Sugar concentration measurement method, sugar concentration measurement device, and sugar concentration measurement program

Abstract

To provide a sugar content measurement method capable of measuring with high accuracy when measuring the sugar content of fruits and vegetables.SOLUTION: The sugar content measurement method for measuring the sugar content of fruits and vegetables by irradiating fruits and vegetables with light from a light source and receiving reflected light that includes diffuse reflection. The method includes: an acquisition step in which fruits or vegetables are irradiated with light in the near-infrared wavelength range emitted from one light source or multiple light sources and acquires the signal values of four different wavelengths from the reflected light that includes the diffuse reflection, or the light in the spectral absorption spectrum that includes these four wavelengths; and an estimated model creation step of creating an estimation model for estimating the sugar content by performing multivariate analysis by using the acquired 4 absorbances with different wavelengths (light signal values) as explanatory variables.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a method for measuring sugar content, a sugar content measuring device, and a sugar content measuring program.

Background Art

[0002] For example, in the non-destructive sugar content measuring device of Patent Document 1, a single or a plurality of light sources that emit light of three wavelengths in the range of 860 nm to 960 nm are used, and the arrangement position of the detector that detects the light absorption is such that, except for the position on the extension line of the straight line connecting the center point of the irradiation region on the surface of the fruit of the incident light emitted from the light source and incident on the fruit and the center of the fruit, and the irradiation region on the surface of the fruit of the incident light and the detection region on the surface of the fruit of the emitted light from the fruit received by the detector do not overlap.

[0003] However, in Patent Document 1, three types of light sources and explanatory variables in the ranges of 860 to 890 nm, 900 to 920 nm, and exceeding 920 to 960 nm are used. However, since each wavelength range is wide, it is necessary to further examine effective wavelengths in each wavelength range, and there is a problem that the non-destructive measurement accuracy of sugar content is low when the three wavelength ranges are applied.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0005]

Non-Patent Document 1

Non-Patent Document 2

[0006] The present invention has been made in view of the above, and an object thereof is to provide a sugar content measurement method, a sugar content measurement device, and a sugar content measurement program capable of measuring the sugar content of fresh fruits and vegetables with high accuracy when measuring the sugar content of fresh fruits and vegetables. [Means for Solving the Problems]

[0007] In order to solve the above-described problems and achieve the object, the present invention is a sugar content measurement method for irradiating light from a light source onto fresh fruits and vegetables, receiving reflected light including diffuse reflection thereof, and measuring the sugar content of the fresh fruits and vegetables, including: an acquisition step of irradiating light in a near-infrared wavelength range irradiated from one light source or a plurality of light sources onto fresh fruits and vegetables, and acquiring signal values of light of a spectral absorption spectrum in four wavelength ranges having different wavelengths or wavelength ranges including these four wavelengths from the reflected light including diffuse reflection thereof; and an estimation model creation step of creating an estimation model for estimating the sugar content by performing multivariate analysis using the four absorbances (signal values of light) having different wavelengths obtained as explanatory variables.

[0008] Further, according to one aspect of the present invention, the four different wavelengths may be 856±2 nm, 876±2 nm or 884±2 nm, 900 to 918 nm, 926±2 nm.

[0009] Further, according to one aspect of the present invention, in the estimation model creation step, multiple regression analysis may be performed as the multivariate analysis to create a multiple regression equation as the estimation model.

[0010] Further, according to one aspect of the present invention, the fresh produce may include the equator part, the scar part, processed products, and crushed materials of the fresh produce.

[0011] Further, according to one aspect of the present invention, the fresh produce may include Western pears, oysters, mangoes, strawberries, bell peppers, oranges (including tangerines), shiranui, tomatoes, cherries, peaches, Japanese pears, apples, plums, and melons.

[0012] Further, according to one aspect of the present invention, the light source may be a ring-shaped light source.

[0013] Further, according to one aspect of the present invention, in the acquisition step, reflected light including diffuse reflection of fresh produce with respect to light irradiated from the ring-shaped light source may be detected at substantially the center of the ring-shaped light source.

[0014] Further, according to one aspect of the present invention, the one light source may be a halogen lamp.

[0015] Further, according to one aspect of the present invention, the plurality of light sources may be a plurality of LEDs having different emission wavelengths.

[0016] Further, according to one aspect of the present invention, the plurality of LEDs may be arranged at a predetermined angle (where the predetermined angle is greater than 10 degrees) with respect to the plane on the side of the fresh produce to be measured.

[0017] Further, in order to solve the above-described problems and achieve the object, the present invention is a sugar content measuring device that irradiates light from a light source onto fresh fruits and vegetables, receives reflected light including diffuse reflection thereof, and measures the sugar content of the fresh fruits and vegetables, comprising: a spectroscopic detection means that irradiates light in the near-infrared wavelength range irradiated from one light source or a plurality of light sources onto fresh fruits and vegetables, and acquires signal values of light of a spectroscopic absorption spectrum in four wavelength ranges having different wavelengths or wavelength ranges including these four wavelengths from the reflected light including diffuse reflection thereof; and an estimation model creation means that creates an estimation model for estimating the sugar content by performing multivariate analysis using the four absorbances (signal values of light) having different wavelengths obtained as explanatory variables.

[0018] Further, according to one aspect of the present invention, the four different wavelengths may be 856±2 nm, 876±2 nm or 884±2 nm, 900 to 918 nm, and 926±2 nm.

[0019] Further, according to one aspect of the present invention, the estimation model creation means may perform multiple regression analysis as the multivariate analysis and create a multiple regression equation as the estimation model.

[0020] Further, according to one aspect of the present invention, the fresh fruits and vegetables may include the equator part, the flower scar part, processed products, and crushed materials of the fresh fruits and vegetables.

[0021] Further, according to one aspect of the present invention, the fresh fruits and vegetables may include Western pears, oysters, mangoes, strawberries, paprika, oranges (including tangerines), shiranui, tomatoes, cherries, peaches, Japanese pears, apples, plums, and melons.

[0022] Further, according to one aspect of the present invention, the light source may be a ring-shaped light source.

[0023] Further, according to one aspect of the present invention, the spectroscopic detection means may detect reflected light including diffuse reflection of fresh fruits and vegetables with respect to light irradiated from the ring-shaped light source at substantially the center of the ring-shaped light source.

[0024] Also, according to one aspect of the present invention, the one light source may be a halogen lamp.

[0025] Also, according to one aspect of the present invention, the plurality of light sources may be a plurality of LEDs having different emission wavelengths.

[0026] Also, according to one aspect of the present invention, the plurality of LEDs may be arranged at a predetermined angle (where the predetermined angle is greater than 10 degrees) with respect to the plane on the side of the fresh produce to be measured.

[0027] Further, in order to solve the above-described problems and achieve the object, the present invention is a sugar concentration measurement program for measuring the sugar concentration of fresh produce, and includes four different wavelengths or a wavelength range including these four wavelengths from the reflected light including diffuse reflection from the fresh produce with respect to the light in the near-infrared wavelength range irradiated from one light source or a plurality of light sources. An acquisition step of acquiring the signal value of the light of the spectral absorption spectrum, and an estimation model creation step of creating an estimation model for estimating the sugar concentration by performing multivariate analysis using the four absorbances (light signal values) of different wavelengths as explanatory variables, and a sugar concentration measurement program for causing a computer to execute.

Effect of the Invention

[0028] According to this invention, when measuring the sugar concentration of fresh produce, there is an effect that it becomes possible to measure with high accuracy.

Brief Description of the Drawings

[0029]

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Embodiments for Carrying Out the Invention

[0030] Hereinafter, examples of preferred embodiments of the sugar concentration measurement method, sugar concentration measurement device, and sugar concentration measurement program according to the present invention will be described in detail with reference to FIGS. 1 to 29. Note that the present invention is not limited by this embodiment.

[0031] [Overview of the Present Invention] First, with reference to FIG. 1, the outline of the present invention will be described. FIG. 1 is an explanatory diagram for explaining the outline of the present invention.

[0032] As described above, in Patent Document 1, three types of light sources of 860 to 890 nm, 900 to 920 nm, and over 920 to 960 nm and explanatory variables (absorbance) are used. However, since the wavelength range is wide, it is necessary to further examine the effective wavelengths in each wavelength range, and there is a problem that the non-destructive measurement accuracy of the sugar content is low when these three wavelength ranges are applied.

[0033] Therefore, in the present invention, in addition to the three types of wavelength ranges, the combination using a fourth wavelength range was further examined.

[0034] In the present invention, as a combination of explanatory variables, by combining the absorbance (light signal value) of 876 ± 2 nm or 884 ± 2 nm, 900 to 918 nm, and 926 ± 2 nm with the absorbance (light signal value) of 856 ± 2 nm as the fourth explanatory variable, it was found that the sugar content can be non-destructively measured with high precision.

[0035] Furthermore, in the present invention, a ring-shaped light source is used, ring-shaped light is irradiated from the ring-shaped light source onto the fresh produce, and the reflected light including the diffuse reflection from the fresh produce is detected at the approximate center of the ring-shaped light source, and it was found that the sugar content can be non-destructively measured with higher precision.

[0036] On the other hand, LEDs (Light Emitting Diodes), which are popular as new light sources, are characterized by being small, having a long lifespan, and low power consumption. Conventionally, in a near-infrared spectrophotometer (non-contact measurement by diffuse reflection light measurement mode (800 - 1000 nm): K-BA100R (high-precision practical machine) manufactured by Kubota Corporation) capable of highly accurate non-destructive measurement of sugar content, a ring-shaped halogen lamp is used as the light source, ring-shaped light is irradiated onto the sample, and the reflected light including the diffuse reflection is detected at the center of the light source and transmitted to the main body using an optical fiber.

[0037] However, with the popularization of LEDs, halogen lamps may become scarce. Also, when using an optical fiber for light guiding, the volume of the device will increase accordingly. On the other hand, MEMS (Micro Electro Mechanical Systems) technology has made it possible to manufacture ultra-small and low-cost spectrometers.

[0038] Therefore, a compact near-infrared spectrophotometer (LED prototype) was prototyped with the sample stage directly above the spectrometer, without using an optical fiber with an ultra-small spectrometer incorporated in the center of an LED ring light source.

[0039] Conventionally, for non-destructive measurement of fruit sugar content using near-infrared spectroscopy, pretreatment has been habitually performed on the non-destructively measured light absorption spectrum. As the pretreatment method, second-order differentiation has been mainly used, but there are many other methods such as centering and first-order differentiation, and not only the conditions of differentiation but also the combination of pretreatment methods need to be considered. Also, high-precision non-destructive measurement is possible by reproducibly measuring small information (see Non-Patent Document 1). Although pretreatment (differentiation, smoothing) of the light absorption spectrum has the effect of removing noise, it also deletes a part of the signal at the same time. However, with the improvement of the performance of spectrophotometers, the need to apply spectral pretreatment has been decreasing.

[0040] In the research and development of non-destructive measurement calibration curves (also called "estimation models" or "model equations"), methods using a large number of explanatory variables such as PLS regression analysis have become mainstream, but the degree of completion of the model equation is difficult to see. When developing a model equation with fewer explanatory variables like multiple regression analysis, the structure of the model equation is easier to explain and has high practicality.

[0041] Also, it is known that near-infrared light absorption spectra are affected by temperature. In practical applications, by non-destructively measuring fruits with different sample temperatures and developing a model equation, it is possible to make the measurement less affected by temperature (see Non-Patent Document 2).

[0042] Therefore, using the aforementioned high-precision practical machine and the LED prototype, a new combination of explanatory variables for non-destructively estimating the sugar content of fresh fruits and vegetables was found without performing preprocessing such as differentiating or smoothing the light absorption spectra obtained by non-destructively measuring fresh fruits and vegetables with different temperatures.

[0043] Excluding unexamined watermelons and the like, the same explanatory variables can be adopted even if the types of fresh fruits and vegetables are different, enabling unified hardware and software design. In addition, it can be effectively utilized without eliminating the signal of the measured near-infrared light absorption spectrum.

[0044] In the high-precision practical machine, since the light-gathering property deteriorates for small fruits (less than approximately 10 g), it was necessary to develop a jig to improve the light-gathering property (see Non-Patent Document 3). In addition, for large fruits such as melons and oranges with thick peels, the measurement time may be as long as approximately 300 ms (milliseconds). On the other hand, in the LED prototype, by installing the LED at an angle of 10 degrees or more, preferably 32 degrees, with respect to the plane (for example, in Patent Document 2, it is set to 10 degrees in order to transmit the measurement light over a long distance through the sample), non-destructive measurement of strawberries, plums, peaches, pears, apples, tomatoes, melons, oranges, etc. is possible in a short measurement time (5 - 30 ms) compared to the high-precision practical machine.

[0045] In the present invention, the fresh fruits and vegetables to be measured include fruits and vegetables, and also include the equatorial part, flower scar part, processed products, and crushed products of the fresh fruits and vegetables. As an example, the case of measuring Western pears, persimmons, mangoes, strawberries, bell peppers, oranges, shiranui mandarins, tomatoes, cherries, peaches, pears, apples, plums, and melons will be described, but the present invention is applicable to other fresh fruits and vegetables in addition to these.

[0046] In FIG. 1, the present invention creates an estimation model creation step (S1) for creating an estimation model for estimating the sugar content from the absorbance (light signal value) at 856±2 nm, which is the fourth explanatory variable, in addition to the three explanatory variables of the absorbance (light signal value) of the target fresh and processed fruits at 876±2 nm or 884±2 nm, 900 - 918 nm, and 926±2 nm. The present invention also includes a measurement step (S2) of measuring the light signal values of the fresh and processed fruits to be measured at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm, and applying the measured absorbance (light signal value) at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm to the estimation model to estimate the sugar content.

[0047] In the estimation model creation step (S1), the light signal values of the target fresh and processed fruits at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm are measured, and a multivariate analysis is performed on the absorbance (light signal value) to create an estimation model for estimating the sugar content in the fresh and processed fruits. Data preprocessing is performed on the absorbance (light signal value) if necessary. In the multivariate analysis, the light signal values of known fresh and processed fruits at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm are measured, and a multivariate analysis is performed on the absorbance (light signal value) to create an estimation model.

[0048] In the measurement step (S2), for the absorbance (light signal value) of the fresh and processed fruits for which the sugar content is to be measured at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm, data preprocessing is performed if necessary. The obtained absorbance (light signal value) is applied to the estimation model to estimate the sugar content. In this way, once the estimation model is created, it is possible to accurately estimate (measure) the sugar content only by obtaining the absorbance (light signal value) of the fresh and processed fruits to be measured at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm.

[0049] [Sugar Content Measurement Method] Referring to FIG. 2, the method for measuring the sugar content according to the present embodiment will be described. FIG. 2 is a flowchart for explaining the method for measuring the sugar content according to the present embodiment.

[0050] As shown in FIG. 2, the method for measuring the sugar content according to the present embodiment includes an estimation model creation step (S1) of creating an estimation model for estimating the sugar content by performing multivariate analysis on the absorbance (light signal value) of 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm of the target fresh produce, and a measurement step (S2) of measuring the light signal values of 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, and 856±2 nm of the fresh produce to be measured, and applying the absorbance (light signal value) to the estimation model to estimate the sugar content.

[0051] Multivariate analysis includes regression analysis and the like. Regression analysis is to create a regression equation (calibration curve) for estimating a specific numerical value and can be used to estimate the sugar content. There are PLS regression analysis, multiple regression analysis, etc. in regression analysis, which can be used to estimate the numerical value of the sugar content. In the multivariate analysis of the present embodiment, the case of using multiple regression analysis will be described, but the present invention is not limited thereto, and discriminant analysis, principal component analysis, cluster analysis, etc. may be used. In addition, instead of multivariate analysis, a model may be created by machine learning or by incorporating machine learning into multivariate analysis.

[0052] The fresh produce to be measured includes fruits and vegetables, and the measurement parts include the equator part, the flower scar part, processed products, and crushed materials of the fresh produce. The fresh produce to be measured is, for example, Western pears, oysters, mangoes, strawberries, bell peppers, oranges, shiranui oranges, tomatoes, cherries, peaches, Japanese pears, apples, plums, and melons, etc.

[0053] In the step of creating the estimation model, first, in order to create the estimation model, fruits and vegetables to be measured are prepared (step S11). The fruits and vegetables to be measured are set on the sample stage of the measuring unit, and near-infrared light (halogen light or a plurality of LED lights) is irradiated from a light source onto the fruits and vegetables, and the reflected light including the diffuse reflected light is detected. The signal values of light at four different wavelengths (for example, 876 ± 2 nm or 884 ± 2 nm, 900 - 918 nm, 926 ± 2 nm, 856 ± 2 nm) in the near-infrared wavelength range, or in a wavelength range including these four different wavelengths, are acquired by a spectroscopic detection device (step S13). Here, the "reflected light including diffuse reflected light" includes the case of only diffuse reflected light and the case of diffuse reflected light + reflected light other than diffuse reflected light (for example, total reflected light). Also, the signal value of light is an electrical signal value (original data) obtained by photoelectric conversion of the reflected light. This signal value of light can be converted into absorbance by a predetermined conversion formula. In the following processes such as preprocessing and multivariate analysis, the signal value of light may be used as it is, or may be used after being converted into absorbance. The reflected light includes both the light reflected from the sample surface and the light diffusely reflected inside the sample. The light source may be a ring-shaped light source. The reflected light including the diffuse reflection of the fruits and vegetables with respect to the light irradiated from the ring-shaped light source may be detected at approximately the center of the ring-shaped light source. The ring-shaped light source may be composed of a halogen lamp. Also, the ring-shaped light source may be composed of a plurality of LEDs with different emission wavelengths. The plurality of LEDs may be arranged at a predetermined angle (greater than 10 degrees, preferably 32 degrees) with respect to the plane on the side of the fruits and vegetables to be measured.

[0054] Next, data preprocessing is performed on the acquired absorbance (signal value of light) as necessary (step S14). In data preprocessing, for example, one or a combination of centering, standardization, normalization, and baseline correction, etc. are performed for signal processing operations.

[0055] On the other hand, the actual measured value of the sugar content of the fruits and vegetables to be measured is acquired (step S12). In step S15, estimation models for estimating the sugar content in the fruits and vegetables are created by performing multivariate analysis on the absorbance (signal value of light) in the near-infrared wavelength range with or without data preprocessing.

[0056] When using regression analysis as a multivariate analysis, for example, a regression equation (calibration curve) for estimating the sugar content value from the spectroscopic absorption spectrum is created as an estimation model. As the regression analysis, for example, multiple regression analysis or PLS regression analysis can be used.

[0057] Next, in the measurement step, a fresh fruit (measurement object) for which the sugar content is to be estimated is prepared (step S21). The fresh fruit to be measured is set on the sample stage of the measuring unit, and the fresh fruit is irradiated with light in the near-infrared wavelength region (halogen light or a plurality of LED lights with different wavelength regions) from a light source, and the reflected light including the diffusely reflected light is detected, and the signal values of light at four different wavelengths in the near-infrared wavelength region (for example, 876 ± 2 nm or 884 ± 2 nm, 900 to 918 nm, 926 ± 2 nm, 856 ± 2 nm), or in a wavelength region including these four different wavelengths, are measured and acquired by a spectroscopic detection device (step S22). Here, the "reflected light including the diffusely reflected light" includes the case of only the diffusely reflected light and the case of the diffusely reflected light + reflected light other than the diffusely reflected light (for example, total reflection light).

[0058] Next, data preprocessing is performed on the absorbances (light signal values) at four different wavelengths as necessary (step S23). The data preprocessing is the same as that in S14. The absorbances (light signal values) at four different wavelengths for which the data preprocessing has been performed, or in a wavelength region including these four different wavelengths, are applied to the estimation model for estimating the sugar content created in step S15 to estimate the sugar content (step S24).

[0059] [Sugar Content Measuring Device] Next, the configuration of the sugar content measuring device of the present invention will be described by taking an embodiment as an example with reference to FIGS. 3 and 4. Note that the sugar content measuring device according to the present embodiment can be suitably used for the aforementioned sugar content measuring method, but the device used for the sugar content measuring method according to the present embodiment is not limited thereto.

[0060] Here, FIG. 3 is a schematic diagram showing an example of the external configuration of the sugar concentration device according to the present embodiment. FIG. 4 is a block diagram showing an example of the configuration of the sugar concentration measuring device according to the present embodiment, and conceptually shows only the part related to the present invention among the configurations.

[0061] As shown in FIGS. 3 and 4, the sugar concentration measuring device 1 includes a measuring unit 11 provided with a light source 12, a spectroscopic detection device 10, and a data processing device 20.

[0062] The data processing device 20 is a device that measures the sugar concentration of the fresh fruit to be measured from the spectroscopic absorption spectra of four wavelengths with different wavelengths in the near-infrared region or a wavelength range including these four different wavelengths acquired by the spectroscopic detection device 10. The data processing device 20 includes a memory 21, a control unit 23, and a calculation processing unit 24, and the measurer inputs measurement conditions and the like to the data processing device 20 using the keyboard and mouse 22. The spectroscopic detection device 10 is connected to the measuring unit 11. The light source 12 is provided in the measuring unit 11. The light source 12 is a device that irradiates the fresh fruit to be measured set on the sample stage of the measuring unit 11 with light of a predetermined wavelength (for example, a wavelength including the near-infrared wavelength region). The light source 12 may be a ring-shaped light source. It is also possible to detect the reflected light including the diffuse reflection of the fresh fruit with respect to the light irradiated from the ring-shaped light source at substantially the center of the ring-shaped light source. The ring-shaped light source may be composed of a halogen lamp. Further, the ring-shaped light source may be composed of a plurality of LEDs with different emission wavelengths. The plurality of LEDs may be arranged on the fresh fruit side to be measured at a predetermined angle (greater than 10 degrees, preferably 32 degrees) with respect to the plane.

[0063] The spectroscopic detection device 10 is connected to the measurement unit 11. The spectroscopic detection device 10 receives the reflected light (including direct reflected light and diffuse reflected light) of the fresh and processed fruits and vegetables, which are the measurement objects, with respect to the light irradiated from the light source 12, and acquires the absorbances at four different wavelengths in the near-infrared wavelength range (the absorbances at 876±2 nm or 884±2 nm, 900 - 918 nm, 926±2 nm, 856±2 nm, or the absorbances (light signal values) in the wavelength range including these four different wavelengths), and transmits them to the data processing device 20. For spectroscopy, various methods such as grating, filter method, pre-spectroscopy, and post-spectroscopy methods can be used. Also, for the optical sensor, an image sensor (polychromator), a photodiode (monochromator), etc. can be used.

[0064] The measurement unit 11 is provided with a light source 12, irradiates the measurement object set on the sample stage with the light source light input from the light source 12, and sends the reflected light including the diffuse reflection from the fresh and processed fruits and vegetables, which are the measurement objects, to the spectroscopic detection device 10.

[0065] The data processing device 20 can be configured by, for example, a personal computer or the like, and includes a memory 21, a control unit 23, and a calculation processing unit 24, and a keyboard / mouse 22, an I / O port (for example, a USB port or the like) 26, a display 30, etc. are connected.

[0066] The memory 21 stores the spectroscopic absorption spectrum transferred from the spectroscopic detection device 10 to the data processing device 20 and acquired by the spectroscopic absorption spectrum acquisition unit 24-1 of the calculation processing unit 24, the actual measured values of the sugar content of the fresh and processed fruits and vegetables used when creating the estimation model by the estimation model creation unit 24-2 of the calculation processing unit 24, the created estimation model 27, etc. The actual measured values of the sugar content of the fresh and processed fruits and vegetables used when creating the estimation model 27 can be input from the keyboard / mouse 22 or the I / O port 26.

[0067] The control unit 23 can perform controls such as an ON / OFF instruction for light irradiation to the light source 12 and a start instruction for spectrum detection of the spectroscopic detection device 10 according to operations of the operator's keyboard / mouse 22, and can also instruct the calculation processing unit 24 to perform processing.

[0068] The spectroscopic absorption spectra of four different wavelengths of fresh produce transferred from the spectroscopic detection device 10, or a wavelength range including these four different wavelengths, are stored in the memory 21 of the data processing device 20. When the measurer instructs the calculation processing unit 24 to perform processing through the keyboard / mouse 22, first, the spectroscopic absorption spectrum acquisition unit 24-1 of the calculation processing unit 24 extracts the spectroscopic absorption spectrum in the near-infrared wavelength range necessary for analysis from the spectroscopic absorption spectrum stored in the memory 21, if necessary, by data preprocessing.

[0069] The estimation model creation unit 24-2 of the calculation processing unit 24 performs multivariate analysis on the extracted spectroscopic absorption spectrum in the near-infrared wavelength range to create an estimation model. In the present embodiment, the estimation model creation unit 24-2 of the calculation processing unit 24 may create, for example, a regression equation (calibration curve) created by PLS regression analysis or multiple regression analysis as a multivariate analysis as an estimation model.

[0070] The estimation model creation unit 24-2 of the calculation processing unit 24 may store the result of the multivariate analysis (estimation model 27) in the memory 21, may output it on the display 30, or may print it via a printer (not shown).

[0071] The estimator 24-3 of the calculation processing unit 24 applies the spectral absorption spectra of four different wavelengths in the near-infrared wavelength range extracted, or a wavelength range including these four different wavelengths, to the estimation model 27 stored in the memory 21 to estimate the sugar content of fresh produce. Note that by storing a plurality of types of estimation models 27 for fresh produce in the memory 21, it becomes possible to measure the sugar content of a plurality of types of fresh produce with a single measuring device. Note that the spectroscopic detection device 10 may be equipped with the functions of the data processing device 20, and the spectroscopic detection device 10 may execute the processing of the data processing device 20 to create an estimation model or the like.

[0072] [Embodiment] The embodiment will be described with reference to FIGS. 6 to 29. In this embodiment, as fresh produce samples, Western pears, oysters, mangoes, strawberries, bell peppers, oranges, shiranui oranges, tomatoes, cherries, peaches, Japanese pears, apples, plums, and melons were used. The spectroscopic detection unit 24-1 acquired the spectral absorption spectra of four different wavelengths in the near-infrared wavelength range of the fresh produce, or a wavelength range including these four different wavelengths. Then, by performing multiple regression analysis using four different absorbances (signal values) in the near-infrared wavelength range of the fresh produce as explanatory variables through the processing of the estimation model creation unit 24-2, multiple regression equations for estimating the sugar content of the fresh produce were created respectively. Also, the multiple regression equations were evaluated. Furthermore, the estimator 24-3 estimated the sugar content of the fresh produce from four different absorbances (signal values) in the near-infrared wavelength range of the fresh produce using the created multiple regression equations.

[0073] (1. Sugar Content Measuring Device) For part or all of the sugar content measuring device 1, a (1) high-precision practical machine and a (2) LED prototype machine, which are near-infrared spectrophotometers using the diffuse reflection light non-contact measurement method, were used.

[0074] (1) High-Precision Practical Machine As a high-precision practical machine, K-BA100R manufactured by Kubota Corporation was used. Fig. 5 shows a perspective view of the main part of the high-precision practical machine. Fig. 5(A) is a schematic perspective view of the measurement part, and Fig. 5(B) shows the cover. As shown in Fig. 5(A), the measurement part of the high-precision practical machine is composed of a main body part 41, a ring-shaped light source 12 (irradiating the sample via an optical fiber from the halogen lamp of the main body), a sample stage 42 composed of a cushion for setting the sample, a light receiving fiber (optical fiber) 43 that receives the reflected light including the diffuse reflection of the sample with respect to the light in the near-infrared region irradiated from the light source 12 and guides the measurement light to the main body, and the like. In addition, a cover 44 shown in Fig. 5(B) is provided in the measurement part of the high-precision practical machine and is removed and used during measurement.

[0075] Tomato pulp and tomato processed products were measured using a cell (for example, CR-A50 manufactured by Konica Minolta). The cell was filled with tomato pulp and tomato processed products, and the cell was set on the sample stage 42 for measurement. The absorbance was calculated from the received signal and linearly interpolated to obtain data at intervals of 2 nm, for example.

[0076] (2) LED prototype An LED prototype was fabricated. Fig. 6 is a diagram showing the main part of the LED prototype. Fig. 6-A is a schematic plan view of the LED prototype seen from above, Fig. 6-B is a schematic cross-sectional view of the LED prototype, Fig. 6-C is a schematic diagram for explaining the path of the light irradiated from the LED with a sample (for example, tomato) set on the sample stage of the LED prototype, and Fig. 6-D shows a schematic perspective view of the LED prototype.

[0077] In Figs. 6-A to 6-D, the LED prototype includes a ring-shaped main body part 51, a ring-shaped light source 12 composed of a plurality of LEDs provided on the upper part of the main body part 51, a sample stage 52 provided inside the main body part 51 on which the sample to be measured is set, and a super-small spectrometer (spectral detection device) 54 that receives the reflected light (diffuse reflected light) of the sample incident on the light incident port 53 of the main body part 51.

[0078] The light source 12 is configured such that three LEDs (center wavelengths: 850, 870, 940 nm) are alternately arranged at 90-degree intervals in a ring shape, with one LED placed at each interval. The diameter is, for example, 36 mm. With these three LEDs (center wavelengths: 850, 870, 940 nm), it is possible to obtain mountain-shaped continuous light in the wavelength range of approximately 800 nm to approximately 1000 nm, and it is possible to acquire the signal values of the light with the above-described four different spectral absorption spectra. Note that the example of the center wavelengths of the three LEDs is not limited to this. The installation angle of each LED with respect to the plane (XY plane) is α (for example, α = 32 degrees) with respect to the sample stage 52 side.

[0079] The sample stage 52 is configured by making a hole with a diameter of 8.5 mm in the center of a rubber plate (for example, thickness 3 mm, diameter 24 mm) so that the center of the hole coincides with the center of the light entrance 53. In the case of melon, measurement may be performed by installing a rubber washer (for example, inner diameter 8 mm, outer diameter 18 mm, thickness 1.5 mm) on this sample stage 52.

[0080] As the ultra-small spectrometer 54, C11708MA (640 - 1050 nm, maximum wavelength resolution 20 nm, number of pixels 256) manufactured by Hamamatsu Photonics is used to detect the measurement light incident from the light entrance 53.

[0081] As shown in Fig. 6-C, in the LED prototype with the above configuration, a measurement object (fresh produce) is set on the sample stage 52. Each LED of the light source 12 irradiates light at a predetermined angle α (for example, α = 32 degrees) from the main body 51 with respect to the horizontal plane (XY plane), and the reflected light (diffuse reflected light) from the measurement object is detected by the ultra-small spectrometer 54 through the light entrance 53.

[0082] The measurement conditions for the spectrum are set as Gain Low, average number of times 3 or 1, number of measurement times 1, and the reference plate used is SRS-99-020 manufactured by Labshere. For Dark and Reference, the measurement is 5 ms, and for the sample, when the light condensing property is poor for 5 ms (for plum, strawberry, tomato), it is set to 10 - 30 ms. Also, the light absorption spectrum obtained by non-destructively measuring the fresh produce is without interpolation (without pre-processing).

[0083] (2. Measurement method) (2-1. Sample, sample temperature, measurement site) The sample temperature was changed in two steps (low temperature, room temperature), and at room temperature, the equatorial part of the fruit (peach, pear, apple, occidental pear (lower part of the fruit), oyster mushroom, plum, mango, orange, shiranuhi, tomato, strawberry, paprika) or the scar part (tomato including cherry tomato, melon, orange, cherry) was measured with a high-precision practical machine or an LED prototype machine facing the light entrance of the sample stage. For tomatoes, a non-destructive measurement test was also carried out by changing the product temperature in three steps.

[0084] For peach, pear, apple, occidental pear, oyster mushroom, plum, mango, melon, paprika, orange (equatorial part measurement), the non-destructive measurement site was used; for tomato, strawberry, orange (scar part measurement), cherry, the whole fruit was used; for shiranuhi, the fruit was longitudinally cut in half, and each sugar content was measured by destructive measurement and used as the target variable.

[0085] (2-2. Measurement of near-infrared spectrum) For occidental pear, oyster mushroom, mango, strawberry, paprika, orange scar part, shiranuhi, tomato equatorial part (fruits of about 10 g or more), tomato crush and tomato processed products (tomato juice, tomato puree, tomato ketchup), only the high-precision practical machine was used; for orange equatorial part, cherry, tomato including cherry tomato, only the LED prototype machine was used for non-destructive measurement. For peach, pear, apple, plum, melon, non-destructive measurement was carried out using both the high-precision practical machine and the LED prototype machine.

[0086] (2-3. Development and evaluation of calibration curve for non-destructive measurement) The sample (known sugar content) for creating the calibration curve is called the "sample for developing non-destructive measurement method", and the sample (unknown sugar content) for evaluating the created calibration curve is called the "sample for evaluating non-destructive measurement method". The data obtained from the sample for developing non-destructive measurement method was subjected to multiple regression analysis using Microsoft Excel to develop a calibration curve for non-destructive measurement (multiple regression equation: estimation model), and the obtained calibration curve (multiple regression equation) was applied to unknown samples (samples for evaluating non-destructive measurement method) for evaluation. RMSE (Root Mean Squared Error) was calculated as the evaluation index.

[0087] The calculation formula for RMSE (non-destructive measurement accuracy of samples for non-destructive measurement method evaluation) is as shown in the following formula (1).

[0088]

Number

[0089] However, Y: Estimated sugar content by non-destructive measurement method X: Actual sugar content n: Number of samples for non-destructive measurement method evaluation

[0090] (3. Measurement results) (3-1. Peach) In the measurement of white peaches and yellow peaches, samples for non-destructive measurement method development (n = 208 for high-precision practical machine, n = 142 for LED prototype) and samples for non-destructive measurement method evaluation (n = 144 for high-precision practical machine, n = 167 for LED prototype) were used.

[0091] Figure 7 shows the measurement results of the high-precision practical machine for peaches. Figure 7(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (876, 902 nm), three (876, 902, 926 nm), and four (856, 876, 902, 926 nm). Figure 7(B) is a diagram showing the correlation (multiple regression analysis) between the measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Figure 7(C) is a diagram showing the correlation between the measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Figures 7(B) and (C), the horizontal axis represents the measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%]. Figure 7(D) shows the results of multiple regression analysis and evaluation results when the four explanatory variables (absorbance at each wavelength) are different combinations of wavelengths.

[0092] As shown in Figure 7(A), when the number of explanatory variables is four, a higher correlation coefficient is obtained compared to the cases of two or three.

[0093] As shown in Fig. 7(B), when the absorbances at 856, 876, 902, and 926 nm were adopted as explanatory variables in the sample for developing the non-destructive measurement method, the intercept was 10.9, and the coefficients were 625.6, -1298.7, 886.5, and -216.4, with a correlation coefficient of 0.96. In the sample for evaluating the non-destructive measurement method shown in Fig. 7(C), the RMSE was also as good as 0.72. As shown in Fig. 7(D), even when the four explanatory variables (absorbances at each wavelength) were combined with different wavelengths, good results were shown for both the correlation coefficient and the RMSE.

[0094] Fig. 8 shows the measurement results with the prototype LED of the peach. Fig. 8(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (876, 909 nm), three (876, 909, 926 nm), and four (857, 876, 909, 926 nm). Fig. 8(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 8(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 8(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%]. Fig. 8(D) shows the results of multiple regression analysis and evaluation results when the four explanatory variables (absorbances at each wavelength) were combined with different wavelengths.

[0095] As shown in Fig. 8(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared to the cases of two or three.

[0096] As shown in Fig. 8(B), when the absorbances at 856.53 (857), 876.35 (876), 909.33 (909), and 926.42 (926) nm were adopted as explanatory variables in the sample for developing the non-destructive measurement method, the intercept was 14.8, and the coefficients were 519.3, -1049, 810.6, and -279.8, with a correlation coefficient of 0.94. In the sample for evaluating the non-destructive measurement method shown in Fig. 8(C), the RMSE was also as good as 0.80. When finally selecting one from the multiple calibration curves developed, it is necessary to determine the line of X:Y = 1:1 while paying attention not only to the correlation coefficient and RMSE but also to the application samples and biases, similar to the high-precision practical machine.

[0097] As shown in Fig. 8(D), even when the four explanatory variables (absorbances at respective wavelengths) were set as different wavelength combinations, good results were shown both in the correlation coefficient and RMSE.

[0098] (3-2. Pear) In the measurement of red pears, samples for developing non-destructive measurement methods (n = 144) and samples for evaluating non-destructive measurement methods for red pears and blue pears (n = 96) were used.

[0099] Fig. 9 shows the measurement results with a high-precision practical instrument for pears. Fig. 9(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at respective wavelengths) were two (876, 910 nm), three (876, 910, 926 nm), and four (856, 876, 910, 926 nm). Fig. 9(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and non-destructive measurement value (estimated value) of the sugar content during development. Fig. 9(C) is a diagram showing the correlation between the actually measured value and non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 9(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0100] As shown in Fig. 9(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared to the cases of two or three.

[0101] As shown in Fig. 9(B), when the absorbances at 856, 876, 910, and 926 nm were adopted as explanatory variables for the samples for developing non-destructive measurement methods, the intercept was 7.1, the coefficients were 502.3, -1015.4, 792.8, -288.6, and the correlation coefficient was 0.96. As shown in Fig. 9(C), the RMSE was also good at 0.58 for the samples for evaluating non-destructive measurement methods.

[0102] Figure 10 shows the measurement results of the prototype LED for pears. Figure 10(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (876, 909 nm), three (876, 909, 926 nm), and four (857, 876, 909, 926 nm). Figure 10(B) is a graph showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Figure 10(C) is a graph showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Figures 10(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0103] As shown in Figure 10(A), when the number of explanatory variables was four, the correlation coefficient was higher than when there were two or three explanatory variables.

[0104] As shown in Figure 10(B), when the absorbances at 856.53 (857), 876.35 (876), 909.33 (909), and 926.42 (926) nm were adopted as explanatory variables for the sample for developing the non-destructive measurement method, the intercept was 12.9, the coefficients were 406.6, -877.7, 725.7, -253.4, and the correlation coefficient was 0.86. As shown in Figure 10(C), the RMSE was also as good as 0.87 for the sample for evaluating the non-destructive measurement method.

[0105] (3-3. Apple) For the measurement of red, yellow, and green apples, samples for developing the non-destructive measurement method (n = 134), samples for evaluating the non-destructive measurement method (high-precision practical machine n = 164, prototype LED n = 132) were used.

[0106] Figure 11 shows the measurement results of the high-precision practical device for apples. Figure 11(A) shows the multiple regression analysis results when the explanatory variables (absorbance at each wavelength) are two (876, 902 nm), three (876, 902, 926 nm), and four (856, 876, 902, 926 nm). Figure 11(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Figure 11(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Figures 11(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0107] As shown in Figure 11(A), when the number of explanatory variables is four, the correlation coefficient is higher than that in the cases of two or three explanatory variables.

[0108] As shown in Figure 11(B), when the absorbances at 856, 876, 902, and 926 nm are adopted as the explanatory variables for the sample for developing the non-destructive measurement method, the intercept is 14.0, the coefficients are 575.7, -1154.4, 780.1, -201.1, n = 134, and the correlation coefficient is 0.96. As shown in Figure 11(C), the RMSE was also good at 0.48 for the sample for evaluating the non-destructive measurement method. Although the correlation coefficient slightly decreased for 902 nm (correlation coefficient 0.96) compared to 910 nm (correlation coefficient 0.97), similar to the case of peaches, 902 nm improved the slope (bias) of the sample for evaluating the non-destructive measurement method.

[0109] Figure 12 shows the measurement results of the LED prototype device for apples. Figure 12(A) shows the multiple regression analysis results when the explanatory variables (absorbance at each wavelength) are two (876, 909 nm), three (876, 909, 926 nm), and four (857, 876, 909, 926 nm). Figure 12(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Figure 12(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Figures 12(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0110] As shown in Fig. 12(A), when four explanatory variables were used, the correlation coefficient was higher than in the cases of two or three explanatory variables.

[0111] As shown in Fig. 12(B), when the absorbances at 856.53(857), 876.35(876), 909.33(909), and 926.42(926) nm were adopted as explanatory variables for the sample for developing the non-destructive measurement method, the intercept was 14.5, the coefficients were 378.1, -771.8, 609, and -214.3, and the correlation coefficient was 0.92. As shown in Fig. 12(C), the RMSE was also as good as 0.88 for the sample for evaluating the non-destructive measurement method. One of the reasons for the slightly larger RMSE for the sample for evaluating the non-destructive measurement method is considered to be that the average number of measurements during near-infrared light absorption spectrum measurement was reduced from three times to one time.

[0112] (3-4. Western pear) For the measurement of Western pears, the sample for developing the non-destructive measurement method had n = 120, and the sample for evaluating the non-destructive measurement method had n = 100.

[0113] Fig. 13 shows the measurement results with a high-precision practical machine for Western pears. Fig. 13(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (874 and 904 nm), three (874, 904, and 926 nm), and four (856, 874, 904, and 926 nm). Fig. 13(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 13(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 13(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0114] As shown in Fig. 13(A), when four explanatory variables were used, the correlation coefficient was higher than in the cases of two or three explanatory variables.

[0115] As shown in Fig. 13(B), when the absorbances at 856, 874, 904, and 926 nm were adopted as explanatory variables in the sample for developing the non-destructive measurement method, the intercept was 14.1, and the coefficients were 609.2, -1086.6, 681.2, and -204.8, with a correlation coefficient of 0.94. As shown in Fig. 13(C), the variety 'Aurora' is a fruit from which juice is difficult to obtain when checking the sugar content, and samples with a large error were found between the non-destructive measurement values (estimated values). However, even in the samples for evaluating the non-destructive measurement method, the RMSE was as good as 0.74.

[0116] (3-5. Oyster) In the measurement of oysters, the number of samples for developing the non-destructive measurement method was n = 76, and the number of samples for evaluating the non-destructive measurement method was n = 178.

[0117] Fig. 14 shows the measurement results using a high-precision practical instrument for oysters. Fig. 14(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (886, 918 nm), three (886, 918, 928 nm), (856, 886, 918 nm), and four (856, 886, 918, 928 nm). Figs. 14(B) and (D) are diagrams showing the correlation (multiple regression analysis) between the actually measured sugar content and the non-destructive measurement values (estimated values) during development. Figs. 14(C) and (E) are diagrams showing the correlation between the actually measured sugar content and the non-destructive measurement values (estimated values) during evaluation. In Figs. 14(B), (C), (D), and (E), the horizontal axis represents the actually measured sugar content [Brix%], and the vertical axis represents the non-destructive measurement values (estimated values) of the sugar content [Brix%].

[0118] As shown in Fig. 14(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared to the cases of two or three explanatory variables.

[0119] As shown in Fig. 14(B), when the absorbances at 856, 886, 918, and 928 nm were adopted as explanatory variables in the sample for non-destructive measurement method development, the intercept was 15.4, the coefficients were 243.3, -689.6, 885, -439.5, and the correlation coefficient was 0.97. As shown in Fig. 14(C), although relatively large errors may occur in hard fruits, it can also be applied to astringent persimmons, and the RMSE of the sample for non-destructive measurement method evaluation was also as good as 1.02. Hereinafter, an example where spectral pretreatment is effective when the light absorption spectrum can be measured in this way is shown. When the second derivative values at 856, 876, 906, and 926 nm after the second derivative processing of the spectrum were adopted as explanatory variables, the correlation coefficient during development was 0.98 (see Fig. 14(D)), and the RMSE during evaluation was 0.86 (see Fig. 14(E)), and the non-destructive measurement values could be improved.

[0120] (3-6. Plum) In the measurement of plums, for the sample for non-destructive measurement method development, the high-precision practical machine had n = 84, the LED prototype had n = 72, and for the sample for non-destructive measurement method evaluation, n = 100.

[0121] Fig. 15 shows the measurement results with the high-precision practical machine for plums. Fig. 15(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (876, 900 nm), three (876, 900, 926 nm), four (856, 876, 900, 926 nm), (856, 886, 900, 926 nm). Fig. 15(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 15(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 15(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0122] As shown in Fig. 15(A), when the number of explanatory variables was four, the correlation coefficient was higher than that in the cases of two or three.

[0123] As shown in Fig. 15(B), in the sample for developing the non-destructive measurement method, when the absorbances at 856, 876, 900, and 926 nm were adopted as explanatory variables, the intercept was 14.1, and the coefficients were 1522.3, -3098.5, 1998.3, and -421.9, and the correlation coefficient was 0.97, which was good. As shown in Fig. 15(C), also in the sample for evaluating the non-destructive measurement method, the RMSE was 0.66, which was good.

[0124] Fig. 16 shows the measurement results with the LED prototype of the peach. Fig. 16(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (886 and 911 nm), three (886, 911, and 928 nm), and four (855, 886, 911, and 928 nm). Figs. 16(B) and (D) are diagrams showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Figs. 16(C) and (E) are diagrams showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Figs. 16(B), (C), (D), and (E), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0125] As shown in Fig. 16(A), when the number of explanatory variables was four, the correlation coefficient was higher than in the cases of two or three.

[0126] As shown in Fig. 16(B), when the absorbances at 854.53 (855), 886.15 (886), 911.25 (911), and 928.31 (928) nm were adopted as explanatory variables in the sample for developing the non-destructive measurement method, a good result was obtained with an intercept of 9.4, coefficients of 339.4, -1462.5, 1505.6, -393, and a correlation coefficient of 0.91. As shown in Fig. 16(C), the RMSE was also as good as 0.97 in the sample for evaluating the non-destructive measurement method. One of the reasons for the lower correlation coefficient compared with the high-precision practical machine is considered to be that the average number of measurements during near-infrared light absorption spectrum measurement was set to 1. Hereinafter, an example where spectral preprocessing is effective when the light absorption spectrum can be measured in this way is shown. In the sample for evaluating the non-destructive measurement method, although the non-destructive measurement values tend to be slightly high (bias = 0.395), when the same explanatory variables are adopted after subjecting the spectrum to SNV (Standard Normal Variate) processing, the correlation coefficient during development slightly decreases to 0.90 (see Fig. 16(D)), but the RMSE during evaluation is 0.96 and the bias is 0.281 (see Fig. 16(E)), and the tendency for the non-destructive measurement values to be high can be improved.

[0127] (3-7. Mango) For the measurement of mango, the sample for developing the non-destructive measurement method had n = 52, and the sample for evaluating the non-destructive measurement method had n = 34.

[0128] Fig. 17 shows the measurement results with the high-precision practical machine for mango. Fig. 17(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) are two (876, 904 nm), three (876, 904, 924 nm), and four (858, 876, 904, 924 nm). Fig. 17(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 17(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 17(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0129] As shown in Fig. 17(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared with the cases of two or three.

[0130] As shown in Fig. 17(B), when the absorbances at 858, 876, 904, and 924 nm were adopted as explanatory variables in the sample for non-destructive measurement method development, the intercept was 13.3, the coefficients were 458, -893.8, 639.5, -208, and the correlation coefficient was 0.95. Also, as shown in Fig. 17(C), the RMSE was also as good as 0.98 in the sample for non-destructive measurement method evaluation.

[0131] (3 - 8. Tomato) (1) Measurement at the equator In the measurement of the equator of tomatoes, the sample temperature was in three stages, and tomatoes of about 10 g or more were used as samples for non-destructive measurement method development (n = 94) and samples for non-destructive measurement method evaluation (n = 177).

[0132] Fig. 18 shows the measurement results with a high-precision practical instrument at the equator of tomatoes. Fig. 18(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (876, 900 nm), three (876, 900, 926 nm), and four (856, 876, 900, 926 nm). Fig. 18(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 18(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 18(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0133] As shown in Fig. 18(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared to the cases of two or three.

[0134] As shown in Fig. 18(B), when the absorbances at 856, 876, 900, and 926 nm were adopted as explanatory variables in the sample for non-destructive measurement method development, the intercept was 8.5, the coefficients were 1525.3, -3181.3, 2079.5, -419.4, and the correlation coefficient was 0.97. As shown in Fig. 18(C), the RMSE was also as good as 0.61 in the sample for non-destructive measurement method evaluation.

[0135] (2) Measurement at the scar In the measurement of the tomato scar part, for the samples for non-destructive measurement development, the samples were measured with a high-precision practical machine at three temperature levels, with tomatoes weighing about 10 g or more (n = 72), and with a LED prototype machine at two temperature levels, including tomatoes weighing less than 10 g, with n = 73.

[0136] Figure 19 shows the measurement results of the tomato scar part with a high-precision practical machine. Figure 19(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (876, 902 nm), three (876, 902, 926 nm), (856, 876, 902 nm), and four (856, 876, 902, 926 nm). Figure 19(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. In Figure 19(B), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0137] As shown in Figure 19(A), when the number of explanatory variables is four, the correlation coefficient is higher than when the number is two or three.

[0138] As shown in Figure 19(B), when the absorbances at 856, 876, 902, and 926 nm are adopted as the explanatory variables for the samples for non-destructive measurement method development, the intercept is 9.1, the coefficients are 1456.6, -2917, 1881.4, -422.3, and the correlation coefficient is 0.95, which is good.

[0139] Figure 20 shows the measurement results of the tomato scar part with a LED prototype machine. Figure 20(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (882, 907 nm), three (882, 907, 925 nm), four (855, 882, 907, 925 nm), and (855, 878, 907, 925 nm). Figure 20(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. In Figure 20(B), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0140] As shown in Fig. 20(A), when four explanatory variables were used, the correlation coefficient was higher than when two or three explanatory variables were used.

[0141] As shown in Fig. 20(B), when the absorbances at 854.53(855), 882.24(882), 907.42(907), and 924.54(925) nm were adopted as explanatory variables for the sample for developing the non-destructive measurement method, the intercept was 16.5, the coefficients were 644.4, -2101.4, 2149.7, and -675.1, and the correlation coefficient was 0.90, which was good. Unlike the high-precision practical machine, tomatoes weighing less than 10 g could also be measured in the same measurement time (5 ms). When the sugar content is low, the error may become large.

[0142] (3-9. Strawberry) In the measurement of strawberries, the sample for developing the non-destructive measurement method had n = 110, and the sample for evaluating the non-destructive measurement method had n = 150.

[0143] Fig. 21 shows the measurement results of strawberries using the high-precision practical machine. Fig. 21(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (886, 902 nm), three (886, 902, 926 nm), four (856, 886, 902, 926 nm), and (856, 876, 902, 926 nm). Fig. 21(B) is a graph showing the correlation (multiple regression analysis) between the actually measured sugar content and the non-destructively measured value (estimated value) during development. Fig. 21(C) is a graph showing the correlation between the actually measured sugar content and the non-destructively measured value (estimated value) during evaluation. In Fig. 21(B) and (C), the horizontal axis represents the actually measured sugar content [Brix%], and the vertical axis represents the non-destructively measured value (estimated value) of sugar content [Brix%].

[0144] As shown in Fig. 21(A), when four explanatory variables were used, the correlation coefficient was higher than when two or three explanatory variables were used.

[0145] As shown in Fig. 21(B), when the absorbances at 856, 886, 902, and 926 nm were adopted as explanatory variables in the sample for non-destructive measurement method development, the intercept was 8.5, and the coefficients were 1525.3, -3181.3, 2079.5, -419.4, and the correlation coefficient was 0.94. As shown in Fig. 21(C), the RMSE was also as good as 0.49 in the sample for non-destructive measurement method evaluation.

[0146] (3-10. Paprika) In the measurement of paprika, the sample for non-destructive measurement method development had n = 112, and the sample for non-destructive measurement method evaluation had n = 174.

[0147] Fig. 22 shows the measurement results with a high-precision practical machine for paprika. Fig. 22(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (876, 902 nm), three (876, 902, 926 nm), and four (856, 876, 902, 926 nm). Fig. 22(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 22(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 22(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0148] As shown in Fig. 22(A), when the number of explanatory variables was four, the correlation coefficient was higher compared to the cases of two or three.

[0149] As shown in Fig. 22(B), when the absorbances at 856, 876, 902, and 926 nm were adopted as explanatory variables in the sample for non-destructive measurement method development, the intercept was 7.9, and the coefficients were 1797.4, -3605.4, 2311.3, -504.2, and the correlation coefficient was 0.88. As shown in Fig. 22(C), the RMSE was also as good as 0.59 in the sample for non-destructive measurement method evaluation.

[0150] (3-11. Melon) For the measurement of melons, the samples for developing non-destructive measurement methods were as follows: for the high-precision practical machine, n = 37; for the LED prototype machine, n = 42; and for the samples for evaluating non-destructive measurement methods, n = 52 for the high-precision practical machine.

[0151] Figure 23 shows the measurement results of the high-precision practical machine for melons. Figure 23(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (876, 902 nm), three (876, 902, 926 nm), four (856, 876, 902, 926 nm), and (856, 884, 902, 926 nm). Figure 23(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured sugar content and the non-destructive measurement value (estimated value) during development. Figure 23(C) is a diagram showing the correlation between the actually measured sugar content and the non-destructive measurement value (estimated value) during evaluation. In Figures 23(B) and (C), the horizontal axis represents the actually measured sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of sugar content [Brix%].

[0152] As shown in Figure 23(A), when the number of explanatory variables was four, the correlation coefficient was higher compared to the cases of two or three.

[0153] As shown in Figure 23(B), when the absorbances at 856, 876, 902, and 926 nm were adopted as explanatory variables for the samples for developing non-destructive measurement methods, the intercept was 9.8, and the coefficients were 575.8, -1223.7, 850.5, and -205.5, with a correlation coefficient of 0.83. As shown in Figure 23(C), the RMSE was also as good as 1.21 for the samples for evaluating non-destructive measurement methods.

[0154] Figure 24 shows the measurement results of the LED prototype machine for melons. Figure 24(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (886, 904 nm), three (886, 904, 926 nm), four (857, 886, 904, 926 nm), and (857, 874, 904, 926 nm). Figure 24(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured sugar content and the non-destructive measurement value (estimated value) during development. In Figure 24(B), the horizontal axis represents the actually measured sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of sugar content [Brix%].

[0155] As shown in Fig. 24(A), when four explanatory variables were used, the correlation coefficient was higher than in the cases of two or three explanatory variables.

[0156] Since there was a melon with a gap generated between the flower scar part, which is the measurement site, and the sample stage, a rubber washer was installed on the sample stage 52 for measurement. As a result, as shown in Fig. 24(B), when absorbances of 856.53(857), 886.15(886), 903.58(904), and 926.43(926) nm were adopted as explanatory variables, good results were obtained with an intercept of 13.1, coefficients of 234.4, -937.6, 851.9, -143.5, and a correlation coefficient of 0.84.

[0157] (3 - 12. Tangerine) (1) Flower scar part measurement For the measurement of the tangerine flower scar part, the sample for developing the non - destructive measurement method had n = 95.

[0158] Fig. 25 shows the measurement results using a high - precision practical instrument for the tangerine flower scar part. Fig. 25(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (874, 906 nm), three (874, 906, 926 nm), and four (856, 874, 906, 926 nm). Fig. 25(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non - destructive measurement value (estimated value) of the sugar content during development. In Fig. 25(B), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non - destructive measurement value (estimated value) of the sugar content [Brix%].

[0159] As shown in Fig. 25(A), when four explanatory variables were used, the correlation coefficient was higher than in the cases of two or three explanatory variables.

[0160] As shown in Fig. 25(B), when absorbances of 856, 874, 906, and 926 nm were adopted as explanatory variables for the sample for developing the non - destructive measurement method, it was good with an intercept of 10.2, coefficients of 848.4, -1570.8, 1086.8, -368.5, and a correlation coefficient of 0.96.

[0161] (2) Equator part measurement For the measurement of the equatorial part of mandarin oranges, the sample for developing the non-destructive measurement method had n = 72.

[0162] Figure 26 shows the measurement results with the LED prototype for the equatorial part of mandarin oranges. Figure 26(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (878, 907 nm), three (878, 907, 928 nm), and four (857, 878, 907, 928 nm). Figure 26(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. In Figure 26(B), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0163] As shown in Figure 26(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared to the cases of two or three.

[0164] As shown in Figure 26(B), in the sample for developing the non-destructive measurement method, when the absorbances at 856.53 (857), 878.32 (878), 907.42 (907), and 928.31 (928) nm were adopted as explanatory variables, the intercept was 10.8, the coefficients were 587.5, -1586.5, 1384.6, -387.4, and the correlation coefficient was 0.93, which was good.

[0165] (3 - 13. Shiranuhi) For the measurement of Shiranuhi, the sample for developing the non-destructive measurement method had n = 36.

[0166] Figure 27 shows the measurement results with the high-precision practical machine for Shiranuhi. Figure 27(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (876, 908 nm), three (876, 908, 926 nm), (856, 876, 908 nm), and four (856, 876, 908, 926 nm). Figure 27(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. In Figure 27(B), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0167] As shown in Fig. 27(A), when four explanatory variables were used, the correlation coefficient was higher than when two or three explanatory variables were used.

[0168] As shown in Fig. 27(B), when the absorbances at 856, 876, 908, and 926 nm were adopted as explanatory variables for the sample for developing the non-destructive measurement method, the intercept was 5.8, the coefficients were 183.5, -418.5, 339, and -102.7, and the correlation coefficient was 0.82, which was good.

[0169] (3-14. Cherry) For the measurement of cherries, n = 50 for the sample for developing the non-destructive measurement method and n = 60 for the sample for evaluating the non-destructive measurement method.

[0170] Fig. 28 shows the measurement results with the LED prototype for cherries. Fig. 28(A) shows the results of multiple regression analysis when the explanatory variables (absorbances at each wavelength) were two (884 and 909 nm), three (884, 909, and 926 nm), four (857, 884, 909, and 926 nm), and (857, 874, 909, and 928 nm). Fig. 28(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during development. Fig. 28(C) is a diagram showing the correlation between the actually measured value and the non-destructive measurement value (estimated value) of the sugar content during evaluation. In Fig. 28(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the non-destructive measurement value (estimated value) of the sugar content [Brix%].

[0171] As shown in Fig. 28(A), when four explanatory variables were used, the correlation coefficient was higher than when two or three explanatory variables were used.

[0172] As shown in Fig. 28(B), when the absorbances at 856.53 (857), 884.19 (884), 909.33 (909), and 926.43 (926) nm were adopted as explanatory variables for the sample for developing the non-destructive measurement method, the intercept was 17.1, the coefficients were 925.7, -3121.7, 3003.1, and -819.5, and the correlation coefficient was 0.96. As shown in Fig. 28(C), the RMSE was also as good as 1.46 for the sample for evaluating the non-destructive measurement method.

[0173] (3 - 15. Tomato Pulp and Tomato Processed Products) For the measurement of tomato pulp and tomato processed products, the number of samples for method development was n = 13, and the number of samples for method evaluation was n = 31.

[0174] Figure 29 shows the measurement results of tomato pulp and tomato processed products using a high-precision practical instrument. Figure 29(A) shows the results of multiple regression analysis when the explanatory variables (absorbance at each wavelength) are two (876, 902 nm), three (876, 902, 926 nm), and four (856, 876, 902, 926 nm). Figure 29(B) is a diagram showing the correlation (multiple regression analysis) between the actually measured value and the measured value (estimated value) of the sugar content during development. Figure 29(C) is a diagram showing the correlation between the actually measured value and the measured value (estimated value) of the sugar content during evaluation. In Figures 29(B) and (C), the horizontal axis represents the actually measured value of the sugar content [Brix%], and the vertical axis represents the measured value (estimated value) of the sugar content [Brix%].

[0175] As shown in Figure 29(A), when the number of explanatory variables was four, a higher correlation coefficient was obtained compared to the cases of two or three explanatory variables.

[0176] As shown in Figure 29(B), when the absorbances at 856, 876, 902, and 926 nm were used as explanatory variables for the samples for method development, the intercept was 10.6, the coefficients were 4681.3, -10265.9, 7346.7, -1730.2, n = 13, and the correlation coefficient was 0.994. As shown in Figure 29(C), the RMSE was also good at 0.98 for the samples for method evaluation.

[0177] (4. Summary) When three absorbances at 876 ± 2 nm or 884 ± 2 nm, 900 - 918 nm, and 926 ± 2 nm were used as explanatory variables in the multiple regression equation, the correlation coefficients included high values of 0.34 - 0.96 but also many low values. In addition, when these three wavelengths were used as explanatory variables, the correlation coefficients in the measurement of oyster and tomato scar parts (high-precision practical instrument) were similar to those of two wavelengths. Therefore, when the absorbance at 856 ± 2 nm was used as the fourth explanatory variable, the correlation coefficients improved to 0.82 - 0.994.

[0178] When the dark and reference values are constant, the signal value of the spectrally decomposed light can be used directly as an explanatory variable without converting it to absorbance or the like. When developing a multiple regression equation for non-destructively estimating fruit sugar content by adopting these four explanatory variables with the wavelength narrowed down, the signs of their coefficients consistently become plus, minus, plus, and minus in order from the lower wavelength for both the high-precision practical machine and the LED prototype machine. In particular, the coefficient of the absorbance at 900 to 918 nm, which is the absorption band of carbohydrates (sugars), is plus, meaning that the higher the absorbance, the higher the sugar content. In addition, since similarly high estimation accuracy results were obtained for tomato fruit crush and tomato processed products, these explanatory variables have high universality.

[0179] When targeting small fruits such as plums, cherries, tomatoes containing fruits less than 10 g, strawberries, and kaki and melons, the non-destructive measurement accuracy can be improved at 884 ± 2 nm rather than at 876 ± 2 nm, or the non-destructive measurement accuracy is about the same at both wavelengths.

[0180] As described above, similar results are consistently obtained for both the high-precision practical machine and the LED prototype machine, but some differences are also recognized. As shown in the results, the explanatory variables to be adopted may deviate slightly depending on the test sample, spectroscope, average number of times, etc. There are cases where 909 nm was adopted in the LED prototype machine when 902 nm was adopted in the high-precision practical machine for peaches and apples, and 900 nm was adopted in the high-precision practical machine and 911 nm was adopted in the LED prototype machine for plums. Also, 918 nm was adopted for kaki. Thus, it was necessary to cover a wide range of 900 to 918 nm in the absorption band of sugars. On the other hand, at other wavelengths (856, 876, 884, 926 nm), there may be a deviation of about ±2 nm as shown in the results.

[0181] When non-destructively measuring small fruits (less than about 10 g), melons, or oranges using a high-precision practical instrument, the light-gathering property deteriorates, and the measurement time becomes as long as about 300 to 350 ms. On the other hand, in the LED prototype, by setting the installation angle of the LED to 32 degrees with respect to the plane as described above, the light-gathering property is improved, and it is possible to measure peaches, pears, apples, as well as strawberries, plums, tomatoes including cherry tomatoes, melons, and oranges in a short measurement time (5 to 30 ms).

[0182] In addition, in the LED prototype, it is possible to measure small fruits such as strawberries and cherry tomatoes in a short time without using a dedicated jig, unlike the high-precision practical instrument. In the case of melons, due to the shape, there may be a gap between the sample stage and the fruit scar part. Therefore, it is necessary to install a rubber washer on the sample stage to unify and perform non-destructive measurement with less influence of stray light. In the scar part of oranges, some have a much deeper indentation than the scar part of melons. As shown in the results, the high-precision practical instrument was more suitable for measuring the scar part of oranges, but even in the measurement of the scar part of oranges using the LED prototype, by reducing the gap between the sample stage and the sample in this way, high-precision non-destructive measurement is possible.

[0183] According to the present invention, by combining the absorbance at 876 ± 2 nm or 884 ± 2 nm, 900 to 918 nm, 926 ± 2 nm with the absorbance at 856 ± 2 nm as the fourth explanatory variable (when the dark and reference values are constant, the spectrally separated signal value can be directly used as the explanatory variable), it becomes possible to measure the sugar content with high precision.

[0184] The present invention can be used not only for non-destructive measurement of fruit sugar content but also for estimating the sugar content of tomato crush and tomato processed products (tomato juice, tomato puree, tomato ketchup), and the applicable samples are not limited to these samples.

[0185] [Other Embodiments] Now, although the embodiments of the present invention have been described so far, the present invention may be implemented in various different embodiments within the scope of the technical idea described in the claims, in addition to the above-described embodiments.

[0186] Also, among the respective processes described in the embodiments, all or part of the processes described as being automatically performed can be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method.

[0187] In addition, regarding the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions of each process, screen examples, and database configurations shown in the above documents and drawings, they can be arbitrarily changed unless otherwise specified.

[0188] Also, regarding the sugar concentration measuring device 1, each illustrated component is a functional concept, and does not necessarily have to be physically configured as illustrated.

[0189] For example, regarding the processing functions provided by each device of the sugar concentration measuring device 1, particularly each processing function performed by the calculation processing unit 24, all or any part thereof may be realized by a CPU (Central Processing Unit) and a program interpreted and executed by the CPU, or may be realized as hardware by wired logic. Note that the program is recorded on a recording medium described later and is mechanically read by the sugar concentration measuring device 1 as necessary. That is, a memory 21 such as a ROM or an HD cooperates as an OS to give commands to the CPU, and a computer program for performing various processes is recorded. This computer program is executed by being loaded into the RAM and cooperates with the CPU to constitute a control unit.

[0190] Further, this computer program may be stored in an application program server connected to the sugar concentration measuring device 1 via an arbitrary network, and it is also possible to download all or part of it as necessary.

[0191] Also, the program according to the present invention may be stored in a computer-readable recording medium, and can also be configured as a program product. Here, this "recording medium" includes any "portable physical medium" such as a memory card, USB memory, SD card, flexible disk, magneto-optical disk, ROM, EPROM, EEPROM, CD-ROM, MO, DVD, and Blu-ray (registered trademark) Disc.

[0192] Also, the "program" is a data processing method described in any language or description method, and is not limited to forms such as source code or binary code. Note that the "program" is not necessarily limited to being configured singly, and also includes those that are distributed as a plurality of modules or libraries, or those that achieve their functions in cooperation with another program represented by an OS (Operating System). Regarding the specific configuration, reading procedure, or installation procedure after reading for reading the recording medium in each device shown in the embodiments, well-known configurations and procedures can be used.

[0193] Various databases and the like stored in the memory 21 are storage means such as a memory device such as a RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, or an optical disk, and store various programs, tables, databases, web page files, etc. used for various processes and website provision.

[0194] Further, the sugar concentration measuring device 1 may be configured as an information processing device such as a known personal computer or workstation, or may be configured by connecting an arbitrary peripheral device to the information processing device. Further, the sugar concentration measuring device 1 may be realized by installing software (including programs, data, etc.) for realizing the method of the present invention in the information processing device.

[0195] Furthermore, the specific form of the distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various additions or according to the functional load. That is, the above-described embodiments may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

Explanation of Reference Numerals

[0196] 1 Sugar concentration measuring device 10 Spectral detection device 11 Measuring unit 12 Light source 20 Data processing device 21 Memory 22 Keyboard / mouse 23 Control unit 24 Calculation processing unit 24-1 Spectral absorption spectrum acquisition unit 24-2 Estimation model creation unit 24-3 Estimation unit 26 I / O port 27 Estimation model 30 Display 41 Main body 42 Sample stage 43 Light receiving fiber 44 Cover 51 Main body 52 Sample stage 53 Light incident port 54 Ultra-small spectrometer

Claims

1. A method for measuring the sugar content of fruits and vegetables, which irradiates light from a light source onto fruits and vegetables, receives the reflected light including the diffuse reflection thereof, and measures the sugar content of the fruits and vegetables, comprising: an acquisition step of irradiating light in the near-infrared wavelength range emitted from one light source or a plurality of light sources onto fruits and vegetables, and acquiring signal values of light of only four spectral absorption spectra having different wavelengths from the reflected light including the diffuse reflection thereof; an estimation model creation step of creating an estimation model for estimating the sugar content by performing multivariate analysis using the absorbances (signal values of light) of only four different wavelengths obtained as explanatory variables; The method for measuring sugar content, characterized by including the above steps.

2. The method for measuring sugar content according to claim 1, wherein the four different wavelengths are 856 ± 2 nm, 876 ± 2 nm or 884 ± 2 nm, 900 to 918 nm, and 926 ± 2 nm.

3. The method for measuring sugar content according to claim 1 or 2, wherein in the estimation model creation step, multiple regression analysis is performed as the multivariate analysis to create a multiple regression equation as the estimation model.

4. The method for measuring sugar content according to any one of claims 1 to 3, wherein the fruits and vegetables include the equatorial part, the flower scar part, processed products, and crushed materials of the fruits and vegetables.

5. The method for measuring sugar content according to any one of claims 1 to 4, wherein the fruits and vegetables include Western pears, oysters, mangoes, strawberries, bell peppers, oranges (including tangerines), shiranui, tomatoes, cherries, peaches, Japanese pears, apples, plums, and melons.

6. The method for measuring sugar content according to any one of claims 1 to 5, wherein the light source is a ring-shaped light source.

7. The method for measuring sugar content according to claim 6, wherein in the acquisition step, the reflected light including the diffuse reflection of the fruits and vegetables with respect to the light emitted from the ring-shaped light source is detected at substantially the center of the ring-shaped light source.

8. The method for measuring sugar content according to claim 6 or 7, wherein the one light source is a halogen lamp.

9. The method for measuring sugar content according to claim 6 or 7, wherein the plurality of light sources are a plurality of LEDs having different emission wavelengths.

10. The method for measuring sugar content according to claim 9, wherein the plurality of LEDs are arranged at a predetermined angle (where the predetermined angle is greater than 10 degrees) with respect to the plane on the side of the fruits and vegetables to be measured.

11. A sugar content measuring device that irradiates fruits and vegetables with light from a light source, receives the reflected light including the diffuse reflection thereof, and measures the sugar content of the fruits and vegetables, a spectroscopic detection means that irradiates fruits and vegetables with light in the near-infrared wavelength range irradiated from one light source or a plurality of light sources, and acquires signal values of light of only four spectroscopic absorption spectra having different wavelengths from the reflected light including the diffuse reflection thereof; an estimation model creation means that creates an estimation model for estimating the sugar content by performing multivariate analysis using the absorbances (signal values of light) of only four different wavelengths obtained as explanatory variables; A sugar content measuring device, characterized by comprising the above.

12. The sugar content measuring device according to claim 11, wherein the four different wavelengths are 856 ± 2 nm, 876 ± 2 nm or 884 ± 2 nm, 900 to 918 nm, and 926 ± 2 nm.

13. The sugar content measuring device according to claim 11 or 12, wherein the estimation model creation means performs multiple regression analysis as the multivariate analysis and creates a multiple regression equation as the estimation model.

14. The sugar content measuring device according to any one of claims 11 to 13, wherein the fruits and vegetables include the equator part, the flower scar part, processed products, and crushed materials of the fruits and vegetables.

15. The sugar content measuring device according to any one of claims 11 to 14, wherein the fruits and vegetables include Western pears, persimmons, mangoes, strawberries, paprika, oranges (including Tangerine), shiranui, tomatoes, cherries, peaches, Japanese pears, apples, plums, and melons.

16. The sugar content measuring device according to any one of claims 11 to 15, wherein the light source is a ring-shaped light source.

17. The sugar content measuring device according to claim 16, wherein the spectroscopic detection means detects the reflected light including the diffuse reflection of the fruits and vegetables with respect to the light irradiated from the ring-shaped light source at substantially the center of the ring-shaped light source.

18. The sugar content measuring device according to claim 16 or 17, wherein the one light source is a halogen lamp.

19. The sugar content measuring device according to claim 16 or 17, wherein the plurality of light sources are a plurality of LEDs having different emission wavelengths.

20. The sugar content measuring device according to claim 19, wherein the plurality of LEDs are arranged on the side of the fruits and vegetables to be measured at a predetermined angle with respect to the plane (where the predetermined angle is greater than 10 degrees).

21. A sugar content measurement program for measuring the sugar content of fruits and vegetables, an acquisition step of acquiring signal values of light of only four spectral absorption spectra having different wavelengths from reflected light including diffuse reflection from fruits and vegetables with respect to light in the near-infrared wavelength range irradiated from one light source or a plurality of light sources; an estimated model creation step of creating an estimated model for estimating the sugar content by performing multivariate analysis using only the absorbances (light signal values) of four different wavelengths obtained as explanatory variables; A sugar content measurement program for causing a computer to execute.

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