WO2017130409A1

WO2017130409A1 - Method for identifying type of oil adhered to paper

Info

Publication number: WO2017130409A1
Application number: PCT/JP2016/052791
Authority: WO
Inventors: 渡辺　直樹; 日香里佐川; 拓矢吉本
Original assignee: 日本たばこ産業株式会社
Priority date: 2016-01-29
Filing date: 2016-01-29
Publication date: 2017-08-03

Abstract

A method for identifying the type of oil adhered to paper includes: a step for acquiring near-infrared spectrum data for the oil to be identified; a step for preparing a library including, as reference spectrum data, near-infrared spectrum data for a plurality of types of sample oils; and a step for identifying the type of the oil to be identified by comparing the near-infrared spectrum data of the oil to be identified with the plurality of items of reference spectrum data in the library.

Description

How to identify the type of oil on paper

The present invention relates to a method for identifying the type of oil adhering to paper.

Conventionally, the identification of the type of oil in the oil stain adhering to the cigarette paper has been carried out by an analysis method using a gas chromatograph. Moreover, in the cigarette manufacturing field, the mechanical oil adhering to the wrapping paper and the natural oil are discriminated by observation using a fluorescence microscope as a primary screening method.

As an oil type identification method, for example, in Patent Document 1, near infrared light is irradiated to a sample, which is an oil to be identified, and the absorbance at a specific wavelength of the near infrared light is measured. A method of discriminating whether a sample oil type is regular gasoline, high-octane gasoline, light oil or kerosene by using three identification steps is disclosed.

Japanese Patent Publication “JP 2008-032694 A (published February 14, 2008)”

When using a gas chromatograph to identify the type of oil that has adhered to paper, it is possible to identify it accurately, but there are problems that require special equipment and skilled skills for implementation. Another problem is that it takes time. And in the method of discriminating oil adhering to the wrapping paper with a fluorescence microscope, it is difficult to discriminate between machine oil and natural oil due to the improvement of manufacturing technology of machine oil.

The method described in Patent Document 1 is a method for identifying the type of gasoline, and is a method for irradiating the oil itself with near-infrared light, so that the oil attached to the cigarette paper is mechanically removed. It is not possible to distinguish between oil and natural oil.

Therefore, the present invention has been made in view of the above problems, and an object of the present invention is to provide a method for easily identifying the type of oil adhering to paper by an economical and efficient means. is there.

In order to solve the above problem, a specifying method according to an aspect of the present invention is a method for specifying the type of oil attached to paper, and irradiates near-infrared rays to the specified target oil attached to the target paper. To prepare a library including a target data acquisition step for acquiring near-infrared spectrum data of the oil of the specific target, and a library including near-infrared spectrum data of a plurality of types of sample oil, which is reference spectrum data. A rally preparation step, and an oil identification step of identifying the type of oil of the identification target by collating near infrared spectrum data of the identification target oil with a plurality of the reference spectrum data in the library, , Including.

According to the method for identifying the type of oil adhering to the paper according to the present invention, the type of oil can be identified easily in a short time without requiring a dedicated facility and skill.

An embodiment of the specific method according to the present invention will be described as follows.

(Overview)
One form of the specifying method according to the present invention is a method for specifying the type of oil attached to paper, and irradiating the target target oil attached to the target paper with near-infrared rays, thereby identifying the specified target oil. Data acquisition process for acquiring near-infrared spectrum data, a library preparation process for preparing a library including near-infrared spectrum data of plural types of sample oil, which is reference spectrum data, and a specific target oil And an oil specifying step of specifying the type of oil to be specified by collating the near infrared spectrum data with a plurality of reference spectrum data in the library.

One form of the specifying method according to the present invention further includes a data selecting step of selecting, from the library, reference spectral data for each sample oil that may have come into contact with the target paper, and in the oil specifying step, About oil, you may collate with the spectrum data for reference selected at the data selection process.

One embodiment of the specifying method according to the present invention may further include a reference data acquisition step of acquiring reference spectrum data by irradiating the sample oil attached to the paper with near infrared rays prior to the library preparation step. Good.

In one form of the specific method according to the present invention, the library may include near-infrared spectrum data of olive oil and near-infrared spectrum data of oil other than at least one kind of olive oil.

In one embodiment of the specifying method according to the present invention, the library preparing step includes preparing a library including the near-infrared spectrum data for each of different types of paper to which the plurality of types of sample oils are attached. In the oil specifying step, the near-infrared spectrum data of the oil to be specified may be collated with a plurality of reference spectrum data for the same type of paper as the target paper in the library. .

(Target data acquisition process)
In the specifying method of the present invention, near-infrared spectrum data of the oil is acquired by irradiating near-infrared rays to the oil attached to the paper, which is the object to be specified, in the target data acquiring step. Specific target oils include industrially used oils and oils used for research. Specific examples of specific target oils include vegetable oils such as olive oil, and mineral oils such as synthetic hydrocarbon oils, inorganic oils, ester oils, paraffins, alicyclic hydrocarbon oils, and vegetable oil oils. Can be mentioned. The paper to which the oil is attached (target paper) may be paper of a type such as paper used industrially, paper used for research. Specific examples of the target paper include cigarette wrapping paper, cigarette chip paper, and cigarette package paper.

As a means for acquiring near infrared spectrum data, a near infrared spectrum measuring instrument such as a spectroscopic measuring instrument can be used. For example, a spectrometric instrument generally used in the technical field can be used. When using to obtain near infrared spectrum data, for example, an arbitrary wavelength region in the range of 400 nm to 2500 nm can be irradiated. In one example, the near infrared wavelength region used for near infrared spectrum data is 1000 nm. A wavelength of ˜2500 nm is preferable, and a wavelength of 1000 nm-2450 nm is more preferable.

The acquired near-infrared spectrum data may be stored in, for example, a near-infrared spectrum measuring device or a computer connected thereto.

(Library preparation process)
In the specific method of the present invention, a library including near-infrared spectrum data of a plurality of types of sample oil, which is reference spectrum data, is prepared in the library preparation step. The library preparation step is performed before the oil identification step, but may be performed before the target data acquisition step or may be performed after the target data acquisition step.

In the library preparation process, the near-infrared spectrum data is already measured spectrum data. The library preparation step can be performed on a computer, for example. In the library preparation process, for example, the reference spectrum data is taken into the computer from the near-infrared spectrum measurement device, and the reference spectrum data stored in the computer or the device connected to the computer is stored in a predetermined folder. , Obtaining reference spectrum data via an internet line, etc., making reference spectrum data ready to be imported into an analysis program, etc., allowing reference spectrum data to be taken into an analysis program, etc., reference spectrum data Can be recognized by the analysis program or the like as reference spectrum data, and / or any reference spectrum data can be selected.

Sample oil includes industrially used oil and oil used for research. Specific examples of specific target oils include vegetable oils such as olive oil, and mineral oils such as synthetic hydrocarbon oils, inorganic oils, ester oils, paraffins, alicyclic hydrocarbon oils, and vegetable oil oils. Can be mentioned. It is preferable that the near-infrared spectrum data of sample oil is the near-infrared spectrum data of sample oil acquired in the state adhering to paper. The paper may be a type of paper such as paper used industrially, paper used for research. Specific examples of the target paper include cigarette wrapping paper, cigarette chip paper, and cigarette package paper. The paper is preferably the same type of paper as the target paper. When the types of paper are the same, the influence on the spectrum derived from the paper can be suppressed, so that the type of oil to be specified can be specified more reliably. Note that “paper type” means not only the difference in the intended use of paper (for example, cigarette paper and cigarette chip paper), but also the difference in materials and specifications (for example, flax cigarette paper and wood) Cigarette paper made of cigarettes) and the like.

The near-infrared spectrum data of a plurality of types of sample oils may be acquired in a reference data acquisition step described later, or may be acquired in advance by other means. In the present specification, the “near infrared spectrum data of plural types of sample oil” in the library preparation process can be collated with the near infrared spectrum data of the oil to be identified in the oil identification process. Also referred to as “spectral data”.

The library contains reference spectrum data. The library may consist only of reference spectral data, or may consist of reference spectral data and other data. Examples of other data include near infrared spectrum data of substances other than oil (for example, water, fragrance, menthol, etc.), and near infrared spectrum data of paper to which nothing is attached. In one example, the library includes near-infrared spectral data for olive oil and near-infrared spectral data for oils other than olive oil. Examples of oils other than the olive oil include oils other than olives such as industrially used oils and oils used for research. The oil other than olive can be mineral oil such as synthetic hydrocarbon oil, inorganic oil, ester oil, paraffin, alicyclic hydrocarbon oil, and vegetable oil oil.

Also, the near-infrared spectrum data, which is reference spectrum data, is data for a plurality of types of sample oil. There may be one or more spectral data for one type of sample oil. When a plurality of (for example, about 200) samples are acquired and used as reference spectrum data for one type of sample oil, the discrimination rate at the time of discrimination is improved. Further, only one spectrum data acquired by attaching to one type of paper for one type of sample oil may be included as reference spectrum data, or each acquired by attaching to a plurality of types of paper. Spectral data may be included as reference spectral data. When each spectrum data acquired by attaching to a plurality of types of paper for one type of sample oil is included as reference spectrum data, each spectrum data may be associated only with the type of sample oil. It may be associated with both the sample oil type and the paper type. In the case of being associated only with the type of oil, the spectrum data for each of the plurality of types of paper can be included as one spectral data group for the oil. In this case, each spectrum data can be collated as a group in the oil specifying step described later.

In this way, if the library is created using the spectrum data for each of a plurality of types of paper as reference spectrum data, which is one spectral data group for the oil, it is less affected by the type of paper measured at the time of discrimination, and is versatile. A high library can be created.

Also, a library may be created by preparing reference spectrum data as a group for each paper type. In this case, since the reference spectral data for the same type of paper as the paper to be specified can be collated in the oil specifying step, the type of oil to be specified can be specified more quickly and accurately.

Further, for a combination of one kind of sample oil and one kind of paper, one spectrum data may be included as reference spectrum data, or a plurality of spectrum data is included as reference spectrum data. May be. When a plurality of spectrum data are included as the reference spectrum data, each spectrum data can be included as a spectrum data group for one combination of the oil and the paper. In this case, each spectrum data can be collated as a group in the oil specifying step described later. When a plurality of spectrum data are included as reference spectrum data, measurement errors can be suppressed, and thus the specific accuracy in the oil specifying step can be increased.

Further, the reference spectrum data may be raw measurement data or data that has been subjected to arbitrary processing. Examples of such processing include first-order differentiation, second-order differentiation, third-order differentiation, fourth-order differentiation, baseline correction, standard normal variate (Standard Normal Variate) processing, etc. It may be spectral data after processing. Such a process simplifies the calculation process in the collation and can more quickly identify the type of oil to be identified. Alternatively, it is possible to acquire reference spectrum data in which the influence of a substance other than the sample oil is reduced, and the type of oil to be identified can be identified with higher accuracy.

(Oil identification process)
In the identification method of the present invention, in the oil identification step, the near-infrared spectrum data of the specific target oil acquired in the target data acquisition step is collated with a plurality of reference spectrum data in the library prepared in the library preparation step. By doing so, the type of oil to be identified is identified.

The reference spectral data to be collated in the oil specifying step may be all reference spectral data in the library or a part of the reference spectral data.

As a method of collating, for example, a method conventionally used when comparing spectra can be cited. The collation can be performed using, for example, a spectrum collation software (analysis program) on a computer. As an example of the collation method, there is a collation method using a correlation method by wavelength, a maximum distance method by wavelength, a Mahalanobis distance in the principal component space, a residual variance in the component space, or the like.

In addition, arbitrary processing may be performed on the spectrum data when collating. Examples of such processing include first-order differentiation, second-order differentiation, third-order differentiation, fourth-order differentiation, baseline correction, and standard normal variable (Standard （Variate) processing.

Also, as a result of the collation, the degree of similarity between the spectrum data of the specific target oil and the reference spectrum data may be quantified as a collation rate (similarity). And you may use this collation rate as a reference | standard at the time of specifying the kind of oil of specification object. That is, the type of sample oil in the reference spectrum data having the highest matching rate is regarded as the same as the type of oil to be specified, and the type of oil to be specified is specified.

Also, a threshold value may be set for the verification rate. For example, the spectral data of the specific target oil and the reference spectral data for each type of oil are collated, and among the respective collation rates of these results, the most similar to the specific target oil When evaluating the verified collation rate, a threshold value is set in advance, and when the collation rate is lower than the threshold value, the specific result obtained by the method of the present invention is determined as having low reliability. The operation of specifying the type of oil to be specified may be performed again using another conventional specifying method. That is, when unidentified oil adhering to the paper is generated, the specifying method of the present invention can be suitably used as the specifying method in the first stage.

When a plurality of spectrum data is included as one spectrum data group as described above for one kind of sample oil, each spectrum data may be collated as a group. In this case, there is one matching result for the plurality of spectrum data. For example, the average of the collation rates of a plurality of spectrum data is calculated as the collation rate of the spectrum data group, or the spectrum data is compared with the spectrum data of the oil to be identified after averaging the plurality of spectrum data. Etc.

Also, spectral data of multiple types of sample oil may be collated as a group. For example, in the case where it is preferable to discriminate between olive oil and other oils, the spectral data of olive oil and the spectral data group of a plurality of sample oils other than olive oil are collated, As a specific result, it may be specified that the oil is olive oil or an oil other than olive oil.

Also, when collating, wavelength regions that are not different between the spectrum of the specific target oil and the spectrum of all sample oils may be excluded from the target of collation. Thereby, since only the wavelength considered to originate in oil can be compared, specific accuracy can be raised.

Also, it may be collated with near-infrared spectrum data of paper to which nothing is attached and / or near-infrared spectrum data of substances other than oil (for example, water, fragrance, menthol, etc.). When these verification rates are higher than the sample oil, the oil to be identified can be identified as a substance other than oil, not oil.

Since the identification method of the present invention through each of the above steps uses near-infrared spectroscopy, it has the effect that the type of oil adhering to the paper can be identified easily in a short time. Moreover, since the said method can use the existing near-infrared spectrum measuring instrument, it has the advantage that a dedicated facility and skill are not required.

(Data selection process)
In one embodiment, the identification method according to the present invention further includes a data selection step of selecting, from the library, reference spectral data for each sample oil that may have come into contact with the target paper. The sample oil is collated with the reference spectrum data selected in the data selection step. The data selection process is performed after the library preparation process.

Here, “each sample oil that may have come into contact with the target paper” in the present embodiment refers to, for example, the target paper and the same kind of oil as the sample oil, a facility such as the same specific factory or laboratory Means a plurality of types of sample oils that are used in the same specific production line or equipment, and that are likely to be in contact with the target paper as compared to other sample oils. Examples of the specific production line here include a hoisting machine and a packaging machine. However, in the present invention, the “sample oil that may have come into contact with the target paper” is not limited to these examples, and the determination of the above possibility may be made under any conditions. It may be performed assuming this.

In the data selection step, it is preferable to select from the library the near-infrared spectrum data of the sample oil obtained in a state where it adheres to the same type of paper as the target paper. When the types of paper are the same, the influence on the spectrum derived from the paper can be suppressed, so that the type of oil to be specified can be specified more reliably.

In one example, reference spectral data for oils that can be used in brands, hoisting machines, and packaging machines are prepared in a predetermined folder for each brand, hoisting machine, and packaging machine, respectively. Also good.

The data selection process can be performed on a computer, for example. In the data selection step, for example, only reference spectral data for each sample oil that may have come into contact with the target paper is stored in a predetermined folder, is in a state that can be imported into an analysis program, etc. Or the like, and / or making the analysis program recognize the data as reference spectrum data to be verified.

In this embodiment, for sample oil, reference spectral data for each sample oil that may have come into contact with the target paper, selected from the library in the data selection step, and the near-infrared spectrum of the specific target oil The data is collated in the above-described oil specifying process.

In the identification method according to the present embodiment, since the types of possible oils can be limited, there is a high possibility that the target oil is identified more accurately. In addition, since the number of spectral data to be collated in the oil specifying step is reduced, it can be specified more quickly.

(Reference data acquisition process)
In one embodiment, the specifying method according to the present invention further includes a reference data acquisition step of acquiring reference spectrum data by irradiating the sample oil attached to the paper with near infrared rays prior to the library preparation step. Including.

As a specific example of the reference data acquisition process, first, a plurality of types of sample oils are attached to a plurality of papers, respectively, to prepare samples. The method for attaching the sample oil to the paper is preferably a method capable of attaching a necessary amount of the sample oil to a necessary portion of the paper. Specifically, there are a method of attaching sample oil to paper using a paint brush, a method of sucking sample oil with a dropper, and dropping it on paper. The paper and sample oil are as described in the library preparation step.

Near infrared spectrum data can be acquired using, for example, a method similar to the above-described target data acquisition process. The measurement conditions are preferably the same.

In this embodiment, the paper irradiated with near infrared rays may be one type of paper or a plurality of types of paper for one type of oil. When one type of paper is used for one type of oil, there are few combinations with each sample oil to be attached, so spectrum data can be obtained from a larger number of samples, and a reference spectrum more suitable for collation Data can be acquired. In addition, when using multiple types of paper, it is possible to create reference spectral data classified for each type of paper, and the near-infrared spectrum of sample oil adhering to the same type of paper as the target paper By collating using data, the specific accuracy of the type of oil to be identified can be increased.

Further, in the present embodiment, reference spectral data may be acquired by irradiating near infrared rays to a paper and / or a substance other than oil. Substances other than oil are preferably attached to the paper. By using the reference spectrum data obtained in this way and grasping the influence of the paper on the spectrum, a part of the wavelength regions can be excluded from the wavelength regions to be verified. Alternatively, substances other than oil can be specified by using the reference spectrum data.

Note that, in order to obtain reference spectrum data more suitable for collation, the spectrum data constituting the reference spectrum data is preferably data obtained under various conditions. For example, even if the same paper is used and the same oil is used, there may be differences between the spectra depending on the pattern of the paper, the shape or orientation of the oil or substance attached to the paper, etc. It is preferable to obtain spectral data of various conditions by irradiating the near infrared rays on the paper from various directions by means of changing the direction of the paper.

Further, the selection of the spectrum necessary for constructing the reference spectrum data may be appropriately performed according to the characteristics of the spectrum of the oil to be specified. For example, it is possible to delete a wavelength region in which there is no difference between the spectrum of the target oil and the spectrum of the sample oil. In addition, when there is a large variation among a plurality of spectra of a specific sample oil, a spectrum that is not suitable for collation may be excluded by setting a threshold value of an abnormal value (outlier) narrow.

The near-infrared spectrum data acquired in the reference data acquisition step may be stored in, for example, a near-infrared spectrum measuring device or a computer connected thereto.

In the present embodiment, prior to the library preparation step, by passing through the reference data acquisition step, more reference spectrum data for collating with the near-infrared spectrum data of the specific target oil can be acquired. Thus, it is possible to create a library that is likely to contain reference spectral data of the same oil as the specific target oil, and the possibility of correctly specifying the specific target oil is further increased.

(Usage example of the present invention)
An example of the use of the present invention is to identify unidentified oil that has adhered to cigarette paper. In this usage example, the target paper is cigarette paper, and the specific target oil attached to the target paper is oil attached to the paper when the cigarette is manufactured.

In the manufacturing process of cigarettes, olive oil is applied to a cutting tool used when cutting cigarettes. If the oil attached to the wrapping paper is the olive oil, the product quality will be small, but other types of oil (for example, machine oil) that may affect the product quality are attached. There is a possibility. Therefore, there is a need to distinguish between olive oil and other types of oil.

Hereinafter, as a use example of the present invention, a method for identifying oil attached to cigarette paper in a cigarette manufacturing factory will be described in detail.

In this cigarette manufacturing factory, different brands of cigarettes are manufactured in a plurality of production lines 1 to 4 as follows. Production lines 1 to 4 use one type of olive oil and three types of machine oil (mineral oil), but the combination of olive oil type and machine oil type is different.

Production line 1: A brand, olive oil O1, machine oil M1, M2, M3, wrapping paper a
Production line 2: B brand, olive oil O2, machine oil M1, M2, M7, wrapping paper b
Production line 3: C brand, olive oil O2, machine oil M2, M5, M6, wrapping paper c
Production line 4: D brand, olive oil O3, machine oil M4, M5, M7, wrapping paper d
First, near infrared spectrum data for all oils used in production lines 1 to 4 is acquired (reference data acquisition step), and a library including these near infrared spectrum data is prepared. (Library preparation process). That is, olive oil O1 to O4 and machine oil M1 to M7 as sample oils are attached to arbitrary cigarettes, the wrapping paper is peeled off from the cigarettes, and each sample oil attached to the paper is irradiated with near-infrared rays, whereby near red External spectrum data (reference spectrum data) is acquired. Separately, prepare a cigarette (blank) with nothing attached to the wrapping paper, a cigarette with water attached to the wrapping paper, and a cigarette with menthol attached to the wrapping paper. Also good. These near-infrared spectral data (reference spectral data, blank, water, and menthol) are stored on a computer. As will be explained below, if the cigarette of a wrapping paper with unidentified oil is generated by carrying out the reference data acquisition process in advance, the reference spectrum of the oil that is likely to be the same as the type of the oil Data can be prepared quickly.

Here, it is assumed that a cigarette in which a substance such as oil adheres to the wrapping paper is found in the production line 2. In this case, the said substance is made into oil of specific object, near infrared rays are irradiated to this, and near infrared spectrum data are acquired (object data acquisition process).

Also, any reference spectrum data can be selected from the near infrared spectrum data stored on the computer (library preparation step). Here, since oil other than olive oil O2 and machine oils M1, M2, and M7 is not used in production line 2, the oil adhering to the cigarette wrapping paper is olive oil O2 and machine oils M1, M2, There is a high possibility that it is one of M7. Therefore, the reference spectral data of the sample oil is selected from the library as olive oil O2 and mechanical oils M1, M2, and M7 as oil that may have come into contact with the target paper (data selection step). Any of the target data acquisition process, the library preparation process, and the data selection process may be performed first.

Next, the near-infrared spectrum data of the oil to be specified is compared with the reference spectrum data of each sample oil selected as described above, thereby specifying the type of oil to be specified (oil specifying step). ). At this time, you may collate with the near-infrared spectrum data about a blank, water, and menthol. Specifically, each verification rate is obtained. For example, when the near-infrared spectrum data of the machine oil M7 is the highest among the verification rates, the type of oil to be identified is identified as the machine oil M7.

If the matching rate is lower than the threshold value described above, the conventional method can be used as the next step.

In addition, the specific method of the present invention is not limited to this use example.

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope shown in the claims, and embodiments obtained by appropriately combining technical means disclosed in different embodiments. Is also included in the technical scope of the present invention. Furthermore, a new technical feature can be formed by combining the technical means disclosed in each embodiment.

Hereinafter, the present invention will be described in more detail based on examples, but the present invention is not limited to the following examples.

[Example 1]
In Example 1, an experiment for specifying the type of oil will be described.

In Example 1, a total of 8 types of oils including 1 type of olive oil and 7 types of mineral oil were used. Each of these oils was applied to 220 cigarettes of the same type for each type of oil, and the wrapping paper was collected from the cigarettes after application. These wrapping papers were cut into squares of about 2 cm on a side so that the oil stains were in the center, and used as samples for analysis. Similarly, 220 wrapping paper samples not coated with oil were prepared as blanks. Table 1 is a list of these samples. O2 oil is olive oil, and M2 to M8 oils are mineral oils.

The near-infrared spectrum of these samples was measured using an NIR analyzer (Foss NIRECO XDS Rapid Analyzer XM-1100 Series (sold by Nireco Corporation)).

Of the 220 spectra obtained from each sample, random 200 spectra are used as data for creating a library, and the operation software (VISION Version) built in the NIR analyzer is used. On 3.5.0.0 Service Pack 6 (sold by Nireco Corporation)), sample selection was performed as follows for each sample type. Select the sample by setting the Mahalanobis distance in the principal component analysis space as the method, the standard normal variable (Standard 処理 Normal 数式 Variate) processing as the formula, the segment of the second derivative is set to 30 nm, the gap is set to 0 nm, and the wavelength region is set to 1000 to 2450 nm. At the same time, an outlier (threshold value 0.02 (Much value)) of these data was detected. The selected samples were stored in a library. Regarding the above setting, the wavelength range of 400 nm to 1000 nm was a constant wavelength without depending on the type of sample, and was determined to be due to the wrapper and was deleted. Regarding the segment of the second derivative, since the spectrum of the O2 oil was highly similar to the spectrum of the M2 oil, it was easy to distinguish by setting the segment roughly. In addition, in the spectra of O2 oil, M6 oil, and M7 oil, there was a large variation between samples, so by setting the outlier threshold value narrower, the main component space of each sample was made finer and fogging with other spaces was achieved. Minimized. The principal component space was 99% cumulative.

Then, on the same operation software as above, the method is the maximum distance method by wavelength, the formula is standard normal variate (Standard NormalateVariate) processing, the second derivative segment is 30 nm, the gap is 0 nm, the wavelength region is 1000 ~ An identification confirmation method was created for each type of sample using the above library, with 2450 nm, discrimination threshold 3.3, and spectral stabilization constant 0.3.

The remaining 20 samples in the spectrum described above were used as the samples to be identified, and identification confirmation was performed for each type of sample using the above identification confirmation method. Table 2 shows the result of identification confirmation for each type of sample and the result of specifying the type of sample.

Sample ID indicates the type of wrapping paper of the sample (for example, P2), the type of oil of the sample (for example, M2), and the number for each sample. ID as indicates a specific result of automatic discrimination. The ID result in the automatic discrimination column indicates the calculated value of the sample of the library with the lowest obtained calculated value when the similarity between the sample of the specific target and each sample of the library is calculated. . Note that the lower the calculated value, the higher the similarity, and when this lowest value is lower than the threshold described below, the sample type of the calculated value showing the lowest value is The result is shown in ID as. P / F in the automatic discrimination column is a pass / fail judgment for the set threshold value (3.3), and the value shown in the ID result is lower than the threshold value and Of the calculated values, only the value indicated in the ID result is lower than the threshold value, indicating that the result is acceptable. Also, if the value shown in the ID result is equal to or greater than the threshold value, it indicates failure, and if two or more of the calculated values are less than the threshold value, Indicated as indistinguishable. These ID result and P / F are mechanically derived on the above-mentioned software (automatic confirmation). The value corresponding to each sample type in the manual confirmation column indicates a calculated value obtained by each identification confirmation method. If the above-mentioned ID result and P / F could not identify the type of the target sample, select the calculated value that shows the lowest value by comparing these values and specify the type of the target sample (Manual confirmation). The final judgment showed these results. When the type was correctly identified by automatic confirmation or manual confirmation, it was indicated as acceptable, and when the type was not correctly identified by automatic confirmation or manual confirmation, it was indicated as failed. There are 20 samples to be specified for each type of sample, and among these, the type of sample can be correctly specified, and the ratio of passing was taken as the discrimination rate.

As shown in Table 2, the discrimination rate of the types of samples other than M5 oil was 95% or more. In addition, all the determination mistakes of M5 oil were due to the determination as M3 oil. In addition, discrimination between olive oil and other oils could be performed reliably.

[Example 2]
In Example 2, an experiment for discriminating between olive oil and other oils will be described.

Samples using a plurality of types of wrapping paper and one olive oil and one kind of mineral oil as the oil applied to the wrapping paper are used as a sample for creating a library and a sample of a specific object. A similar experiment was performed under similar setting conditions. Table 3 is a list of used wrapping paper and used oil. Table 4 shows the result of identification confirmation for each type of sample and the result of specifying the type of sample. Each item of Sample ID means the type of the sample paper roll (for example, P7), the type of the sample oil (for example, olive oil: symbol O1), and the numerical values after these are the same as the same type of paper roll Means three measurements with a sample of the oil type. Other items are the same as in Table 2.

Similarly, a sample using a fragrance was prepared, and the spectrum data of the fragrance was collated with the spectrum data of the olive oil, the mineral oil, and the blank spectrum data. In addition, for the wrapping paper (blank) to which nothing adhered, the spectrum data was collated with the spectrum data of the olive oil, the mineral oil, and the spectrum data of the blank. The results are shown in Table 4.

As shown in Table 3, the material of the wrapping paper of P1 to P4 is flax, and the manufacturers and specifications are different from each other. The material of the wrapping paper of P5 to P7 is wood pulp, the manufacturer is the same, but the specifications are different. The material of the P8 wrapping paper is 90% wood and 10% flax pulp paper, and the surface is coated with fragrance (scented). The material of the P9 wrapping paper is flax pulp, and the surface is coated with a fragrance. The type of O1 oil is olive oil, and the type of M1 oil is mineral oil (mineral oil).

As shown in Table 4, the discrimination between olive oil and mineral oil could be performed reliably. Moreover, even if it was a case where a fragrance | flavor and a blank are samples of a specific object, it was able to be determined that it was not olive oil and mineral oil in a high ratio.

The present invention can be suitably used in fields where it is necessary to specify the type of oil attached to paper.

Claims

A method for identifying the type of oil attached to paper,
A target data acquisition step of acquiring near-infrared spectrum data of the specific target oil by irradiating the specific target oil attached to the target paper with near infrared;
A library preparation step of preparing a library including near-infrared spectrum data of plural types of sample oil, which is reference spectrum data;
An oil identification step for identifying the type of oil of the identification target by comparing the near-infrared spectral data of the identification target oil with a plurality of the reference spectrum data in the library. A specific method to be characterized.
Further comprising a data selection step of selecting from the library the reference spectral data for each sample oil that may have contacted the subject paper;
2. The specifying method according to claim 1, wherein in the oil specifying step, the sample oil is collated with the reference spectrum data selected in the data selecting step.
The reference data acquisition step of acquiring the reference spectrum data by irradiating the sample oil attached to the paper with near-infrared rays prior to the library preparation step. 2. The specifying method according to 2.
The library according to any one of claims 1 to 3, wherein the library includes near-infrared spectrum data of olive oil and near-infrared spectrum data of oil other than at least one kind of olive oil. Identification method.
In the library preparation step, reference spectrum data is prepared as a group for each paper type, and in the oil identification step, the near infrared spectrum data of the oil to be identified is stored in the library. The identification method according to any one of claims 1 to 4, wherein collation is performed with a plurality of the reference spectrum data for the same type of paper as the target paper.