JP2016223969A - Rice starch property evaluation method based on gelatinization viscosity property - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for easily, quickly, inexpensively and accurately evaluating a property of rice starch for every subspecies related to functionality such as a content of indigestible starch, even when samples are relative species, based on gelatinization viscosity property.SOLUTION: A ratio of a gelatinization property value based on maximum viscosity, minimum viscosity, final viscosity, brake-down, setback, and consistency, and gelatinization start temperature are used as explanatory variables of multiple regression analysis, for evaluating rice starch property for every subspecies. The rice starch property is any of a content of indigestible starch, a content of middle long chain fraction having 13 or more of glucose polymerization degree of amylopectin, and a content of amylose.SELECTED DRAWING: Figure 2

Description

  The present invention is based on the gelatinization viscosity characteristics of rice starch, in order to be able to discriminate between similar subspecies such as Japanese rice and Indian rice and hard rices, resistant starch content Further, the present invention relates to a technique for simply and rapidly evaluating starch characteristics such as amylose content or content of amidopectin medium and long chain fractions closely related to resistant starch.

  In recent years, Japan has become a society with a declining birthrate and an aging society, and it is necessary to promote health maintenance and disease prevention with a diet rich in functional ingredients.

  Rice with a high amylose content in starch and rice with a long amylopectin chain are indigestible, have a moderate increase in blood glucose after meals, and are expected to prevent the onset of diabetes (Non-patent Documents 1 and 2). Therefore, it is extremely important to develop a technique for measuring the contents of resistant starch and related sugar chain fractions of various rice easily and quickly with high accuracy.

  Conventionally, the amylopectin chain length distribution of rice starch has been measured by high performance liquid chromatography after debranching with isoamylase (Non-patent Document 3).

  In addition, it has been reported that the amylose molecular weight distribution and amylopectin chain length distribution of rice starch analyzed by a fluorescent labeling gel filtration method affect the thermal gelatinization characteristics and taste of rice starch (Non-Patent Document 4).

  Although these conventional measurement methods provide accurate measurement values such as amylopectin chain length distribution, they do not measure the resistant starch content that is expected to prevent disease in rice starch. Is a costly method because it requires expensive equipment such as a high performance liquid chromatograph, and it is simple, quick, low cost and highly resistant to starch in the fields of breeding selection and food processing. In order to evaluate the sugar chain fraction content closely related to the content or resistant starch content, a new measurement method was required.

  In addition, the present inventor has known a gelatinization characteristic measuring device that displays food property values of cereals (Patent Document 1).

  However, this gelatinization property measuring apparatus uses an estimation formula with the gelatinization property as a variable, but the target of estimation is food physical properties such as rice physical properties in rice and raw noodle hardness in wheat, which is difficult. It was not intended to estimate the content of functional components such as the digestible starch content and the content of the long chain fraction in amylopectin which is closely related to the resistant starch.

  Moreover, based on the gelatinization viscosity characteristics of rice starch, there is also known a method for evaluating rice starch characteristics for estimating the amylose content, resistant starch content, amylopectin sugar chain fraction content, etc. of rice (Patent Document 2). . This evaluation method enables high discrimination among a wide range of sample rices including Japanese rice and Indian rice.

  However, even with this evaluation method, Japanese rice that currently accounts for over 99% of Japan's produced rice, or conversely, Indian rice that occupies most of South Asian countries such as India and Thailand, or hard rice The problem remains that identification between related species such as each other is difficult despite the high necessity.

Japanese Patent No. 3908227 Japanese Patent Laying-Open No. 2015-55548

Kenichi Otsubo, Sumiko Nakamura, Kazunori Utsunomiya, Yasunobu Masuda, Keisuke Tsuji: Efforts to prevent and commercialize hard rice and diabetes. Food industry, 53 (14), 46-51, 2010. Ken’ichi Ohtsubo, Sumiko Nakamura, Keisuke Tsuji, Kazunori Utsunomiya, Yasunobu Masuda, Mineo Hasegawa: Possibility of diabetes prevention by high-amylose rice.Rice studies, present and future, Sankyosyuppan Inc., pp.109-115, 2012. Masako Asaoka, Kazutoshi Okuno, Yasumi Sugimoto, Masahiro Yano, Takeshi Omura, and Hidetsugu Fuwa: Structure and properties of endosperm starch and wated soluble polysaccharides from sugary mutant of rice, Starch, 37, 364-366 (11), 1985. Toshinari Igarashi, Isao Hanashiro, and Yasuhito Takeda: Molecular structures and some properties of rice starches from Hokkaido cultivars.J. Applied Glycoscience, 55, 5-12, 2008. Toshinari Igarashi, Masafumi Kinoshita, Hideki Kanda, Tomoko Nakamori, and Toshimi Kusume: Evaluation of hardness of waxy rice cake based on the amylopectin chain-length distribution.J. Applied Glycoscience, 55, 13-19, 2008.

  Therefore, the present invention provides simple, quick, and low-cost, low-cost, low-cost rice starch properties related to functionality, such as resistant starch content, even when the samples are closely related species. And it aims at providing the method of evaluating with high precision.

  In order to solve the above-mentioned problems, the present inventors have intensively studied and, as a result, conducted a multivariate analysis using the ratio of the ratio of the polished rice flour or rice starch sample as a variable instead of the gelatinized viscosity characteristic value, and the analysis value. By applying the estimation formula newly found by the present inventors, it is possible to easily and quickly reduce the low-starch starch content, the amylose content, or the long-chain fraction content in amylopectin, which is closely related to the resistant starch content. In addition, the inventors have found that the estimation can be performed with high accuracy and have arrived at the present invention.

  That is, the present invention uses the ratio of gelatinization characteristic values consisting of maximum viscosity, minimum viscosity, final viscosity, breakdown, setback, consistency and gelatinization start temperature as explanatory variables for multiple regression analysis. It is a rice starch characteristic evaluation method characterized by evaluating the rice starch characteristic.

  Moreover, the rice starch characteristic is characterized by the indigestible starch content.

  In addition, the characteristic of rice starch is that the amylopectin has a medium and long chain fraction content of 13 or more in the degree of glucose polymerization.

  Moreover, the rice starch characteristic is characterized by amylose content.

  In addition, when the estimation formula for the resistant starch content of the Japanese subspecies is a milled rice flour,

It is characterized by being.

  In addition, when the estimation formula for the resistant starch content of Japanese subspecies is rice starch,

It is characterized by being.

  The formula for estimating the content of medium and long chain fractions of amylopectin with a degree of glucose polymerization of 13 or more is:

It is characterized by being.

  In addition, when the estimation formula for the amylose content of the Japanese subspecies is a milled rice flour,

It is characterized by being.

  Moreover, when the estimation formula of the amylose content of the Japanese-type subspecies is a sample of rice starch,

It is characterized by being.

  In addition, when the estimation formula of the hard-to-digestible starch content of hard rice is rice starch,

It is characterized by being.

  In addition, when the estimation formula for the amylose content of hard rice is rice starch,

It is characterized by being.

  In addition, the amylose content, long-chain content of amylopectin, or resistant starch content of Japanese rice is the objective variable, and the viscosity of the polished rice sample suspension measured with a rapid visco analyzer is the explanatory variable, using the least square method. Is a method for estimating the amylose content, long-chain content of amylopectin or resistant starch content of Japanese rice based on the gelatinized viscosity characteristics, which is characterized in that an estimation formula that minimizes the error is created by the method.

  According to the present invention, indigestible starch, which is expected to have functions such as prevention of diabetes and prevention of obesity by slowing the increase in postprandial blood glucose in humans, is a related subtype such as Japanese rice or Indian rice Measure the content of closely related rice, such as rice and hard rice, by measuring the gelatinization viscosity of polished rice flour or rice starch that requires only a gelatinization viscosity measuring device, quickly, accurately, and at low cost. be able to.

  In addition, according to the present invention, by measuring the gelatinization viscosity of rice flour or rice starch, it is possible to estimate the content of medium and long chain fractions of amylopectin having a glucose polymerization degree of 13 or more even in closely related rice. Can be estimated easily and with high accuracy.

  Furthermore, since a formula for estimating the amylose content of closely related rice has been established by simple gelatinization viscosity measurement, it is possible to easily and accurately estimate the quality characteristics of the sample rice.

It is a graph which shows the example of a measurement of the gelatinization viscosity characteristic by a rapid visco analyzer, and each gelatinization characteristic value. It is a graph which shows the estimation result of the indigestible starch content of Japanese type rice which uses milled rice powder as a sample, and the test result by an unknown sample. It is a graph which shows the estimation result of the indigestible starch content of Japanese type | mold rice which uses milled rice powder as a sample, and the test result (comparative example) by an unknown sample. It is a graph which shows the estimation formula of the resistant starch content of Japanese type | mold rice which uses rice starch as a sample, and the test result by an unknown sample. It is a graph which shows the estimation result of the indigestible starch content of Japanese type rice which uses rice starch as a sample, and the test result (comparative example) by an unknown sample. It is a graph which shows the estimation of the long chain content (Fb1 _{+ 2 + 3} ) in amylopectin of Japanese-type rice which uses rice starch as a sample. It is a graph which shows the estimation result of the amylose content of the Japanese-type rice which uses milled rice powder as a sample, and the test result by an unknown sample. It is a graph which shows the test result (comparative example) of the estimation formula by the estimation of an amylose content of a Japanese type rice which uses milled rice powder as a sample, and an unknown sample. It is a graph which shows the test result of the estimation formula by the estimation of the amylose content of a Japanese type rice which uses rice starch as a sample, and an unknown sample. It is a graph which shows the test result (comparative example) of the estimation formula by the estimation of the amylose content of a Japanese type rice which uses rice starch as a sample, and an unknown sample. It is a graph which shows the estimation formula of the resistant starch content of the hard rice which uses milled rice powder as a sample, and the test result by an unknown sample. It is a graph which shows the estimation result of the amylose content of the hard rice which uses rice starch as a sample, and the test result by an unknown sample. It is a graph which shows the estimation formula of the amylose content by the least squares method (PLS method) and resistant starch, and its test result.

  The present invention is based on the gelatinization viscosity characteristics of polished rice flour or rice starch, such as the content of resistant starch and sugar chain fraction content closely related to resistant starch, even between closely related rice samples. The present invention relates to a technique for simply and quickly evaluating starch characteristics with high accuracy.

  The present invention adopts a ratio of gelatinized viscosity parameters as explanatory variables in the estimation formula, and uses a ratio of gelatinized viscosity parameters, so that rice of close relatives such as Japanese rices, Indian rices, or hard rices can be used. It enables high-precision discrimination between the two, and at the same time enables high-precision estimation based on the gelatinization viscosity measurement result using polished rice flour as a sample.

  In addition, the present invention uses, as objective variables, resistant starch, amyloelement content, amylopectin medium-long chain content, etc., of closely related species such as Japanese rice and Indian rice, and these are highly accurate between closely related species. As a second method for discriminating, a multiple regression analysis for minimizing a prediction error is performed using all viscosities at regular intervals when measuring gelatinized viscosity characteristics as explanatory variables.

  Here, the polished rice flour in the present invention is a general Japanese type rice or Indian type rice, a newly cultivated new trait rice, and a milled rice of amylopectin long chain type rice, an impact grinder or a roll type. It refers to powdered by pulverization using a pulverizer or an airflow pulverizer.

  The rice starch in the present invention is a product obtained by defatting and deproteinizing a polished rice sample, which is obtained by removing protein with a dilute alkali at a low temperature and subsequently defatted with ethanol and acetone. The protein may be deproteinized by heating, or degreased by hot butanol or hexane.

  The gelatinization viscosity characteristics in the present invention include rice flour or rice starch as a sample, and a general rotational viscosity such as a rapid visco analyzer (RVA) manufactured by Parten, an amylograph manufactured by Brabender, a trace viscograph, and a rheometer manufactured by Anton Paar. Using a measuring device, suspending the sample in water or other dispersion medium, raising, holding, or lowering the temperature while stirring, and detecting the viscosity of the solution or suspension during that time as the rotational resistance value Measured, gelatinization start temperature, maximum viscosity, minimum viscosity, final viscosity or breakdown (difference between maximum and minimum viscosity), setback (difference between final viscosity and maximum viscosity), consistency (final viscosity and minimum viscosity) A characteristic expressed as a parameter such as (difference).

  FIG. 1 shows an example of gelatinization characteristic values measured with a Parten Rapid Visco Analyzer.

In this specification and drawings, etc., the gelatinization start temperature is PT, the maximum viscosity is V _MAX , the minimum viscosity is V _MIN , the final viscosity is V _FIN , the breakdown is BD, the setback is SB2, and the consistency is C. May be written.

  The multiple regression analysis in the present invention is a kind of multivariate analysis in which one objective variable is predicted by a plurality of explanatory variables. According to the multiple regression analysis, which explanatory variable influences the objective variable to what extent. You can know if you are. For example, if there are n independent variables, the multiple regression equation is

It becomes. The coefficient applied to each independent variable is called “partial regression coefficient”. The suitability of the model is expressed as a coefficient of determination (the square of the multiple correlation coefficient) and can be tested by analysis of variance.

  The starch characteristic in the present invention refers to a functional characteristic of starch, for example, a physiological characteristic such as digestion and absorption after feeding, and a physical characteristic such as viscosity characteristic of gelatinized starch.

  The indigestible starch in the present invention refers to starch that is difficult to digest and absorb in the human stomach and small intestine after ingestion, and when starch molecules are mutually cross-linked by a covalent bond or chemically modified. In the case of crystallization by hydrogen bonding, when starch molecules are structurally covered with other materials, gelatinized starch is aged, etc. Similarly, it refers to starch that has been reported to have an intestinal regulating effect, and can be measured by, for example, an enzyme kit manufactured by Megazyme.

  The medium and long chain fraction of amylopectin in the present invention is a glucose polymer constituting amylopectin, which is the main component of starch, and gel filtration chromatography after debranching α-1,6 bonds by isoamylase. In the case of separation / fractionation by molecular weight, the sugar chain fraction consisting of long molecules having a glucose polymerization degree of 13 or more is indicated.

  EXAMPLES The present invention will be described in detail below based on examples, but the present invention is not limited to these examples.

<Example 1> (Measurement of resistant starch content of sample rice)
Each sample of 2 g of polished rice was suspended in 10 mL of 0.1% aqueous sodium hydroxide solution, shaken at 4 ° C. for 3 hours, and deproteinized. After light centrifugation, the precipitate was washed three times with pure water, twice with 99% ethanol, then washed with acetone and air-dried to obtain a sample starch.

  Table 1, Table 2, Table 3, Table 4, and Table 6 show the results of taking a starch sample having a dry matter weight of 100 mg in a test tube and measuring the amylose content by the iodometric method.

  Moreover, the result of having measured the resistant starch content using the same sample and using the resistant starch measurement kit made by Megazyme is as shown in Table 1, Table 2, Table 3, Table 4, and Table 6. EM10 (sample rice number 1 in Table 1), EM72 (sample rice number 4 in Table 1), EM174 (sample rice number 7 in Table 1), etc. showed high values.

<Example 2> (Quantification of sugar chain fractions having different chain lengths)
4 mg of the starch sample prepared in Example 1 was suspended in 1.6 mL of distilled water, the starch was gelatinized at 100 ° C., and 16 μL of 1M acetate buffer pH 3.5 was added to adjust the pH to 3.5. 0.67 μL (0.03 U / mg) was added and reacted at 45 ° C. for 15 hours to debranch the 1,6-glucoside bond of starch. Subsequently, the enzyme was inactivated by heating at 100 ° C. for 10 minutes, cooled in water, and used as a dry sample with a centrifugal concentrator (Tomy CC-105).

  To this sample, 111 μL of dimethyl sulfoxide was added and dissolved by heating. To this, 89 μL of distilled water and 200 μL of 2-aminopyridine were added and mixed well, and incubated at 60 ° C. for 1 hour in the dark. Thereafter, 200 μL of sodium cyanoborohydride was added and incubated for 24 hours to prepare a sample for HPLC. Next, this sample was filtered through a membrane filter (manufactured by Millipore, pore size 0.2 μm), and 20 μL of the filtrate was injected into HPLC manufactured by JASCO Corporation for analysis. The column was connected with SHODEXOHPAKSB-803HQ and SHODEXOHPAKSB-802.5 and detected with a fluorescence detector LP2020. Tables 1, 5 and 6 show the measurement results of the amylopectin chain length distribution of the rice sample starch.

<Example 3> (Measurement of gelatinization viscosity characteristics of rice starch or polished rice powder)
25 mL of pure water was added to 3 g of dried rice starch or polished rice powder, and the viscosity characteristics were measured under the following measurement conditions. That is, after stirring at 50 ° C. for 1 minute, the temperature was raised from 50 ° C. to 93 ° C. over 4 minutes, stirred at 93 ° C. for 7 minutes, cooled from 93 ° C. to 50 ° C. for 4 minutes, and stirred at 50 ° C. for 3 minutes. Viscosity was measured. The difference between the highest viscosity and the lowest viscosity was taken as breakdown, the difference between the final viscosity and the lowest viscosity was taken as consistency, and the difference between the highest viscosity and the final viscosity was taken as setback.

  Tables 1 and 7 show the gelatinization characteristic values of each sample rice by Rapid Visco Analyzer (RVA). In the maximum viscosity, high amylose rice Koshi no Kaori (sample rice number 26 in Table 1), Yoshika Koshi (sample rice number 23 in Table 1), good taste rice yumepirika (sample rice number 13 in Table 1) EM10 (sample rice number 1 in Table 1), EM145 (sample rice number 8 in Table 1), and glutinous rice (sample rice numbers 10, 11, and 12 in Table 1) are low. The value is shown. These results are presumed to be due to the influence of starch properties of amylose and amylopectin without being affected by each enzyme activity, protein, and lipid because of the gelatinization property test using starch.

  In addition, at the gelatinization start temperature, super hard rice (sample rice numbers 1 to 9 in Table 1) showed a very high value, and subsequently, high amylose rice Koshi Kaori (sample rice number 26 in Table 1) and Super hybrid rice (sample rice number 31 in Table 1) of a Japan-India hybrid showed a high value. Moreover, in the maximum viscosity / minimum viscosity, super hard rice (sample rice numbers 1 to 9 in Table 1) shows a very low value, and indica rice (sample rice numbers 225 to 32 in Table 1) also has a relatively low value. However, among Indica rice, hybrid rice of high amylose rice (sample rice number 31 in Table 1) showed a high value. Good taste rice (sample rice numbers 13 to 17 in Table 1) and general-purpose rice (sample rice numbers 18 to 20 in Table 1) showed almost high values. Next, in the consistency value which is an index of aging, Indian type dream color (sample rice number 32 in Table 1), super hard rice (sample rice numbers 1 to 9 in Table 1) are high, Next, indica Hoshiyutaka (sample rice number 25 in Table 1), Japanese-type hard rice carnaroli (sample rice number 24 in Table 1), and indica high amylose rice Koshigoe (sample rice number 26 in Table 1) are high. The value is shown.

<Example 4> (Preparation of an estimation formula for the resistant starch content using the gelatinization characteristics of Japanese-type rice milled rice as a variable, and test using an unknown sample)
As shown in FIG. 2A, the gelatinization characteristic value (Table 7) of the polished rice powder of 21 varieties of Japanese rice is used as an explanatory variable, the objective variable is the resistant starch content (RS), and multiple regression analysis is performed. Estimates were made with PT and SB2 / C, V _MAX / V _MIN and V _MAX / V _FIN as variables.

  The coefficient of determination (square of the multiple correlation coefficient) of this estimation equation shows 0.80, and as shown in FIG. 2B, the test with 17 unknown sample types shows a coefficient of determination of 0.72, The estimation formula showed a very high correlation even with unknown samples, and it became clear that the estimation formula was widely applicable.

<Comparative example 1> (Preparation formula of indigestible starch content using the gelatinization characteristics of polished rice powder of Japanese rice as a variable and testing with unknown samples)
As shown in FIG. 3A, multiple regression analysis was performed with the gelatinization characteristic values (Table 7) of the polished rice powder of Japanese rice of the same 21 varieties as in Example 4 as explanatory variables and the objective variable as the resistant starch content. Thus, an estimation formula having PT and V _MIN as variables was created.

  The coefficient of determination of this estimation formula was 0.79. However, as shown in FIG. 3B, in the test using 17 unknown samples, the coefficient of determination was 0.55, and the estimation formula of A is for the unknown sample. It became clear that the correlation and applicability were significantly inferior to those of Example 4.

<Example 5> (Preparation of an estimation formula for resistant starch content using the gelatinization characteristics of Japanese-style rice starch as a variable, and test using an unknown sample)
As shown in FIG. 4A, the gelatinization characteristic values (Table 8) of starches of 11 Japanese rice varieties are used as explanatory variables, the objective variable is the resistant starch content, and SB2 / C, V is determined by multiple regression analysis. Estimation formulas using _MAX / V _FIN and V _MAX / V _MIN as variables were prepared.

  The coefficient of determination of this estimation formula is 1.00, and as shown in FIG. 4B, the coefficient of determination is 0.83 in the test of 12 unknown samples. It was revealed that the estimation formula showed high correlation and was widely applicable.

<Comparative example 2> (Preparation formula of resistant starch content using the gelatinization characteristics of Japanese rice starch as a variable and testing with unknown samples)
As shown in FIG. 5A, the gelatinization characteristic values (Table 8) of 11 Japanese rice varieties are used as explanatory variables, the objective variable is the resistant starch content, and the breakdown (BD) is determined by multiple regression analysis. The estimation formula using was used as a variable.

  The determination coefficient of this estimation formula is 0.72, and as shown in FIG. 5B, the determination coefficient is 0.16 in the test of 12 unknown samples, and the estimation formula of A is the estimation formula of the fifth embodiment. It became clear that the estimation formula is significantly inferior to.

<Example 6> (Preparation of an amylopectin medium-long chain estimation formula using a variable of the gelatinization characteristics of Japanese-style rice starch and testing with unknown samples)
The medium variable chain content (APL) of the amylopectin of 13 varieties of Japanese rice starch shown in Table 5 having a degree of glucose polymerization of 13 or more is an objective variable, and SB2 / C, V _{MAX of} gelatinization characteristic values shown in Table 1 Multiple regression analysis was performed using / V _FIN and V _MAX / V _MIN as explanatory variables, and an estimation formula was created.

  As a result, as shown in FIG. 6A, the coefficient of determination of this estimation formula shows 0.99, and the coefficient of determination in the medium and long chain of amylopectin of the six types of Japanese rice varieties shown in Table 1 is 0.97. As shown, the estimation formula of A shows a high correlation with an unknown sample, and it has been clarified that the estimation formula has wide applicability.

<Example 7> (Preparation of an estimation formula of amylose content using the gelatinization characteristics of polished rice powder of Japanese-style rice as a variable and testing with an unknown sample)
The gelatinization characteristic values (Table 7) of 21 types of Japanese rice flour were used as explanatory variables, the objective variable was amylose content (AC), and PT and SB2 / C, V _MAX / V _MIN and An estimation formula using V _MAX / V _FIN as a variable was created.

  As shown in A of FIG. 7, the coefficient of determination of this estimation formula shows 0.89, and as shown in B of FIG. 7, in the test using 17 unknown sample varieties, the coefficient of determination shows 0.71. The estimation formula showed a very high correlation even with unknown samples, and it became clear that the estimation formula was widely applicable.

<Comparative Example 3> (Preparation of an estimation formula for amylose content using the gelatinization characteristics of Japanese-style rice milling powder as a variable and testing with an unknown sample)
The pasteurization characteristic value (Table 7) of the polished rice powder of 21 Japanese varieties as in Example 7 is an explanatory variable, the objective variable is amylose content, and an estimation equation with PT and C as variables is obtained by multiple regression analysis. Created.

  As shown in A of FIG. 8, the coefficient of determination of this estimation formula showed 0.87, but as shown in B of FIG. 8, in the test with 17 unknown samples, the coefficient of determination showed 0.30, It became clear that the estimation formula of A is significantly inferior to the estimation formula of Example 7 in terms of correlation and applicability to unknown samples.

<Example 8> (Preparation of an estimation formula for amylose content using the gelatinization characteristics of Japanese-style rice starch as a variable and testing with an unknown sample)
The gelatinization characteristics (Table 8) of 11 Japanese rice varieties are used as explanatory variables, the objective variable is amylose content, and SB2 / C, V _MAX / V _FIN, and V _MAX / V _MIN are variables by multiple regression analysis. The estimation formula used for is made.

  As a result, as shown in A of FIG. 9, the coefficient of determination of this estimation formula shows 0.86, and as shown in B of FIG. 9, the coefficient of determination shows 0.76 in the test of 12 unknown samples. The estimation formula of A showed a high correlation even with an unknown sample, and it became clear that the estimation formula has wide applicability.

<Comparative Example 4> (Preparation of an estimation formula for amylose content using the gelatinization characteristics of Japanese-style rice starch as a variable and testing with an unknown sample)
An estimation formula was created using the gelatinization characteristic values (Table 8) of 11 Japanese rice varieties as explanatory variables, the objective variable as amylose content, and the gelatinization start temperature (PT) as a variable by multiple regression analysis.

  As a result, as shown in FIG. 10A, the coefficient of determination of this estimation formula shows 0.37, and as shown in FIG. 10B, the coefficient of determination shows 0.16 in the test of 12 unknown samples. It became clear that the estimation formula of A is a significantly inferior formula compared with the estimation formula of Example 8.

<Example 9> (Preparation of an estimation formula for the resistant starch content using the gelatinization characteristic of hard rice milled rice as a variable, and test using an unknown sample)
By using the gelatinization characteristic values (Table 7) of hard rice milling powders of 8 varieties as explanatory variables, the objective variable is the indigestible starch content of Table 2, and by multiple regression analysis, SB2 / C, V _MAX / V _MIN and An estimation formula using V _MAX / V _FIN as a variable was created.

  As shown in A of FIG. 11, the coefficient of determination of this estimation formula shows 0.83, and as shown in B of FIG. 11, in the test using eight unknown sample varieties, the coefficient of determination shows 0.69. The estimation formula showed a very high correlation even with unknown samples, and it became clear that the estimation formula was widely applicable.

<Example 10> (Preparation of an estimation formula of amylose content using the gelatinization characteristic of polished rice powder of hard rice as a variable and testing with an unknown sample)
By using the gelatinization characteristic values (Table 7) of the polished rice powders of 8 varieties of hard rice as explanatory variables and the objective variable as the amylose content in Table 1, SB2 / C, V _MAX / V _MIN and V _MAX / An estimation formula with V _FIN as a variable was created.

  As shown in A of FIG. 12, the determination coefficient of this estimation formula shows 0.87, and as shown in B of FIG. 12, in the test using eight unknown sample varieties, the determination coefficient shows 0.77. The estimation formula showed a very high correlation even with unknown samples, indicating that the estimation formula has wide applicability.

<Example 11> (Development of estimation formulas for amylose and resistant starch by PLS analysis)
11 samples of Japanese rice were used as samples, the viscosity of Rapid Visco Analyzer (RVA) at a fixed time (4 seconds) was used as a variable, and 284 points of data were used as variables for each sample. An estimation formula was created by the least squares method (PLS method) using digestible starch content as an objective variable, and suitability was tested using 10 unknown samples of Japanese rice as samples.

When the coefficient b _n according to the data collection point X _n are shown in Table 10.

  The estimation accuracy of the amylose content at the time of creating this estimation formula was 0.9899 with a coefficient of determination, and the accuracy of the unknown sample was 0.8994 with a coefficient of determination.

  Moreover, the coefficient of determination of the estimation formula of the resistant starch content was 0.9605, and the coefficient of determination of the unknown sample was 0.9667. As shown in FIG. 13, these results are very high estimation formulas, and the applicability to unknown samples was very good.

Claims (12)

Using the ratio of gelatinization characteristic values consisting of maximum viscosity, minimum viscosity, final viscosity, breakdown, setback, and consistency and the gelatinization start temperature as explanatory variables for multiple regression analysis, subtype rice starch characteristics are evaluated. A method for evaluating the characteristics of rice starch. The method for evaluating rice starch characteristics according to claim 1, wherein the rice starch characteristics are indigestible starch content. 2. The method for evaluating rice starch characteristics according to claim 1, wherein the rice starch characteristics are a medium and long chain fraction content in which the degree of glucose polymerization of amylopectin is 13 or more. The method for evaluating rice starch characteristics according to claim 1, wherein the rice starch characteristics are amylose content. When the estimation formula for the resistant starch content of Japanese subspecies is the milled rice flour,
The method for evaluating characteristics of rice starch according to claim 2, wherein: When the estimation formula for the resistant starch content of Japanese subspecies is rice starch,
The method for evaluating characteristics of rice starch according to claim 2, wherein: The formula for estimating the content of medium and long chain fractions of amylopectin with a degree of glucose polymerization of 13 or more is:
The method for evaluating characteristics of rice starch according to claim 3, wherein: If the formula for estimating the amylose content of Japanese subspecies is a milled rice flour,
The method for evaluating rice starch characteristics according to claim 4, wherein: When the estimation formula for the amylose content of the Japanese subspecies is rice starch,
The method for evaluating rice starch characteristics according to claim 4, wherein: If the estimation formula of hard-to-digestible starch content of hard rice is rice starch,
The method for evaluating characteristics of rice starch according to claim 2, wherein: When the estimation formula for the amylose content of hard rice is rice starch,
The method for evaluating rice starch characteristics according to claim 4, wherein: The amylose content, long-chain content of amylopectin, or resistant starch content of Japanese rice is the objective variable, and the viscosity of the polished rice sample suspension measured by Rapid Visco Analyzer is the explanatory variable, and the error is calculated by the least square method. A method for estimating amylose content, long-chain content of amylopectin, or resistant starch content of Japanese rice based on gelatinization viscosity characteristics, characterized in that an estimation formula that minimizes the content is prepared.