CN111521575B - Quality control material selection method and device - Google Patents
Quality control material selection method and device Download PDFInfo
- Publication number
- CN111521575B CN111521575B CN202010441346.0A CN202010441346A CN111521575B CN 111521575 B CN111521575 B CN 111521575B CN 202010441346 A CN202010441346 A CN 202010441346A CN 111521575 B CN111521575 B CN 111521575B
- Authority
- CN
- China
- Prior art keywords
- quality control
- data
- derivative
- distance
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a method and a device for selecting quality control substances, wherein the method comprises the following steps: respectively carrying out non-contact imaging processing on a tested sample and an optional quality control substance to obtain a hyperspectral image; collecting spectral data of a tested sample and spectral data of selectable quality control substances in a hyperspectral image; taking the spectral data of a tested sample as target data, taking the spectral data of selectable quality control substances as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result; and selecting a target quality control substance according to the comparative analysis result. The invention can improve the accuracy and efficiency of quality control material selection, thereby achieving the purpose of improving the analysis accuracy of unknown samples.
Description
Technical Field
The invention relates to a standard substance quality control technology, in particular to a quality control substance selection method and a device.
Background
The modern geological industry carries out component analysis on rock minerals, quality control is mostly carried out depending on standard substances, the selection principle is matrix similarity of a sample and the standard substances, and the higher the similarity is, the more the accuracy of an analysis result is favorably improved. The selection of the existing standard substance depends on subjective judgment of experimenters, namely, the selection is carried out according to the actual requirements of the inspection and detection items and the applicable requirements of the existing standard substance. For example, in the analysis of copper content in a loess sample, a loess-type standard substance is first selected, and a standard substance having copper content similar to that of the sample to be analyzed is selected. In the actual situation, before the analyzed sample is not analyzed, the person cannot judge which standard substance has the copper content similar to that of the analyzed sample; if all the loess standard substances are selected to be analyzed together with the sample, unnecessary waste is caused. There is a need for an efficient and environment-friendly method for selecting quality control/standard substances, which can accurately guide the selection of the quality control/standard substances and avoid pollution and waste.
Disclosure of Invention
The invention provides a quality control material selection method and a quality control material selection device, which are used for improving the accuracy and efficiency of quality control material selection.
Therefore, the invention provides the following technical scheme:
a method of quality control substance selection, the method comprising:
respectively carrying out non-contact imaging processing on a tested sample and an optional quality control substance to obtain hyperspectral images;
collecting spectral data of a tested sample and spectral data of selectable quality control substances in a hyperspectral image;
taking the spectral data of a tested sample as target data, taking the spectral data of a selectable quality control substance as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result;
and selecting the target quality control substance according to the comparative analysis result.
Optionally, the performing non-contact imaging processing on the measured sample and the selectable quality control substance respectively to obtain a hyperspectral image includes:
and respectively carrying out non-contact imaging processing on the tested sample and the selectable quality control substances by using a hyperspectral imaging system to obtain a hyperspectral image.
Optionally, the performing a comparison analysis on the similarity between the target data and the comparison data to obtain a comparison analysis result includes:
respectively correcting the target data and the comparison data to obtain corrected data;
determining an interested area in a hyperspectral imaging graph of the tested sample and the selectable quality control substances;
determining an average spectral curve of the region of interest of the tested sample and an average spectral curve of the region of interest of the selectable quality control material according to the corrected data;
and performing similarity comparison analysis by using the spectral curve to obtain a comparison analysis result.
Optionally, the respectively performing modification processing on the target data and the comparison data includes:
correcting the target data and the contrast data respectively, wherein the correcting comprises: radiation correction and black and white correction;
and converting the corrected data into relative reflectivity data, and performing minimum noise separation transformation on the relative reflectivity data.
Optionally, the performing a similarity comparison analysis by using the spectral curve to obtain a comparison analysis result includes:
calculating the first derivative distance of each wave band of the measured sample spectral curve and the blank sample spectral curve, and selecting the maximum value as the maximum first derivative distance of the measured sample spectral curve;
calculating the first derivative distance of each wave band of the spectrum curve of the selectable quality control substance and the spectrum curve of the blank sample, and selecting the maximum value as the maximum first derivative distance of the spectrum curve of the selectable quality control substance;
determining whether the distance of the first derivative of each wave band of the spectrum curve of the selectable quality control substance is significantly different from the distance of the first derivative of each wave band of the spectrum curve of the sample to be detected, and whether the maximum distance of the first derivatives of the optional quality control substance and the distance of the first derivative of each wave band of the spectrum curve of the sample to be detected are in the same wave band;
the selecting the target quality control substance according to the comparative analysis result comprises:
and if the distance of the first derivative of each wave band of the spectrum curve of the selectable quality control substance has no significant difference with the distance of the first derivative of each wave band of the spectrum curve of the tested sample, and the maximum distance of the first derivative of the selectable quality control substance and the first derivative of each wave band of the spectrum curve of the tested sample is positioned in the same wave band, selecting the selectable quality control substance as the target quality control substance.
A mass control material selection device, the device comprising:
the imaging processing module is used for respectively carrying out non-contact imaging processing on the tested sample and the selectable quality control substance to obtain a hyperspectral image;
the data acquisition module is used for acquiring the spectral data of the tested sample and the spectral data of the selectable quality control substances in the hyperspectral image;
the comparison analysis module is used for taking the spectral data of the tested sample as target data and the spectral data of the selectable quality control substances as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result;
and the screening module is used for selecting the target quality control substances according to the comparative analysis result obtained by the comparative analysis module.
Optionally, the imaging processing module is specifically configured to perform non-contact imaging on the measured sample and the selectable quality control substance by using a hyperspectral imaging system.
Optionally, the comparative analysis module comprises:
the data processing unit is used for respectively carrying out correction processing on the target data and the comparison data to obtain corrected data;
the area determining unit is used for determining an interested area in the hyperspectral imaging graphs of the tested sample and the selectable quality control substances;
the spectral curve determining unit is used for determining an average spectral curve of the region of interest of the tested sample and an average spectral curve of the region of interest of the selectable quality control substances according to the corrected data;
and the comparison unit is used for performing similarity comparison analysis by using the spectrum curve to obtain a comparison analysis result.
Optionally, the data processing unit comprises:
a syndrome unit for correcting the target data and the contrast data, respectively, the correcting including: radiation correction and black and white correction;
and the conversion subunit is used for converting the corrected data into relative reflectivity data and performing minimum noise separation transformation on the relative reflectivity data.
Optionally, the comparing unit includes:
the first calculating subunit is used for calculating the first derivative distance of each wave band of the measured sample spectral curve and the blank sample spectral curve, and selecting the maximum value as the maximum first derivative distance of the measured sample spectral curve;
the second calculating subunit is used for calculating the first derivative distance of each wave band of the selectable quality control substance spectral curve and the blank sample spectral curve, and selecting the maximum value as the maximum first derivative distance of the selectable quality control substance spectral curve;
the band determining subunit is configured to determine whether a distance of a first derivative of each band of the selectable quality control substance spectral curve is significantly different from a distance of a first derivative of each band of the measured sample spectral curve, and whether a distance of a maximum first derivative of the distances is located in the same band;
the screening module is specifically configured to select the selectable quality control substance as the target quality control substance when the band determining subunit determines that the distance of the first-order derivative of each band of the selectable quality control substance spectral curve has no significant difference from the distance of the first-order derivative of each band of the measured sample spectral curve, and the distance of the maximum first-order derivative of the two is in the same band.
The quality control material selection method and the quality control material selection device provided by the embodiment of the invention fully utilize the capability of analyzing the overall characteristics of the sample by a hyperspectral technology, namely analyzing the matrix information of the sample, can automatically complete result analysis by programming the overall comparison of spectrograms and the analysis of data similarity, have higher accuracy and normalization compared with the traditional manual selection method, and improve the working efficiency. The device can carry out nondestructive non-contact analysis on the sample, can set selection conditions according to the characteristics of the sample to be analyzed, and has the characteristics of high analysis speed, strong pertinence and environmental protection.
Drawings
FIG. 1 is a flowchart of a method for selecting a quality control material according to an embodiment of the present invention;
FIG. 2 is a flow chart of a similarity comparison analysis performed on target data and comparison data in an embodiment of the present invention;
FIG. 3 is a graph of first derivative distance as a function of wavelength for a plurality of soil samples and a plurality of quality control material spectra curves to be selected;
fig. 4 is a block diagram showing the structure of a quality control substance selecting device according to the embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method and a device for selecting a quality control substance, which are used for respectively carrying out non-contact imaging processing on a tested sample and the selectable quality control substance to obtain a hyperspectral image; collecting the spectral data of a tested sample and the spectral data of optional quality control substances in a hyperspectral image; taking the spectral data of a tested sample as target data, taking the spectral data of a selectable quality control substance as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result; selecting a target quality control substance according to the comparative analysis result; therefore, automatic comparative analysis of the quality control substances is realized, and the target quality control substances are selected.
As shown in fig. 1, it is a flowchart of a quality control material selection method according to an embodiment of the present invention, including the following steps:
and 101, respectively carrying out non-contact imaging processing on the tested sample and the optional quality control substance to obtain a hyperspectral image.
Specifically, a hyperspectral imaging system such as a hyperspectral camera can be used to perform non-contact imaging on the measured sample and the selectable quality control substance respectively.
The hyperspectral imaging technology is based on image data technology of a plurality of narrow wave bands, combines the imaging technology with the spectrum technology, detects two-dimensional geometric space and one-dimensional spectral information of a target, and acquires continuous and narrow wave band image data with high spectral resolution.
And 102, collecting the spectral data of the tested sample and the spectral data of the optional quality control substances in the hyperspectral image.
The hyperspectral image is finely divided in the spectral dimension, and not only is the difference of the traditional black and white images or R, G and B images, but also N channels are arranged in the spectral dimension. Therefore, what can be obtained through the hyperspectral camera is a data cube, not only has the information of the image, but also expands on the spectral dimension, and as a result, not only can the spectral data of each point on the image be obtained, but also the image information of any spectral band can be obtained.
And 103, taking the spectral data of the detected sample as target data, taking the spectral data of the selectable quality control substance as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result.
The specific process of performing the similarity comparison analysis on the target data and the comparison data will be described in detail later.
And 104, selecting a target quality control substance according to the comparative analysis result.
As shown in fig. 2, the process of performing similarity comparison analysis on the target data and the comparison data according to the embodiment of the present invention includes the following steps:
The correction processing mainly comprises the following steps: data correction, data conversion, and the like, specifically as follows:
(1) Respectively correcting the target data and the contrast data, mainly comprising radiation calibration and black and white calibration, specifically, respectively performing radiation calibration on the target data and the contrast data; the radiation calibrated data is then corrected for black and white.
Wherein, the radiation correction refers to the process of correcting systematic and random radiation distortion or distortion generated by external factors and data acquisition and transmission systems, and eliminating or correcting image distortion caused by radiation errors.
The reasons for the radiation error may be: sensor response characteristics, solar radiation conditions, and atmospheric transmission conditions, among others.
In an embodiment of the present invention, the radiation calibration may be performed according to the following formula:
L λ ＝DN×gain+bias
in the formula, L λ For the measured spectral radiance, DN is the electrical signal value recorded by the instrument, gain is the slope of the response function (channel gain), and bias is the intercept of the response function.
The black and white correction means that due to the existence of dark current in a dark box, the images acquired at the wave band with weak illumination intensity contain large noise due to uneven light source intensity distribution, sample difference and the like under different wave band conditions, so that the black and white correction needs to be performed on the acquired hyperspectral images to eliminate the influence of partial noise. Firstly scanning a standard white correction plate under the same system condition with sample collection to obtain a full white calibration image W; then, covering a lens cover of the camera to acquire an image to obtain a completely black calibration image B; finally, finishing image calibration according to the following correction formula, and changing the acquired original image I into a correction image R, namely:
(2) In the embodiment of the present invention, there are two main types of data conversion: firstly, the corrected data is converted into relative reflectivity data, and then the relative reflectivity data is subjected to minimum noise separation transformation, so that the aim of data noise reduction is fulfilled.
Specifically, from the obtained images, an image of the area where the sample is located in the sample tray may be selected as the analysis object.
And 204, performing similarity comparison analysis by using the average spectrum curve to obtain a comparison analysis result.
In the case that the type of the optional quality control substance cannot be judged, the optional quality control substance can be subjected to spectrum recognition (for example, the spectrum recognition function of the ENVI software can be used for realizing the spectrum recognition function) to determine the type (rock, soil, sediment and the like) of the optional quality control substance. For the case that the types of the selectable quality control substances (rocks, soil, sediments and the like) are known, the similarity between the selectable quality control substances and the specific element content of the tested sample needs to be judged.
In the embodiment of the present invention, the first derivative distance of the curve may be used to determine the similarity between the selectable quality control substance and the sample to be tested.
The first derivative of the curve represents the slope (rate of change) of the curve at a certain point, and is calculated by the formula:
the curve first derivative distance is defined as the absolute value of the difference of the first derivative of the two spectral curves with the same wavelength range and the same wavelength interval. The calculation formula is as follows:
D＝|f'(x i )-g'(x i )|
wherein D is the first derivative distance of the two spectral curves, f' (x) i ) Is the first derivative of the spectral curve, g' (x), of the selectable quality control substance or sample i ) The first derivative of the spectral curve of the blank sample, x, represents the wavelength information.
In the embodiment of the present invention, the blank sample refers to a sample having the same matrix type as the sample to be analyzed and containing a low content of the target element. In actual operation, one of the quality control substances to be selected with the lowest content of the target element can be selected as a blank sample.
By using the formula, the blank sample spectral curve is used as a reference spectral curve, and the distance of the first-order derivative of each wave band of the spectral curve of each selectable quality control substance and the distance of the first-order derivative of each wave band of the spectral curve of the measured sample can be respectively obtained by subtracting the first-order derivative of each wave band of the blank spectral curve from the first-order derivative of each wave band of the blank spectral curve.
And for the tested sample and the optional quality control substance, the first-order derivative distance of each waveband is plotted along with the change relation of the wavelength, the first-order derivative distance between the curves can amplify the slight difference between the first-order derivative distance and the first-order derivative distance, and the first-order derivative distances corresponding to different wavebands have an indicative effect on the content information of a certain element.
Specifically, after the first derivative distance of each wave band of the measured sample spectrum curve and the blank sample spectrum curve is obtained through calculation, the maximum value is selected as the maximum first derivative distance of the measured sample spectrum curve; similarly, after the first derivative distance of each wave band of the selectable quality control substance spectral curve and the blank sample spectral curve is obtained through calculation, the maximum value is selected as the maximum first derivative distance of the selectable quality control substance spectral curve.
And during comparison and analysis, determining whether the distance of the first derivative of each waveband of the spectrum curve of the selectable quality control substance is significantly different from the distance of the first derivative of each waveband of the spectrum curve of the sample to be detected, and whether the maximum distance of the first derivative of the selectable quality control substance and the first derivative of each waveband of the spectrum curve of the sample to be detected is in the same waveband.
Accordingly, based on the results of the comparative analysis, in step 104, the results obtained from the comparative analysis are: and when the distance of the first derivative of each waveband of the spectral curve of the selectable quality control substance has no significant difference with the distance of the first derivative of each waveband of the spectral curve of the tested sample, and the maximum distance of the first derivative of the two is positioned in the same waveband, the selectable quality control substance is selected as the target quality control substance.
As shown in fig. 3, the first derivative distance of the spectral curves of the soil samples and the quality control substances to be selected is a graph of the variation of the first derivative distance with the wavelength, wherein the spectral curves of the soil samples, the blank samples and the quality control substances to be selected are collected under the same instrument parameters, and the soil samples and the quality control substances to be selected have different copper contents. The ordinate in the figure is the first derivative distance.
As shown in FIG. 3, the Cu content of the soil sample is 50-150. Mu.g-g -1 In the range, the distance maximum value of the first derivative is distributed in a wave band interval of 720-780 nm; the Cu content of the soil sample is between 200 and 600 mu g.g -1 In the range, the distance maximum of the first derivative is distributed in a wave band interval of 540-600 nm. That is, the maximum first derivative distance of samples with different copper content ranges is different from the located wave band, and the maximum first derivative distance can be used as the basis for judging and selecting the quality control material.
The difference between the two similar spectral curves can be amplified through the first derivative distance transformation, and the identification of the slight difference between the two spectral curves is facilitated. Judging whether the first derivative distance of each wave band of the spectral curve of the selectable quality control substance and the first derivative distance of each wave band of the spectral curve of the tested sample have significant difference or not according to the fact whether the first derivative distance of each wave band of the spectral curve of the selectable quality control substance and the first derivative distance of each wave band of the spectral curve of the tested sample have significant difference or not; according to the relation between the content range of the element to be researched in the sample and the wave band where the maximum value of the first derivative is located, the content range of the specific element can be further judged.
Accordingly, when the target quality control substance is selected according to the comparative analysis result in step 104, the selectable quality control substance may be selected as the target quality control substance when the distance of the first derivative of each band of the selectable quality control substance spectral curve is not significantly different from the distance of the first derivative of each band of the measured sample spectral curve, and the maximum distance of the first derivative of the two is in the same band.
In practical application, the same amount of the tested sample and the optional standard substance/quality control substance (the granularity of the tested sample is the same) can be respectively taken and paved on the same glass sample tray, and the hyperspectral spectrograms are respectively subjected to non-contact scanning imaging by using a hyperspectral imaging system under the same operating parameters to obtain the hyperspectral spectrograms. Respectively carrying out the same data processing operation on the data obtained by the two methods, wherein the same data processing operation comprises radiation correction, black-and-white correction, data conversion and the like, then determining an average spectral curve of an interested area by using the processed data, carrying out similarity comparison analysis on the target sample and the comparison sample based on the spectral curve, and selecting a target quality control substance according to the comparison analysis result; thereby realizing automatic comparative analysis of the quality control substances and further selecting the target quality control substances.
The hyperspectral spectrogram is the comprehensive embodiment of the overall characteristics of the sample, so that the quality control substance selection method provided by the embodiment of the invention can be used for distinguishing different geological samples by utilizing an indoor hyperspectral imaging technology, selecting the standard substance or the quality control substance similar to the matrix of the sample to be detected, effectively avoiding the identification error caused by the external appearance difference of the sample compared with the traditional manual selection method, having higher accuracy and normalization and improving the working efficiency.
Correspondingly, the embodiment of the invention also provides a quality control substance selection device, which is a structural block diagram of the device as shown in fig. 4.
In this embodiment, the apparatus includes the following modules:
the imaging processing module 301 is configured to perform non-contact imaging processing on the detected sample and the selectable quality control substance respectively to obtain a hyperspectral image;
the data acquisition module 302 is used for acquiring the spectral data of the tested sample and the spectral data of the selectable quality control substances in the hyperspectral image;
the comparison analysis module 303 is configured to use the spectral data of the detected sample as target data, use the spectral data of the selectable quality control substance as comparison data, and perform similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result;
and the screening module 304 is configured to select a target quality control substance according to the comparative analysis result obtained by the comparative analysis module 303.
The imaging processing module 301 may perform non-contact imaging on the sample to be measured and the selectable quality control substance by using a hyperspectral imaging system, such as a hyperspectral camera.
The comparative analysis module 303 may specifically include the following units:
the data processing unit is used for respectively carrying out correction processing on the target data and the comparison data to obtain corrected data;
the area determining unit is used for determining an interested area in the hyperspectral imaging map of the tested sample and the selectable quality control substances;
the spectral curve determining unit is used for determining an average spectral curve of the region of interest of the tested sample and an average spectral curve of the region of interest of the selectable quality control material according to the corrected data;
and the comparison unit is used for carrying out similarity comparison analysis by utilizing the average spectrum curve to obtain a comparison analysis result.
The data processing unit mainly includes data correction processing and data conversion processing, and accordingly, the data processing unit may include the following sub-units:
a syndrome unit for correcting the target data and the contrast data, respectively, the correcting including: radiation correction and black and white correction;
and the conversion subunit is used for converting the corrected data into relative reflectivity data and carrying out minimum noise separation conversion on the relative reflectivity data.
Through the correction processing of the data processing unit on the data, some error data generated by the influence of various factors can be eliminated, so that the subsequent comparative analysis on the selectable quality control substances and the tested sample is more accurate.
In the embodiment of the present invention, the comparing unit may specifically perform similarity determination on the selectable quality control substance and the detected sample by using the first derivative distance of the curve, that is, calculate the first derivative distance of the spectrum curve by using the spectrum curve of the detected sample and the spectrum curve of the selectable quality control substance, and perform comparison analysis.
The definition of the first derivative distance of the spectral curve, i.e. the specific calculation method, has been described in detail above, and is not described herein again.
The difference between two similar spectral curves can be amplified through first derivative distance transformation, which is beneficial to identifying the slight difference between the two spectral curves. Judging whether the first derivative distance of the optional quality control substance spectral curve in each wave band and the first derivative distance of the measured sample spectral curve in each wave band have significance difference (such as P < 0.05) or not, and judging whether the first derivative distance and the first derivative distance of the optional quality control substance spectral curve in each wave band have significance matrix difference or not; according to the relation between the content range of the element to be researched in the sample and the wave band where the maximum value of the first derivative is located, the content range of the specific element can be further judged.
Correspondingly, the comparison unit may specifically include the following sub-units:
the first calculating subunit is used for calculating the first derivative distance of each wave band of the measured sample spectral curve and the blank sample spectral curve, and selecting the maximum value as the maximum first derivative distance of the measured sample spectral curve;
the second calculating subunit is used for calculating the first-order derivative distance of each wave band of the spectrum curve of the selectable quality control substance and the spectrum curve of the blank sample, and selecting the maximum value of the first-order derivative distances as the maximum first-order derivative distance of the spectrum curve of the selectable quality control substance;
and the judging subunit is configured to determine whether the distance of the first-order derivative of each band of the spectrum curve of the selectable quality control substance is significantly different from the distance of the first-order derivative of each band of the spectrum curve of the sample to be detected (for example, P < 0.05), and whether the distance of the maximum first-order derivative of the distance of the first-order derivative of each band of the spectrum curve of the selectable quality control substance and the distance of the first-order derivative of each band of the spectrum curve of the sample to be detected are located in the same band.
Accordingly, the screening module 304 may select the selectable quality control substance as the target quality control substance when the determining subunit determines that the distance of the first derivative of each wavelength band of the selectable quality control substance spectral curve is not significantly different from the distance of the first derivative of each wavelength band of the measured sample spectral curve, and the distance of the maximum first derivative of the two is in the same wavelength band.
The quality control material selection device provided by the embodiment of the invention utilizes an indoor hyperspectral imaging technology to distinguish different geological samples essentially, selects the standard material or the quality control material similar to the detected sample matrix, can effectively avoid the identification error caused by the external appearance difference of the sample, has higher accuracy and normalization compared with the traditional manual selection method, and improves the working efficiency.
It should be noted that, for each embodiment of the quality control substance selection device, since the function implementation of each module and unit is similar to that in the corresponding method, the description of each embodiment of the quality control substance selection device is relatively simple, and relevant parts can be referred to the description of the corresponding parts of the method embodiment.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Furthermore, the above-described system embodiments are merely illustrative, wherein modules and units illustrated as separate components may or may not be physically separate, i.e., may be located on one network element, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those skilled in the art will appreciate that all or part of the steps in the above method embodiments may be implemented by a program to instruct associated hardware to perform the steps, and the program may be stored in a computer readable storage medium, referred to herein as a storage medium, such as: ROM/RAM, magnetic disk, optical disk, etc.
Accordingly, an embodiment of the present invention further provides an apparatus for a quality control substance selection method, where the apparatus is an electronic device, and for example, the apparatus may be a mobile terminal, a computer, a tablet device, a personal digital assistant, or the like. The electronic device may include one or more processors, memory; wherein the memory is configured to store computer-executable instructions and the processor is configured to execute the computer-executable instructions to implement the methods of the previous embodiments.
The foregoing detailed description of the embodiments of the present invention has been presented for purposes of illustration and description, and is intended to be exhaustive or to limit the invention to the precise forms disclosed, and forms of manufacture. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention, and the content of this description shall not be construed as limiting the present invention. Therefore, any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A method of selecting a quality control substance, the method comprising:
respectively carrying out non-contact imaging processing on a tested sample and the selectable quality control substances to obtain a hyperspectral image;
collecting the spectral data of a tested sample and the spectral data of optional quality control substances in a hyperspectral image;
taking the spectral data of a tested sample as target data, taking the spectral data of a selectable quality control substance as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result;
selecting a target quality control substance according to the comparative analysis result;
wherein, the performing a similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result comprises:
respectively correcting the target data and the comparison data to obtain corrected data;
determining a region of interest in the hyperspectral imaging maps of the tested sample and the selectable quality control substances;
determining an average spectrum curve of the region of interest of the tested sample and an average spectrum curve of the region of interest of the selectable quality control material according to the corrected data;
carrying out similarity comparison analysis by using the average spectrum curve to obtain a comparison analysis result;
wherein, the using the average spectrum curve to perform similarity comparison analysis to obtain a comparison analysis result comprises:
calculating the first derivative distance of each wave band of the measured sample average spectrum curve and the blank sample spectrum curve, and selecting the maximum value as the maximum first derivative distance of the measured sample average spectrum curve;
calculating the distance between the average spectrum curve of the selectable quality control substances and the first derivative of each wave band of the spectrum curve of the blank sample, and selecting the maximum value as the maximum first derivative distance of the average spectrum curve of the selectable quality control substances;
determining whether the distance of the first derivative of each wave band of the average spectrum curve of the selectable quality control substances is significantly different from the distance of the first derivative of each wave band of the average spectrum curve of the sample to be detected, and whether the distance of the maximum first derivative of the distance of the first derivative of each wave band of the average spectrum curve of the selectable quality control substances and the distance of the first derivative of each wave band of the average spectrum curve of the sample to be detected are in the same wave band;
the selecting the target quality control substance according to the comparative analysis result comprises:
and if the distance of the first derivative of each wave band of the average spectrum curve of the selectable quality control substances is not significantly different from the distance of the first derivative of each wave band of the average spectrum curve of the tested sample, and the maximum distance of the first derivative of the selectable quality control substances and the first derivative of each wave band of the average spectrum curve of the tested sample is positioned in the same wave band, selecting the selectable quality control substances as the target quality control substances.
2. The method of claim 1, wherein the performing non-contact imaging processing on the measured sample and the selectable quality control substance to obtain the hyperspectral image comprises:
and respectively carrying out non-contact imaging processing on the tested sample and the selectable quality control substance by utilizing a hyperspectral imaging system to obtain a hyperspectral image.
3. The method of claim 2, wherein the respectively modifying the target data and the comparison data comprises:
correcting the target data and the contrast data respectively, wherein the correcting comprises: radiation correction and black and white correction;
and converting the corrected data into relative reflectivity data, and performing minimum noise separation transformation on the relative reflectivity data.
4. A mass control material selection device, comprising:
the imaging processing module is used for respectively carrying out non-contact imaging processing on the tested sample and the selectable quality control substance to obtain a hyperspectral image;
the data acquisition module is used for acquiring the spectral data of the tested sample and the spectral data of the selectable quality control substances in the hyperspectral image;
the comparison analysis module is used for taking the spectral data of the tested sample as target data and the spectral data of the selectable quality control substances as comparison data, and performing similarity comparison analysis on the target data and the comparison data to obtain a comparison analysis result;
the screening module is used for selecting the target quality control substances according to the comparative analysis result obtained by the comparative analysis module;
the comparative analysis module comprises:
the data processing unit is used for respectively correcting the target data and the comparison data to obtain corrected data;
the area determining unit is used for determining an interested area in the hyperspectral imaging map of the tested sample and the selectable quality control substances;
the spectral curve determining unit is used for determining an average spectral curve of the region of interest of the tested sample and an average spectral curve of the region of interest of the selectable quality control substances according to the corrected data;
the comparison unit is used for carrying out similarity comparison analysis by utilizing the average spectrum curve to obtain a comparison analysis result;
the comparison unit includes:
the first calculating subunit is used for calculating the first derivative distance of each wave band of the measured sample average spectrum curve and the blank sample spectrum curve, and selecting the maximum value as the maximum first derivative distance of the measured sample average spectrum curve;
the second calculating subunit is used for calculating the first derivative distance of each waveband of the average spectrum curve of the selectable quality control substance and the spectrum curve of the blank sample, and selecting the maximum value of the first derivative distances as the maximum first derivative distance of the average spectrum curve of the selectable quality control substance;
the judging subunit is used for determining whether the distance of the first-order derivative of each waveband of the average spectrum curve of the selectable quality control substances is significantly different from the distance of the first-order derivative of each waveband of the average spectrum curve of the sample to be detected, and whether the distance of the maximum first-order derivative of the distances is located in the same waveband;
the screening module is specifically configured to select the selectable quality control substance as the target quality control substance when the determining subunit determines that the distance of the first-order derivative of each band of the selectable quality control substance average spectral curve is not significantly different from the distance of the first-order derivative of each band of the measured sample average spectral curve, and the distance of the maximum first-order derivative of the two is in the same band.
5. The apparatus of claim 4,
the imaging processing module is specifically used for performing non-contact imaging on the tested sample and the selectable quality control substances by utilizing a hyperspectral imaging system.
6. The apparatus of claim 4, wherein the data processing unit comprises:
a syndrome unit for correcting the target data and the contrast data, respectively, the correcting including: radiation correction and black and white correction;
and the conversion subunit is used for converting the corrected data into relative reflectivity data and performing minimum noise separation transformation on the relative reflectivity data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010441346.0A CN111521575B (en) | 2020-05-22 | 2020-05-22 | Quality control material selection method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010441346.0A CN111521575B (en) | 2020-05-22 | 2020-05-22 | Quality control material selection method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111521575A CN111521575A (en) | 2020-08-11 |
| CN111521575B true CN111521575B (en) | 2023-03-14 |
Family
ID=71905434
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010441346.0A Active CN111521575B (en) | 2020-05-22 | 2020-05-22 | Quality control material selection method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111521575B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112129720B (en) * | 2020-09-17 | 2021-10-29 | 山东大学 | Lining detection method and system based on hyperspectral information |
| CN112906750B (en) * | 2021-01-25 | 2022-04-29 | 浙江大学 | Hyperspectral image-based material analysis method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288551A (en) * | 2011-04-01 | 2011-12-21 | 尚振刚 | Method for distinguishing traditional Chinese medicinal material by utilizing hyperspectral data |
| CN103558167A (en) * | 2013-10-31 | 2014-02-05 | 华南理工大学 | Method for rapidly measuring content of sodium chloride in salted meat |
| CN106568737A (en) * | 2016-10-28 | 2017-04-19 | 核工业北京地质研究院 | Method used for inversion analysis of potassic salt content via ground surface imaging hyperspectrum |
| CN106918565A (en) * | 2017-03-02 | 2017-07-04 | 中南大学 | Heavy metal-polluted soil Cd contents Inverse modeling and its spectral response characteristics wave band recognition methods based on indoor standard specimen bloom spectrum signature |
| CN107132205A (en) * | 2017-06-12 | 2017-09-05 | 华中科技大学 | One kind is based on the enhanced Spectral matching method and system of all band feature |
| CN110865092A (en) * | 2019-12-10 | 2020-03-06 | 中国科学院金属研究所 | In-situ analysis method for representing component distribution of high-temperature alloy by utilizing X-ray fluorescence spectrum |
-
2020
- 2020-05-22 CN CN202010441346.0A patent/CN111521575B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288551A (en) * | 2011-04-01 | 2011-12-21 | 尚振刚 | Method for distinguishing traditional Chinese medicinal material by utilizing hyperspectral data |
| CN103558167A (en) * | 2013-10-31 | 2014-02-05 | 华南理工大学 | Method for rapidly measuring content of sodium chloride in salted meat |
| CN106568737A (en) * | 2016-10-28 | 2017-04-19 | 核工业北京地质研究院 | Method used for inversion analysis of potassic salt content via ground surface imaging hyperspectrum |
| CN106918565A (en) * | 2017-03-02 | 2017-07-04 | 中南大学 | Heavy metal-polluted soil Cd contents Inverse modeling and its spectral response characteristics wave band recognition methods based on indoor standard specimen bloom spectrum signature |
| CN107132205A (en) * | 2017-06-12 | 2017-09-05 | 华中科技大学 | One kind is based on the enhanced Spectral matching method and system of all band feature |
| CN110865092A (en) * | 2019-12-10 | 2020-03-06 | 中国科学院金属研究所 | In-situ analysis method for representing component distribution of high-temperature alloy by utilizing X-ray fluorescence spectrum |
Non-Patent Citations (1)
| Title |
|---|
| 浅谈食品理化检测实验室质量控制;张林田 等;《理化检验(化学分册)》;20130430;第49卷(第4期);第473-476页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111521575A (en) | 2020-08-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107037012B (en) | Echelle spectrometer dynamic correcting method for laser induced breakdown spectroscopy acquisition | |
| CN111521575B (en) | Quality control material selection method and device | |
| Grotti et al. | An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning | |
| WO2012156667A1 (en) | Spectroscopic apparatus and methods for determining components present in a sample | |
| CN106295696A (en) | A kind of multi-source Remote Sensing Images radiation normalization method | |
| CN101059452A (en) | Fruit quality damage-free detection method and system based on multiple spectral imaging technique | |
| CN108827934B (en) | Blind source separation Raman scattering image-based packaged food quality nondestructive testing method | |
| CN111626224B (en) | Near infrared spectrum and SSA optimization-based ELM (enzyme-linked immunosorbent assay) quick coal gangue identification method | |
| CN113959961B (en) | Hyperspectral image-based tannin additive anti-counterfeiting detection method and system | |
| CN110555395A (en) | Classified evaluation method for nitrogen content grade of rape canopy | |
| CN115235997A (en) | Soil texture inversion method based on satellite hyperspectral image | |
| CN201041553Y (en) | Fruit quality non-damage detection system base on multi-spectrum imaging technology | |
| Liu et al. | Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system | |
| CN113272639B (en) | Method for extracting spectral information of substance to be detected | |
| US20230298853A1 (en) | Method and system for generating a diffraction image | |
| CN111398138A (en) | Optical detection system and method of dry type blood cell analysis device | |
| CN114062282B (en) | Method and device for rapidly judging dry matters of fruits on tree | |
| Fan et al. | Identification of milled rice varieties using machine vision | |
| CN112945902A (en) | Method for detecting azodicarbonamide in flour by applying near-infrared hyperspectral imaging technology | |
| CN104677826B (en) | A kind of height correction system and method | |
| CN111398323A (en) | Calculation method for automatically acquiring X-ray analysis position in mineral automatic analysis system | |
| CN118392801B (en) | Method, equipment, medium and product for automatic modeling of agricultural product spectrum nondestructive testing | |
| CN111640085A (en) | Image processing method and apparatus, detection method and apparatus, and storage medium | |
| KR102727706B1 (en) | Method for extracting spectral information of a target substance | |
| CN113588095B (en) | Point source target infrared radiation quantitative processing method and device based on frame accumulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |