CN114136918B - Near infrared-based rice taste quality evaluation method - Google Patents
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Abstract
The invention discloses a near infrared-based rice taste quality evaluation method, which relates to the technical field of rice taste quality detection and comprises the following steps: s1, establishing a near infrared diffuse reflection model of the content of rice components; s2, collecting samples with different rice taste qualities, and predicting the content of the taste sample components by a near infrared component model; s3, detecting a taste value of the rice taste sample; s4, correlating the predicted value of the content of each sample component in the S2 with the taste value measured in the S3, and establishing a multiple regression model of the taste value; s5, predicting the taste value of the sample to be detected by using the model constructed in the steps S1-S4. The invention has the beneficial effects that: the invention can rapidly and nondestructively detect the taste value of rice, the taste value can be predicted by directly carrying out spectrum acquisition on a small amount of rice samples without pretreatment on the samples during detection, and the prediction result is close to the sensory evaluation result, so that the accuracy is high.
Description
Technical Field
The invention relates to the technical field of rice taste quality detection, in particular to a near infrared-based rice taste quality evaluation method
Background
The rice is the most important grain crop in the world at present, is staple food of more than half of the population worldwide, and along with the continuous emergence of new rice varieties in the market, the requirements of consumers on the taste of the rice are continuously improved, and the consumers in different areas have obvious difference in perception of the taste of the rice, so that the taste evaluation of the rice is particularly difficult and has no unified standard.
Common physicochemical indexes for evaluating the taste quality of rice are amylose, protein, fat and starch-related indexes (gum consistency, gelatinization temperature, alkali extinction value, etc.), and although the trend of the results of preliminary evaluation of the taste quality by these physicochemical indexes is consistent, the accuracy needs to be improved. The traditional evaluation method of the rice cooking taste quality is sensory evaluation, and the sensory evaluation is comprehensive evaluation of the characteristics of appearance smell, structure, palatability and the like of rice by tasters directly through eyes, nose and mouth. The method has strong subjectivity, high price, large sample consumption, low detection speed and damage to the sample.
Thus, researchers have attempted to evaluate taste quality with more accuracy and scientificity through instrumentation. The current common taste evaluation instruments comprise a viscosity rapid measuring instrument, a texture analyzer, a taste analyzer and the like, wherein the viscosity rapid measuring instrument and the texture analyzer are used for evaluating the taste on the basis of physicochemical indexes closely related to the taste. The taste meter uses an internal near infrared instrument and combines sensory evaluation, a mathematical model is established through analysis, and the taste value of rice is calculated by means of computer software and related application software. Although the accuracy of the taste values detected by the instruments is high, the rice is processed and used as a research object, so that the defects of low detection speed, complicated pretreatment process, large sample consumption and rice damage cannot be completely solved. Meanwhile, paddy processing cannot be continued to be planted, so that breeders cannot perform quality screening in low generation, the breeding time is prolonged, and the breeding efficiency is reduced.
Near infrared spectroscopy (NIRS) is a rapid and non-destructive method, mainly using different hydrogen-containing groups at wave number 13333-4000cm -1 The difference of frequency multiplication and frequency combination absorption in the (wavelength 750-2500 nm) interval is analyzed, and near infrared is widely applied to the analysis of rice quality. At present, the physicochemical indexes related to the taste quality are successfully established by a near infrared model, and the sample consumption is related from a large amount to a small amount even a single particle. Therefore, the near infrared technology can be combined to carry out the content of the components of a small amount of riceAnd carrying out nondestructive prediction, and establishing a model in association with the taste value to realize rapid detection of the taste quality of rice.
The patent with publication number of CN111007040A discloses a method for rapidly evaluating the quality of rice taste by near infrared spectroscopy, but the method still needs to select, dehulling, hulling and milling samples to prepare polished rice, and then modeling and detecting the correlation between the near infrared spectrum of polished rice and the taste value. The operation process is slow, the pretreatment process is complicated, the sample consumption is large, and the sample needs to be damaged. Since the taste quality is evaluated on rice, the sensitivity and accuracy of near infrared detection are reduced by external chaff, and thus the taste quality of rice is evaluated by rice, polished rice or brown rice in the conventional near infrared taste quality instrument.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, the rice taste detection has the problems of slow operation process, complicated pretreatment process, large sample consumption and sample damage, and provides a near infrared-based method for rapidly evaluating the rice taste quality, which replaces the existing taste quality detection method taking brown rice, polished rice or rice as an object, and realizes the rapid and nondestructive rice taste quality detection.
The invention solves the technical problems by the following technical means:
a near infrared-based rice taste quality evaluation method comprises the following steps:
s1, collecting a plurality of rice samples with different component contents and balanced moisture, collecting near infrared diffuse reflection spectrums of each sample, detecting the component content of each sample, and finally establishing a near infrared diffuse reflection model of chemical component content of rice by using a PLS algorithm;
s2, collecting samples with different rice taste quality, constructing a rice taste sample set, and predicting the component content of each sample in the sample set by using a near infrared diffuse reflection model;
s3, detecting taste values of all samples in the rice taste sample set;
s4, correlating the predicted value of the content of each sample component in the S2 with the taste value measured in the S3, and establishing a multiple regression model of the taste value;
s5, collecting near infrared diffuse reflection spectrums of rice samples to be analyzed, predicting the content of each chemical component by using the model constructed in the step S1, and predicting by using the regression model constructed in the step S4 according to the predicted values of the content of each chemical component to obtain the taste value of each sample.
The beneficial effects are that: according to the invention, the rice is used for directly measuring the content of main components of the rice based on a near infrared spectrum technology, and then a regression analysis model between the main components of the rice and the taste quality is used for evaluating the taste of the rice. The method has simple pretreatment, no sample damage and small dosage, and can realize the taste quality screening of rice in the low generation.
According to the invention, paddy is taken as a detection object, main chemical components (amylose, protein and fat) of paddy are predicted by a paddy near infrared diffuse reflection model with a wide application range, and as the correlation of the chemical components of paddy in the taste quality of paddy is strong, a taste value regression analysis model is established by the correlation between taste values of the main components of paddy, namely amylose, protein, fat and the taste quality of paddy for evaluating the taste quality of paddy, so as to predict the taste quality of paddy. In order to improve the accuracy of the model, a plurality of rice materials with different chemical values and different sources are collected, and a near infrared model of the rice components, which has wide range, high accuracy and strong correlation with taste quality, is established.
The invention can rapidly and nondestructively detect the taste value of rice, the taste value can be predicted by directly carrying out spectrum acquisition on a small amount of rice samples without pretreatment on the samples during detection, and the prediction result is close to the sensory evaluation result, so that the accuracy is high.
Preferably, the components in step S1 include amylose, protein and fat.
Preferably, the construction of the near infrared diffuse reflection model in the step S1 includes the following steps:
(1) The dry moisture content of the rice sample is 12% -14%, and the water is balanced in a dryer for 2 weeks;
(2) Each sample was selected10g of full and mature rice is put into a glass bottle with the diameter of 22mm, the glass bottle is placed in a sample tank of a BrookMPA near infrared instrument to collect near infrared diffuse reflection spectrum, each sample is repeatedly collected twice, the rice in the bottle is re-mixed uniformly during the two repetition periods, and the average spectrum of the two repetition periods is used as the spectrum of the sample; the spectrum scanning range is 4000-12000cm -1 At an interval of 8cm -1 The number of scans was 32;
(3) Detecting the amylose content, the protein content and the fat content of the sample by a chemical method;
(4) And (5) establishing a near infrared model of the component content of the rice.
Preferably, the amylose content detection in the step (3) is according to the flow injector method in NY/T2639-2014, the protein content detection is according to the Kai nitrogen determination method in GB/T5511-2008, and the fat content detection is according to the Soxhlet extraction method in GB 5009.6-2016.
Preferably, the pretreatment method of the amylose content model in the step (4) is multi-element scattering correction, and the spectrum interval is 8794.31-4798.3cm -1 The factor number is 13; the pretreatment method for protein correction set model selection is first derivative+MSC, and the spectrum interval is 8655.4-7498.3cm -1 ，6341.1-5762.6cm -1 ，5184-4026.8cm -1 The factor number is 11; the pretreatment method for selecting the fat correction set model is to eliminate constant offset, and the spectrum interval is 8859.8-7432.1cm -1 The factor number is 4.
Preferably, in the step S2, when each sample in the rice taste sample set is predicted by using the near infrared diffuse reflection model, the pretreatment and spectrum collection method of the rice are the same as in the step S1.
Preferably, in the step S3, the sample in the rice taste sample set is dehulled, milled into polished rice, cooked, cooled, and then the taste value of the sample is detected by using a zozhu STA-1A rice taste meter.
Preferably, in the step S4, a multiple regression equation is established with the amylose content AC, the protein content PC, and the fat content FC as independent variables and the taste value TV as dependent variables.
Preferably, the multiple regression equation is calculated as tv= 119.938-0.774×ac-11.533 ×pc+15.432×fc.
Preferably, in order to check the accuracy of the taste value model, conventional sensory evaluation is performed on the rice sample to be analyzed, and the sensory score obtained by the sensory evaluation is compared with the taste value obtained by prediction.
The invention has the advantages that: according to the invention, the rice is used for directly measuring the content of main components of the rice based on a near infrared spectrum technology, and then a regression analysis model between the main components of the rice and the taste quality is used for evaluating the taste of the rice. The method has simple pretreatment, no sample damage and small dosage, and can realize the taste quality screening of rice in the low generation.
According to the invention, paddy is taken as a detection object, main chemical components (amylose, protein and fat) of paddy are predicted by a paddy near infrared diffuse reflection model with a wide application range, and as the correlation of the chemical components of paddy in the taste quality of paddy is strong, a taste value regression analysis model is established by the correlation between taste values of the main components of paddy, namely amylose, protein, fat and the taste quality of paddy for evaluating the taste quality of paddy, so as to predict the taste quality of paddy. In order to improve the accuracy of the model, a plurality of rice materials with different chemical values and different sources are collected, and a near infrared model of the rice components, which has wide range, high accuracy and strong correlation with taste quality, is established.
The invention can rapidly and nondestructively detect the taste value of rice, the taste value can be predicted by directly carrying out spectrum acquisition on a small amount of rice samples without pretreatment on the samples during detection, and the prediction result is close to the sensory evaluation result, so that the accuracy is high.
Drawings
FIG. 1 is a flow chart showing the evaluation method of rice taste quality in example 1 of the present invention;
FIG. 2 is a near infrared spectrum representative of rice in example 1 of the present invention;
FIG. 3 shows the results of a near infrared diffuse reflection model cross-test of the rice fraction in example 1 of the present invention (A: amylose content, B; protein content, C: fat content);
FIG. 4 shows the results of near infrared diffuse reflection model verification of the rice fraction in example 1 of the present invention (A: amylose content, B; protein content, C: fat content);
FIG. 5 is a regression model of taste values in example 1 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The test materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
Those of skill in the art, without any particular mention of the techniques or conditions, may follow the techniques or conditions described in the literature in this field or follow the product specifications.
The near infrared-based rice taste quality evaluation method specifically comprises the following steps:
s1, establishing a near infrared diffuse reflection model of the content of the rice components. In this embodiment, in order to verify the validity of the constructed near infrared diffuse reflection model of the rice component content, the used samples are divided into a correction set for constructing the model and a verification set for verifying and evaluating the predictive effect of the constructed model. The method comprises the following specific steps:
(1) Rice samples having differences in amylose, protein and fat content, of which 388 parts of amylose material (221 parts of indica rice, 167 parts of japonica rice), 178 parts of protein material (73 parts of indica rice, 105 parts of japonica rice) and 158 parts of fat content (68 parts of indica rice, 90 parts of japonica rice) were collected in 502 parts of 2017-2020. Naturally sun-drying rice sample until the water content is about 12-14%, and placing into a dryer to balance water for 2 weeks.
(2) Selecting 10g rice with plump grain, placing into a glass bottle with diameter of 22mm, and placing into MPAAnd the diffuse reflection spectrum acquisition is carried out on a detection window of the transformation spectrometer. Diffuse reflection spectrum wave number range 4000-12000cm -1 At an interval of 8cm -1 The number of scans was 32. Each sample was repeatedly collected 2 spectra, and the rice in the bottle was remixed during the two replicates, and the average spectrum of the two replicates was taken as the sample spectrum. The near infrared spectrum is shown in FIG. 1.
(3) Detecting the amylose content, the protein content and the fat content of the sample: the amylose content detection adopts an iodine color reaction in NY/T2639-2014, the protein content detection adopts a Kai-type nitrogen determination method in GB/T5511-2008, and the fat content detection adopts a Soxhlet extraction method in GB 5009.6-2016.
(4) Samples were run using KS algorithm at 2:1 is divided into a correction set and a test set.
Wherein, the distribution ranges of the amylose content, the protein content and the fat content of the correction set are respectively 1.2% -24.6%, 6.5% -10.8% and 2.2% -4.2%, and the standard deviation is respectively 5.4%, 0.7% and 0.4%; the distribution ranges of the amylose content, the protein content and the fat content of the test set are respectively 1.2% -23.6%, 6.5% -9.7% and 2.3% -4.2%, and the standard deviations are respectively 5.3%, 0.7% and 0.4%.
(5) And (3) correlating the spectrum and chemical value of the correction set sample, selecting a proper pretreatment method and spectrum interval, and establishing a near infrared PLS model by a cross validation method.
Wherein the pretreatment method of the established amylose content model is multi-element scattering correction, and the spectrum interval is 8794.31-4798.3cm -1 The factor number is 13; the pretreatment method for protein correction set model selection is first derivative+MSC, and the spectrum interval is 8655.4-7498.3cm -1 ，6341.1-5762.6cm -1 ，5184-4026.8cm -1 The factor number is 11; the pretreatment method for selecting the fat correction set model is to eliminate constant offset, and the spectrum interval is 8859.8-7432.1cm -1 The factor number is 4. The cross-check results of the model are shown in fig. 3.
(6) The test set verifies the effect of the model, and the verification result of the model is shown in fig. 4. As can be seen from fig. 4, the model has good prediction effect on the samples of the validation set, and the determination coefficients between the predicted values and the true values of the amylose, protein and fat contents of the samples of the validation set, the predicted root mean square error, and the correction set are close. The model is indicated to be effectively suitable for prediction of the content of the rice components to be detected.
S2, collecting 100 japonica rice taste samples, and predicting the amylose content, the protein content and the fat content of the taste samples by using a near-infrared component model. Wherein the average value, standard deviation and range of the amylose content are respectively 10.8%, 3.3% and 2.6-20.5%, the average value, standard deviation and range of the protein content are respectively 8.4%, 0.8% and 6.9-10.2%, and the average value, standard deviation and range of the fat content are respectively 3.2%, 0.3% and 2.4-3.9%.
S3, husking rice of the taste sample, grinding into polished rice, and detecting the taste value of the polished round-grained nonglutinous rice taste sample by using an STA-1A type taste meter (zozhu, japan). The method comprises the following specific steps:
weighing 30g of polished rice, cleaning, soaking, steaming rice, preserving heat, cooling and pressing rice cakes according to the standard of preparing rice cakes by using a zozhu taste meter, preparing 2 rice cakes by each sample, detecting taste values by using the taste meter, and taking the average value of the taste values of the 2 rice cakes as a final result.
Wherein the average value, standard deviation and range of the taste value are 64.1, 9.9 and 30.5-80.4 respectively.
S4, establishing a taste value regression analysis model.
(1) And (6) constructing a taste value model. And (3) correlating the component of S2 with the taste value of S3, and establishing a multiple regression equation by taking the amylose content, the protein content and the fat content as independent variables and the taste value as dependent variables. The calculation formula is as follows: tv= 119.938-0.774×ac-11.533 ×pc+15.432×fc, R 2 0.8206, wherein AC represents amylose content, PC represents protein content, FC represents fat content, TV represents taste value, and the result of regression analysis of taste value is shown in FIG. 5.
(2) And (5) verifying a taste value model. 21 rice varieties with different taste qualities are selected from the reference materials of the 'Anhui province high-quality rice variety taste quality evaluation meeting' to serve as verification samples of a taste value model, and the method is used for evaluating the prediction effect of the method on the rice taste quality. After the taste prediction value of the verification sample is obtained by adopting the method, the taste prediction value is compared with the corresponding sensory score, and the consistency of the measured result and the sensory evaluation result is evaluated. The method comprises the following specific steps:
a. selecting 21 parts of taste value verification samples with different taste values, predicting the amylose content, the protein content and the fat content of the samples according to the method in S2, and detecting the taste values according to the method in S3; predicting a taste value according to the taste value model in S4, and verifying the taste value of the sample;
b. sensory evaluation is carried out on the taste value model verification sample, and the specific steps are as follows: selecting 20 reference polished round-grained nonglutinous rice, and performing sensory evaluation of rice taste by referring to a sensory evaluation method (GB/T15682-2008) of rice cooking edible quality. The taste of cooked rice was scored for each rice sample with "five-you rice No. 4 (Heilongjiang, five-times)" as a control. The performance of the three indexes including smell, appearance and palatability are compared with a control sample item by item for evaluation. The rice performance in each index is classified into 5 grades of "poor, identical, slightly good, good" and is respectively marked as-2, -1, 0, 1 and 2. The total score for the sensory evaluation of the taste of each sample was calculated according to the following formula:
E＝E ck +25×(0.15S+0.15A+0.7T)
wherein E is the total taste score of each sample; eck is the total taste score of the control, and this value was set to 75 in the study based on the score of the control in the taste meter; s is the odor score; a is appearance score; t is a palatability score.
c. The evaluation effect of the model on the quality of the cooked food and the flavor was verified, and the specific results are shown in table 1. As can be seen from table 1, the consistency between the predicted value of the model and the taste value obtained by sensory evaluation is high, which indicates that the taste quality detection method used in the present invention has good accuracy.
Table 1 verifies the effect of the model on the evaluation of the quality of the cooked and the taste
S5, collecting near infrared diffuse reflection spectrum of the rice sample to be analyzed, predicting the content of each component by using the model constructed in the S1, and predicting by using the regression model constructed in the S4 according to the predicted value of the content of each component to obtain the taste value of each sample.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A near infrared-based rice taste quality evaluation method is characterized by comprising the following steps of: the method comprises the following steps:
s1, collecting a plurality of rice samples with different component contents and balanced moisture, collecting near infrared diffuse reflection spectrums of each sample, detecting the component content of each sample, and finally establishing a near infrared diffuse reflection model of the rice component content by using a PLS algorithm;
the construction of the near infrared diffuse reflection model comprises the following steps:
(1) The dry moisture content of the rice sample is 12% -14%, and the water is balanced in a dryer for 2 weeks;
(2) 10g of full and mature rice is selected from each sample, the rice is put into a glass bottle with the diameter of 22mm, the glass bottle is placed in a sample groove of a BrookMPA near infrared instrument to collect near infrared diffuse reflection spectrum, each sample is repeatedly collected for two times, the rice in the bottle is re-mixed uniformly during the two times of repetition, and the average spectrum of the two times of repetition is used as the spectrum of the sample; the spectrum scanning range is 4000-12000cm -1 At an interval of 8cm -1 The number of scans was 32;
(3) Detecting the amylose content, the protein content and the fat content of the sample by a chemical method;
(4) Establishing a near infrared model of the component content of the rice;
s2, collecting samples with different rice taste quality, constructing a rice taste sample set, and predicting the component content of each sample in the sample set by using a near infrared diffuse reflection model;
s3, detecting taste values of all samples in the rice taste sample set;
s4, correlating the predicted value of the content of each sample component in the S2 with the taste value measured in the S3, and establishing a multiple regression model of the taste value;
s5, collecting near infrared diffuse reflection spectrums of rice samples to be analyzed, predicting the content of each component by using the model constructed in the step S1, and predicting by using the regression model constructed in the step S4 according to the predicted values of the content of each component to obtain the taste value of each sample.
2. The near infrared-based rice taste quality evaluation method according to claim 1, characterized in that: the components in the step S1 comprise amylose, protein and fat.
3. The near infrared-based rice taste quality evaluation method according to claim 1, characterized in that: the pretreatment method of the amylose content model in the step (4) comprises the steps of multiplex scattering correction and spectrum interval 8794.31-4798.3cm -1 The factor number is 13; the pretreatment method for protein correction set model selection is first derivative+MSC, and the spectrum interval is 8655.4-7498.3cm -1 ，6341.1-5762.6cm -1 ，5184-4026.8cm -1 The factor number is 11; the pretreatment method for selecting the fat correction set model is to eliminate constant offset, and the spectrum interval is 8859.8-7432.1cm -1 The factor number is 4.
4. The near infrared-based rice taste quality evaluation method according to claim 1, characterized in that: in the step S2, when each sample in the rice taste sample set is predicted by using the near infrared diffuse reflection model, the pretreatment and spectrum acquisition method of the rice are the same as in the step S1.
5. The near infrared-based rice taste quality evaluation method according to claim 1, characterized in that: in the step S4, a multiple regression equation is established with the amylose content AC, the protein content PC, and the fat content FC as independent variables and the taste value TV as dependent variables.
6. The near infrared-based rice taste quality evaluation method of claim 5, wherein: the calculation formula of the multiple regression equation is TV= 119.938-0.774×AC-11.533 ×PC+15.432×FC.
7. The near infrared-based rice taste quality evaluation method according to claim 1, characterized in that: carrying out traditional sensory evaluation on the rice sample to be analyzed, and comparing the sensory score obtained by the sensory evaluation with the taste value obtained by prediction.
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