Manufacturing method of measuring device

Abstract

To provide a method for manufacturing a measurement device which can reduce the instrument error of a measurement device.SOLUTION: The method for manufacturing a measurement device is a method for manufacturing a measurement device having a plurality of sensors and outputting an output signal on the basis of detection signals of the sensors. The method for manufacturing the measurement device includes the steps of: acquiring the output characteristics of N number of sensors; and selecting j number of sensors for one measurement device from the N number of sensors on the basis of the output characteristics of the N number of sensors (N>>j) and forming a plurality of measurement devices.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for manufacturing a measuring device. [Background technology]

[0002] Near-infrared spectrometers are widely used as devices for sorting agricultural products such as fruits and measuring various parameters (such as sugar content). However, near-infrared spectrometers have differences (instrumental error) between multiple devices, even if they are of the same model, and even when measuring the same object using multiple devices, there is variation in the measured values ​​obtained. For this reason, it is necessary to ensure that multiple devices can obtain approximately the same measured values. Furthermore, even with the same device, periodic calibration is required to address changes in measured values ​​over time.

[0003] When measuring agricultural products such as fruits, there are no standard samples whose characteristics are retained for a long period of time. This means that spectroscopic measurements of dozens of samples and the target variables (e.g., sugar content, protein content, etc.) must be performed to confirm the estimated accuracy of the calibration curve. While calibration is relatively easy for items that are easy to analyze, such as sugar content, there are quality items for which calibration is extremely difficult or impossible, such as calibration curves that estimate human perception of taste and texture. This has been an obstacle to the development of agricultural product measurement using near-infrared spectroscopy. In addition to near-infrared spectroscopy, variations in sensor output characteristics can lead to variations in output characteristics between multiple measuring devices, which can result in insufficient reliability of the measuring devices. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6080410 [Patent Document 2] Patent Publication No. 2021-139818 Summary of the Invention [Problem to be solved by the invention]

[0005] The present invention provides a method for manufacturing a measuring device that can reduce instrumental error between measuring devices. [Means for solving the problem]

[0006] In order to solve the above problems, a method for manufacturing a measuring device according to the present invention is a method for manufacturing a measuring device that includes a plurality of sensors and outputs an output signal based on detection signals from the plurality of sensors. This manufacturing method includes the steps of acquiring output characteristics of each of N sensors, and selecting j sensors (N>>j) for one measuring device from the N sensors based on the output characteristics of each of the N sensors to configure a plurality of measuring devices. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide a method for manufacturing a measuring device that can reduce instrumental error between measuring devices. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a schematic diagram illustrating the configuration of a measuring device 10 manufactured by a manufacturing method according to a first embodiment. [Figure 2] 10 is a graph illustrating the effect of providing a plurality of spectroscopic sensors 14 in one measurement device 10. [Figure 3] 4 is a flowchart illustrating a method for manufacturing the measurement device according to the first embodiment. [Figure 4] 2 is a schematic diagram illustrating an overview of a manufacturing method for the measurement device according to the first embodiment. FIG. [Figure 5] 10 is a flowchart illustrating a method for manufacturing a measurement device according to a second embodiment. [Figure 6] FIG. 10 is a schematic diagram illustrating an overview of a manufacturing method for a measuring device according to a second embodiment. [Figure 7] 10 is a flowchart illustrating a method for manufacturing a measurement device according to a third embodiment. [Figure 8]FIG. 10 is a schematic diagram illustrating an overview of a manufacturing method for a measurement device according to a third embodiment. [Figure 9] 10 is a graph illustrating the effects of various embodiments. [Figure 10] 10 is a graph illustrating the effects of various embodiments. [Figure 11] 10 is a graph illustrating the effects of various embodiments. [Figure 12] 10 is a flowchart illustrating a method for manufacturing a measurement device according to a modified example of the third embodiment. [Figure 13] The results of applying principal component analysis to the detected spectral signals shown in FIG. 2 and then applying sensor selection by k-means clustering in the third embodiment are shown below. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, the present embodiment will be described with reference to the accompanying drawings. In the accompanying drawings, functionally identical elements may be designated by the same numerals. Note that the accompanying drawings show embodiments and implementation examples according to the principles of the present disclosure, but these are for understanding the present disclosure and are not to be used to interpret the present disclosure in a limiting manner. The descriptions in this specification are merely typical examples and are not intended to limit the scope or application of the present disclosure in any way.

[0010] Although the present embodiment has been described in sufficient detail to enable those skilled in the art to implement the present disclosure, it should be understood that other implementations and forms are possible, and that changes in configuration and structure and substitutions of various elements are possible without departing from the scope and spirit of the technical ideas of the present disclosure. Therefore, the following description should not be interpreted as being limited thereto.

[0011] [First embodiment] First, a method for manufacturing a measuring device according to a first embodiment of the present invention will be described. Fig. 1 illustrates the configuration of a measuring device 10 manufactured by the manufacturing method according to the first embodiment. The upper view of Fig. 1 shows the structure of the measuring surface (lower surface) of the measuring device 10, and the lower view shows a side cross-sectional view.

[0012] As an example, this measuring device 10 is a near-infrared spectrometer including a light source 11, an optical fiber 12, a light-shielding rubber 13, a plurality of spectroscopic sensors 14, and an entrance slit 15. The near-infrared spectrometer shown in FIG. 1 is an example of the measuring device 10 that is the subject of the manufacturing method of the present invention. The light source 11 is a light source that emits measurement light to be irradiated onto a measurement object SP (such as a fruit). The optical fiber 12 is a light-guiding member that extends from the light source 11 toward the measurement surface (lower surface) and guides the measurement light toward the measurement surface.

[0013] The light-shielding rubber 13 is formed on the underside of the measuring device 10 so as to surround the output end face of the optical fiber 12, and prevents the output light from entering the spectroscopic sensor 14 without passing through the measurement target SP (stray light).

[0014] The multiple spectroscopic sensors 14 are arranged, for example, at equal intervals on a circumference surrounding the optical fiber 12 on the measurement surface and measure the spectrum of the reflected light LB from the measurement target SP. Measuring the spectrum of the reflected light LB using the average or median of the detection values ​​of the multiple spectroscopic sensors 14 reduces variations in characteristics among the multiple spectroscopic sensors 14 and thus reduces variations in performance among the multiple measurement devices 10. The number of spectroscopic sensors 14 installed in one measurement device 10 is not limited to a specific number. FIG. 1 illustrates an example in which one measurement device 10 includes 16 spectroscopic sensors 14, each arranged at equal intervals of 22.5 degrees. The arrangement of the spectroscopic sensors 14 is also not limited to a specific arrangement. For example, instead of being arranged at equal intervals on the circumference as shown in FIG. 1, the spectroscopic sensors 14 can be arranged at equal intervals in a grid pattern, for example. An entrance slit 15 is provided in front of the multiple spectroscopic sensors 14 to limit the reflected light LB. The spectroscopic sensor 14 may be, for example, an ultra-compact grating spectrometer C14384MA-01 (product name) provided by Hamamatsu Photonics KK Such a spectrometer can measure light in the near-infrared region with high sensitivity, but may contain errors in sensitivity or in the correspondence between the detection element installed and the measured wavelength.

[0015] The detection signals from the multiple spectroscopic sensors 14 are input to an arithmetic control unit (not shown), and the average or median of the detection values ​​from the multiple spectroscopic sensors 14 can be output as the measurement value of the measuring device 10. By calculating the average or median of the detection values ​​from the multiple spectroscopic sensors 14, even if there is variation in the performance of the spectroscopic sensors 14, it is possible to output a measurement value that eliminates the influence of the variation.

[0016] 2A and 2B are graphs illustrating the effect of providing multiple spectroscopic sensors 14 in one measurement device 10. FIG. 2A shows the variation in detected spectral signals among multiple measurement devices 10 when each measurement device 10 has a single spectroscopic sensor 14. FIG. 2B shows the variation in detected spectral signals among multiple measurement devices 10 when each measurement device 10 has four spectroscopic sensors 14. FIG. 2C shows the variation in detected spectral signals among multiple measurement devices 10 when each measurement device 10 has 16 spectroscopic sensors 14.

[0017] As shown in FIG. 2, the more spectroscopic sensors 14 are installed in one measuring device 10, the smaller the variation in the detection signals between the multiple measuring devices 10 becomes, since the average or median value of the detection values ​​of the multiple spectroscopic sensors 14 is used as the output signal.

[0018] However, even if the number of spectroscopic sensors 14 mounted on one measuring device 10 increases, there still exists variation in the detection signals between the measuring devices 10. Therefore, in this embodiment, it is proposed to reduce such variation by adopting the manufacturing method described below.

[0019] A manufacturing method according to the first embodiment will be described with reference to Figures 3 and 4. Figure 3 is a flowchart illustrating the manufacturing method according to the first embodiment, and Figure 4 shows an outline of the manufacturing method. In this method, in order to manufacture a measuring device 10 having j spectroscopic sensors 14 per device, N spectroscopic sensors 14 (N>>j) are prepared, and these N spectroscopic sensors 14 form a population.

[0020] Then, the output characteristics of each of the plurality of spectroscopic sensors 14 in this population are measured and acquired (step S11). The measurement of the output characteristics may be performed by measuring the output signal of each spectroscopic sensor 14, or the output characteristics may be estimated based on visual inspection or the like. Note that if the spectroscopic sensor 14 has a plurality of output channels, the measurement of the output characteristics may be performed after performing dimensionality reduction using principal component analysis, t-distributed stochastic neighbor embedding (t-SNE), nonnegative matrix factorization (NMF), or the like.

[0021] Assume that there are a plurality of (e.g., N) spectroscopic sensors 14, and information regarding their output characteristics is obtained in advance. In this case, N spectroscopic sensors 14 are suitably combined in groups of j (N>>j) to create a large number of combinations of j spectroscopic sensors 14, and the output characteristics of a plurality of measuring devices 10 for each combination are calculated on a computer. Then, the output characteristics for all possible combinations of j spectroscopic sensors 14 are calculated "brute-force." Then, the combination that minimizes the variation in output characteristics among the plurality of measuring devices 10 is determined, and a plurality of measuring devices 10 each including j spectroscopic sensors 14 is configured according to that combination (step S13).

[0022] According to the manufacturing method of this first embodiment, the output characteristics are measured in a brute-force manner for all combinations of N spectroscopic sensors 14 that constitute a plurality of measuring devices 10, each having j spectroscopic sensors 14, making it possible to uniformize the output characteristics among the plurality of measuring devices 10.

[0023] [Second embodiment] Next, a manufacturing method according to a second embodiment will be described with reference to FIGS. 5 and 6. The manufacturing method according to the second embodiment also targets, as an example, a measuring device 10 as shown in FIG. 1. FIG. 5 is a flowchart illustrating the manufacturing method according to the second embodiment, and FIG. 6 shows an outline of the manufacturing method. This method also manufactures measuring devices 10 each having j spectroscopic sensors 14, so N spectroscopic sensors 14 (N>>j) are prepared, and these N spectroscopic sensors 14 form a population. Then, the output characteristics of each of the N spectroscopic sensors 14 in this population are measured (step S21). Up to this point, the process is the same as in the first embodiment.

[0024] Thereafter, in the manufacturing method of the second embodiment, the spectroscopic sensors 14 are grouped (classified) into a plurality of groups (for example, k groups) according to the output characteristics of the spectroscopic sensors 14. In this second embodiment, as an example, as shown in FIG. 6, groups (A) to (D) are defined in a feature space, and grouping is performed so that spectroscopic sensors 14 with similar output characteristics are spaced apart in the feature space (step S22). Then, j spectroscopic sensors 14 are selected for each of the groups (A) to (D), and a plurality of measuring devices 10 each including j spectroscopic sensors 14 are configured (step S23). The number of groups k is not particularly limited by j or N, and can be set to a value that allows optimal grouping.

[0025] As an example of an algorithm that can be used for selecting the spectroscopic sensor 14, the Kennard-Stone (KS) method and its derived algorithms can be used. This is an algorithm that first selects the two most distant points in a feature space, and then sequentially selects the point with the furthest minimum distance from all the selected points. The KS method uses Euclidean distance to calculate distance, but derived algorithms may use Mahalanobis generalized distance.

[0026] In the second embodiment, the spectroscopic sensors 14 with similar output characteristics are grouped so that they are farther apart, and therefore each of the groups (A) to (D) can be configured as a collection of spectroscopic sensors 14 with different output characteristics. Therefore, by selecting spectroscopic sensors 14 belonging to the same group, the spectroscopic sensors 14 can be distributed to the multiple measuring devices 10 without bias in output characteristics, and the output characteristics can be made uniform among the multiple measuring devices 10.

[0027] [Third embodiment] Next, a manufacturing method according to a third embodiment will be described with reference to FIGS. 7 and 8. The manufacturing method according to the third embodiment also targets, as an example, a measuring device 10 as shown in FIG. 1. FIG. 7 is a flowchart illustrating the manufacturing method according to the third embodiment, and FIG. 8 shows an outline of the manufacturing method. This method also manufactures measuring devices 10 each having j spectroscopic sensors 14, so N spectroscopic sensors 14 (N>>j) are prepared, and these N spectroscopic sensors 14 form a population. Then, the output characteristics of each of the N spectroscopic sensors 14 in this population are measured (step S31). Up to this point, the process is the same as in the first and second embodiments.

[0028] Thereafter, in the manufacturing method of the third embodiment, the spectroscopic sensors 14 are grouped (classified) into a plurality of groups according to the output characteristics of the spectroscopic sensors 14. As an example, the grouping can be performed in the same manner as in the second embodiment, by defining, for example, four groups (A) to (D) in four quadrants of the feature space.

[0029] The difference from the second embodiment is that, while the second embodiment performs grouping so that spectroscopic sensors 14 with similar output characteristics are spaced far apart in the feature space, the third embodiment performs grouping so that spectroscopic sensors with similar output characteristics are grouped together (step S32). Then, a predetermined number of spectroscopic sensors 14 are selected from each of the plurality of groups (A) to (D) to configure a plurality of measuring devices 10, each having j spectroscopic sensors 14 (step S33). It is preferable that the number of spectroscopic sensors 14 selected from each of the plurality of groups (A) to (D) to configure one measuring device 10 is the same for all groups, but the number of selected sensors may be different from one group to another depending on conditions.

[0030] In the third embodiment, spectroscopic sensors 14 with similar output characteristics are grouped into the same group, and therefore each of groups (A) to (D) can be configured as a collection of spectroscopic sensors 14 with similar output characteristics. Therefore, by selecting a predetermined number of spectroscopic sensors from each of groups (A) to (D), the spectroscopic sensors 14 can be distributed to the multiple measuring devices 10 without bias in output characteristics, and the output characteristics can be made uniform among the multiple measuring devices 10.

[0031] To classify the spectroscopic sensors 14 into groups, algorithms such as hierarchical clustering, k-means clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), k-nearest neighbor search, and spectral clustering may be used.

[0032] 9 is a graph illustrating the effects of the second and third embodiments. Specifically, the upper graph in FIG. 9 shows variations in output characteristics among a plurality of measuring devices 10 when the N spectroscopic sensors 14 are not grouped, but are instead randomly combined to assemble measuring devices 10 each having j spectroscopic sensors 14. In the upper graph, small dots indicate the output characteristics (output values ​​1 and 2) of each spectroscopic sensor 14, and large round dots indicate the output characteristics of the measuring device 10. When random combinations are performed, as shown in the upper graph, the output characteristics of some measuring devices 10 may be significantly different from those of other measuring devices 10.

[0033] 9 shows the variation in output characteristics among a plurality of measuring devices 10 when N spectroscopic sensors 14 are grouped and then the Kennard-Stone method is used to select the spectroscopic sensors 14. It can be seen that the variation in output characteristics among a plurality of measuring devices 10 is suppressed compared to the upper graph (when the measuring devices are randomly combined).

[0034] The three graphs in Fig. 10 show the variation in output characteristics among multiple measurement devices 10 when N spectroscopic sensors 14 are grouped and then the spectroscopic sensors 14 are selected using hierarchical cluster analysis (top graph), K-means cluster analysis (middle graph), and spectral clustering (bottom graph). It can be seen that the variation in output characteristics among multiple measurement devices 10 is suppressed compared to the top graph in Fig. 9 (when the devices are randomly combined).

[0035] 11 is a graph comparing the standard deviations of the output values ​​(output value 1, output value 2) of the measurement device 10. As shown in FIG. 11, the standard deviation is large when the spectroscopic sensors 14 are randomly combined, but the standard deviation is small when the spectroscopic sensors 14 are selected from each group using the method of this embodiment (Knnard-Stone method, hierarchical cluster analysis, K-means cluster analysis, and spectral clustering).

[0036] In the third embodiment, after grouping of N spectroscopic sensors has been performed, a new spectroscopic sensor other than the N spectroscopic sensors may be added to the population. In that case, it is not necessary to perform new grouping of all N spectroscopic sensors belonging to the population, and grouping can be performed only on the spectroscopic sensor to be newly added. Fig. 12 is a flowchart illustrating a manufacturing method of a modified example of the third embodiment when a new spectroscopic sensor is added to the population.

[0037] First, the output characteristics of each of the spectroscopic sensors 14 newly added to the population are measured (step S34). Next, for each group consisting of N existing (grouped) spectroscopic sensors, for example, four groups (A) to (D), the average output characteristics of the spectroscopic sensors belonging to each group are calculated (step S35).

[0038] Then, each newly added spectroscopic sensor 14 is placed in a group whose output characteristics are closest to the average output characteristics of the group (step S36). Finally, a predetermined number of spectroscopic sensors 14 are selected from each of the plurality of groups (A) to (D), and a plurality of measuring devices 10, each having j spectroscopic sensors 14, are configured (step S37).

[0039] Although the second and third embodiments have been described with examples in which there are two output values, the number of output values ​​is not limited. Furthermore, if there are a large number of output values ​​and it is difficult to grasp the output characteristics of each sensor, the number of dimensions of the output characteristics may be compressed in advance using techniques such as principal component analysis, t-SNE (t-distribution type stochastic neighbor embedding), or NMF (non-negative matrix factorization).

[0040] Figure 13 shows the results of applying principal component analysis to the detected spectral signals shown in Figure 2, followed by sensor selection using k-means clustering according to the third embodiment. The original detected spectral signals consist of 700 output values ​​from 1100 nm to 2498 nm at 2 nm intervals, but by using principal component analysis, the signals are compressed into two-dimensional principal components that indicate the characteristics of each spectroscopic sensor, making it possible to visually grasp the differences in output characteristics between spectroscopic sensors.

[0041] As shown in Figure 13, it can be confirmed that the variation in output characteristics among multiple measuring devices 10 is smaller when sensor selection using k-means clustering is applied compared to when N spectroscopic sensors 14 are not grouped and N spectroscopic sensors 14 are randomly combined to assemble a measuring device 10 having j spectroscopic sensors 14 per device.

[0042] (others) In the embodiment described above, the measuring device 10 is described as an example, having a plurality of spectroscopic sensors 14 as sensors. However, the spectroscopic sensors 14 are described as an example, and the present invention can also be applied to other types of sensors in general, where there is variation in output characteristics among the plurality of sensors. For example, by applying a method similar to that described above to a method for manufacturing a measuring device having a plurality of thermocouples as sensors, it is possible to reduce variation in output characteristics among the plurality of measuring devices.

[0043] The present invention is not limited to the above-described embodiments, and various modifications are possible. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to embodiments including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment. It is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to delete part of the configuration of each embodiment, or to add or replace other configurations. [Explanation of symbols]

[0044] 10...measuring device, 11...light source, 12...optical fiber, 13...light-shielding rubber, 14...spectroscopy sensor, 15...entrance slit, SP...measurement object, LB...measurement light.

Claims

1. A method for manufacturing a measuring device that includes a plurality of sensors and outputs an output signal based on detection signals of the plurality of sensors, comprising: acquiring output characteristics of each of the N sensors; and selecting j sensors (N>>j) for one measurement device from among the N sensors based on the output characteristics of each of the N sensors to configure a plurality of measurement devices; The step of selecting j sensors includes: calculating output characteristics of all combinations of selecting the j sensors from the N sensors; selecting a combination that minimizes the variation in the output characteristics of the plurality of measuring devices; A method for manufacturing a measuring device.

2. A method for manufacturing a measuring device that includes a plurality of sensors and outputs an output signal based on detection signals of the plurality of sensors, comprising: acquiring output characteristics of each of the N sensors; and selecting j sensors (N>>j) for one measurement device from among the N sensors based on the output characteristics of each of the N sensors to configure a plurality of measurement devices; further comprising a step of classifying the plurality of sensors into a plurality of groups according to output characteristics of the plurality of sensors such that sensors having similar output characteristics are spaced apart from each other in a feature space; the step of selecting j sensors includes selecting j sensors in each of the plurality of groups. A method for manufacturing a measuring device.

3. A method for manufacturing a measuring device that includes a plurality of sensors and outputs an output signal based on detection signals of the plurality of sensors, comprising: acquiring output characteristics of each of the N sensors; and selecting j sensors (N>>j) for one measurement device from among the N sensors based on the output characteristics of each of the N sensors to configure a plurality of measurement devices; further comprising a step of classifying the plurality of sensors into a plurality of groups according to output characteristics of the plurality of sensors such that sensors having similar output characteristics are grouped together, wherein the step of selecting the j number of sensors includes selecting a predetermined number of sensors from each of the plurality of groups. A method for manufacturing a measuring device.

4. The manufacturing method according to claim 2, wherein the step of selecting the j sensors for the one measurement device to configure the plurality of measurement devices uses the Kennard-Stone (KS) method and its derived algorithms.

5. 4. The method for manufacturing a measurement device according to claim 3, wherein the step of classifying the plurality of sensors into a plurality of groups includes hierarchical clustering, k-means clustering, DBSCAN (Density-Based Spatial Clustering of Application with Noise), k-nearest neighbor search, or spectral clustering.

6. After the N sensors are classified into a plurality of groups, when a new spectroscopic sensor is added to the population of the N spectroscopic sensors, The step of classifying the plurality of sensors into a plurality of groups includes: measuring the output characteristics of each newly added sensor to the population; calculating an average output characteristic of the sensors belonging to the plurality of groups; classifying the newly added sensor into one of the plurality of groups based on a relationship between its output characteristic and the average output characteristic of the plurality of groups; The method for manufacturing the measuring device of claim 3 , comprising:

7. 4. The method for manufacturing a measurement device according to claim 1, wherein the step of acquiring the output characteristics of each of the N sensors includes a step of applying principal component analysis, t-SNE (t-distribution type stochastic neighbor embedding), or NMF (non-negative matrix factorization) to the output characteristics of the N sensors to reduce the dimensionality of the output characteristics.

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