CN114136918A - Near-infrared-based rice taste quality evaluation method - Google Patents
Near-infrared-based rice taste quality evaluation method Download PDFInfo
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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 rice component content; 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 the taste value of the rice taste sample; s4, correlating the predicted value of each sample component content in the S2 with the taste value measured in the S3, and establishing a multiple regression model of the taste value; and S5, predicting the taste value of the sample to be tested by using the model constructed in the steps S1-S4. The invention has the beneficial effects that: the method can be used for rapidly and nondestructively detecting the taste value of the rice, samples do not need to be pretreated during detection, the taste value can be predicted only by directly carrying out spectrum acquisition on a small amount of rice samples, the prediction result is close to the sensory evaluation result, and the accuracy is high.
Description
Technical Field
The invention relates to the technical field of detection of taste quality of rice, in particular to a near-infrared-based taste quality evaluation method of rice
Background
The rice is the most important food crop in the world at present, is staple food of more than half of the population in the world, and with the continuous emergence of new varieties of rice in the market, the requirements of consumers on the taste of the rice are continuously improved, and the perception of the consumers in different areas on the taste of the rice has obvious difference, so that the taste evaluation of the rice is particularly difficult and has no uniform standard.
Common physicochemical indexes for evaluating the taste quality of rice are amylose, protein, fat and indexes related to starch (gel consistency, gelatinization temperature, alkali elimination value and the like), and although the results of the initial evaluation of the taste quality through the physicochemical indexes have consistent trends, the accuracy needs to be improved. The traditional evaluation method of rice cooking taste quality is sensory evaluation, and the sensory evaluation is comprehensive evaluation of characteristics such as appearance smell, structure, palatability and the like of rice by tasters through eyes, noses and mouths. The method has the advantages of strong subjectivity, high price, large sample consumption, low detection speed and damage to the sample.
Therefore, researchers have attempted to evaluate the taste quality more accurately and scientifically by instruments. At present, common taste evaluation instruments comprise a viscosity tacheometer, a texture instrument, a taste meter and the like, and the viscosity tacheometer and the texture instrument are used for evaluating the taste on the basis of physicochemical indexes closely related to the taste. The taste meter is characterized in that an internal near infrared instrument is combined with sensory evaluation, a mathematical model is established through analysis, and the taste value of the rice is calculated by means of computer software and related application software. Although the taste value detected by the instruments is high in accuracy, the rice is processed and treated as a research object, so that the defects that the detection speed is low, the pretreatment process is complicated, the sample consumption is large, and the rice can be damaged cannot be completely solved. Meanwhile, the rice cannot be continuously planted after being processed, so that the quality screening of the breeding personnel cannot be performed in low generations, the breeding time is prolonged, and the breeding efficiency is reduced.
Near infrared spectroscopy (NIRS) is a rapid, non-destructive method of detection, which mainly uses different hydrogen-containing groups at wavenumber 13333--1The difference between the frequency doubling and the frequency combination absorption in the region (wavelength 750-. At present, physicochemical indexes related to taste quality are successfully established by a near infrared model, and the dosage of a sample is related to a large amount, a small amount and even a single particle. Therefore, the near infrared technology can be combined to carry out nondestructive prediction on the component content of a small amount of rice, and a model is established in association with the taste value, so that the taste quality of the rice can be rapidly detected.
Patent publication No. CN111007040A discloses a method for rapidly evaluating taste quality of rice by near infrared spectroscopy, but it still needs to select, hulle, hull, and mill rice, then make fine rice, correlate the near infrared spectrum of fine rice with taste value, then model, and then detect. 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 for rice and the outer husk reduces the sensitivity and accuracy of near-infrared detection, the taste quality of rice is evaluated by rice, polished rice or brown rice in the existing near-infrared taste quality instrument evaluation.
Disclosure of Invention
The invention aims to solve the technical problems that the rice taste detection in the prior art has the problems of slow operation process, complicated pretreatment process, large sample consumption and sample damage, provides a near-infrared-based method for quickly evaluating the rice taste quality, replaces the existing taste quality detection method taking brown rice, polished rice or rice as an object, and realizes the more quick and nondestructive detection of the rice taste quality.
The invention solves the technical problems through 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 spectrum of each sample, detecting the component content of each sample, and finally establishing a near-infrared diffuse reflection model of the chemical component contents of the rice by using a PLS algorithm;
s2, collecting samples with different rice taste qualities, 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 the taste value of each sample in the rice taste sample set;
s4, correlating the predicted value of each sample component content in the S2 with the taste value measured in the S3, and establishing a multiple regression model of the taste value;
s5, acquiring near infrared diffuse reflection spectrums of the rice samples to be analyzed, predicting the chemical component content of the rice samples by using the model constructed in the step S1, and then predicting the taste value of each sample by using the regression model constructed in the step S4 according to the predicted value of the component content.
Has the advantages that: in the invention, the rice is used for directly measuring the content of the main components of the rice based on the near infrared spectrum technology, and then the taste of the rice is evaluated by using a regression analysis model between the main components of the rice and the taste quality. The method has the advantages of simple pretreatment, no damage to samples and low consumption, and can realize the taste quality screening of rice in low generation.
The method takes rice as a detection object, and predicts the main chemical components (amylose, protein and fat) of the rice through a rice near-infrared diffuse reflection model with a wide application range, and because the chemical components of the rice have strong correlation with the taste quality of the rice, a taste value regression analysis model is established through the correlation between the amylose, the protein and the fat as the main components of the rice and the taste value of the rice taste quality evaluated by a taste meter to predict the taste quality of the rice. 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 rice component near-infrared model with wide range, high accuracy and strong correlation with taste quality is established.
The method can be used for rapidly and nondestructively detecting the taste value of the rice, the samples do not need to be pretreated during detection, the taste value can be predicted only by directly carrying out spectrum acquisition on a small amount of rice samples, the prediction result is close to the sensory evaluation result, and the accuracy is high.
Preferably, the ingredients in step S1 include amylose, protein, and fat.
Preferably, the constructing of the near-infrared diffuse reflection model in step S1 includes the following steps:
(1) the dry moisture content of the rice sample is 12% -14%, and the moisture is balanced in a dryer for 2 weeks;
(2) selecting 10g of full and mature paddy for each sample, putting the paddy into a glass bottle with the diameter of 22mm, placing the glass bottle in a sample groove of a Bruker MPA near-infrared instrument to collect near-infrared diffuse reflection spectrum, repeatedly collecting the spectrum for two times for each sample, uniformly mixing the paddy in the bottle during the two times of repetition, and taking the average spectrum of the two times of repetition as the spectrum of the sample; the spectral scanning range is 4000-12000cm-1At an interval of 8cm-1The number of scanning times is 32;
(3) detecting the amylose content, the protein content and the fat content of a sample by a chemical method;
(4) and establishing a near-infrared model of the component content of the rice.
Preferably, the detection of the amylose content in the step (3) is carried out according to a flow injection instrument method in NY/T2639-.
Preferably, the pretreatment method of the amylose content model in the step (4) is multivariate scattering correction, and the spectral interval is 8794.31-4798.3cm-1The factor number is 13; the pretreatment method selected by the protein correction set model is the first derivative + MSC, and the spectral interval is 8655.4-7498.3cm-1，6341.1-5762.6cm-1，5184-4026.8cm-1The factor number is 11; the preprocessing method selected by the fat correction set model is to eliminate constant offset, and the spectral interval is 8859.8-7432.1cm-1The number of factors is 4.
Preferably, in the step S2, when the near-infrared diffuse reflection model is used to predict each sample in the rice taste sample set, the pretreatment of rice and the spectrum collection method are the same as those in the step S1.
Preferably, in the step S3, the samples collected from the rice taste samples are milled into polished rice by husking, cooked, cooled, and then the taste value of the samples is measured by using a rice taste meter of zhozhu STA-1A.
Preferably, in 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 test the accuracy of the taste value model, the rice sample to be analyzed is subjected to traditional sensory evaluation, and sensory scores obtained by the sensory evaluation are compared with taste values obtained by prediction.
The invention has the advantages that: in the invention, the rice is used for directly measuring the content of the main components of the rice based on the near infrared spectrum technology, and then the taste of the rice is evaluated by using a regression analysis model between the main components of the rice and the taste quality. The method has the advantages of simple pretreatment, no damage to samples and low consumption, and can realize the taste quality screening of rice in low generation.
The method takes rice as a detection object, and predicts the main chemical components (amylose, protein and fat) of the rice through a rice near-infrared diffuse reflection model with a wide application range, and because the chemical components of the rice have strong correlation with the taste quality of the rice, a taste value regression analysis model is established through the correlation between the amylose, the protein and the fat as the main components of the rice and the taste value of the rice taste quality evaluated by a taste meter to predict the taste quality of the rice. 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 rice component near-infrared model with wide range, high accuracy and strong correlation with taste quality is established.
The method can be used for rapidly and nondestructively detecting the taste value of the rice, the samples do not need to be pretreated during detection, the taste value can be predicted only by directly carrying out spectrum acquisition on a small amount of rice samples, the prediction result is close to the sensory evaluation result, and the accuracy is high.
Drawings
FIG. 1 is a flow chart of a method for evaluating the taste quality of rice in example 1 of the present invention;
FIG. 2 is a representative near infrared spectrum of rice in example 1 of the present invention;
FIG. 3 shows the cross-examination result of near-infrared diffuse reflection model for rice components in example 1 of the present invention (A: amylose content, B; protein content, C: fat content);
FIG. 4 shows the near infrared diffuse reflection model verification results of the rice components in example 1 of the present invention (A: amylose content, B; protein content, C: fat content);
FIG. 5 is a taste value regression model in example 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Test materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
The specific techniques or conditions not specified in the examples can be performed according to the techniques or conditions described in the literature in the field or according to the product specification.
The rice taste quality evaluation method based on near infrared comprises the following steps:
s1, establishing a near infrared diffuse reflection model of the rice component content. 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 and a verification set, wherein the correction set is used for constructing the model, and the verification set is used for verifying and evaluating the prediction effect of the constructed model. The method comprises the following specific steps:
(1) the rice samples with different amylose, protein and fat contents collected in the year of 502 parts of 2017-containing materials 2020 are collected, wherein 388 parts of amylose materials (indica type rice 221 parts and japonica rice 167 parts), 178 parts of protein materials (indica type rice 73 parts and japonica rice 105 parts) and 158 parts of fat contents (indica type rice 68 parts and japonica rice 90 parts). Rice samples were naturally sun-dried to a moisture content of about 12-14% and placed in a desiccator to equilibrate moisture for 2 weeks.
(2) And (3) selecting 10g of plump rice seeds, putting the plump rice seeds into a small glass bottle with the diameter of 22mm, and putting the glass bottle in a detection window of an MPA type conversion spectrometer for diffuse reflection spectrum collection. Wave number range of diffuse reflection spectrum 4000-12000cm-1At an interval of 8cm-1The number of scans was 32. The spectra were collected for 2 replicates per sample, 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 amylose content, protein content and fat content of a sample: the detection of the amylose content adopts iodine color reaction in NY/T2639-2014, the detection of the protein content adopts a Kjeldahl method in GB/T5511-2008, and the detection of the fat content adopts a Soxhlet extraction method in GB 5009.6-2016.
(4) Samples were run using the KS algorithm as 2: 1 is divided into a correction set and a check set.
Wherein, the distribution ranges of amylose content, protein content and fat content of the correction set are respectively 1.2-24.6%, 6.5-10.8% and 2.2-4.2%, and the standard deviations are 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 of the sample in the correction set with the chemical value, selecting a proper pretreatment method and a spectrum interval, and establishing a near-infrared PLS model by using a cross-validation method.
Wherein the amylose is established to containThe preprocessing method of the quantity model is multivariate scattering correction, and the spectral interval is 8794.31-4798.3cm-1The factor number is 13; the pretreatment method selected by the protein correction set model is the first derivative + MSC, and the spectral interval is 8655.4-7498.3cm-1，6341.1-5762.6cm-1，5184-4026.8cm-1The factor number is 11; the preprocessing method selected by the fat correction set model is to eliminate constant offset, and the spectral interval is 8859.8-7432.1cm-1The number of factors is 4. The cross-test results for 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 a good prediction effect on the verification set samples, and the decision coefficient and the prediction root mean square error between the predicted values and the truth values of the amylose, protein and fat contents of the verification set samples are close to those of the correction set. The model can be effectively applied to the prediction of the content of the rice components to be detected.
S2, collecting 100 parts of japonica rice taste samples, and predicting amylose content, protein content and fat content of the taste samples by a near-infrared component model. Wherein, the average value, the standard deviation and the range of the amylose content are respectively 10.8 percent, 3.3 percent and 2.6 to 20.5 percent, the average value, the standard deviation and the range of the protein content are respectively 8.4 percent, 0.8 percent and 6.9 to 10.2 percent, and the average value, the standard deviation and the range of the fat content are respectively 3.2 percent, 0.3 percent and 2.4 to 3.9 percent.
S3, husking the rice of the taste sample, grinding into polished rice, and detecting the taste value of the taste sample of the japonica rice by using an STA-1A type taste meter (Zhuo bamboo, Japan). The method comprises the following specific steps:
weighing 30g of polished rice, cleaning according to the standard of preparing rice cakes according to a table of bamboo, soaking, steaming rice, keeping the temperature, cooling and pressing the rice cakes, preparing 2 rice cakes for each sample, detecting the table value by using the table of table, and taking the average value of the table values of the 2 rice cakes as the final result.
Wherein, the average value, standard deviation and range of the taste value are respectively 64.1, 9.9 and 30.5-80.4.
S4, establishing a taste value regression analysis model.
(1) And (5) constructing a taste value model. The composition of S2And (4) correlating 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, R20.8206, where AC represents amylose content, PC represents protein content, FC represents fat content, TV is taste value, and the results of the regression analysis of taste values are shown in FIG. 5.
(2) And (5) verifying the taste value model. 21 rice varieties with different taste and quality are selected from the reference materials of the appraisal society of the taste and quality of high-quality rice varieties in Anhui province as verification samples of a taste value model and are used for evaluating the prediction effect of the method on the taste and quality of the rice. After the taste predicted value of the sample is verified by adopting the method, the predicted 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 taste value verification samples with different taste values, predicting amylose content, protein content and fat content of the samples according to the method in S2, and detecting the taste values according to the method in S3; the taste value of the sample is verified according to the taste value model prediction taste value in S4;
b. sensory evaluation is carried out on the taste value model verification sample, and the specific steps are as follows: selecting 20 reference japonica rice, and performing sensory evaluation on rice taste according to a sensory evaluation method (GB/T15682-2008) for cooked rice eating quality. The taste of the cooked rice was scored for each portion of the rice sample, using "wuyou rice No. 4 (black dragon river, wuchang)" as a control. And (4) evaluating the performances according to three indexes of smell, appearance and palatability by comparing with a control sample item by item. The performance of rice in each index is divided into 5 grades of 'poor, identical, slightly good and good', which are respectively marked as-2, -1, 0, 1 and 2. The taste sensory evaluation total score of each sample is calculated according to the following formula:
E＝Eck+25×(0.15S+0.15A+0.7T)
wherein E is the total taste of each sample; eck is the total score of the control for food flavor, which was set to 75 in this study based on the score of the control in the food flavor meter; s is the odor score; a is an appearance score; t is the palatability score.
c. The evaluation effect of the model on the cooking and taste quality 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 invention has good accuracy.
Table 1 demonstrates the evaluation effect of the model on cooking and taste quality
S5, acquiring near infrared diffuse reflection spectrums of the rice samples to be analyzed, predicting the content of each component of the rice samples by using the model constructed in the S1, and then predicting by using the regression model constructed in the S4 according to the predicted values of the content of each component to obtain the taste value of each sample.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A near-infrared-based rice taste quality evaluation method is characterized by comprising the following steps: 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 spectrum 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;
s2, collecting samples with different rice taste qualities, 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 the taste value of each sample in the rice taste sample set;
s4, correlating the predicted value of each sample component content in the S2 with the taste value measured in the S3, and establishing a multiple regression model of the taste value;
s5, acquiring near infrared diffuse reflection spectrums of the rice samples to be analyzed, predicting the content of each component of the rice samples by using the model constructed in the step S1, and then 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 ingredients in step S1 include amylose, protein, and fat.
3. The near-infrared-based rice taste quality evaluation method according to claim 1, characterized in that: 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 moisture is balanced in a dryer for 2 weeks;
(2) selecting 10g of full and mature paddy for each sample, putting the paddy into a glass bottle with the diameter of 22mm, placing the glass bottle in a sample groove of a Bruker MPA near-infrared instrument to collect near-infrared diffuse reflection spectrum, repeatedly collecting the spectrum for two times for each sample, uniformly mixing the paddy in the bottle during the two times of repetition, and taking the average spectrum of the two times of repetition as the spectrum of the sample; the spectral scanning range is 4000-12000cm-1At an interval of 8cm-1The number of scanning times is 32;
(3) detecting the amylose content, the protein content and the fat content of a sample by a chemical method;
(4) and establishing a near-infrared model of the component content of the rice.
4. The near-infrared-based rice taste quality evaluation method according to claim 3, characterized in that: the detection of the amylose content in the step (3) is carried out according to a flow injection instrument method in NY/T2639-2014, the detection of the protein content is carried out according to a Kjeldahl method in GB/T5511-2008, and the detection of the fat content is carried out according to a Soxhlet extraction method in GB 5009.6-2016.
5. The near-infrared-based rice taste quality evaluation method according to claim 3, characterized in that: the pretreatment method of the amylose content model in the step (4) is multivariate scattering correction, and the spectral interval is 8794.31-4798.3cm-1The factor number is 13; the pretreatment method selected by the protein correction set model is the first derivative + MSC, and the spectral interval is 8655.4-7498.3cm-1，6341.1-5762.6cm-1，5184-4026.8cm-1The factor number is 11; the preprocessing method selected by the fat correction set model is to eliminate constant offset, and the spectral interval is 8859.8-7432.1cm-1The number of factors is 4.
6. The near-infrared-based rice taste quality evaluation method according to claim 1, characterized in that: in the step S2, when the near-infrared diffuse reflection model is used to predict each sample in the rice taste sample set, the pretreatment and spectrum collection methods of rice are the same as those in the step S1.
7. The near-infrared-based rice taste quality evaluation method according to claim 1, characterized in that: in the step S3, the samples in the rice taste sample set are hulled and milled into polished rice, cooked, cooled, and then the taste value of the sample rice is detected by using a rice taste meter of bamboo STA-1A.
8. The near-infrared-based rice taste quality evaluation method according to claim 1, characterized in that: in 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.
9. The near-infrared-based rice taste quality evaluation method according to claim 8, characterized in that: the calculation formula of the multiple regression equation is 119.938-0.774 × AC-11.533 × PC +15.432 × FC.
10. The near-infrared-based rice taste quality evaluation method according to claim 1, characterized in that: and (3) 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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