Quantitative method for rice protein content

Abstract

To provide a method for determining the quantity of protein contained in rice that can classify and determine the quantity of quickly digestible protein easily in a short time.SOLUTION: A method for determining the quantity of protein contained in rice includes: for every type of a plurality of types of rice grains being a sample for calibration curve creation, acquiring, by a transmission method, an optical spectrum of light of a wavelength band including a wavelength range less than 1000 nm; for every type of the rice grains being the sample for calibration curve creation, determining the quantity of quickly digestible protein or slowly digestible protein by a known quantity determination method; analyzing information on the acquired optical spectrum and information on the determination of the quantity of protein by PLS regression analysis to set a regression equation indicating the relationship between the information on the optical spectrum and the information on the determination of the quantity of protein; and acquiring, by the transmission method, an optical spectrum of light of the wavelength band including a wavelength range less than 1000 nm for rice grains being a measurement target sample, and calculating the content of protein of a target type based on the set regression equation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This invention relates to a method for quantifying protein contained in rice by spectroscopy. [Background technology]

[0002] Analysis of the components contained in food has been conducted for a long time. Particular attention has been paid to starch and sugars, which affect calories and are related to obesity and diabetes. Near-infrared spectroscopy, which can be performed non-destructively and in a short time, is widely used for food component analysis.

[0003] Patent Document 1 describes a non-destructive analytical method for triacylglycerols contained in a single grain of brown rice, in which the near-infrared spectrum of diffuse reflection is measured using near-infrared light in a wavelength range of 1000 to 2500 nm or a part thereof, the triacylglycerol content of the brown rice is quantified, and the spectral data obtained in all or a part of the wavelength range in which the near-infrared spectrum was measured and the quantified triacylglycerol content are analyzed by PLS regression analysis to determine factors related to the triacylglycerol content.

[0004] Non-Patent Document 1 describes the use of near-infrared spectroscopy as a new method for chemical analysis of food. It also describes that a rice taste measuring device that uses near-infrared spectroscopy can measure the moisture, protein, amylose, iodine coloration, and other properties of rice. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-72726 [Non-patent literature]

[0006] [Non-Patent Document 1] Miyuki Kondo, "Chemical Analysis of Foods by Near-Infrared Spectroscopy," Journal of Nagoya Bunri University, No. 7, 2007, pp. 23-28 Summary of the Invention [Problem to be solved by the invention]

[0007] The protein content of rice is thought to affect its taste, and it has been measured as shown in Non-Patent Document 1. However, what is measured is the total amount of protein, and there is generally no interest in measuring the different types of protein separately.

[0008] Rice proteins are divided into digestible and indigestible proteins. Rice with low levels of digestible proteins is useful for dietary therapy for patients who need to restrict protein intake, and is also suitable for sake rice. Indigestible proteins are also involved in the stickiness and taste of rice.

[0009] In this way, the differential quantification of digestible and indigestible proteins can be utilized in various rice breeding projects, for human health, and for the production of cooked rice and sake with excellent taste. However, while a typical near-infrared analyzer used to judge the taste of rice can measure the total protein content of rice, it cannot distinguish between digestible and indigestible proteins.

[0010] Although there are methods for differentially quantifying digestible and indigestible proteins, these methods typically require chemical analysis over a period of about four days, which is time-consuming and expensive.

[0011] An object of the present invention is to provide a method for quantifying proteins contained in rice, which can easily and quickly distinguish and quantify easily digestible proteins and indigestible proteins. [Means for solving the problem]

[0012] In order to solve the above problems, the method for quantifying rice protein of the present invention includes: obtaining an optical spectrum of light having a wavelength band including a wavelength band of less than 1000 nm for each of a plurality of types of rice grains that are samples for preparing a calibration curve, using a transmission method; Quantifying the digestible protein or indigestible protein for each type of rice grain in the sample for creating the calibration curve using a known quantification method; The acquired optical spectrum information and protein quantification information are analyzed using PLS regression analysis to establish a regression equation that shows the relationship between the optical spectrum information and protein quantification. The optical spectrum of light in a wavelength range that includes the wavelength band below 1000 nm is obtained for the rice grain sample being measured using the transmission method, and the protein content of the target type is calculated based on a set regression equation. Examples of easily digestible proteins to be quantified include glutelin β, globulin, glutelin α, and glutelin precursors, and examples of indigestible proteins include prolamins.

[0013] When acquiring the light spectrum, digestible proteins can be measured with high accuracy by using light having a wavelength band that includes a wavelength band of less than 800 nm.

[0014] A regression equation for brown rice set using brown rice as a sample for creating a calibration curve, A regression equation for polished rice was prepared using polished rice as a sample for creating a calibration curve. When the sample to be measured is brown rice, a regression equation for brown rice may be used, and when the sample to be measured is polished rice, a regression equation for polished rice may be used to quantify the target protein. [Effects of the Invention]

[0015] According to the method for quantifying rice protein of the present invention, the amount of easily digestible protein or indigestible protein contained in rice can be quantified in a short time and at low cost. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a flowchart showing an outline of a method for quantifying rice protein. [Figure 2] An example of an electrophoretic image is shown below. [Figure 3]1 is a graph showing example light spectra of brown rice and polished rice samples. [Figure 4] 10 is a graph showing an example of the light spectrum of brown rice subjected to first derivative processing. [Figure 5] 10 is a graph showing an example of the light spectrum of polished rice subjected to Detrend processing, SNV processing, and first derivative processing. [Figure 6] 1 is a graph showing the regression coefficient of digestible protein in brown rice. [Figure 7] 1 is a graph showing the regression coefficient of digestible protein in polished rice. [Figure 8] 1 is a graph showing the regression coefficient of total protein of brown rice. [Figure 9] 1 is a graph showing the regression coefficient of total protein in polished rice. [Figure 10] FIG. 1 is a scatter plot showing an example of the correspondence between calculated values ​​and manual analysis values ​​of easily digestible protein content in brown rice. [Figure 11] FIG. 1 is a scatter plot showing an example of the correspondence between calculated values ​​and manual analysis values ​​of easily digestible protein content in polished rice. [Figure 12] 10 is a graph showing another example of the regression coefficient of digestible protein in brown rice. [Figure 13] 10 is a graph showing another example of the regression coefficient of digestible protein of polished rice. [Figure 14] FIG. 10 is a scatter plot showing another example of the correspondence between calculated values ​​and manual analysis values ​​of easily digestible protein content in brown rice. [Figure 15] FIG. 10 is a scatter plot showing another example of the correspondence between calculated values ​​and manual analysis values ​​of digestible protein content in polished rice. DETAILED DESCRIPTION OF THE INVENTION

[0017] An embodiment of the present invention will be described.

[0018] First, we will explain the proteins to which this quantification method of the present invention is applicable. Rice proteins specifically accumulate in two types of protein bodies (PBs) present in the starchy endosperm: PB-I and PB-II. PB-I contains low-molecular-weight polypeptides with apparent molecular weights of 10 kDa, 13 kDa, and 16 kDa, all of which are prolamins. PB-II, on the other hand, contains polypeptides with apparent molecular weights of 22-23 kDa, 26 kDa, and 37-39 kDa. The 22-23 kDa and 37-39 kDa polypeptides are glutelins, and the 26 kDa polypeptide is a globulin. The 22-23 kDa and 37-39 kDa glutelins are produced by processing a 57 kDa glutelin precursor.

[0019] In PB-I, prolamin polypeptides are stacked in layers within the endoplasmic reticulum membrane as they are synthesized, and the 10, 16, and 13a (kDa) polypeptides form strong bonds with each other via disulfide bonds, which in turn form strong hydrophobic bonds with the major 13 kDa prolamin, giving the structure a physically strong structure. It has been revealed that PB-I is not digested in the human body and is excreted from the body.

[0020] On the other hand, PB-II is first synthesized on membrane-bound polysomes as a precursor polypeptide with an apparent molecular size of 57 kDa and then transported to the vacuole via the Golgi apparatus. During this process, it is cleaved into two subunits of approximately 20 kDa and 40 kDa. A specific 26 kDa polypeptide present in B-II is also synthesized on membrane-bound polysomes, but unlike glutelin, it is not synthesized as a precursor. Close observation of PB-II under an electron microscope reveals that it is composed of clusters of electron-dense glutelin blocks. It is believed that upon water absorption, these blocks within PB-II separate, making the entire PB-II structure more susceptible to breakdown. In fact, electron microscopic examination of the internal structure of rice cooked in a pressure cooker has confirmed that the structure of PB-II, like starch, is completely destroyed, while the morphology of PB-I is maintained. Therefore, rice that is low in PB-II, which is composed of easily digestible proteins such as glutelin and globulin, and high in indigestible proteins such as prolamins may be able to reduce protein intake while maintaining the same calories.

[0021] In this invention, indigestible proteins such as prolamins and easily digestible proteins such as glutelin and globulin are separately quantified. Here, the separate quantification may refer not only to the content of each type of protein (e.g., content relative to the weight of rice grains) but also to the content relative to total protein, or the ratio of easily digestible protein to indigestible protein, etc. Therefore, analytical methods that calculate only the total protein content are not the method for quantifying rice protein of the present invention.

[0022] The spectroscopic analyzer used to acquire the optical spectrum will be described. An analyzer capable of measuring the absorbance of a rice grain sample using light with wavelengths in a band including a wavelength band of less than 1000 nm is used. Near-infrared light (electromagnetic waves) is a wavelength region between the visible and infrared regions, and is generally in the 800 to 2500 nm range, as shown in Non-Patent Document 1. In this invention, light with at least the shortest wavelength range in the near-infrared region is used. Furthermore, light in the visible region with shorter wavelengths than this may also be used. Therefore, in this invention, an analyzer capable of acquiring an optical spectrum in these wavelength regions is used. The analyzer is also capable of measuring the absorbance of a rice grain sample using a transmission method.

[0023] As will be described later, the quantification method of the present invention sets a regression equation that shows the relationship between optical spectrum information and protein quantification, and performs calculations based on that regression equation. Therefore, an analytical device equipped with a central processing unit (CPU) and a memory device and capable of installing and executing a program that performs calculations based on the determined regression equation is preferred. Such spectroscopic analyzers are commercially available. Here, an example will be described using an Inflammatic 9500 (PerkinElmer) as the spectroscopic analyzer.

[0024] Figure 1 is a flowchart outlining the process for setting a regression equation in a method for quantifying rice protein. First, a calibration curve is created, i.e., a sufficient number of samples are prepared to set the regression equation. These samples are rice grains, and can be brown rice or polished rice. However, brown rice samples are prepared for the analysis of brown rice, and polished rice samples are prepared for the analysis of polished rice. Here, we will use polished rice samples as an example, but the principles are the same for brown rice.

[0025] The number of samples (number of types) is preferably 50 or more; here, 100 types of samples were prepared. These samples are used to create a calibration curve. Each type of sample must be large enough to obtain an optical spectrum and perform quantitative analysis other than spectroscopic analysis, but it is preferable that the quality of the rice grains within each type of sample is as uniform as possible. Furthermore, it is preferable that the protein content of the type being measured be distributed among different types of samples so as to cover a wide range of the desired measurement range.

[0026] In this example, in addition to the 100 samples for creating the calibration curve, 20 samples were prepared as samples for evaluating the calibration curve. 74 rice varieties produced in Ehime and 46 varieties selected from brands across the country were used to prepare a total of 120 samples.

[0027] For each type of sample used to create a calibration curve, an optical spectrum is acquired, and quantitative analysis is then performed using a known analytical method other than spectroscopic analysis (measurement of manual analytical values), and the optical spectrum information and the manual analytical values ​​are associated and saved. Each type of sample may be divided into two, and an optical spectrum may be acquired using one sample, and quantitative analysis may be performed using the other. Furthermore, since optical spectrum acquisition can be performed non-destructively, quantitative analysis may be performed using the same sample after optical spectrum acquisition.

[0028] Similarly, for the samples for calibration curve evaluation, optical spectra are acquired and manual analytical values ​​are measured using a method other than spectroscopic analysis. It is also possible to separate the samples for calibration curve creation and the samples for calibration curve evaluation beforehand, and then acquire optical spectra and perform quantitative analysis using a method other than spectroscopic analysis. Alternatively, both samples can be analyzed in the same way at this stage, and then the data for calibration curve creation and calibration curve evaluation can be separated based on the results. In this example, the 120 samples are handled without being separated at this stage.

[0029] This section explains quantitative analysis by manual analysis. Quantitative analysis here uses an established analytical method. Any method that can accurately measure the target protein is sufficient, even if it is time-consuming and costly. An example of quantitative analysis by manual analysis is described in detail below.

[0030] 10 g of brown rice was polished to 91% using a small Kett Pearlest rice polisher to prepare the polished rice sample. Brown rice or polished rice was then ground to a 100-mesh mesh using a UDY Cyclone Sample Mill. 700 μL of extraction buffer (0.125 M Tris-HCl (pH 6.8) containing 8 M urea, 4% SDS, 20% glycerin, and 5% 2-mercaptoethanol) was added to 30 mg of brown rice flour or polished rice flour, stirred, and then left at 33°C for 24 hours. The supernatant obtained after centrifugation at 10,000 rpm for 10 minutes was used as the protein extract.

[0031] Protein extracts were separated by SDS-PAGE electrophoresis according to Larmmli (1970). The polyacrylamide gel used was ATTO's pre-prepared e-Pagel E-R1020L gel, ATTO's EzRun powder electrophoresis buffer, ATTO's EzStandard molecular weight markers, and the electrophoresis tank was an AE-6530P Rapidus Minislab e-Pagel electrophoresis system. After electrophoresis, the gel was stained using the GenScript eStein 2.0 protein staining system. The electrophoretic images were scanned using an Epson GT-X970 scanner, and the band intensities were quantified using Totallab's QuantV 12.2 image analysis software to calculate the percentage of each band.

[0032] Figure 2 shows an example of an electrophoretic image. The electrophoretic images of three varieties of rice are displayed side by side. At the top, a band corresponding to prolamin appears. This prolamin is a protein that is difficult to digest, i.e., an indigestible protein. Furthermore, bands corresponding to glutelin β, globulin, glutelin α, and glutelin precursors appear. These are easily digestible proteins, i.e., easily digestible proteins.

[0033] In parallel with this, the total protein content was measured as follows: 200 mg of brown rice or polished rice samples, crushed to 100 mesh using the method described above, were subjected to measurement of nitrogen content using Elementar Analytical's fully automated elemental analyzer, Vario MAX CNS, and the total protein content was calculated by multiplying the nitrogen content by a coefficient (5.95). The content of each protein was calculated by multiplying this total protein content by the proportion of each band determined by image analysis.

[0034] Although this manual quantitative analysis method can distinguish between digestible and indigestible proteins, it takes about four days to perform and is expensive. However, once this analysis is performed and a calibration curve (regression equation) is established, the present invention makes it possible to measure digestible and indigestible proteins without chemical analysis.

[0035] We will explain how to obtain an optical spectrum using the Inflamatic 9500 as an example. Simply insert a sample grain of rice into the inlet of this spectroscopic analyzer, and a single measurement will be performed in about 30 seconds. No pretreatment such as crushing the rice grains is required. The absorbance spectrum is collected when light passes through a sample (whole rice grains) filled into an 18 mm optical path length cell. After measurement, the sample rice grains can be collected as is. By performing this process for all types of sample, an optical spectrum can be obtained for each.

[0036] The optical spectrum is obtained by measuring absorbance using the transmission method. Therefore, information on the components of the entire rice grain, including the center, can be obtained, not just the surface. Unlike the oil contained in rice bran, rice protein is distributed throughout the rice grain, so it is important to be able to obtain information on the interior as well.

[0037] Spectroscopic analysis is performed over a wavelength range from the visible to near-infrared regions. The spectrum is automatically obtained by measuring absorbance in 0.5 nm increments from 570 nm to 1100 nm. Figure 3 shows examples of the optical spectra of brown rice and polished rice samples.

[0038] Next, we will explain how to determine the calibration curve. First, select the data for creating the calibration curve. In this example, spectra were acquired and quantitatively analyzed for 120 samples, but data from 100 samples was selected for creating the calibration curve so that the content of the protein to be measured was widely distributed, and data from the remaining samples was used for evaluating the calibration curve.

[0039] Next, a regression analysis is performed. While the raw spectrum information showing the relationship between wavelength and absorbance can be used, it is preferable to perform preprocessing, particularly first-order derivative processing. For polished rice, detrend processing and SNV processing were performed, followed by first-order derivative processing. Figure 4 shows an example of the optical spectrum of brown rice after first-order derivative processing, and Figure 5 shows an example of the optical spectrum of polished rice after detrend processing, SNV processing, and first-order derivative processing.

[0040] For the preprocessed optical spectrum of a certain sample, if the evaluation values ​​at wavelengths λ1, λ2, λ3... are x1, x2, x3... and the corresponding regression coefficients are a1, a2, a3..., the content y of a certain protein in that sample is linearly related as follows: y=a1·x1+a2·x2+a3·x3+

[0041] PLS regression analysis is performed on the information of samples selected for creating the calibration curve, and the regression coefficients a1, a2, a3, etc. are determined. This allows the regression equation to be established. Figure 6 is a graph showing the regression coefficients for digestible protein in brown rice, and Figure 7 is a graph showing the regression coefficients for digestible protein in polished rice. Here, the total amount of glutelin beta, globulin, glutelin alpha, and glutelin precursors is defined as the digestible protein content, and the regression coefficients, which are the correlation factors between this total amount and spectral information, are determined. In the example of digestible protein, information from 815 nm to 1097.5 nm, which is a portion of the spectral information in the wavelength range from 570 nm to 1100 nm, is used. Therefore, information on wavelengths in the range close to 800 nm, which is considered the lower limit of the near-infrared range, is also used. In this example, PLS regression analysis is performed on the information of 100 samples selected for creating the calibration curve. Therefore, the information from the 20 samples used for calibration curve evaluation is not involved in establishing the regression equation.

[0042] Furthermore, if quantitative analysis information on the amount of total protein is available, the regression coefficient for the amount of total protein can be calculated in a similar manner. Figure 8 is a graph showing the regression coefficient for total protein in brown rice, and Figure 9 is a graph showing the regression coefficient for total protein in polished rice. Here, the total protein content is defined as the sum of the easily digestible proteins glutelin β, globulin, glutelin α, and glutelin precursors, and the indigestible protein prolamin. In the example of total protein, the information from 850 nm to 1097.5 nm inclusive is used from the spectral information in the wavelength range of 570 nm to 1100 nm.

[0043] Next, we will explain the evaluation of the calibration curve. The 20 samples used for the calibration curve evaluation were subjected to the same pretreatment as the calibration curve evaluation samples, and the regression equation established using the above procedure was then applied to calculate the target protein content. Because the calibration curve evaluation samples were not used to create the regression equation, the calculation was performed on an unknown sample. The validity of the regression equation was evaluated by examining the correspondence between the calculated values ​​and the manual analytical values ​​obtained through quantitative analysis, i.e., the actual measured values. Figure 10 is a scatter plot showing an example of the correspondence between the calculated and manual analytical values ​​for the digestible protein content of brown rice, and Figure 11 is a scatter plot showing an example of the correspondence between the calculated and manual analytical values ​​for the digestible protein content of polished rice. The distribution of the results for each sample is shown, with the calculated values ​​on the vertical axis and the manual analytical values ​​on the horizontal axis. Figures 10 and 11 also display a regression line showing the correlation between the calculated values ​​and the manual analytical values. For brown rice, the coefficient of determination is 0.8097, with a standard error of 0.66. For polished rice, the coefficient of determination was 0.8938 and the standard error was 0.48, indicating a strong positive correlation in both cases.

[0044] Another example of PLS ​​regression analysis will now be described. Figure 12 is a graph showing another example of the regression coefficients for digestible protein in brown rice, and Figure 13 is a graph showing another example of the regression coefficients for digestible protein in polished rice. The sample and raw spectrum information (absorbance spectrum) are the same as in the above example. This is an example of PLS ​​regression analysis that uses information on light in an even shorter wavelength range than the above example, using information from 730 nm to 1097.5 nm. Therefore, information on light in a range that is generally considered to be the visible range is also used.

[0045] Figure 14 is a scatter plot showing another example of the correspondence between the calculated digestible protein content of brown rice and the manually analyzed value, and Figure 15 is a scatter plot showing another example of the correspondence between the calculated digestible protein content of polished rice and the manually analyzed value. These plots show examples of applying the regression coefficients of Figures 12 and 13 to calibration curve evaluation samples. The coefficient of determination for brown rice was 0.8421 with a standard error of 0.59, while the coefficient of determination for polished rice was 0.9004 with a standard error of 0.46. Thus, accuracy is improved by using spectral information for light in the range shorter than 800 nm. Furthermore, calculations were performed using wavelengths shorter than 730 nm, but no significant improvement in accuracy was observed. Therefore, using wavelengths of 730 nm or longer is preferable for highly accurate calculations.

[0046] The regression equation is determined through the above steps. The optical spectrum can then be acquired using a spectrophotometer, and the quantitative value of the target protein can be calculated based on the regression equation. This eliminates the need for conventional chemical analysis, which is time-consuming and costly. The calculation program for the regression equation calculation can be installed on a general-purpose computer such as a PC and executed by inputting the optical spectrum information into the general-purpose computer. However, if a spectrophotometer with a computing unit is used and the calculation program is installed, the content of digestible and indigestible proteins can be obtained non-destructively and quickly by simply adding sample rice grains. By installing separate regression equations for digestible and indigestible proteins, as well as regression equations for even smaller protein types, the content of multiple types of proteins can be classified and calculated simultaneously in a single operation.

[0047] Furthermore, since the regression equations for the same type of protein are different for brown rice and polished rice, if you install regression equations for both brown rice and polished rice and switch between them by operating a button or menu screen, you can create a protein analyzer that can be used for both brown rice and polished rice.

Claims

1. For each of the plurality of types of rice grains used as samples for creating a calibration curve, an optical spectrum of light having a wavelength band including a wavelength band of less than 1000 nm is obtained by a transmission method; The total amount of digestible protein or the total amount of indigestible protein is quantified for each type of rice grain in the sample for preparing the calibration curve by a known quantification method; analyzing the acquired light spectrum information and the quantitative information of the total amount of digestible proteins or the total amount of indigestible proteins by PLS regression analysis to set a regression equation showing the relationship between the light spectrum information and the quantitative information of the total amount of digestible proteins or the total amount of indigestible proteins; A method for quantifying rice protein, which involves obtaining an optical spectrum of light with wavelengths in a band including a wavelength band of less than 1000 nm for rice grains, which are the sample to be measured, using a transmission method, and calculating the total amount of digestible protein or the total amount of indigestible protein based on a set regression equation.

2. 2. The method for quantifying rice-containing proteins according to claim 1, wherein the easily digestible proteins to be quantified are the total amount of glutelin β, globulin, glutelin α, and glutelin precursors.

3. 2. The method for quantifying rice protein according to claim 1, wherein the indigestible protein to be quantified is the total amount of prolamins.

4. 3. The method for quantifying rice protein according to claim 2, wherein light having a wavelength band including a wavelength band of less than 800 nm is used.

5. A regression equation for brown rice set using brown rice as a sample for creating a calibration curve, A regression equation for polished rice was prepared using polished rice as a sample for creating a calibration curve. A method for quantifying rice-containing protein according to any one of claims 1 to 4, wherein the target protein is calculated using a regression equation for brown rice when the sample to be measured is brown rice, and using a regression equation for polished rice when the sample to be measured is polished rice.

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