Alkali decay test method

Abstract

To improve the evaluation quality of the rice dissolution process in alkaline solutions. [Solution] The alkali decay test method involves obtaining multiple images by imaging rice immersed in an alkaline solution over time, determining the brightness distribution for each image, and obtaining a feature profile based on the temporal change in brightness distribution. The rice is evaluated using the first characteristic value, which is the feature at a predetermined time elapsed from the start of the profile, as an evaluation parameter. The predetermined time is set so that the elapsed time is after the point in the profile when the feature becomes 50% of its maximum value.

Description

Technical Field

[0001] The present disclosure relates to an alkali disintegration test method for evaluating the dissolution characteristics of rice and the like using an alkaline solution.

Background Art

[0002] As one of the methods for evaluating the quality of rice, an alkali disintegration test is known. In the alkali disintegration test, the resistance of rice to an alkaline solution is utilized, which varies depending on various conditions such as variety, production area, production year, etc., and quality evaluation is performed based on the disintegration property of rice immersed in the alkaline solution. In a typical alkali disintegration test, dozens of grains of rice are immersed in an alkaline solution and left for several hours. In the process, the disintegration property is evaluated by visually observing the appearance of the alkaline solution becoming turbid due to the dissolution of the rice. The disintegration property evaluated by such an alkali disintegration test is regarded as an index of qualitative properties related to palatability such as cooking characteristics and taste in edible rice, and is known to be closely related to brewing conditions in sake rice.

[0003] In the conventional alkali disintegration test, since the evaluation is based on visual observation as described above, there are still problems with stability because there are quite a few experiential and subjective elements of the operator. Therefore, in Patent Document 1, alkaline solutions of different concentrations are prepared, and the disintegration property is evaluated based on the time required for the rice to dissolve in each alkaline solution, aiming to improve the evaluation accuracy of sake brewing suitability. However, in Patent Document 1, although the evaluation is performed based on the time required for the rice to dissolve when the rice is immersed in alkaline solutions of various concentrations, the determination of whether the rice has dissolved is still performed by visual observation, so the problem regarding the stability of the conventional alkali disintegration test remains.

[0004] Therefore, the inventor of this application developed the technology described in Patent Document 2. In Patent Document 2, by analyzing multiple images obtained by imaging the process of rice dissolving in an alkaline solution over time, the changes in the brightness distribution in the images are quantified as a time-series profile. Then, by using the characteristic time corresponding to feature points selected from the time-series profile according to predetermined criteria as evaluation parameters, it becomes possible to quantitatively evaluate the quality of rice without including empirical or subjective elements. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2017-161399 [Patent Document 2] Patent No. 7402451 [Overview of the project] [Problems that the invention aims to solve]

[0006] In the above-mentioned Patent Document 2, the characteristic time until the brightness value decreases from its maximum value to approximately 50% in the time-series profile is used as an evaluation parameter for quantitative quality evaluation. Such a characteristic time corresponds to the stage in the process where the area of ​​turbidity in the alkaline solution gradually expands as the dissolution of rice immersed in the alkaline solution progresses, and the area of ​​turbidity is relatively concentrated around the position where the rice was before dissolution (i.e., the area of ​​turbidity has not spread sufficiently into the alkaline solution, and the rice has not completely dissolved in the alkaline solution).

[0007] On the other hand, the inventor's research has shown that the palatability of rice and brewing conditions are also influenced by the properties of the rice at a stage where the turbidity expands and the dissolution of the rice in the alkaline solution progresses further. Patent document 2, mentioned above, does not take into account the properties of the rice at this stage where the dissolution of the rice in the alkaline solution progresses further, and there is room for further improvement in evaluation quality.

[0008] At least one embodiment of this disclosure has been made in view of the above-mentioned problems and aims to provide an alkali disintegration test method that can improve the evaluation quality of the rice dissolution process in an alkaline solution. [Means for solving the problem]

[0009] (1) An alkali disintegration test method according to one embodiment solves the above problem, The process of soaking the rice in an alkaline solution, The process involves acquiring multiple images by imaging the rice immersed in the alkaline solution over time, A step of determining the brightness distribution for each of the aforementioned multiple images, A step of obtaining a feature profile based on the temporal change of the luminance distribution, A step of evaluating the rice using the first characteristic value, which is the feature quantity at a predetermined time elapsed from the start of the profile, as an evaluation parameter, Equipped with, The predetermined time is set so that the elapsed time is after the time when the feature quantity in the profile becomes 50% of its maximum value.

[0010] According to the embodiment described in (1) above, quantitative evaluation of disintegration is possible by analyzing multiple images obtained by imaging the dissolution of rice immersed in an alkaline solution over time. The temporal change in the brightness distribution contained in the multiple images is obtained as a feature profile, and the feature at a predetermined time elapsed from the start of this profile is identified as the first characteristic value. The predetermined time is set so that the elapsed time is after the point in the profile when the feature value reaches 50% of its maximum value. By using this identified first characteristic value as an evaluation parameter, the quality of the rice can be appropriately evaluated by considering the disintegration at a stage when the dissolution of the rice in the alkaline solution has progressed relatively far.

[0011] (2) In other embodiments, in the embodiment of (1) above, The predetermined time is set so that the elapsed time is later than the time when the slope of the profile is at its maximum.

[0012] According to the embodiment of (2) above, a predetermined time is set such that the elapsed time corresponding to the first characteristic value is after the time when the slope in the profile is maximum. This makes it possible to suitably evaluate the characteristics of the rice at a stage when the dissolution of the rice immersed in the alkaline solution has progressed to a certain extent, based on the first characteristic value, which is a characteristic quantity at the time when a predetermined time has elapsed from the start time in the profile.

[0013] (3) In other embodiments, in the embodiment of (1) or (2) above, The predetermined period is x times the time required for the profile from the start to the point in time when the feature quantity reaches 50% of its maximum value (where x is a number greater than or equal to 1).

[0014] According to the embodiment of (3) above, a predetermined period is set such that the elapsed time corresponding to the first characteristic value is x times the time required from the start to the point in time when the feature quantity reaches 50% of the maximum value. This makes it possible to suitably evaluate the characteristics of rice at a stage in which the dissolution of rice immersed in an alkaline solution has progressed to a certain extent, based on the first characteristic value, which is the feature quantity at the point in the profile after a predetermined time has elapsed from the start.

[0015] (4) An alkali disintegration test method according to one embodiment is: The process of soaking the rice in an alkaline solution, The process involves acquiring multiple images by imaging the rice immersed in the alkaline solution over time, A step of determining the brightness distribution for each of the aforementioned multiple images, A step of obtaining a feature profile based on the temporal change of the luminance distribution, A step of evaluating the rice, using a second characteristic value, which is the derivative of the profile at the point in time when the feature quantity of the profile reaches a predetermined ratio with respect to the maximum value, as an evaluation parameter, It is equipped with.

[0016] According to the aspect (4) above, by analyzing a plurality of images obtained by imaging over time the state of dissolution of rice immersed in an alkaline solution, a quantitative evaluation of the disintegration property becomes possible. The temporal change in the luminance distribution included in the plurality of images is obtained as a profile of the feature quantity, and the differential value at the time point when the feature quantity in the profile reaches a preset ratio with respect to the maximum value is specified as the second characteristic value. By using the second characteristic value thus specified as an evaluation parameter, it is possible to suitably evaluate the quality of rice in consideration of the disintegration property at the stage where the dissolution of rice in the alkaline solution has relatively progressed.

[0017] (5) In another aspect, in the aspect (4) above, The preset ratio is 50%.

[0018] According to the aspect (5) above, the differential value at the time point when the feature quantity in the profile reaches 50% with respect to the maximum value is specified as the second characteristic value. Since the second characteristic value can be used to infer the subsequent transition of the feature quantity based on the differential value at that time point, it is possible to suitably evaluate the disintegration property at the stage where the dissolution of rice in the alkaline solution has progressed further than 50%.

[0019] (6) In another aspect, in any one of the aspects (1) to (5) above, In the step of evaluating the rice, the rice is evaluated based on a first index corresponding to the first characteristic value or the second characteristic value, and a second index corresponding to the elapsed time from the start time point to the time point when the feature quantity in the profile reaches a preset ratio with respect to the maximum value in the profile.

[0020] According to the aspect (6) above, it is possible to suitably evaluate the characteristics of rice that affect palatability and brewing conditions based on the combination of the first index and the second index based on different viewpoints.

[0021] (7) In another aspect, according to the aspect (6) above, In the process of evaluating the rice, the rice is evaluated by classifying it into a plurality of classes defined by a first reference value corresponding to the first indicator and a second reference value corresponding to the second indicator.

[0022] According to the embodiment of (7) above, the quality evaluation can be suitably performed by classifying the characteristics of the rice into one of several classes based on the results of comparing the first and second indicators obtained from the profile with the corresponding first and second reference values, respectively.

[0023] (8) In other embodiments, according to the embodiment of (6) above, The first and second reference values ​​are the average values ​​of the first and second indicators in multiple samples with different varieties or production conditions, respectively.

[0024] According to the embodiment of (8) above, quality evaluation by class classification can be suitably performed by using the average values ​​of the first and second indicators in multiple samples with different varieties or production conditions as the first and second reference values ​​that define each class.

[0025] (9) In other embodiments, according to any one of the embodiments (1) to (8) above, The aforementioned feature is the luminance variance value of the luminance distribution.

[0026] According to the embodiment described in (9) above, by selecting the luminance variance value of the luminance distribution as a feature quantity that defines the profile, a suitable evaluation becomes possible.

[0027] (10) In other embodiments, according to any one embodiment of (1) to (9) above, The rice grains are immersed in the alkaline solution in separate containers.

[0028] According to the embodiment of (10) above, when performing this method on multiple grains of rice, immersing each grain of rice in a separate container makes it possible to perform a highly accurate evaluation while eliminating the influence of inter-grain interactions.

[0029] (11) In other embodiments, according to any one embodiment of (1) to (10) above, The aforementioned alkaline solution has an alkaline concentration of 3% or more.

[0030] According to the method described in (11) above, by conducting the test using an alkaline solution with an alkali concentration of 3% or more (more preferably 5% or more), evaluation can be performed in a shorter test time compared to conventional alkali decay tests. As mentioned above, this method allows for quantitative evaluation using evaluation parameters, so even when using such a high-concentration alkaline solution, accurate evaluation is possible (conventional alkali decay tests relied on visual observation for evaluation, so using a high-concentration alkaline solution reduced the evaluation accuracy). [Effects of the Invention]

[0031] According to at least one embodiment of this disclosure, an alkali disintegration test method is provided that can improve the evaluation quality of the rice dissolution process in an alkaline solution. [Brief explanation of the drawing]

[0032] [Figure 1] This is a flowchart showing the alkali disintegration test method according to one embodiment, step by step. [Figure 2] This is a schematic diagram showing how multiple grains of rice are immersed in an alkaline solution in separate containers. [Figure 3A] Figure 2 shows an example of an image captured of a single grain of rice being immersed in an alkaline solution in one of the cells. [Figure 3B] This is another example of an image taken in one of the cells in Figure 2, showing a single grain of rice being immersed in an alkaline solution. [Figure 4] This is an example of a profile obtained in step S15 of Figure 1. [Figure 5] This is the normalized profile obtained by applying normalization processing to the profile in Figure 4. [Figure 6]Figure 5 is an explanatory diagram showing the first characteristic value identified based on the first criterion in the normalized profile. [Figure 7] This is a modified version of Figure 6. [Figure 8] Figure 5 is an explanatory diagram showing the second characteristic value identified based on the second criterion in the normalized profile. [Figure 9] This is an example of a quality evaluation based on the first and second indicators performed in step S18 of Figure 1. [Figure 10] This is another example of a quality assessment based on the first and second indicators performed in step S18 of Figure 1. [Modes for carrying out the invention]

[0033] Embodiments of the present invention will be described below with reference to the attached drawings. However, unless otherwise specified, the dimensions, materials, shapes, relative arrangements, etc., of the components described in this embodiment are not intended to limit the scope of the present invention, but are merely illustrative examples.

[0034] An alkali disintegration test method according to at least one embodiment of this disclosure is a method for conducting a test to evaluate the disintegration (solubility) of rice immersed in an alkaline solution. Figure 1 is a flowchart showing the alkali disintegration test method according to one embodiment, step by step.

[0035] First, the rice to be tested is prepared (Step S11). The rice may be for consumption or for brewing, and relevant conditions such as variety, production area, production year, and producer are specified in advance. The number of grains prepared may be just one, but in this embodiment, multiple grains are prepared to eliminate the influence of individual differences, and the evaluation results for each grain are statistically processed as described later to improve the evaluation accuracy.

[0036] Next, the rice prepared in step S11 is immersed in the alkaline solution, which is the test solution (step S12). The alkaline solution is prepared in a predetermined container, and the rice to be tested is immersed in it by placing it inside. Preferably, this container has a shape suitable for imaging the state of the rice as it is immersed in the alkaline solution, as will be described later. In this embodiment, a bottomed container is used that has an opening facing upwards where imaging is performed, and a flat bottom so that the position of the rice placed inside is stable. Examples of such containers include petri dishes and test tubes made from transparent plastic or glass materials.

[0037] The alkaline solution used in the test is preferably a strong alkaline component, such as KOH, NaOH, LiOH, RbOH, CsOH, Ca(OH) 2、 Sr(OH)2, etc., is used. The alkali concentration of the alkaline solution is preferably in the range of, for example, 1.7% by weight.

[0038] In step S11, if multiple grains of rice are prepared, it is preferable that each grain be immersed in an alkaline solution in a separate container. Figure 2 is a schematic diagram showing how multiple grains of rice 1 are immersed in an alkaline solution (not shown because it is transparent) in separate containers. In this example, multiple cells 10 arranged adjacently in a matrix are used as separate containers, and an alkaline solution 12 is stored in each cell 10. Then, one grain of rice 1 is placed into each cell 10 and immersed in the alkaline solution 12. If multiple grains of rice 1 were immersed in the alkaline solution 12 in the same container, the interaction between the grains may affect the dissolution process. In this embodiment, however, by immersing each grain of rice 1 in a separate container, the influence of interaction between two grains is eliminated, making it possible to perform a highly accurate evaluation.

[0039] Next, an image is acquired by imaging the rice 1 immersed in the alkaline solution 12 (step S13). The imaging of the rice in step S13 is repeated over time, so that the process of the rice 1 dissolving in the alkaline solution 12 is captured across multiple images. In other words, these multiple images record the state of the rice 1 at each moment as it dissolves in the alkaline solution 12 over time.

[0040] Furthermore, if one grain of rice 1 is placed in each cell 10 as shown in Figure 2, imaging in step S13 may also be performed for each cell 10. Figures 3A and 3B are examples of images taken of a single grain of rice immersed in alkaline solution 12 in one cell 10 of Figure 2. In this example, in order to perform stable imaging, an image scanner is used as the imaging device, and the effects of ambient light are eliminated by conducting the test and taking the photograph in a dark place with black paper as the background.

[0041] Figure 3A is an image of rice 1 immediately after being immersed in the alkaline solution 12 (i.e., in the initial stage of dissolution). Since dissolution has not yet progressed, the original outline of rice 1 is clearly visible. On the other hand, Figure 3B is an image of rice 1 taken some time after being immersed in the alkaline solution 12. It shows the white area 14 that has spread around the rice 1 due to its dissolution. Figures 3A and 3B show representative images at two different time points. By acquiring multiple images at different time points in this way, the process of a single grain of rice 1 dissolving over time is recorded.

[0042] Furthermore, when imaging is performed while immersing rice 1 in alkaline solution 12, it is preferable to maintain a stable measurement environment. For example, ambient temperature affects the solubility of rice 1 (for example, the higher the ambient temperature, the faster the dissolution rate of rice 1), so it is advisable to manage the ambient temperature to remain stable while the test is being conducted. However, if the ambient temperature changes, the effects of such changes should be taken into consideration in advance, and the test results should be appropriately corrected to reflect these changes in the final test results.

[0043] In step S13, multiple images are acquired by imaging the process of rice 1 dissolving in each cell 10 over time. These acquired images are managed in a manner that allows for individual identification by imaging time, cell number, etc.

[0044] Next, the luminance distribution is determined for each image acquired in step S13 (step S14). Typically, an image consists of many pixels, and the luminance distribution is determined by identifying the luminance value of each pixel through analog analysis of the image. For example, if the luminance value of each pixel is represented by 8 bits, the luminance value of each pixel will be determined to be in the range of 0 to 255. This allows for the determination of a luminance distribution, for example, where the coordinate data of each pixel is associated with the luminance value of each pixel.

[0045] Furthermore, if the image acquired in step S13 is a color image, the brightness value of each pixel may be determined by performing a grayscale conversion or a conversion to brightness information using YUV conversion before performing analog analysis.

[0046] Next, a profile P showing the temporal change in the luminance distribution obtained in step S14 is determined (step S15). In this embodiment, the luminance variance value is obtained from the luminance distribution of each image captured at different times, and its temporal change is obtained as profile P. Luminance variance value I Disp For example, average brightness I Avg It can be calculated using the following formula. TIFF2026046185000002.tif16170TIFF2026046185000003.tif16170Here I xy is the luminance at coordinate values ​​(x, y) on a two-dimensional plane, where X and Y are the coordinates in the X-axis and Y-axis directions, respectively.

[0047] Figure 4 shows an example of profile P obtained in step S15 of Figure 1. Figure 4 shows profile P for rice belonging to a specific variety. Profile P shows that at time 0, rice 1 is immersed in alkaline solution 12, and thereafter the luminance dispersion value increases from the initial value, reaching a peak value Dmax at time tp, and then gradually asymptotically approaches zero. This profile shape corresponds to a series of behaviors in which, in the initial stage, the luminance dispersion value increases as rice 1 absorbs the alkaline solution 12, expanding or the inside of the rice turns white, and then as dissolution progresses, the cloudy area gradually spreads to the surroundings, and finally, as it completely dissolves in the alkaline solution 12, it asymptotically approaches a uniform transparent state or a uniform luminance distribution state.

[0048] In this embodiment, the behavior of how the turbidity range changes during the dissolution process of rice 1 is quantified using an evaluation parameter called the luminance dispersion value. However, it is also possible to use other parameters as the evaluation parameter, such as the difference between the maximum and minimum luminance values ​​in each image or color information.

[0049] Next, normalization is performed on the profile P obtained in step S15 (step S16). Figure 5 shows the normalized profile P' obtained by performing normalization on the profile P in Figure 4. In the normalization process, each data point included in profile P is divided by the peak value Dmax. In the normalized profile P' obtained by this normalization process, the peak value is normalized to "1". By performing normalization on profile P in this way, it becomes possible to evaluate rice belonging to different conditions equally.

[0050] In this embodiment, normalization processing is performed to perform a relative evaluation of rice belonging to different conditions. However, if an absolute evaluation (an evaluation of rice belonging to a certain condition without comparison to rice belonging to other conditions) is performed, the above-mentioned normalization processing may be omitted.

[0051] Next, based on the normalized profile P', a characteristic value R that satisfies a predetermined criterion is determined (step S17). The criteria for determining the characteristic value R in step S17 can be set from several perspectives, as described below.

[0052] Figure 6 is an explanatory diagram showing the first characteristic value R1 identified based on the first criterion in the normalized profile P' shown in Figure 5. The first criterion is defined so that the first characteristic value R1 is selected from the normalized profile P' at a predetermined time point tq, which is set to a predetermined time T from the start time (=0). In this case, the predetermined time T is set so that the elapsed time point tq is after the time point th, when the characteristic value in the normalized profile P' becomes 50% of the maximum value (=1). In step S18, by using the first characteristic value R1 identified in this way as an evaluation parameter, the disintegration properties at a stage where the dissolution of rice 1 in the alkaline solution 12 has progressed relatively well can be appropriately evaluated.

[0053] Furthermore, the predetermined time T may be set so that the elapsed time tq is later than the time tr where the slope of the normalized profile P' is maximum (i.e., the time when the differential function of the normalized profile P' with respect to time has its maximum value). In the example in Figure 6, the case where time tr coincides with time th is illustrated.

[0054] Figure 7 is a modified version of Figure 6. In this modified version, the predetermined period T is set to x times the time Ta required from the start time (=0) to the point th where the feature quantity reaches 50% of the maximum value (=1) in the normalized profile P' (where x is a value of 1 or greater). This ensures that, according to the first criterion, the feature quantity (normalized luminance distribution value) at time tq, which is the result of a predetermined time T (=Ta × x) from the start time (=0), is selected as the first characteristic value R1 (i.e., T = Ta × x). This allows the profile features to be obtained for the elapsed time, with time th as the reference time.

[0055] Figure 8 is an explanatory diagram showing the second characteristic value R2 identified based on the second criterion in the normalized profile P' shown in Figure 5. The second criterion is defined so that the second characteristic value R2 is selected as the derivative value at time th when the number of features in the normalized profile P' reaches a predetermined ratio (e.g., 50%) relative to the maximum value (=1). Since the second characteristic value R2 identified by such a second criterion is a derivative value that indicates the slope of the normalized profile P' at time th, it is a suitable evaluation parameter for predicting the change in the normalized profile P' after time th. In other words, if the second characteristic value R2 is large, the slope of the normalized profile P' at time th is steep, so it is predicted that the number of features (luminance distribution values) will asymptotically approach zero relatively early after time th. On the other hand, if the second characteristic value R2 is small, the slope of the normalized profile P' at time th is gentle, so it is predicted that it will take a relatively long time for the number of features (luminance distribution values) to asymptotically approach zero after time th. In step S18, by using the second characteristic value R2 obtained in this way as an evaluation parameter, it is possible to predict the changes in the feature quantity after time th, thus allowing for a suitable evaluation of the disintegration properties of rice 1 at a stage where dissolution has progressed relatively well in the alkaline solution 12. Furthermore, since the evaluation based on the second characteristic value R2 can be performed at time th, the time required for the test can also be shortened.

[0056] Furthermore, if multiple grains of rice 1 are prepared, the characteristic time may be determined by conducting the same test on each grain and statistically processing the multiple test results obtained from each. For example, if multiple grains of rice belonging to the same variety and under the same conditions are prepared as shown in Figure 2, the average of the characteristic times obtained from each grain or the median of the group may be adopted as the final evaluation parameter. By treating the statistically processed results of multiple test results as the final test result, the accuracy of the test can be further improved.

[0057] Next, a quality evaluation is performed based on the characteristic values ​​obtained in step S17 (step S18). In step S18, the disintegration properties at a stage in which the dissolution of rice 1 has progressed relatively well in the alkaline solution 12 can be suitably evaluated by using the characteristic values ​​(first characteristic value R1 or second characteristic value R2) obtained based on the criteria described above.

[0058] Furthermore, the quality evaluation in step S18 may be performed based on a single characteristic value as described above, or it may be performed by combining multiple characteristic values. In the latter case, in addition to the first indicator I1 corresponding to the characteristic value obtained in step S17, the evaluation can be performed by combining a second indicator I2 corresponding to other characteristic values. The other characteristic values ​​corresponding to the second indicator I2 are not limited, but for example, the elapsed time from the start point (=0) to the point th where the feature quantity reaches a predetermined ratio (=50%) of the maximum value Dmax in the normalized profile P' can be used.

[0059] Figure 9 shows an example of quality evaluation based on the first indicator I1 and the second indicator I2 performed in step S18 of Figure 1. In Figure 9, the vertical axis shows the first indicator I1 (corresponding to the second characteristic value R2 mentioned above), and the horizontal axis shows the second indicator I2 (the elapsed time from the start point (=0) to the point th where the number of features reaches a predetermined ratio (=50%) relative to the maximum value Dmax in the normalized profile P').

[0060] Thus, in the two-dimensional space defined by the first index I1 and the second index I2, multiple classes C1 to C4 that can be classified by the first criterion value L1 and the second criterion value L2 are defined, as shown in Figure 9. The first criterion value L1 is defined as the criterion value corresponding to the first index I1, and is, for example, the average value of the first index I1 obtained for multiple varieties. The second criterion value L2 is defined as the criterion value corresponding to the first index I2, and is, for example, the average value of the second index I2 obtained for multiple varieties.

[0061] The classes C1 to C4, classified by the first criterion value L1 and the second criterion value L2, are specifically as follows: First, class C1 is defined as the class corresponding to the region where the first index I1 is greater than or equal to the first criterion value L1 and the second index I2 is greater than or equal to the second criterion value L2. Class C2 is defined as the class corresponding to the region where the first index I1 is greater than or equal to the first criterion value L1 and the second index I2 is less than the second criterion value L2. Class C3 is defined as the class corresponding to the region where the first index I1 is less than the first criterion value L1 and the second index I2 is greater than or equal to the second criterion value L2. Class C4 is defined as the class corresponding to the region where the first index I1 is less than the first criterion value L1 and the second index I2 is less than the second criterion value L2.

[0062] In Figure 9, characteristic points corresponding to combinations of the first index I1 and the second index I2, determined based on the normalized profile P' corresponding to several varieties A to C, are mapped onto the two-dimensional space defined in this way. In this evaluation example, the characteristic points corresponding to variety A belong to class C1, the characteristic points corresponding to variety B belong to class C3, and the characteristic points corresponding to variety C belong to class C4. In step S18, the characteristic points corresponding to the combinations of the first index I1 and the second index I2, determined based on the normalized profile P' obtained from the alkali disintegration test of rice 1 of each variety, are mapped onto the two-dimensional space, and it is determined which class each variety belongs to. This allows for a comparison of the characteristics of rice 1 of each variety on the distribution map shown in the two-dimensional space, enabling a detailed characteristic analysis of the differences in characteristics between varieties.

[0063] Figure 10 shows another example of quality evaluation based on the first indicator I1 and the second indicator I2, which is performed in step S18 of Figure 1. In this evaluation example, similar to Figure 9 above, characteristic points corresponding to combinations of the first indicator I1 and the second indicator I2, determined based on the standardized profile P' corresponding to different production conditions (e.g., production site, production year, etc.), are mapped onto a two-dimensional space defined by the first indicator I1 and the second indicator I2. In this evaluation example, the characteristic points corresponding to production conditions a and b belong to class C1, and the characteristic points corresponding to production condition c belong to class C4. In step S18, it is determined which class the characteristic points corresponding to combinations of the first indicator I1 and the second indicator I2, determined based on the standardized profile P' obtained from alkali disintegration tests of rice 1 under each production condition, belong to. This allows for a comparison of the characteristics of rice 1 under each production condition on a distribution map shown in two-dimensional space, and enables a detailed characteristic analysis of the characteristic differences due to production conditions.

[0064] Furthermore, the first and second reference values ​​L1 and L2, which define the boundaries of each class in Figures 9 and 10, are obtained as the average value of each data mapped in the two-dimensional space, as described above, and can be stabilized by increasing the number of mapped data. In addition, the first and second reference values ​​L1 and L2, which are defined as the average values ​​of the first and second indicators I1 and I2, may be used as reference values ​​for absolute evaluation, or as reference values ​​for relative evaluation for each variety or production condition.

[0065] In addition, while the evaluation examples in Figures 9 and 10 show examples of analysis using data mapping in a two-dimensional space defined by two indicators (first indicator I1 and second indicator I2), data mapping in a multi-dimensional space defined by three or more indicators may also be performed.

[0066] As described above, according to each of the embodiments described above, it is possible to provide an alkali disintegration test method that can improve the evaluation quality of the dissolution process of rice 1 in an alkaline solution 12. [Explanation of symbols]

[0067] 1 rice 10 cells 12. Alkaline solution 14. Area of ​​cloudiness P Profile P' Normalized Profile R1 Primary characteristic value R2 Second characteristic value L1 First Reference Value L2 Second Reference Value I1 1st indicator I2 Second indicator

Claims

1. The process of soaking the rice in an alkaline solution, The process involves acquiring multiple images by imaging the rice immersed in the alkaline solution over time, A step of determining the brightness distribution for each of the aforementioned multiple images, A step of obtaining a feature profile based on the temporal change of the luminance distribution, A step of evaluating the rice using the first characteristic value, which is the feature quantity at a predetermined time elapsed from the start of the profile, as an evaluation parameter, Equipped with, An alkali disintegration test method wherein the predetermined time is set so that the elapsed time is after the time when the feature quantity in the profile becomes 50% of the maximum value.

2. The alkali disintegration test method according to claim 1, wherein the predetermined time is set so that the elapsed time is later than the time when the slope of the profile is at its maximum.

3. The alkali disintegration test method according to claim 1 or 2, wherein the predetermined period is x times the time required from the start of the profile until the feature quantity reaches 50% of its maximum value (where x is a value of 1 or more).

4. The process of soaking the rice in an alkaline solution, The process involves acquiring multiple images by imaging the rice immersed in the alkaline solution over time, A step of determining the brightness distribution for each of the aforementioned multiple images, A step of obtaining a feature profile based on the temporal change of the luminance distribution, A step of evaluating the rice, using a second characteristic value, which is the derivative of the profile at the point in time when the feature quantity of the profile reaches a predetermined ratio with respect to the maximum value, as an evaluation parameter, A method for testing alkali disintegration, comprising the components described above.

5. The alkali disintegration test method according to claim 4, wherein the aforementioned predetermined ratio is 50%.

6. The alkali disintegration test method according to claim 1 or 4, wherein in the step of evaluating the rice, the rice is evaluated based on a first index corresponding to the first characteristic value or the second characteristic value, and a second index in the profile corresponding to the elapsed time from the start to the point in time when the characteristic quantity becomes a predetermined ratio with respect to the maximum value.

7. The alkali disintegration test method according to claim 6, wherein in the step of evaluating the rice, the rice is evaluated by classifying it into a plurality of classes defined by a first reference value corresponding to the first indicator and a second reference line corresponding to the second indicator.

8. The alkali disintegration test method according to claim 7, wherein the first reference value and the second reference value are the average values ​​of the first indicator and the second indicator in a plurality of samples with different varieties or production conditions, respectively.

9. The alkali decay test method according to claim 1 or 4, wherein the feature quantity is the luminance variance value of the luminance distribution.

10. The alkali disintegration test method according to claim 1 or 4, wherein the rice grains are immersed in the alkaline solution in separate containers.

11. The alkali disintegration test method according to claim 1 or 4, wherein the alkali solution has an alkali concentration of 3% or more.

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