Calibration curve generation device, calibration curve generation method, and calibration curve generation program

Abstract

To provide a calibration curve generation device, a calibration curve generation method, and a calibration curve generation program, which prevent an inspection device from erroneously determining inspection results.SOLUTION: A calibration curve generation device 2 for generating a calibration curve to be referenced by an inspection device 11 configured to inspect an article based on a spectroscopic spectrum of the article as measured by a spectrometric device 10 comprises: a preprocessing unit 20 configured to preprocess multiple absorption spectra obtained by the spectrometric device 10 for multiple articles with known true values; a classification unit 21 for classifying the preprocessed multiple absorption spectra into teacher data and test data; and a calibration curve computation unit 22 configured to compute a calibration curve from the teacher data and the true values. The classification unit 21 computes a statistical value obtained from each preprocessed absorption spectrum to select absorption spectra to be classified into the teacher data so as to completely cover all the statistical values.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a calibration curve generating device, a calibration curve generating method, and a calibration curve generating program. [Background technology]

[0002] Patent Document 1 discloses an inspection device that detects whether the components of a molded product are appropriate by directing the transmitted light emitted from a light source that has passed through the molded product into a sensor as signal light and analyzing the signal light. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-112199 Summary of the Invention [Problem to be solved by the invention]

[0004] An inspection device such as that described in Patent Document 1 inspects whether the components of a molded product are appropriate based on a calibration curve. The calibration curve is generated from training data whose true values ​​are known. However, if the calibration curve is generated from training data with low comprehensiveness, the accuracy of the calibration curve will decrease, which may cause the inspection device to erroneously determine the inspection results.

[0005] The present invention has been made in consideration of the above-mentioned circumstances, and aims to provide a calibration curve generating device, a calibration curve generating method, and a calibration curve generating program that can prevent the inspection device from making an erroneous judgment on the inspection results. [Means for solving the problem]

[0006] The calibration curve generating device according to the present invention is a calibration curve generating device (2) that generates a calibration curve to be referenced by an inspection device (11) that inspects an item based on the spectroscopic spectrum of the item measured by a spectroscopic measuring device (10), and includes a preprocessing unit (20) that preprocesses a plurality of absorption spectra obtained by the spectroscopic measuring device for a plurality of items whose true values ​​are known, a classification unit (21) that classifies the preprocessed absorption spectra into teacher data and test data, a calibration curve calculation unit (22) that calculates the calibration curve from the teacher data and the true values, and a calibration curve evaluation unit (23) that evaluates the calibration curve from the test data and the true values, and the classification unit is configured to calculate statistical values ​​obtained from each of the preprocessed absorption spectra and select absorption spectra to be classified into the teacher data so as to evenly cover the statistical values.

[0007] With this configuration, the calibration curve generating device according to the present invention improves the accuracy of the calibration curve by selecting absorption spectra to be classified into training data so as to provide high comprehensiveness for the statistical values ​​obtained from each absorption spectrum, thereby preventing the testing device from making erroneous judgments about the test results.

[0008] In the calibration curve generating device according to the present invention, the statistical values ​​may include a first type of statistical values ​​and a second type of statistical values, and the classifying unit may select absorption spectra to be classified into the training data so as to evenly cover the first type of statistical values, and may select absorption spectra to be classified into the training data from the absorption spectra excluding the absorption spectra classified into the training data from the plurality of absorption spectra so as to evenly cover the second type of statistical values.

[0009] With this configuration, the calibration curve generating device according to the present invention improves the accuracy of the calibration curve by selecting absorption spectra to be classified into training data so as to achieve high comprehensiveness for the multiple statistical values ​​obtained from each absorption spectrum, thereby preventing the testing device from making erroneous judgments about the test results.

[0010] In the calibration curve generating device according to the present invention, the statistical value may be any one of an average value, a maximum value, a minimum value, a standard deviation, and a range.

[0011] With this configuration, the calibration curve generating device according to the present invention improves the accuracy of the calibration curve by selecting absorption spectra to be classified into training data so as to have high comprehensiveness with respect to any of the average value, maximum value, minimum value, standard deviation, and range as statistical values ​​obtained from each absorption spectrum, thereby preventing the testing device from making erroneous judgments about the test results.

[0012] The calibration curve generation method according to the present invention is a calibration curve generation method for generating a calibration curve to be referenced by an inspection device (11) that inspects an article based on the spectroscopic spectrum of the article measured by a spectroscopic measurement device (10), the calibration curve comprising: a preprocessing step for preprocessing a plurality of absorption spectra obtained by the spectroscopic measurement device for a plurality of articles whose true values ​​are known; a classification step for classifying the preprocessed absorption spectra into teacher data and test data; a calibration curve calculation step for calculating the calibration curve from the teacher data and the true values; and a calibration curve evaluation step for evaluating the calibration curve from the test data and the true values, wherein the classification step calculates statistical values ​​obtained from each of the preprocessed absorption spectra and selects absorption spectra to be classified into the teacher data so as to evenly cover the statistical values.

[0013] In this way, the calibration curve generation method according to the present invention improves the accuracy of the calibration curve by selecting absorption spectra to be classified into training data so as to provide high comprehensiveness for the statistical values ​​obtained from each absorption spectrum, thereby preventing the testing device from making erroneous judgments about the test results.

[0014] The calibration curve generation program according to the present invention is a calibration curve generation program that causes a computer to generate a calibration curve to be referenced by an inspection device (11) that inspects an article based on the spectroscopic spectrum of the article measured by a spectroscopic measurement device (10). The calibration curve generation program includes a preprocessing step of preprocessing a plurality of absorption spectra obtained by the spectroscopic measurement device for a plurality of articles whose true values ​​are known, a classification step of classifying the preprocessed absorption spectra into teacher data and test data, a calibration curve calculation step of calculating the calibration curve from the teacher data and the true values, and a calibration curve evaluation step of evaluating the calibration curve from the test data and the true values. The classification step calculates statistical values ​​obtained from each of the preprocessed absorption spectra, and selects absorption spectra to be classified into the teacher data so as to evenly cover all of the statistical values.

[0015] In this way, the calibration curve generation program according to the present invention improves the accuracy of the calibration curve by selecting absorption spectra to be classified into training data so as to provide high comprehensiveness for the statistical values ​​obtained from each absorption spectrum, thereby preventing the testing device from making erroneous judgments about the test results. [Effects of the Invention]

[0016] According to the present invention, it is possible to provide a calibration curve generating device, a calibration curve generating method, and a calibration curve generating program that can prevent the inspection device from making erroneous judgments about the inspection results. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a functional block diagram of an article inspection device equipped with a calibration curve generating device according to one embodiment of the present invention. [Figure 2] FIG. 2 is a conceptual diagram showing the number of absorption spectra classified into training data by a classification unit included in a calibration curve generating device according to one embodiment of the present invention. [Figure 3]FIG. 3 is a functional block diagram showing in detail the input and output of a calibration curve calculation unit that constitutes a calibration curve generating device according to one embodiment of the present invention. [Figure 4] FIG. 4 is a functional block diagram showing in detail the input and output of a calibration curve evaluation unit that constitutes a calibration curve generating device according to one embodiment of the present invention. [Figure 5] FIG. 5 is a conceptual diagram showing an example of a display for confirming the manner of classification by the classification unit constituting the calibration curve generating device according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an article inspection system including a calibration curve generating device according to one embodiment of the present invention will be described with reference to the drawings. As shown in FIG. 1, the article inspection system according to this embodiment includes an article inspection device 1 and a calibration curve generating device 2.

[0019] When an item to be inspected, which is being transported individually along a transport path by a transport unit, reaches a predetermined inspection position, the item inspection device 1 irradiates light onto the item, which is in a fixed position at the predetermined inspection position, and inspects the quality of the item based on the spectral spectrum of the transmitted light that passes through the item upon irradiation with this light (also called irradiated light).

[0020] The items to be inspected include unpackaged items with an outer diameter of several to several tens of mm that can be transported individually, bite-sized items, as well as items and molded products of a predetermined shape manufactured using existing manufacturing equipment or manufacturing equipment without inspection capabilities, and especially items that do not change shape during transportation.

[0021] Examples of such articles include pharmaceutical preparations such as tablets, capsules, lozenges, and drops, as well as candy and chocolate. The following description will be given taking as an example an article to be inspected a tablet W that is circular in plan view, has a height (thickness) smaller than its diameter, and is roughly cylindrical in side view. Note that the article to be inspected is not limited to a circular shape in plan view, and articles of various shapes such as an oval shape or a polygonal shape can also be used.

[0022] Examples of the conveying unit include a conveying belt, a conveying disk, a conveying chute, and the like, which are configured to align and convey articles individually.

[0023] The article inspection device 1 according to this embodiment includes a spectroscopic measurement device 10 and an inspection device 11.

[0024] The spectroscopic measurement device 10 includes a light source unit and a light detection unit. The spectroscopic measurement device 10 irradiates a tablet W to be measured with broadband light (visible light, near-infrared to terahertz light (terahertz waves)), and measures the spectrum of the light transmitted through the tablet W in response to the irradiation of this light.

[0025] In this embodiment, the inspection device 11 is composed of a computer unit equipped with a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), a storage device such as a hard disk drive, and a communication port.

[0026] The ROM and storage device of this computer unit store a program for causing the computer device to function as the inspection device 11. That is, the CPU executes the program stored in the ROM and storage device using the RAM as a working area, causing the computer unit to function as the inspection device 11 in this embodiment.

[0027] The inspection device 11 calculates the absorbance A of the tablet W at each wavelength λ from the light intensity Ii at the wavelength λ of the reference spectrum stored in the storage device and the light intensity I at the wavelength λ of the spectrum of the tablet W measured by the spectrometer 10, as follows: A=-log 10 The absorption spectrum of the tablet W is calculated by performing calculation according to (I / Ii). In this embodiment, the spectrum measured by the spectroscopic measurement device 10 in the absence of a measurement object is used as the reference spectrum.

[0028] The inspection device 11 performs preprocessing for a predetermined wavelength range of the absorption spectrum of the tablet W. The predetermined wavelength range is received from the calibration curve generating device 2 connected via a communication port and stored in the storage device of the inspection device 11.

[0029] The preprocessing performed by the inspection device 11 is the same as the preprocessing performed by the preprocessing unit 20 of the calibration curve generating device 2 described later, and therefore will not be described again. When the preprocessing of the absorption spectrum of the tablet W is completed, the inspection device 11 refers to the calibration curve stored in the storage device and calculates a measurement value corresponding to the absorption spectrum of the tablet W.

[0030] In this embodiment, the measured value represents the content of the test component contained in the tablet W. The calibration curve is received from the calibration curve generating device 2 connected via a communication port and stored in the storage device of the inspection device 11.

[0031] The inspection device 11 judges whether the quality of the tablets W is good or bad based on the measured values. The inspection device 11 outputs a sorting signal based on the quality or bad result of the judgment to a sorting device (not shown) that sorts the tablets W into normal products and defective products. The sorting device sorts the tablets W into normal products and defective products based on the sorting signal.

[0032] The calibration curve generating device 2 generates a calibration curve to be referenced by the inspection device 11 in order to inspect the tablet W measured by the spectroscopic measurement device 10. In this embodiment, the calibration curve generating device 2 is configured by a computer device including a CPU, RAM, ROM, a storage device such as a hard disk drive, a communication port, a display device, and an input device.

[0033] The ROM and storage device of this computer device store a program for causing the computer device to function as the calibration curve generating device 2. That is, the CPU executes the program stored in the ROM and storage device using the RAM as a working area, causing the computer device to function as the calibration curve generating device 2 in this embodiment.

[0034] The display device is, for example, a liquid crystal display device. The input device is, for example, a keyboard device or a pointing device. The input device may be, for example, a touch pad integrated with the display device. A cable for communicating with the inspection device 11 is connected to the communication port.

[0035] The storage device of the calibration curve generating device 2 stores the absorption spectra of a plurality of tablets whose true values ​​are known in association with the true values. The true values ​​of the plurality of tablets are measured in advance by an apparatus capable of high-precision measurement, such as a high-performance liquid chromatography apparatus. The absorption spectra of the plurality of tablets are measured in advance by the article inspection device 1.

[0036] In this embodiment, the absorption spectra of a plurality of tablets are grouped according to true values ​​and stored in the storage device of the calibration curve generating device 2. For example, the absorption spectra of 80 tablets with a true value of around 2 are assigned to the first group. The absorption spectra of 80 tablets with a true value of around 4 are assigned to the second group. The absorption spectra of 80 tablets with a true value of around 8 are assigned to the third group. The absorption spectra of 80 tablets with a true value of around 12 are assigned to the fourth group.

[0037] The calibration curve generating device 2 includes a preprocessing unit 20, a classification unit 21, a calibration curve calculation unit 22, and a calibration curve evaluation unit .

[0038] The preprocessing unit 20 performs predetermined preprocessing on the absorption spectra stored in the storage device to convert them into a data format suitable for multivariate analysis, which will be described later. The predetermined preprocessing includes at least one of the four arithmetic operations, differentiation, integration, and normalization.

[0039] The classification unit 21 classifies the preprocessed absorption spectra into teacher data Tr and test data Te. In this embodiment, the classification unit 21 classifies the preprocessed absorption spectra of 40 tablets from each of the first to fourth groups as teacher data Tr, and classifies the preprocessed absorption spectra of the remaining 40 tablets from each of the first to fourth groups as test data Te.

[0040] The classification unit 21 calculates first to fourth type statistical values ​​of each preprocessed absorption spectrum. In this embodiment, the first type statistical value is an average value, the second type statistical value is a maximum value, the third type statistical value is a minimum value, and the fourth type statistical value is a standard deviation.

[0041] As shown in FIG. 2, for each of the first to fourth groups, the classification unit 21 selects 10 absorption spectra to be classified into the training data Tr from the 80 absorption spectra that have been preprocessed so as to cover the entire average value.

[0042] In addition, for each of the first to fourth groups, the classification unit 21 selects 10 absorption spectra to be classified as teacher data Tr from 70 absorption spectra obtained by excluding the 10 absorption spectra classified as teacher data Tr from the 80 absorption spectra that have been preprocessed, so as to evenly cover the maximum values.

[0043] In addition, for each of the first to fourth groups, the classification unit 21 selects 10 absorption spectra to be classified as teacher data Tr from the 70 absorption spectra remaining after excluding the 20 absorption spectra classified as teacher data Tr from the 60 absorption spectra that have undergone preprocessing, so as to evenly cover the minimum values.

[0044] In addition, for each of the first to fourth groups, the classification unit 21 selects 10 absorption spectra to be classified as training data Tr from 50 absorption spectra, excluding the 30 absorption spectra classified as training data Tr from the 80 absorption spectra that have been preprocessed, so as to cover the entire standard deviation.

[0045] In this way, for each of the first to fourth groups, the classification unit 21 selects 40 absorption spectra from the 80 preprocessed absorption spectra to classify as training data Tr, and classifies the remaining 40 absorption spectra as test data Te.

[0046] 3, the calibration curve calculation unit 22 calculates a calibration curve representing the correlation between the teacher data Tr and the true value by performing multivariate analysis on the teacher data Tr and the true value associated with the teacher data Tr. The calibration curve calculation unit 22 performs multivariate analysis using, for example, simple regression analysis, multiple regression analysis, quantification type 1, quantification type 2, quantification type 3, discriminant analysis, logistic regression analysis, principal component analysis, partial least squares regression (PLS regression), factor analysis, cluster analysis, correspondence analysis, multidimensional scaling, conjoint analysis, support vector machine, decision tree, random forest, naive Bayes, neural network, deep learning, etc.

[0047] In this embodiment, the calibration curve calculation unit 22 performs multivariate analysis using PLS regression. The calibration curve calculated by the calibration curve calculation unit 22 is transmitted to the inspection device 11 via a communication port in response to an operation of the input device, for example, and stored in a storage device of the inspection device 11.

[0048] As shown in FIG. 4, the calibration curve evaluation unit 23 calculates an evaluation value of the calibration curve calculated by the calibration curve calculation unit 22 using the test data Te and the true value associated with the test data Te, and evaluates the accuracy of the calibration curve based on the evaluation value.

[0049] In this embodiment, the calibration curve evaluation unit 23 calculates an evaluation value of the calibration curve based on at least one index such as the mean square error, the root mean square error, the mean absolute error, the average error, the prediction standard error, the coefficient of determination, and the coefficient of determination corrected for degrees of freedom, etc. The calibration curve evaluation unit 23 displays the calculated evaluation value on, for example, a display device.

[0050] As described above, the calibration curve generating device 2 according to this embodiment improves the accuracy of the calibration curve by selecting absorption spectra to be classified into training data so as to increase the comprehensiveness of the statistical values ​​obtained from each absorption spectrum, thereby preventing the inspection device 11 from making erroneous judgments about the inspection results.

[0051] In the above-described embodiment, an example was described in which the first type of statistical value was an average value, the second type of statistical value was a maximum value, the third type of statistical value was a minimum value, and the fourth type of statistical value was a standard deviation.

[0052] On the other hand, the first to fourth types of statistical values ​​may be other combinations as long as the types do not overlap. Furthermore, each of the first to fourth types of statistical values ​​may be an average value, a maximum value, a minimum value, a standard deviation, or another type of statistical value such as a range (maximum value - minimum value). Furthermore, the classification unit 21 may allow the user to select the first to fourth types of statistical values ​​via an input device of the calibration curve generating device 2.

[0053] Furthermore, as shown in FIG. 5, the classification unit 21 may display an image on the display device of the calibration curve generating device 2 to allow the operator to confirm that the training data Tr has been selected from a statistically sufficient number of absorption spectra so as to provide high comprehensiveness for the statistical values.

[0054] FIG. 5 shows an example of an image display that allows the operator to confirm that 10 absorption spectra, 1st, 10th, 19th, 28th, 37th, 46th, 55th, 64th, 73rd, and 80th in ascending order of average value, have been selected as training data Tr from 80 absorption spectra.

[0055] Furthermore, when the inspection device 11 detects a tablet W that exceeds the allowable range for the statistical values ​​of the first to fourth types, the inspection device 11 may send a sorting signal to sort the tablet W into a category different from normal products and defective products, thereby prompting the calibration curve generating device 2 to regenerate the calibration curve.

[0056] In this case, when classifying the preprocessed absorption spectrum into teacher data Tr and test data Te, the classification unit 21 calculates the allowable ranges for the statistical values ​​of the first to fourth types, and transmits the calculated allowable ranges together with the calibration curve to the inspection device 11 via the communication port, where they are stored in the memory device of the inspection device 11.

[0057] While embodiments of the present invention have been disclosed above, it will be apparent to those skilled in the art that modifications may be made without departing from the scope of the present invention. All such modifications and equivalents are intended to be encompassed by the following claims. [Explanation of symbols]

[0058] 2. Calibration curve generator 10 Spectrometer 11 Inspection equipment 20 Pretreatment section 21 Classification Department 22 Calibration curve calculation section 23 Calibration curve evaluation section

Claims

1. A calibration curve generating device (2) that generates a calibration curve to be referenced by an inspection device (11) that inspects an article based on a spectroscopic spectrum of the article measured by a spectroscopic measurement device (10), comprising: a pre-processing unit (20) that pre-processes a plurality of absorption spectra obtained by the spectroscopic measurement device for a plurality of articles whose true values ​​are known; a classification unit (21) that classifies the plurality of absorption spectra that have been subjected to the preprocessing into teacher data and test data; a calibration curve calculation unit (22) for calculating the calibration curve from the teacher data and the true value; a calibration curve evaluation unit (23) that evaluates the calibration curve from the test data and the true value, The classification unit Calculating statistics obtained from each of the pre-processed absorption spectra; A calibration curve generating device that selects absorption spectra to be classified into the training data so as to cover all of the statistical values.

2. the statistical values ​​include a first type of statistical value and a second type of statistical value; The classification unit selecting absorption spectra to be classified into the training data so as to evenly cover the first type of statistical values; 2. The calibration curve generating device according to claim 1, wherein the absorption spectra to be classified as the training data are selected from the absorption spectra excluding the absorption spectra classified as the training data from the plurality of absorption spectra so as to evenly cover the second type of statistical values.

3. The calibration curve generating device according to claim 1 , wherein the statistical value is any one of an average value, a maximum value, a minimum value, a standard deviation, and a range.

4. A calibration curve generating method for generating a calibration curve to be referenced by an inspection device (11) that inspects an object based on a spectroscopic spectrum of the object measured by a spectroscopic measurement device (10), comprising: a pre-processing step of pre-processing a plurality of absorption spectra obtained by the spectroscopic measurement device for a plurality of articles whose true values ​​are known; a classification step of classifying the plurality of preprocessed absorption spectra into training data and test data; a calibration curve calculation step of calculating the calibration curve from the teacher data and the true value; a calibration curve evaluation step of evaluating the calibration curve from the test data and the true value, The classification step includes: Calculating statistics obtained from each of the pre-processed absorption spectra; A calibration curve generating method for selecting absorption spectra to be classified into the training data so as to cover all of the statistical values.

5. A calibration curve generation program that causes a computer to generate a calibration curve to be referenced by an inspection device (11) that inspects an item based on a spectroscopic spectrum of the item measured by a spectroscopic measurement device (10), comprising: a pre-processing step of pre-processing a plurality of absorption spectra obtained by the spectroscopic measurement device for a plurality of articles whose true values ​​are known; a classification step of classifying the plurality of preprocessed absorption spectra into training data and test data; a calibration curve calculation step of calculating the calibration curve from the teacher data and the true value; a calibration curve evaluation step of evaluating the calibration curve from the test data and the true value, The classification step includes: Calculating statistics obtained from each of the pre-processed absorption spectra; a calibration curve generation program for selecting absorption spectra to be classified into the training data so as to cover all of the statistical values ​​evenly;

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