JP2005134379A - Method for determining granular material - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for determining a granular material, which determines the granular material of a size being an object to be detected by one scan from many-sized granular materials arranged on a disc, and does not make a granular material count value incorrect even in the case a plurality of granular materials exist side by side. <P>SOLUTION: In the method, a PD detection signal is determined by using a plurality of thresholds corresponding to the granular material sizes to be detected, and determined results corresponding to respective thresholds are stored on independent memory maps. When counting the granular materials, the granular material size and the granular material count value are determined by combining data which are stored on the same position of respective memory maps and contained in a scan window zone. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  In the present invention, in the granular material of various sizes, the granular material is correctly discriminated when counting the granular material in a certain range according to the size, or specified in the sample injected on the analysis disk. In particular, the present invention relates to a method for accurately determining the size and number of granular materials.

A conventional granular material discrimination method will be described with reference to FIG.
In the figure, a granular material 51 as a measurement object, a track 52, and a laser beam 53 are shown.

  In a conventional analyzer that injects a sample into an analysis disk and counts the number of specific granular materials 51 present in the sample, a track 52 spirals on the disk in the same manner as an optical disk such as a CD-ROM. The laser beam 53 is controlled so as to move along the track 52 on the track 52 when the disk rotates.

  On the other hand, the granular material 51 which is a measurement object is larger than the width of the track 52 and exists across a plurality of tracks 52. When the laser moves on the track 52, the granular material 51 is formed on the track 52. Depending on whether or not there is, a signal change occurs in a laser light receiving section (Photo Detector, hereinafter referred to as PD) that receives the laser beam 53 that has passed through the disk and performs photoelectric conversion.

  By processing the signal change, “1” is stored in the memory when it is determined that there is the granular material 51, and “0” is stored in the memory in the other cases, and “1” is stored from the obtained data array. By detecting the length of the disk in the radial direction, the size of the granular material is discriminated and the number is counted.

  As a granular material size discrimination and counting method performed in this way, a method is used in which a rectangular scanning window is used to switch and detect the size of the scanning window for each size of the granular material to be obtained (for example, Patent Document 1). reference).

FIG. 19B is a diagram for explaining a particle size determination and counting method using a scanning window.
For example, when trying to detect a granular material having a size corresponding to six tracks from a granular material having a size corresponding to 1 to 11 tracks, first, the X direction is used by using a window having a size of 6 × X1. Scanning is performed while shifting one by one, and the number of points where all the lines contain “1” is counted.

Next, scanning is performed while shifting one by one in the X direction using a window having a size of 7 × X1, and the number of points where all the lines include “1” is counted.
As a result, the number of granular materials existing over 6 or more tracks and the number of granular materials existing over 7 or more tracks are obtained, and the number of granular materials of the size of 6 tracks is obtained from the difference between them. Can be requested.

  Here, X1 is an integer value larger than the position shift range of “1” due to disk rotation unevenness and signal detection unevenness, and even if a position shift of “1” occurs in each track, it is detected from the same granular material. Detected as “1”.

FIG. 20A is a diagram showing detection of particulate matter in another conventional particulate matter discrimination method.
The analysis disk has light reflectivity and transparency, and is a base disk in which a guide or data recording track 201 is spirally engraved, an upper cover having an inlet, an adhesion and flow of the upper cover and the base disk. It is composed of an adhesive layer for the purpose of forming a path.

  The outer shape of the analysis disk is the same as that of the optical disk such as CD-ROM and CD-RW except for the thickness. When the analysis disk is carried into the analyzer, the analysis disk is chucked with a motor having a turntable. Thus, rotational motion can be performed about the center of the disk diameter.

  The specimen for inspection is injected into the analysis disk, passes through the flow path formed on the adhesive layer, the upper cover lower surface, and the base disk upper surface, and is centrifuged using the centrifugal force generated by rotating the analysis disk. Through the pretreatment, the particulate matter that is the measurement target component in the specimen reaches the measurement area.

  In the measurement area, the particulate matter in the specimen exists on the surface of the base disk due to an adsorption factor (such as an antibody) that adsorbs the specific particulate matter coated on the top surface of the base disk. As shown in a), the width of the track 201 is larger than the track 201, and the track 201 extends over a plurality of tracks 201. Therefore, the laser beam 202 is caused to follow the track 201 and the difference signal of the transmitted light is detected. The presence or absence of the upper granular material can be determined.

More specifically, the analyzer has a two-part PD that receives the laser beam 202 that has passed through the analysis disk, and the laser emitted from the optical pickup when there is no particulate matter on the analysis disk. The spot is positioned so as to be in the center of the PD.
When the granular material crosses over the laser, the position of the laser spot located at the center of the two-divided PD changes due to the change in the degree of light refraction.

  By changing the signal of the PD divided into two, the change in the position of the laser spot is detected as an S-shaped pattern (hereinafter referred to as an S-shaped curve) having a maximum value and a minimum value corresponding to the size of the measurement object. The presence or absence of particulate matter on the track can be determined based on the presence or absence of the S curve.

  The difference signal of the two-divided PD is stored in the memory at regular intervals, and when the S-shaped curve is detected, it is determined that there is a granular material 203, and “1” is stored in the memory, otherwise , “0” is stored in the memory.

  In this analysis, it is necessary to count for each size of the granular material. For size determination and counting, a rectangular window is used to switch the window for each desired size and scan the memory to meet the window conditions. A method of determining the presence / absence of a granular material of a desired size based on the presence / absence of a match is used (for example, see Patent Document 1).

FIG. 20B is a diagram illustrating a conventional particulate matter discrimination method using the operation window 204 for particulate matter size discrimination.
For example, when trying to detect a granular material having a size corresponding to six tracks out of various granular materials having a size corresponding to 1 to 11 tracks, first, a window having a size of 6 × X1 is used. Then, scanning is performed while shifting one by one in the X direction, which is the track direction, and detection is made of locations where each row contains “1”.

Here, after “1” is detected in all the rows of the window, when the window is shifted by one in the X direction to detect the next position, the previously detected “1” is read again. May end up.
Therefore, “1” read once is erased from the memory so that one particle is not counted repeatedly.

After the scanning with the window of 6 × X1 is completed, the scanning is performed while shifting one by one in the X direction using the window with the size of 7 × X1, and “1” is obtained in the previous scanning. Is deleted from the memory, the S-curve detection operation is performed again and stored in the memory.
Then, the same scanning as in the case of 6 × X1 is performed, and the number of locations where each line of the window includes “1” is counted.

Here, X1 indicating the width of the detection window is an integer value larger than the position shift range of “1” detection due to disk rotation unevenness or signal detection unevenness, and “1” granular material and 7 or more tracks in each track. Thus, the number of granular materials existing over the two tracks can be obtained, and the number of granular materials having a size corresponding to six tracks can be obtained from the difference. Even if the positional deviation occurs, it can be detected as “1” detected from the same granular material.
JP 2000-287077 A

  The conventional particulate matter discriminating method is performed as described above, and when the particulate matter exists adjacent to the radial direction of the disk, it continues in the section corresponding to the particulate matter existing on the memory. “1” is stored. When the memory is scanned using the scan window, “1” continues, so the boundary of the granular material cannot be detected correctly, and a plurality of adjacent granular materials are detected as one granular material. There was a problem that it was.

  Further, in the above conventional method, it is necessary to perform scanning a plurality of times while changing the scanning window size in order to determine the granular material size, and furthermore, only one memory array is used. Therefore, there is a problem in that it takes time to determine and count the size of the granular material because it requires a storage process in the memory the same number of times, and thus a scanning operation is required a plurality of times.

  Further, in the conventional particulate matter discriminating method, when a plurality of particulate matter are adjacent on the disc for analysis, the S-shaped curve is detected in the track direction by the number of particulate matter, but in the radial direction, the end of the particulate matter is detected. Since the end of the granular material on the track is detected, the start of another granular material is detected on the next track, and the start of another granular material is detected on the next track. 1 "continues.

  In this case, when scanning is performed on the memory with the detection window, there are actually a plurality of granular materials, but as a detection result, it is detected that there is one large granular material, and there is a possibility that it cannot be counted properly. There was a problem.

  The present invention has been made in order to solve the above-described problems. The granular material size is determined by one scan, and even when there are adjacent granular materials, the granular materials are independent. An object of the present invention is to provide a high-speed and high-accuracy granular material discrimination method capable of detecting correctly.

  In addition, the present invention has been made to solve the above-described conventional problems, and even when a plurality of particles on the track are adjacent in the radial direction, the particle size can be accurately determined. An object of the present invention is to provide a particulate matter discrimination method.

  The particulate matter discriminating method according to the present invention (Claim 1) is an independent method for identifying a particulate matter, which is a measurement object, according to a specific signal pattern that appears when a memory representing an electric signal distribution is scanned along a certain scanning direction. A plurality of different recognition conditions based on the signal level of the specific signal pattern, a flag when the determination result based on each recognition condition is true, and a flag when the determination result is false, Are stored in a plurality of memories provided corresponding to each recognition condition, and the array of the flags stored in the plurality of memories is scanned with a scanning window having the same predetermined size. In addition, on the array of flags stored in a plurality of memories, the arrangement of flags in the same area on the memory representing the distribution of the electrical signals is compared to determine the independence of the granular material. And the particulate matter determination step that is intended to comprising a. In addition, the independence of the granular material described in the present specification means that when there are a plurality of adjacent granular materials, the state is an aggregation of a single granular material. By determining or discriminating, it is possible to measure the number of granular materials in a plurality of adjacent granular materials.

  Further, the particulate matter discrimination method according to the present invention (invention 2) is the particulate matter discrimination method according to claim 1, wherein the specific signal pattern has a maximum value and a minimum value according to the size of the measurement object. The plurality of recognition conditions include a condition that the size of the S-curve is greater than a predetermined first threshold, and the first recognition condition. A plurality of conditions each having a threshold value sequentially larger than the threshold value are characterized in that

  Further, in the particulate matter discrimination method according to claim 1 or 2, the particulate matter judgment step according to the present invention (Claim 3) is a flag when the judgment result is true in the plurality of scanning windows. Are determined to be present continuously over a predetermined number of rows on the flag array, and the independence of the granular material is determined by combining the determination results in the respective windows. It is characterized by this.

  In addition, the particulate matter discriminating method according to the present invention (Claim 4) is a particulate matter that is a measurement object by a specific signal pattern that appears when a memory representing the distribution of electrical signals is scanned along a certain scanning direction. Of different recognition conditions based on the signal level of the specific signal pattern, a flag when the determination result by each recognition condition is true, and a case where the determination result is false. A data storage step of storing flags in two memories provided corresponding to the respective recognition conditions, and an array of the flags stored in the two memories, two having the same predetermined size Scan in a scanning window, compare the arrangement of flags in the same area on the memory indicating the distribution of the electrical signal on the array of flags stored in the two memories, And the particulate matter determination step of determining neutrality and is characterized in that it comprises a.

  Moreover, the granular material discrimination method according to the present invention (Claim 5) is the granular material discrimination method according to Claim 4, wherein the specific signal pattern has a maximum value and a minimum value according to the size of the measurement object. And the two recognition conditions include a condition that the size of the S-curve is greater than a predetermined first threshold value, and a size of the S-curve that is greater than the first threshold value. The condition is that the condition is larger than a predetermined large second threshold value.

  Further, the particulate matter discrimination method according to the present invention (invention 6) is the particulate matter discrimination method according to claim 4 or 5, wherein the particulate matter judgment step is performed when the judgment result is true within the two scanning windows. It is determined whether each flag is continuously present over a predetermined number of rows on the flag array, and a combination of determination results in each scanning window is the determination using the first threshold value. When the result is true and the determination result using the second threshold is true, it is recognized as an independent granular object, the determination result using the first threshold is true, and the second threshold When the determination result using is false, the recognition number of the granular material is recognized as a plurality calculated from the number of rows in which the determination result is true in the scanning window. It is a feature.

  Further, according to the present invention (Claim 7), the particulate matter discriminating method is a particulate matter which is an object to be measured by a specific signal pattern which appears when a memory representing the distribution of electrical signals is scanned along a certain scanning direction. A granular material discrimination method for discriminating the independence of a predetermined signal, wherein a predetermined recognition condition based on the shape of the specific signal pattern, a flag when a determination result based on the recognition condition is true, and a determination result when the determination result is false A data storage step of storing a flag in a memory, and an array of the flag stored in the memory are scanned with a scanning window having a predetermined size, and a flag of when the determination result in the scanning window is true And a granular material determination step of determining the independence of the granular material from the arrangement.

  Further, the particulate matter discrimination method according to the present invention (invention 8) is the particulate matter discrimination method according to claim 7, wherein the shape of the specific signal pattern is a maximum value corresponding to the size of the measurement object. An S-curve having a minimum value, and the predetermined recognition condition is that a distance from a change start position to a change end position in the track direction of the S-curve is greater than a predetermined value. It is what.

  Further, the particulate matter discrimination method according to the present invention (invention 9) is the particulate matter discrimination method according to claim 7, wherein the shape of the specific signal pattern is a maximum value according to the size of the measurement object. The S-curve having a minimum value, and the predetermined recognition condition is that the distance from the change start position to the change end position in the track direction of the S-curve is greater than a predetermined value, and the adjacent S-characters It is a condition that the length from the change start position to the change end position in the track direction of the curve is different.

  Further, the particulate matter discriminating method according to the present invention (invention 10) is the particulate matter discriminating method according to any one of claims 1 to 9, wherein the signal pattern is applied to an analysis medium into which the measurement object is injected. A step of generating by irradiating a laser beam and optically reading, and a reference object having a predetermined size corresponding to the size of the measurement object are provided in a predetermined region of the analysis medium in advance and measured. A step of setting a signal pattern obtained by previously reading the reference object as a reference pattern, and a step of performing a measurement based on a result of comparison with the reference pattern in the data storage step. is there.

  Further, the particulate matter discrimination method according to the present invention (invention 11) is the particulate matter discrimination method according to claim 10, wherein the specific signal pattern and the reference pattern correspond to the size of the measurement object. It is an S-shaped curve having a maximum value and a minimum value, and the result of comparison with the reference pattern is only when either the maximum value or the minimum value of the measurement object exists within the distribution range of the reference pattern. The measurement is performed.

  Further, the particulate matter discrimination method according to the present invention (invention 12) is the particulate matter discrimination method according to claim 10, wherein the specific signal pattern and the reference pattern are maximal in accordance with the size of the measurement object. An S-shaped curve having a value and a minimum value, and the result of comparison with the reference pattern is measured only when both the maximum value and the minimum value of the measurement object are within the distribution range of the reference pattern. It is characterized by this.

  A particulate matter discriminating method according to the present invention (invention 13) is the particulate matter discriminating method according to any one of claims 1 to 9, wherein the particulate matter discriminating method is provided so as to be movable with respect to the analysis disk. An optical system including an objective lens, an actuator that drives the objective lens in the rotation axis direction and the radial direction of the analysis disk, and a photodetector that photoelectrically converts reflected light from the analysis disk. An optical pickup, a spindle motor that is a rotation driving means for the analysis disk, a servo control circuit that performs focus servo control, tracking servo control, and spindle servo control based on a signal from the optical pickup, and the optical pickup The laser beam emitted from the laser beam passes through the analysis disk, receives the transmitted light, and performs photoelectric conversion. A photo detector (hereinafter referred to as PD), an S-curve detection circuit that detects an S-curve based on an electrical signal output from the PD, and a memory that stores the output of the S-curve detection circuit And a granular material recognition device that recognizes the granular material based on the data in the memory.

  Further, the particulate matter discriminating method according to the present invention (Claim 14) is a method for discriminating the independence of the particulate matter, which is the measurement object injected into the analysis disk, in the presence or absence of the particulate matter and the particulate matter. The step of storing the binary data array consisting of “0” and “1” determined based on the size in the memory, and the particle size from the data array in the track direction and the radial direction of the analysis disk And a step of discriminating.

  In addition, according to the particulate matter discriminating method of the present invention (Claim 15), in the particulate matter discriminating method according to Claim 14, the data array in the track direction has a maximum value and a minimum value which are detection signals of the particulate matter. When the S-curve is large, the number of “1” is large, and when the S-curve is small, the number of “1” is small.

  Further, according to the particulate matter discriminating method of the present invention (invention 16), in the particulate matter discriminating method according to claim 14, “1” is continuous in the radial data array, and the track is located in the middle row. When it is detected that the number of “1” s in the direction data array is decreasing, it is determined that there are a plurality of granular materials.

  According to another aspect of the present invention (Claim 17), there is provided a method for discriminating the independence of a granular material which is a measurement object injected into an analysis disk. Next, the method includes a step of writing a data array on the memory based on the size of an S-shaped curve having a maximum value and a minimum value, which are detection signals of granular materials.

  Further, the particulate matter discrimination method according to the present invention (invention 18) is characterized in that in the particulate matter discrimination method according to claim 17, the encoded data array does not continue with "1" continuously. To do.

  Further, the particulate matter discriminating method according to the present invention (invention 19) is a method for discriminating the independence of the particulate matter, which is the measurement object injected into the analysis disk, and the data arrangement at the time of the particulate matter detection and the previous If the S-curve of the data array at the time of detecting the particulate matter is small compared to the data array of rows, the data is not stored in the memory.

  In addition, according to the particulate matter discriminating method of the present invention (Claim 20), in the particulate matter discriminating method according to Claim 19, the data array includes data indicating the presence / absence of the particulate matter and data indicating the size of the particulate matter. It is characterized by comprising.

  In addition, according to the particulate matter discriminating method of the present invention (claim 21), in the particulate matter discriminating method according to claim 19, "0" is stored in the memory next to "11" which is a data array indicating the presence or absence of the particulate matter. A step of writing on the memory, and a step of writing a data array in which “1” is not continuous, which is determined based on the size of the S-curve having a maximum value and a minimum value, which are detection signals of the particulate matter, to the memory. It is characterized by including.

  Further, according to the granular material discrimination method of the present invention (Claim 22), in the granular material identification method according to Claim 14, "1" is continuous in the radial data array, and the data array in the track direction is the same. When the number of “1” in each row is the same number in each row, it is determined that there are a plurality of granular materials.

  A particulate matter discriminating method according to the present invention (Claim 23) is the particulate matter discriminating method according to any one of Claims 14 to 22, and is provided so as to be movable with respect to the analysis disk. An optical system including an objective lens, an actuator that drives the objective lens in the rotation axis direction and the radial direction of the analysis disk, and a photodetector that photoelectrically converts reflected light from the analysis disk. An optical pickup, a spindle motor that is a rotation driving means for the analysis disk, a servo control circuit that performs focus servo control, tracking servo control, and spindle servo control based on a signal from the optical pickup, and the optical pickup The laser light emitted from the laser beam passes through the analysis disk, receives the transmitted light, and performs photoelectric conversion. D, an S-curve detection circuit that detects an S-curve based on the electrical signal output from the PD, and an array of binary data that is processed based on the output signal of the S-curve detection circuit And a granular material recognition circuit that recognizes the granular material based on the data in the memory.

  According to the particulate matter discriminating method according to the present invention (claim 1), the particulate matter which is a measurement object by a specific signal pattern which appears when a memory representing the distribution of electrical signals is scanned along a certain scanning direction. A plurality of different recognition conditions based on the signal level of the specific signal pattern, a flag when the determination result by each recognition condition is true, and a case where the determination result is false. A data storage step of storing flags in a plurality of memories provided corresponding to respective recognition conditions, and a scanning window having the same predetermined size in the array of the flags stored in the plurality of memories Independence of the granular material by comparing the arrangement of the flags in the same area on the memory representing the distribution of the electric signal on the array of the flags stored in a plurality of memories. The particulate matter judgment step for judging is included, and by comparing the flag states between the plurality of memory maps, the particulate matter can be recognized without erroneously recognizing the plurality of particulate matter continuous in the radial direction of the disk. The effect that it is possible to count the number is obtained.

  Moreover, according to the granular material discrimination method according to the present invention (Claim 2), in the granular material identification method according to Claim 1, the specific signal pattern has a maximum value according to the size of the measurement object. The S-curve has a minimum value, and the plurality of recognition conditions include a condition that the size of the S-curve is larger than a predetermined first threshold and a threshold that is sequentially larger than the first threshold. Therefore, by setting an appropriate threshold value, it becomes possible to count the number of particles without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk. The effect is obtained.

  Moreover, according to the particulate matter discrimination method according to the present invention (invention 3), in the particulate matter discrimination method according to claim 1 or 2, in the particulate matter judgment step, the judgment result is true in the plurality of scanning windows. It is determined whether each flag is continuously present over a predetermined number of rows in the flag array, and the independence of the granular material, that is, the combination of the determination results in each window Since the size and the number of particles are measured, it is possible to count the number of particles without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk from the determination result of the scanning window. can get.

  Further, according to the granular object discrimination method of the present invention (Claim 4), the object to be measured is based on a specific signal pattern that appears when the memory representing the electric signal distribution is scanned along a certain scanning direction. A method for discriminating the independence of a granular object, wherein two different recognition conditions based on the signal level of the specific signal pattern, a flag when a determination result by each recognition condition is true, and a determination result is false The data storage step for storing the flags of the cases in the two memories provided corresponding to the respective recognition conditions, and the arrangement of the flags stored in the two memories have the same predetermined size. Scan with two scanning windows, compare the arrangement of flags in the same area on the memory representing the distribution of the electrical signal on the array of flags stored in the two memories, and And a granular object determination step for determining the independence of the objects. By comparing the flag states of the two memory maps respectively corresponding to the thresholds having different sizes, a plurality of consecutive in the radial direction of the disk is obtained. It is possible to count the number of granular materials without erroneously recognizing the granular materials.

  Moreover, according to the granular material discrimination method according to the present invention (Claim 5), in the granular material identification method according to Claim 4, the specific signal pattern has a maximum value corresponding to the size of the measurement object. The S-curve has a minimum value, and the two recognition conditions include a condition that the size of the S-curve is larger than a predetermined first threshold, and the size of the S-curve is the first threshold. It is assumed that the condition is larger than a predetermined second threshold value that is larger than that. By setting an appropriate threshold value, the number of particles can be determined without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk. It is possible to obtain an effect that it is possible to perform counting.

  Further, according to the particulate matter discriminating method according to the present invention (Claim 6), in the particulate matter discriminating method according to Claim 4 or 5, the particulate matter judgment step is such that the judgment result is true within the two scanning windows. It is respectively determined whether or not the flag at the time of the flag is continuously present over a predetermined number of rows on the flag array, and the combination of the determination results in each scanning window uses the first threshold value. When the determination result is true and the determination result using the second threshold is true, it is recognized as one independent granular material, the determination result using the first threshold is true and the second threshold When the determination result using the threshold value is false, the number of recognition of the granular material is recognized as a plurality calculated from the number of rows in which the determination result is true in the scanning window. So that the scanning window Than the determination result, the effect is obtained that it is possible to count the granules number without erroneously recognizing a plurality of granular materials to be continuous in the radial direction of the disk.

  Moreover, according to the granular object discrimination method of the present invention (Claim 7), the measurement object is based on a specific signal pattern that appears when a memory representing the distribution of electrical signals is scanned along a certain scanning direction. A particulate matter discrimination method for discriminating the independence of a particulate matter, wherein a predetermined recognition condition based on the shape of the specific signal pattern, a flag when a judgment result based on the recognition condition is true, and a judgment result is false A data storage step for storing a flag in the memory, and an array of the flags stored in the memory with a scan window having a predetermined size, and when the determination result in the scan window is true The particulate matter judgment step for judging the independence of the particulate matter from the flag sequence is included. Therefore, by setting the particulate matter recognition condition corresponding to the particulate matter size to be detected in advance, the particulate matter is run. Even if there are a plurality of adjacent particles in the vertical direction with respect to the direction, the number of particles can be counted without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk without making a mistake in the target particles. The effect that it becomes possible to obtain is obtained.

  Further, according to the particulate matter discrimination method according to the present invention (invention 8), in the particulate matter discrimination method according to claim 7, the shape of the specific signal pattern is a maximum corresponding to the size of the measurement object. Since the predetermined recognition condition is that the distance from the change start position to the change end position in the track direction of the S curve is greater than a predetermined value. By setting an appropriate threshold value, it is possible to count the number of particles without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk.

  Further, according to the particulate matter discrimination method according to the present invention (invention 9), in the particulate matter discrimination method according to claim 7, the shape of the specific signal pattern is a maximum corresponding to the size of the measurement object. The predetermined recognition condition is that the distance from the change start position to the change end position in the track direction of the S curve is greater than a predetermined value and adjacent to the S curve. Since the length from the change start position to the change end position in the track direction of the S-shaped curve is different, by setting an appropriate threshold value, a plurality of particles that are continuous in the radial direction of the disc are erroneously recognized. The effect that it becomes possible to count the number of granular materials is obtained.

  Moreover, according to the particulate matter discrimination method according to the present invention (claim 10), in the particulate matter discrimination method according to any one of claims 1 to 9, the signal pattern is used for analysis in which the measurement object is injected. A step of generating by irradiating the medium with laser light and optically reading it, and a reference object having a predetermined size corresponding to the size of the measurement object are provided in a predetermined region of the analysis medium in advance. Since it includes a step of setting a signal pattern obtained by reading the reference object before measurement as a reference pattern, and a step of performing measurement based on a result of comparison with the reference pattern in the data storage step, By comparing the reference value of the detection signal before input with the detection signal at the time of actual granular object recognition, it is possible to measure only the granular object of the size to be measured. Even if there are several adjacent in the vertical direction, it is possible to count the number of particles without mistakenly identifying the particles that are continuous in the radial direction of the disc without making a mistake in the target particles. The effect of becoming is obtained.

  Further, according to the particulate matter discrimination method according to the present invention (invention 11), in the particulate matter discrimination method according to claim 10, the specific signal pattern and the reference pattern have a size of the measurement object. It is an S-shaped curve having a corresponding maximum value and minimum value, and the result of comparison with the reference pattern indicates that either the maximum value or the minimum value of the measurement object exists within the distribution range of the reference pattern. Since the measurement is performed only at the time, it is possible to count the number of particles without erroneously recognizing a plurality of particles continuous in the radial direction of the disk by setting an appropriate threshold value. Is obtained.

  Further, according to the particulate matter discrimination method according to the present invention (claim 12), in the particulate matter discrimination method according to claim 10, the specific signal pattern and the reference pattern correspond to a size of the measurement object. S-curve having a maximum value and a minimum value, and the result of comparison with the reference pattern is measured only when both the maximum value and the minimum value of the measurement object are within the distribution range of the reference pattern. Therefore, by setting an appropriate threshold value, it is possible to count the number of particles without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk.

  Further, according to the particulate matter discriminating method of the present invention (Claims 14 to 16), in the method for discriminating the independence of the particulate matter as the measurement object injected into the analysis disk, the presence or absence of the particulate matter, and From the step of storing, in the memory, an array of binary data consisting of “0” and “1” determined based on the size of the granular material, and the data array in the track direction and radial direction of the analysis disk. The step of determining the size of the granular material is included, so that even when a plurality of granular materials on the track are adjacent in the radial direction, the particle size can be accurately determined and counted. is there.

  Further, according to the particulate matter discriminating method of the present invention (Claims 17, 18, 20 to 22), in the method for discriminating the independence of the particulate matter which is the measurement object injected into the analysis disk, the particulate matter Next, the step of writing “0” in the memory next to “11”, which is a data array indicating the presence or absence, and the maximum value and the minimum value as detection signals of the granular material when the laser beam crosses the granular material next. And the step of writing the data array encoded based on the size of the S-curve having a memory on the memory. In the step of detecting the presence or absence of the granular material, the number of “1” in each line of the window is calculated. There is no need to monitor, and it is only necessary to determine the arrangement of “11”, so that it is possible to detect the granular material without operating a complicated detection window. In the step of determining the size of the granular material, the track Even when a plurality of granular materials are adjacent to each other in the radial direction, it is possible to accurately determine and count the size of the granular material, and when the data array indicating the size of the S-curve is 5 digits or more Can be subdivided more finely by increasing the number of data that can be handled, which is advantageous when subdivision is required.

  Further, according to the particulate matter discriminating method of the present invention (Claim 19), in the method for discriminating the independence of the particulate matter that is the measurement object injected into the analytical disk, When the S-curve of the data array at the time of the granular object detection is small compared to the data array of the previous row, the data is not stored in the memory, so that a plurality of granular objects on the track are adjacent in the radial direction. Even if it is, the particle size can be accurately determined and counted.

Hereinafter, an embodiment of the particulate matter discrimination method of the present invention will be described in detail with reference to the drawings.
(Embodiment 1)
FIG. 1 shows a block diagram of an analyzer to which the particulate matter discrimination method according to Embodiment 1 of the present invention is applied. Hereinafter, the basic operation of the apparatus will be described.
When the analysis disk 1 on which the particulate matter, which is the analysis object 8, is arranged, is mounted on the analyzer, the servo control circuit 3 connected to the microcomputer 2 controls the rotation of the spindle motor 4 (CAV control).

  Next, the laser beam 7 is irradiated from the optical pickup 5 to the analysis disk 1. The servo control circuit 3 performs focus servo, tracking servo, and traverse control to trace a track formed by pits or grooves based on the reproduction output signal of the optical pickup 5, and also an analysis disk The address information recorded in one track is detected, and the spindle motor 4 is rotationally controlled (CLV control) so that the linear velocity is constant.

  In this way, tracing, which is the operation of causing the optical pickup 5 to follow the track on the disk 1, is performed, and the reflected light or transmitted light from the analysis disk 1 at that time is detected by the PD (light detector) 6. However, FIG. 1 shows the case of transmitted light, and a scanning operation for analyzing the analysis object 8 based on the signal becomes possible.

  Next, from a signal detected by the PD 6 (hereinafter referred to as a PD detection signal) in granular object recognition at the time of scanning, which is a feature of the present invention, a single unit using the S-curve detection circuit 9 and the granular object recognition circuit 10 is used. The detection of a granular material is demonstrated using FIG.

FIG. 2A shows an example in which the centers of three granular materials having different sizes are located on the same track 21.
FIG. 2B shows the waveform of the PD detection signal when the track 21 is traced in the example of FIG.

  As shown in FIG. 2B, the waveform is constant when there is no particulate matter, whereas an S-shaped curve is drawn when there is a particulate matter. In the present embodiment, the amplitude of this S-shaped curve is proportional to the size of the granular material.

  Therefore, the S-curve detection circuit 9 detects the change of the PD detection signal based on a predetermined recognition condition, and when the S-curve portion is not detected, “0” is written as a flag on the memory 11 and the S-curve is detected. When the curve portion is detected, “1” is written as a flag on the memory 11.

  The predetermined recognition condition is determined using, for example, the amplitude of the PD detection signal and a predetermined threshold. If the amplitude is smaller than the predetermined threshold, it is determined that the S-curve portion has not been detected. When it is larger than the predetermined threshold, it is determined that the S-curve portion is detected.

  As described above, the S-curve detection circuit 9 reflects the presence or absence of the S-curve in the PD detection signal on the memory as needed. The data in the memory 11 generated in this way is a two-dimensional memory array in which one direction indicates the track direction of the disk and the other direction indicates the radial direction of the disk.

Further, in the present embodiment, a plurality of memory arrays reflecting the determination results based on the respective recognition conditions are constructed using a plurality of recognition conditions at the time of one scan.
In the present embodiment, it is assumed that there are three types of granular materials A, B, and C having different sizes (the size is assumed to be granular material A <granular material B <granular material C), and the predetermined recognition described above. As the conditions, four threshold values (threshold value a1 <threshold value a2 <threshold value a3 <threshold value a4) are used as threshold value a1, threshold value a2, threshold value a3, and threshold value a4.

Hereinafter, a recognition condition in this embodiment, that is, a method for setting each threshold will be described.
As shown in FIG. 2 (a), consider a track that passes through the centers of three types of particulate matter A, particulate matter B, and particulate matter C. FIG. 2B is a graph showing the relationship between the S-shaped curves of the granular material A, the granular material B, and the granular material C generated on the track and the four threshold values a1, a2, a3, and a4. .

  As shown in FIG. 2B, the threshold values a1 to a4 are set so that there is a difference in the size of the S-curve that can be detected by each threshold value.

  Hereinafter, a detailed description will be given using an example of the memory arrays A1 to A4 constructed corresponding to each threshold value. FIG. 3 shows a table representing the relationship between the type of granular material and the threshold value in the present embodiment. The numbers in the figure indicate the number of rows including “1” that appear continuously in the radial direction on the memory array when the granular materials A, B, and C having different sizes exist. .

  For example, in the memory array 1 corresponding to the threshold value a1, the granular material A has “1” continuously appearing over 5 rows, the memory array 2 corresponding to the threshold value a2 has 3 rows, and the memory array corresponding to the threshold value a3. In 3, “1” appears only in one line. In the memory array 4 corresponding to the largest threshold value a4, “1” does not appear at all. Thus, as the threshold value increases, the number of “1” appearing on the memory array decreases.

  Moreover, when FIG. 3 is seen according to the kind of granular material, the magnitude | size of the threshold value from which "1" does not appear at all becomes large according to the size of a granular material. For example, in the smallest granular material A, the number of occurrences of “1” is 0 with respect to a threshold having a size equal to or larger than the threshold a2, but in the largest granular material C, “ The number of occurrences of “1” is 0.

The table shown in FIG. 3 is used for particle size determination described later. Therefore, this table needs to be obtained in advance, but it must be possible to uniquely determine the type of granular material from the minimum threshold at which “1” does not appear. Each threshold value is set to an appropriate value so that all kinds of granular materials can be uniquely determined.
The memory arrays A1 to A4 are generated using the threshold values a1 to a4 set as described above.

  Next, as shown in FIG. 4, a method for recognizing a plurality of particulates continuously present in the radial direction of the analysis disk as separate particulates from the generated memory arrays A1 to A4 is shown in FIG. It explains using.

  FIG. 5 shows the state of each memory array in the region including the granular material of FIG. 4, in particular, FIG. 5 (a) shows a memory array A1 corresponding to the threshold value a1, and FIG. 5 (b) shows a memory corresponding to the threshold value a2. The array A2, FIG. 5C shows the memory array A3 corresponding to the threshold value a3, and FIG. 5D shows the memory array A4 corresponding to the threshold value a4.

Scanning the position where the scanning point becomes “1” while shifting the scanning point by 1 to the right end in the row direction from the upper left of FIG. 5A as the base point, and when the scanning to the right end is completed, the left end of the next row is Scanning is performed with the base point shifted by 1 to the right end in the row direction. This scanning is repeated.
When it is detected that “1” exists at the scanning point, a granular material recognition process described later is performed. When this granular object recognition process is performed, the scanning is continued by shifting the scanning point by one.

Hereinafter, the above-described granular object recognition processing will be described with reference to FIG.
FIG. 6 is a diagram illustrating a state in the scanning window for explaining the granular material size determination processing. Note that “1” in FIG. 6 indicates that there is at least one “1” in the corresponding row in the scanning window, and “0” indicates that all the corresponding rows in the scanning window are “0”. . That is, “1” and “0” in FIG. 6 are the results of calculating the OR in the row direction for each row of the scanning window. The state of the scanning window 41 shown in FIG. 5 is shown in FIG. Such a notation will be used to represent the state in the scanning window 41 that appears in the following description.

FIG. 7 is a flowchart of the particulate matter recognition process in the present embodiment.
First, in step S1, the scanning window 41 is generated in which the range is expanded as long as “1” continues in the row direction from the scanning point. This scanning window has a predetermined width in the column direction. The predetermined width is obtained by measuring in advance, for example, a width that allows all “1” s generated for one granular object to fall within the scanning window.

Next, in step S2, a granular material size determination process, which will be described later, is performed to determine the size of the granular material.
In step S3, referring to the table shown in FIG. 3, the rows in the scan window 41 of each memory array are cleared in order from the top by the number of consecutive “1” s corresponding to the obtained size of the granular material (1). All the data in the row is set to “0”).

  In step S4, it is determined whether or not all the scanning windows 41 are “0” on the memory array A1. If the determination result is true, that is, all of them are “0”, the granular object recognition process is terminated. On the other hand, if the determination result is false, that is, “1” remains in any row, the process returns to step S1 to continue the granular object recognition process.

  FIG. 6 (b) shows the state of the scanning window and the count status when the process reaches step S4 for the first time. In step S2, the granular material C is detected. In step S3, the memory array A1 has five rows, A2 has three rows, and A3 has one row in which “1” is present. At this time, since “1” still remains in the scanning window 41, the process returns to step S2 to continue the granular object recognition process.

  FIG. 6C shows the state of the scanning window and the count state when the process reaches step S4 for the second time. In step S2, the granular material B is detected. In step S3, the memory array A1 has three rows, and A2 has a row where “1” exists over one row. At this time, since “1” still remains in the scanning window 41, the process returns to step S2 to continue the granular object recognition process.

  Thereafter, the processing from step S2 to step S4 is repeated in the same manner. In step S2, the granular material A is detected the third time, the fourth time, and the fifth time, and in each subsequent step S3, the row where “1” exists in only one row in the memory array A1 is cleared. FIG. 6 (d) shows the state of the scanning window and the count state when the process reaches step S4 for the fifth time. At this time, since all the scanning windows 41 are “0”, the granular object recognition processing is ended.

Next, the granular material size determination process performed in step S2 described above will be described.
FIG. 8 is a flowchart of the granular material size determination process in the present embodiment.
In step S11, the variable n is initialized with 1. This variable n is introduced in order to express an iterative process in the description of this flowchart.

In step S12, it is determined whether or not all the scanning windows 41 are “0” on the memory array An.
If the determination result in step S12 is true, that is, all are “0”, the process proceeds to step S13. In step S13, the type of granular material is determined from the value of n with reference to the table of FIG. For example, if n = 3, it is determined that the minimum threshold value at which “1” does not appear is the granular material B having the threshold value a3.

  On the other hand, if the determination result in step S12 is false, that is, “1” exists in any row, the process proceeds to step S14, the value of n is incremented by 1, and the process returns to step S12 and continues.

  By performing such a scan on the memory array over the entire memory array, the particle count is performed while determining the particle size.

  As described above, according to the first embodiment, the S-curve detection circuit 9 forms a plurality of memory arrays corresponding to a plurality of different recognition conditions, and detects S-curve under different recognition conditions (thresholds). By analyzing the combination of appearance patterns of “1” on the memory map under the respective recognition conditions by the granular object recognition circuit 10, without erroneously recognizing a plurality of granular objects continuous in the radial direction of the disk, The number of granular materials can be counted.

  In the first embodiment, when the width of the S-curve increases in proportion to the size of the granular material, the same granular material size is set by setting the threshold value in the time axis direction instead of the amplitude direction. Needless to say, the discrimination method can be applied.

Further, even when the PD detection signal of the first embodiment is a convex signal as shown in FIG. 9 instead of the S-curve shown in FIG. Thresholds a1, a2, a
By setting as 3 and a4 and constructing a memory array, similar particle size determination and counting can be performed.

(Embodiment 2)
Next, the particulate matter discrimination method according to Embodiment 2 of the present invention will be described. A block diagram of an apparatus to which this embodiment is applied is the same as the configuration shown in FIG.

Hereinafter, a characteristic operation of the particulate matter determination method according to the second exemplary embodiment will be described.
Here, the threshold value b1 and the threshold value b2 (threshold value b1 <threshold value b2) corresponding to the amplitude of the S-shaped curve, and the memory array B1 and the memory array B2 generated by the respective threshold values are considered.

FIG. 10 shows the number of “1” appearing continuously in the radial direction on the memory array when the granular materials D, E and F having different sizes exist.
Looking at FIG. 10 for each type of granular material, the number of rows of “1” continuously appearing in the memory array B2 corresponding to the threshold value b2 increases according to the size of the granular material. For example, in the smallest granular material D, the number of occurrences of “1” is 0 with respect to the threshold value b2, whereas in the largest granular material F, the number of occurrences of “1” is 3.

  The table shown in FIG. 10 is used for granular material size determination described later. Therefore, this table needs to be obtained in advance, but it must be possible to uniquely determine the type of granular material from the number of rows in which “1” appears continuously with respect to the threshold value b2. Each threshold value is set to an appropriate value so that all kinds of granular materials can be uniquely determined.

  The memory arrays B1 and B2 are generated using the threshold values b1 and b2 set as described above. Next, referring to FIG. 5, a method of recognizing the granular materials continuously present in the radial direction of the analysis disk as separate granular materials from the generated memory arrays B1 and B2 as shown in FIG. To do.

  FIG. 5 shows the state of each memory array in the region including the granular material of FIG. 4. In particular, FIG. 5 (a) corresponds to the memory array B1 corresponding to the threshold value b1, and FIG. 5 (b) corresponds to the threshold value b2. The memory array B2 is shown.

Scanning the position where the scanning point becomes “1” while shifting the scanning point by 1 to the right end in the row direction from the upper left of FIG. 5A as the base point, and when the scanning to the right end is completed, the left end of the next row is Scanning is performed with the base point shifted by 1 to the right end in the row direction. This scanning is repeated.
When it is detected that “1” exists at the scanning point, a granular material recognition process described later is performed. When this granular object recognition process is performed, the scanning is continued by shifting the scanning point by one.

  Hereinafter, the above-described granular object recognition processing will be described with reference to FIG. FIG. 11 shows the state of the scanning window 41 for explaining the granular material size discrimination processing. Note that the notation method representing the state of the scanning window in FIG. 11 is the same as that in FIG.

FIG. 12 is a flowchart of the particulate matter recognition process in the present embodiment.
When the process is started, in step S21, the scanning window 41 is generated in which the range is expanded as long as “1” continues in the row direction from the scanning point. The scanning window 41 has a predetermined width in the column direction. The predetermined width is obtained by measuring in advance, for example, a width that allows all “1” s generated for one granular object to be accommodated in the scanning window 41.

  Next, in step S22, each row in the scan window 41 is scanned from above on the memory array B2, and the number of rows in which “1” continues is counted from the row where “1” is first found, and the table of FIG. To determine the size of the granular material. For example, when the state of the scanning window 41 is as shown in FIG. 11A, the first “1” continuously exists on the memory array B2 in three lines from the third line to the fifth line. Therefore, it is determined from the table of FIG.

  Next, in step S23, with reference to the table shown in FIG. 10, the rows in the scan window 41 of each memory array are cleared in order from the top by the number of consecutive “1” s corresponding to the obtained size of the granular material ( All the data in one line is set to “0”).

  In step S24, it is determined whether or not all the scanning windows 41 are “0” on the memory array B1. If the determination result is true, that is, all of them are “0”, the granular object recognition process is terminated. On the other hand, if the determination result is false, that is, “1” remains in any row, the process returns to step S21 to continue the particulate matter recognition process.

  FIG. 11B shows the state of the scanning window 41 and the count state when the process reaches step S24 for the first time. In step S22, the particulate matter F is detected, and in step S23, the memory array B1 has five rows and B2 has three rows in which “1” exists. At this time, since “1” still remains in the scanning window 41, the process returns to step S22 to continue the granular object recognition process.

  FIG. 11C shows the state of the scanning window 41 and the count state when the process reaches step S24 for the second time. In step S22, the particulate matter E is detected. In step S23, the memory array B1 has three rows, and B2 has one row in which “1” exists over one row. At this time, since “1” still remains in the scanning window 41, the process returns to step S22 to continue the granular object recognition process.

Thereafter, the processing from step S22 to step S24 is similarly repeated. In step S22, the particulate matter D is detected the third time, the fourth time, and the fifth time, and in each subsequent step S23, a row where “1” exists in only one row in the memory array B1 is cleared. FIG. 11D shows the state of the scanning window 41 and the count state when the process reaches step S24 for the fifth time. At this time, since all the scanning windows 41 are “0”, the granular object recognition processing is ended.
By performing such a scan on the memory array over the entire memory array, the particle count is performed while determining the particle size.

  As described above, in the second embodiment, the memory array has a two-plane configuration of the memory array B1 for the threshold value b1 and the memory array B2 for the threshold value b2, so that “1” on each memory array is obtained. By analyzing the combinations of the appearance patterns, it is possible to recognize the particle size and to count the number of particles without erroneously recognizing a plurality of particles that are continuous in the radial direction of the disk.

  Of course, when the width of the S-curve increases in proportion to the size of the granular material, a similar granular material size determination method can be applied by setting the threshold value not in the amplitude direction but in the time axis direction. Yes.

Even if the PD detection signal of the second embodiment is a convex signal as shown in FIG. 13 instead of the S-curve shown in FIG. Threshold b1
And b4, and by constructing a memory array, similar particle size determination and counting can be performed.

(Embodiment 3)
Next, the particulate matter discrimination method according to Embodiment 3 of the present invention will be described. FIG. 14A shows an arrangement diagram of the granular material on the optical disc in the granular material discrimination method according to the third embodiment of the present invention. FIG. 14B shows a signal pattern corresponding to the granular material on the optical disk shown in FIG. Further, FIG. 15 shows a memory array showing the result determined by the granular material recognition condition with respect to FIG.

  In addition, the block diagram of the apparatus to which the particulate matter determination method according to the third embodiment of the present invention is applied is the same as the configuration shown in FIG.

Hereinafter, a characteristic operation of the particulate matter determination method according to the third exemplary embodiment will be described.
As shown in FIG. 14A, it is assumed that the granular material G, the granular material H, and the granular material I are arranged adjacent to each of the tracks 1 to 16 on the optical disk. That is, the granular material G is disposed on the track 2 and the track 3, the granular material H is disposed on the track 4 to the track 8, and the granular material I is disposed on the track 9 to the track 15.

  In this case, the signal pattern generated along each track is an S-shaped pattern (hereinafter referred to as an S-shaped curve) as shown in FIG. This S-shaped curve appears in the range of two intersections of each track and the outermost peripheral part of each granular material.

Next, the recognition conditions of the granular material when only the granular material H is set as the measurement object will be described. FIG. 16 is a view when the center (one-dot chain line) of two tracks out of the track intersecting the granular object H of the measurement object passes through the center o of the granular object, P is the track pitch of the optical disk, R represents the radius of the granular material H. At this time, the distance d in the figure can be calculated from the following equation (1).

  Using 2d, which is twice the distance d, the recognition condition is that the size of the signal pattern in the time direction (distance from the change start position to the change end position in the track direction) is 2d or more. .

  This recognition condition is applied to the S-curve appearing from track 1 to track 16, and when the result is true, "1" is written on the memory array, and conversely, when the result is false, the memory array “0” is written to. The memory array generated in this way is shown in FIG.

  Next, in the memory array shown in FIG. 15, it is determined that “1” does not exist continuously in the vertical direction, that is, a portion where “0” is present above and below “1” is present as a granular material to be measured. (Criteria A).

  Accordingly, “1” does not exist on the memory array for the non-measuring granular material G, and for the non-measuring granular material I, a plurality of “1” s continue in the vertical direction. Removed by A.

That is, the number of granular materials detected from the memory array shown in FIG. 15 is one.
In this way, it is possible to count only the measurement target granular material H even when the granular materials having different sizes are adjacent to each other.

  Next, the granular material recognition conditions will be described. As shown in FIG. 16, when the center line of the two tracks passing through the granular material passes through the center of the granular material, the length of the S-curve in the two tracks in the time direction (start of change in the track direction) The distance from the position to the change end position) is equal. In this case, when the recognition condition is applied to the length of the S-curve in the two tracks in the time direction (distance from the change start position to the change end position in the track direction), each length is expressed by the equation (1). 2), two “1” s are written on the memory array.

  When the determination criterion A is applied to this memory array, a plurality of “1” s are continuously present in the vertical direction on the memory array, and therefore are removed according to the determination criterion and are not counted as granular materials. Therefore, when the lengths of adjacent S-curves in the time axis direction are equal, “1” is written in the memory array only at the portion corresponding to the S-curve measured first, and the S of the same length that appears next is written. “0” is written instead of “1” on the memory array at the location corresponding to the character curve. As a result, the determination condition A can be applied, and accurate counting of particulate matter is possible.

  As described above, according to the third embodiment, the granular material recognition condition corresponding to the granular material size to be detected is set, and “1” is set in the memory map only when an S-character larger than the condition is detected. Since writing is performed, even when the granular materials are adjacent in the vertical direction, “1” does not continue in the vertical direction, and one granular material can be recognized correctly. Further, by counting only the case where only one “1” stands after the wind scan, the size and number of granular materials to be detected in one scan can be known.

(Embodiment 4)
Next, the particulate matter discrimination method according to Embodiment 4 of the present invention will be described. FIG. 17 shows an example of the arrangement range of the measurement object on the optical disc and the arrangement range of the reference object for obtaining the reference pattern. FIG. 18 shows an S-shaped curve obtained from the reference object and the measurement object.

  Further, the block diagram of the apparatus to which the particulate matter discrimination method according to the fourth embodiment is applied is the same as the configuration shown in FIG.

Hereinafter, a characteristic operation of the particulate matter determination method according to the fourth exemplary embodiment will be described.
In FIG. 17, the area A is the arrangement range of the measurement object, the area B is the arrangement range of the reference object A equivalent to the minimum value of the measurement object, and the area C is the size of the measurement object. The arrangement | positioning range of the reference | standard target object B equivalent to the maximum value of is shown.

  Before counting the measurement object using such an optical disc, first, S-shaped curves corresponding to the reference object A and the reference object B arranged in the region B and the region C are acquired. A section a in FIG. 18 indicates an S-shaped curve corresponding to the reference object A, and a section b in FIG. 18 indicates an S-shaped curve corresponding to the reference object B. And the maximum value level of the amplitude of the S-shaped curve corresponding to the obtained reference object is stored. That is, the reference line B in FIG. 18 indicates the maximum value level of the S-shaped curve corresponding to the reference object A, and the reference line A in FIG. 18 indicates the maximum value level of the S-shaped curve corresponding to the reference object B.

  Similarly, the minimum value level of the amplitude of the S-curve of the reference object is stored. That is, the reference line C in FIG. 18 indicates the minimum value level of the S-shaped curve corresponding to the reference object A, and the reference line D in FIG. 18 indicates the minimum value level of the S-shaped curve corresponding to the reference object B. Next, the S-shaped curve of the measurement object arranged in the region A is acquired using the same method as in the first embodiment.

  The maximum value level or minimum value level of the S-curve of the measurement object is within the range of the maximum value level (reference line AB) obtained from the reference object or the range of the minimum value level obtained from the reference object It is determined whether or not it exists within (reference line CD section). As a result, the S-curve obtained from the measurement object is within the maximum value level range (reference line AB) obtained from the reference object or the minimum value level range obtained from the reference object (reference value). The data recording process to the memory similar to the first embodiment and the second embodiment is performed using the S-shaped curve only when it exists in any of the lines (C-D section). Alternatively, the granular material is counted by the same method as in the second embodiment.

  Alternatively, the maximum value level and the minimum value level of the S-curve of the measurement object are within the range of the maximum value level (reference line AB) obtained from the reference object and obtained from the reference object. It is determined whether or not it exists within the minimum value level range (reference line CD section). As a result, the S-curve obtained from the measurement object is within the range of the maximum value level obtained from the reference object (reference line AB) and the range of the minimum value level obtained from the reference object ( The data recording process to the memory similar to the first and second embodiments is performed using the S-shaped curve only when it exists within the reference line (C-D section). The particulate matter is counted by the same method as in the second mode.

  Thus, by selecting the S-curve of the measurement object based on the S-curve of the reference object, the signal of the S-curve obtained from the object of a size other than the target measurement object is , Removed in advance. That is, when the S-curve obtained from the measurement object is the sections c and d and the section e in FIG. 18, the processes after the data recording process to the memory are performed using the S-curve of only the section d. As a result, it is possible to accurately count particulate matter using the S-curve signal of only the measurement object to be counted.

  As described above, according to the fourth embodiment, the size (diameter) of the measurement object serving as a reference in advance on the analysis disk is utilized using the characteristic that the amplitude of the pattern is proportional to the size (diameter) of the object. The maximum value and the minimum value are obtained, and the distribution of the maximum value and the minimum value of the signal obtained by measuring this is obtained, and the amplitude of the signal obtained at the time of measuring the measurement object is the above reference. By measuring only those that fall within the distribution range of the maximum and minimum amplitudes of the signal, it is possible to perform measurement in one scan without measuring objects other than the target measurement object size. Only the target object can be measured.

(Embodiment 5)
FIG. 21 (a) is a diagram showing particulate matter detection in the particulate matter discrimination method according to Embodiment 5 of the present invention.
The analysis disk has light reflectivity and transparency, and a guide disk or a data recording track 1 is spirally engraved on the base disk, an upper cover having an injection port, and the adhesion and flow of the upper cover and the base disk. It is composed of an adhesive layer for the purpose of forming a path.

  The test specimen is injected into the analysis disk, passes through the flow path formed on the adhesive layer, the upper cover lower surface, and the base disk upper surface, undergoes a pretreatment such as centrifugation, and is a granular component that is a measurement target component in the specimen. An object reaches the measurement area.

  In the measurement area, the particulate matter in the sample is present on the surface of the base disk by the particulate matter adsorption factor (antibodies, etc.) applied to the top surface of the base disc, and the size of the particulate matter is as shown in FIG. In addition, the track 211 is larger than the width of the track 211 and extends across a plurality of tracks 211.

  The analyzer has a two-part PD that receives the laser beam 212 transmitted through the analysis disk. If there is no particulate matter on the analysis disk, the laser spot emitted from the optical pickup is at the center of the PD. It is arranged to come.

  When a granular object crosses the laser, the position of the laser spot on the PD changes due to the change in the light refraction, and the spot position change is detected as an S-shaped signal by subtracting the two-part PD signal. The

  At this time, as shown in the right figure of FIG. 21 (a), the size of the S-shaped curve 215 varies depending on the size of the crossed granular material 213, and the larger the crossed granular material 213, the S-shaped curve. 215 also increases.

  FIG.21 (b) is the figure which expanded the S-shaped curve 215 in the granular material discrimination | determination method by Embodiment 5 of this invention.

  When the S-curve 215 is detected, “1” is not simply stored in the memory, but the size of the S-curve is shown in FIG. 21B based on the magnitude of ΔZ or ΔT. “1000” when the character curve is determined to be the smallest, and “1100” and “1110” as the S curve increases, and “1111” when the S curve is determined to be the largest. A data array in which the size of the S-curve is divided into several stages is stored in the memory.

About the granular material discrimination | determination method by this Embodiment 5 comprised as mentioned above, the operation | movement and an effect | action are demonstrated below.
FIG. 22 is a diagram showing a granular material discrimination method according to Embodiment 5 of the present invention.

  First, when trying to detect a granular material having a size of seven tracks, as shown in FIG. 22 (a), an operation (detection) window 214 having a size of 7 × X1 is used. Scanning is performed while shifting one by one in the direction, and a portion where the first line of the detection window 214 includes at least one “1” is first detected.

  When “1” is detected, the detection window 214 is shifted in the track direction one by one while monitoring the total of “1” in each row. Finally, the number of “1” in each row is determined. The window 214 is shifted to a position that does not change even if the detection window 214 is shifted in the track direction.

  The fact that the number of “1” s in each row does not change even when the detection window 214 is moved in the track direction indicates that all “1” s around the detection window are within the detection window.

Next, it is determined whether or not all the rows in the window having the size of 7 × X1 contain one or more “1” at the position thus detected.
If “1” is not included in any of the rows, the granular material has a size of less than seven tracks and is not counted.

If every row in the window having a size of 7 × X1 contains one or more “1” s, a count is performed.
Further, when all the rows in the window having a size of 7 × X1 include one or more “1”, the increase / decrease in the number of “1” in each track is examined, and as shown in FIG. In the middle, a portion where constriction occurs as indicated by α is separately counted as having a plurality of granular materials.

In addition, even if there is no constriction, regarding a place where the number of “1” is constant without increasing or decreasing, it is assumed that a plurality of small granular materials are arranged, and this is also counted separately.
“1” at the position read once is erased from the memory, the next window scan is performed, and finally, the number of granular materials having a size of 7 or more tracks by taking the difference between the two numbers. Can be counted.

After the scanning with the window of 7 × X1 is completed, the scanning is performed while shifting one by one in the X direction using the window with the size of 8 × X1, and “1” in the previous scanning. Is deleted from the memory, the S-character detection operation is performed again and stored in the memory.
Then, scanning is performed while shifting one by one in the X direction using a window of 8 × X1, and processing similar to that for a 7 × X1 window is performed.

  By this series of window operations, the number of granular materials for seven or more tracks, the number of granular materials for eight or more tracks, and the number of granular materials formed from a plurality of granular materials can be determined. It is possible to determine the number of granular materials for seven.

  In addition, although the arrangement | sequence of "1" was represented by 4 columns in 4 steps | paragraphs "1000"-"1111" here, in order to raise resolution, 5 columns ("10000"-"11111"), 6 columns ("100000" Needless to say, the same effect can be obtained without any problem.

  As described above, according to the granular material discrimination method according to the fifth embodiment, the laser light 212 is irradiated onto the analysis disk into which the plurality of granular materials 213 are injected, and the laser light 212 irradiated with the PD is detected. In the method of processing the detected signal and storing it in a memory to determine the independence of the granular material, “0” and “0” determined based on the presence or absence of the granular material 213 and the size of the granular material 213 1 ”is stored in the memory, and the particle size is determined from the data array in the track direction and the radial direction of the analysis disk. When 215 is detected, the size of the S-shaped curve 215 is divided into several stages, and information indicating the size of the S-shaped curve 215 is added to the data array on the memory, so that a plurality of granular materials are obtained. Ranaru of whether or can determine what is consists of one granulate 1, there is an effect that more precise granulate number can be obtained in the.

(Embodiment 6)
FIG. 23 is a diagram (a) illustrating the granular material discrimination method according to the sixth embodiment of the present invention, and a diagram (b) illustrating a data array stored in the memory.
Here, since the detection method of a granular material is the same as that of Embodiment 1, description is abbreviate | omitted. The difference from the first embodiment is that the data array indicating the presence or absence of the granular material is “11”, and when the S-curve is detected, “110” is stored in the memory for window detection, and then the S-curve Is classified into several stages, and when it is determined that there is a granular material having the smallest S-shaped curve, “0001” is obtained, and when it is determined that there is a granular material having the largest S-shaped curve, “ 1010 "is assigned to an array of" 1 "and" 0 "in which" 1 "is not consecutive and is stored in the memory.

  For example, when the array representing the size of the S-curve is 4 digits, the size of the S-curve is “0000”, “0001”, “0010”, “0100”, “0101”, “1000”, “ 1001 ”and“ 1010 ”.

  As described above, it is not necessary to monitor the number of “1” in each row of the window in the position detecting step by separating the array indicating the presence or absence of the granular material and the array indicating the size of the S-shaped curve. Compared to the first mode, the operation of the detection window is not complicated.

  In the case of 4 digits, the number of divisions is 7 steps and 8 steps, which is almost the same as in the first embodiment, but in the case of 5 digits, a total of 13 steps can be increased by 5 steps compared to the first embodiment. This is advantageous when subdividing.

About the granular material discrimination | determination method by this Embodiment 6 comprised as mentioned above, the operation | movement and an effect | action are demonstrated below.
First, when trying to detect a granular material having a size of seven tracks, a 7 × X1 size window shown in FIG. 23A is used to shift one by one in the X direction as the track direction. Scanning is performed, and as shown in the range of β in FIG. 23 (b), counting is performed for the places where all the rows include “11”.

  If the matching condition is met, the data is further analyzed for the range of γ shown in FIG. 23B, and if the S-curve size tends to decrease in the middle of each column in the window, Judge that there are particulates and count separately.

  “1” read once is erased from the memory so that one granular object is not counted again, and before scanning in another window, the S-shaped detection operation is performed again and stored in the memory. .

  Next, the same scanning is performed in a window of size 8 × X1, and the number of places where all the rows include “11” is counted, and the size of the S-curve tends to decrease in the middle of each column in the window. Count where there was.

  By this series of window operations, the number of granular materials for seven or more tracks, the number of granular materials for eight or more tracks, and the number of granular materials formed from a plurality of granular materials are known. It is possible to determine the number of the granular material for this.

  Here, although the data array indicating the size of the S-shaped curve is four columns, it goes without saying that the same effect can be obtained by using five columns, six columns,... To increase the resolution.

  Even in the case of three rows, the size of the S-shaped curve can be divided into five stages, and it is possible to determine whether or not there are sufficiently a plurality of granular materials even in the three rows.

  As described above, according to the granular material discrimination method according to the sixth embodiment, the laser light 212 is irradiated on the analysis disk into which the plurality of granular materials 213 are injected, and the laser light 212 irradiated by the PD is detected. In the method of processing the detected signal and storing it in the memory to determine the independence of the granular material, “0” is stored in the memory next to “11” which is a data array indicating the presence or absence of the granular material 213. Since the writing step and the step of writing the data array encoded on the basis of the size of the S-curve 215 detected by the PD when the laser beam 212 crosses the granular material 213 next are included. When the S-curve is detected, the size of the S-curve is divided into several levels, and in addition to the information indicating the presence or absence of the S-curve in the data array on the memory, the information indicating the size of the S-curve By adding, the operation of the detection window is easy, and it can be judged whether the granular material consists of a plurality of granular materials or one granular material, and the number of granular materials can be determined more accurately. There is an effect that can be obtained.

(Embodiment 7)
FIG. 24A is a diagram illustrating a granular material discrimination method according to the seventh embodiment of the present invention, and FIG. 24B is a diagram illustrating a data array stored in a memory. In FIG. 24B, the data array in the vertical direction indicates the radius of the granular material.

  The difference from the fifth and sixth embodiments is that a routine for comparing the size of the S-curve and the S-curve of the previous line is provided when the S-curve is detected, and the S-curve of the previous line is larger. For example, the array is not stored in the memory. When an S-shaped curve is detected, the presence or absence of particulate matter is confirmed in the data array in the previous row.

  If there is no granular object in the previous line, “110” is stored in the memory for window detection, and then the size of the S-curve is classified into several levels, and the level is set to “1”. A data array that matches the size of the S-curve detected among the data arrays allocated to the non-consecutive arrays “1” and “0” is stored in the memory.

For example, when the array representing the size of the S-curve is 4 digits, the size of the S-curve is “0000”, “0001”, “0010”, “0100”, “0101”, “1000”, “ 1001 ”and“ 1010 ”.
In the next line, when an S-shaped curve is detected, the presence or absence of particulate matter is confirmed in the data array of the previous line.

The data array indicating the size of the S-curve in the previous line is compared with the detected S-curve. If the detected S-curve is larger than the S-curve in the previous line, “110” is used for window detection. The data is stored in the memory, and then the size of the S-curve is classified into several stages, and the stages are classified into “1” and “0” arrays in which “1” is not continuous. A data array suitable for the detected size of the S-shaped curve is stored in the memory.
If the detected S-curve is smaller than the S-curve in the previous row, the data array indicating the presence / absence and size of the S-curve is not stored in the memory.

  As described above, in the granular material discrimination method according to the seventh embodiment, for example, when granular materials for 6 to 7 tracks are to be detected, first, a 3 × X1 size window is used to store data on the memory. An array in which “11” is arranged in three or more rows is detected.

  Since the number of “11” on the memory is about half that of the number of tracks that actually crossed the granular material, the size of the granular material that can be detected by this 3 × X1 is larger than the size of six double tracks. It becomes a granular material.

  After detecting particles having a size of 6 tracks or more by 3 × X1, similarly, particles having a size of 8 tracks or more are detected using a window of size 4 × X1.

  By subtracting the number of particles having a size of 6 tracks or more from the number of particles having a size of 8 tracks or more, the particles of 6 to 7 tracks can be detected.

  As described above, according to the seventh embodiment, the laser beam 212 is irradiated onto the analysis disk into which the plurality of granular materials 213 are injected, the laser beam 212 irradiated by the PD is detected, and the detected signal is obtained. In a method of processing data and storing it in a memory to determine the independence of the granular material, the S-curve of the data array at the time of detecting the granular material is compared with the data array at the time of detecting the granular material and the data array at the previous row If the S curve is small, the data is not stored in the memory, so if the S curve detected is smaller than the S curve in the previous row, the data array is not stored in the memory, so that a plurality of granular objects are adjacent to each other. Even in this case, since the data is not connected in the vertical direction and can be clearly distinguished, there is an effect that a more accurate number of granular materials can be obtained.

  The particulate matter discriminating method according to the present invention has an effect of enabling the particulate matter count without erroneously recognizing a plurality of particulate matter continuous in the radial direction of the disk, and is an analysis device for counting the analysis object. This is useful as a particulate matter counting method. Further, even when the granular materials are arranged adjacent to each other, each granular material is accurately determined as an object, which is useful for measurement of a measurement object, number determination, and the like.

The block diagram of the analyzer which used the granular material discrimination | determination method in Embodiment 1 of this invention The figure which shows an example of the granular material obtained by the said analyzer, and an S-shaped curve The figure which shows an example of the granular material obtained by the said analyzer, and an S-shaped curve The figure which shows the table showing the relationship between the kind of said granular material, and a threshold value The figure which shows the example of the granular material arranged in the radial direction of the disk in the said Embodiment 1 and Embodiment 2. FIG. The figure for demonstrating the example of a memory array in the said Embodiment 1 and Embodiment 2 The figure for demonstrating the example of a memory array in the said Embodiment 1 and Embodiment 2 The figure for demonstrating the example of a memory array in the said Embodiment 1 and Embodiment 2 The figure for demonstrating the example of a memory array in the said Embodiment 1 and Embodiment 2 The figure which shows the state of the scanning window of the granular material discrimination | determination method in the said Embodiment 1. The figure which shows the state of the scanning window of the granular material discrimination | determination method in the said Embodiment 1. The figure which shows the state of the scanning window of the granular material discrimination | determination method in the said Embodiment 1. The figure which shows the state of the scanning window of the granular material discrimination | determination method in the said Embodiment 1. The figure which shows the flow of the granular material recognition process by the granular material identification method in the said Embodiment 1. The figure which shows the flow of the granular material size determination process by the granular material identification method in the said Embodiment 1. The figure for demonstrating the example of the convex signal by the granular material discrimination | determination method in the said Embodiment 1. FIG. The figure which shows the table showing the relationship between the kind of granular material and threshold value by the granular material discrimination | determination method in the said Embodiment 2. The figure which shows the state of the scanning window by the granular material discrimination | determination method in the said Embodiment 2. The figure which shows the state of the scanning window by the granular material discrimination | determination method in the said Embodiment 2. The figure which shows the state of the scanning window by the granular material discrimination | determination method in the said Embodiment 2. The figure which shows the state of the scanning window by the granular material discrimination | determination method in the said Embodiment 2. The figure which shows the flow of the granular material recognition process by the granular material identification method in the said Embodiment 2. The figure for demonstrating the example of the convex signal by the granular material discrimination | determination method in the said Embodiment 2. The arrangement | positioning figure of the granular material on an optical disk by the granular material identification method in Embodiment 3 of this invention The figure which shows the example of the signal pattern obtained from arrangement | positioning of the granular material on an optical disk by the granular material identification method in Embodiment 3 of this invention. The figure for demonstrating the example of the memory arrangement | sequence by the granular material discrimination | determination method in the said Embodiment 3. The figure which shows the relationship between the granular material and track | truck by the granular material identification method in the said Embodiment 3. The figure which shows the example of the arrangement | positioning range of the measurement target object on the optical disk by the granular material discrimination | determination method of Embodiment 4 of this invention, and the arrangement | positioning range of the reference | standard object for obtaining a reference | standard pattern The figure for demonstrating the example of the S-shaped curve obtained from the reference | standard target object and a measurement target object by the granular material discrimination | determination method in the said Embodiment 4. Relationship diagram between granular materials and trucks by the granular material discrimination method in the prior art The figure for demonstrating the example of the memory arrangement | sequence by the granular material discrimination | determination method in a prior art. The figure which shows the granular material detection in the conventional granular material discrimination | determination method The figure which shows the granular material discrimination method using the conventional window The figure which shows the granular material detection in the granular material identification method by Embodiment 1 of this invention. Enlarged view of the S-curve signal in the granular material discrimination method according to Embodiment 1 of the present invention. The figure which shows the granular material discrimination | determination method by Embodiment 1 of this invention. The figure which described the graph which showed the number of "1" detected with the window in the granular material discrimination | determination method by Embodiment 1 of this invention The figure which shows the granular material discrimination | determination method by Embodiment 2 of this invention. The figure which shows the data arrangement | sequence stored in the memory in the granular material discrimination | determination method by Embodiment 2 of this invention. The figure which shows the granular material discrimination | determination method by Embodiment 3 of this invention. The figure which shows the data arrangement | sequence stored in the memory in the granular material discrimination | determination method by Embodiment 3 of this invention.

Explanation of symbols

1 Disc for analysis 2 Microcomputer 3 Servo control circuit 4 Spindle motor 5 Optical pickup 6 PD
7 Laser light 8 Analysis object 9 S-curve detection circuit 10 Granular object recognition circuit 11 Memory 21 Track 22 Granular object 31 Track 32 Granular object 41 Scanning window 51 Granular object 52 Track 53 Laser light 202 Laser light 211 Track 212 Laser light 213 Granularity Object 214 Operation window 215 S-curve

Claims (23)

A particulate matter discrimination method for discriminating the independence of a particulate matter that is a measurement object based on a specific signal pattern that appears when a memory on an electric signal distribution is scanned along a certain scanning direction,
A plurality of different recognition conditions based on the signal level of the specific signal pattern, a flag when the determination result based on each recognition condition is true, and a flag when the determination result is false correspond to each recognition condition. A data storing step of storing the data in a plurality of memories provided respectively;
A memory representing the distribution of the electrical signal on the array of flags stored in the plurality of memories by scanning the array of flags stored in the plurality of memories with a scanning window having the same predetermined size. A particulate matter determination step of comparing the flag sequence of the same region above and determining the independence of the particulate matter,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 1,
The specific signal pattern is an S-shaped pattern (hereinafter referred to as an S-shaped curve) having a maximum value and a minimum value according to the size of the measurement object,
The plurality of recognition conditions are a condition that the size of the S-shaped curve is larger than a predetermined first threshold value and a plurality of conditions that sequentially take a larger threshold value than the first threshold value, respectively.
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 1 or 2,
The particulate matter determination step includes
It is determined whether or not a flag when the determination result is true in the plurality of scanning windows continuously exists over a predetermined number of rows on the flag array, and the determination result in each window is determined. The combination determines the independence of the granular material.
A particulate matter discrimination method characterized by the above. A particulate matter discrimination method for discriminating the independence of a particulate matter that is a measurement object based on a specific signal pattern that appears when a memory on an electric signal distribution is scanned along a certain scanning direction,
Two different recognition conditions based on the signal level of the specific signal pattern, a flag when the determination result based on each recognition condition is true, and a flag when the determination result is false correspond to each recognition condition. A data storing step of storing in each of the two memories provided;
The array of flags stored in the two memories is scanned with two scanning windows having the same predetermined size, and the distribution of the electrical signal is indicated on the array of flags stored in the two memories. A particulate matter determination step of comparing the sequence of flags in the same area on the memory and determining the independence of the particulate matter,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 4,
The specific signal pattern is an S-shaped curve having a maximum value and a minimum value according to the size of the measurement object,
The two recognition conditions are a condition that the size of the S-shaped curve is larger than a predetermined first threshold value, and a predetermined second threshold value where the size of the S-shaped curve is larger than the first threshold value. Is also a condition that
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 4 or 5,
The particulate matter determination step includes
It is determined whether or not a flag when the determination result is true in the two scanning windows continuously exists over a predetermined number of rows on the flag array, and the determination result in each scanning window When the determination result using the first threshold is true and the determination result using the second threshold is true, the combination is recognized as an independent granular material, and the first threshold is When the determination result used is true and the determination result using the second threshold is false, there is a continuous flag indicating the number of recognition of the particulate matter when the determination result is true in the scanning window. Recognize as multiple numbers calculated from the number of lines,
A particulate matter discrimination method characterized by the above. A particulate matter discrimination method for discriminating the independence of a particulate matter that is a measurement object based on a specific signal pattern that appears when a memory on an electric signal distribution is scanned along a certain scanning direction,
A data storage step of storing a predetermined recognition condition based on the shape of the specific signal pattern, a flag when the determination result by the recognition condition is true, and a flag when the determination result is false;
A granular material that scans the array of flags stored in the memory with a scanning window having a predetermined size, and determines the independence of the granular material from the arrangement of the flags when the determination result in the scanning window is true A determination step,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 7,
The shape of the specific signal pattern is
An S-shaped curve having a maximum value and a minimum value according to the size of the measurement object;
The predetermined recognition condition is:
The condition is that the distance from the change start position to the change end position in the track direction of the S-shaped curve is larger than a predetermined value.
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 7,
The shape of the specific signal pattern is
An S-shaped curve having a maximum value and a minimum value according to the size of the measurement object;
The predetermined recognition condition is:
The distance from the change start position to the change end position in the track direction of the S-shaped curve is greater than a predetermined value, and the length from the change start position to the change end position in the track direction of the adjacent S-shaped curve is different. The condition is
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to any one of claims 1 to 9,
Generating the signal pattern by optically reading the analysis medium into which the measurement object has been injected by irradiating a laser beam; and
A reference object having a predetermined size corresponding to the size of the measurement object is provided in a predetermined area of the analysis medium in advance, and a signal pattern obtained by reading the reference object before measurement is used as a reference pattern. Process,
In the data storage step, measuring based on the comparison result with the reference pattern,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 10,
The specific signal pattern and the reference pattern are:
An S-shaped curve having a maximum value and a minimum value according to the size of the measurement object;
The result of comparison with the reference pattern is
Measurement is performed only when either the maximum value or the minimum value of the measurement object exists within the distribution range of the reference pattern.
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 10,
The specific signal pattern and the reference pattern are:
An S-shaped curve having a maximum value and a minimum value according to the size of the measurement object;
The result of comparison with the reference pattern is measured only when both the maximum value and the minimum value of the measurement object are within the distribution range of the reference pattern.
A particulate matter discrimination method characterized by the above. The particulate matter discrimination method according to any one of claims 1 to 9,
An optical system including a light source, an objective lens, and the like that is movable with respect to the analysis disk, an actuator that drives the objective lens in a rotation axis direction and a radial direction of the analysis disk, and the analysis disk An optical pickup composed of a photodetector or the like that photoelectrically converts reflected light from
A spindle motor which is a rotation driving means of the analysis disk;
A servo control circuit that performs focus servo control, tracking servo control, and spindle servo control based on the signal from the optical pickup,
Laser light irradiated from the optical pickup transmits the analysis disk, receives the transmitted light, and performs photoelectric conversion (Photo Detector, hereinafter referred to as PD);
An S-curve detection circuit for detecting an S-curve based on an electrical signal output from the PD;
A memory for storing the output of the S-curve detection circuit;
A particulate recognition circuit for recognizing particulates based on the data in the memory;
Realized by a particulate matter discrimination device comprising:
A particulate matter discrimination method characterized by the above. In the method of determining the independence of the granular material which is the measurement object injected into the analysis disk,
Storing an array of binary data consisting of “0” and “1” determined based on the presence or absence of the granular material and the size of the granular material in the memory;
Discriminating the particle size from the data array in the track direction and radial direction of the disk for analysis,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 14,
In the data arrangement in the track direction, the number of “1” is large when the S-shaped curve having the maximum value and the minimum value as the detection signal of the granular material is large, and the number of “1” when the S-shaped curve is small. Less is,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 14,
When it is detected that “1” is continuous in the data array in the radial direction and the number of “1” in the data array in the track direction is decreasing in the middle row, there are a plurality of granular materials. To determine,
A particulate matter discrimination method characterized by the above. In the method of determining the independence of the granular material which is the measurement object injected into the analysis disk,
Writing a data array on the memory based on the size of the S-curve having a maximum value and a minimum value, which are detection signals of the granular material, next to the data array indicating the presence or absence of the granular material;
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 17,
In the encoded data array, “1” does not continue continuously,
A particulate matter discrimination method characterized by the above. In the method of determining the independence of the granular material which is the measurement object injected into the analysis disk,
When the S-curve of the data array at the time of granular object detection is small compared with the data array at the time of granular object detection and the data array of the previous row, the data is not stored in the memory.
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 19,
The data array is composed of data indicating the presence or absence of particulate matter, and data indicating the size of the particulate matter.
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 19,
Writing “0” on the memory next to “11”, which is a data array indicating the presence or absence of the granular material;
Next, the step of writing a data array in which “1” determined based on the size of the S-curve having a maximum value and a minimum value, which are detection signals of the granular material, is not continuous, on the memory,
A particulate matter discrimination method characterized by the above. In the granular material discrimination method according to claim 14,
When “1” is continuous in the data array in the radial direction and the number of “1” in the data array in the track direction is the same number in each row, it is determined that the plurality of granular materials.
A particulate matter discrimination method characterized by the above. 23. The particulate matter discrimination method according to claim 14, wherein
An optical system including a light source, an objective lens, and the like that is movable with respect to the analysis disk, an actuator that drives the objective lens in a rotation axis direction and a radial direction of the analysis disk, and the analysis disk An optical pickup composed of a photodetector or the like that photoelectrically converts reflected light from
A spindle motor which is a rotation driving means of the analysis disk;
A servo control circuit that performs focus servo control, tracking servo control, and spindle servo control based on the signal from the optical pickup,
A laser beam emitted from the optical pickup passes through the analysis disk, receives the transmitted light, and performs photoelectric conversion;
An S-curve detection circuit for detecting an S-curve based on an electrical signal output from the PD;
A memory for storing an array of binary data processed based on the output signal of the S-curve detection circuit;
A particulate recognition circuit for recognizing particulates based on the data in the memory;
Realized by a particulate matter discrimination device comprising:
A particulate matter discrimination method characterized by the above.

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