CN114384041A - Method for constructing near-infrared model of soluble sugar content of peanuts with different seed coat colors - Google Patents
Method for constructing near-infrared model of soluble sugar content of peanuts with different seed coat colors Download PDFInfo
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Abstract
The invention discloses a method for constructing a near-infrared model of soluble sugar content of peanuts with different seed coat colors, and belongs to the field of agricultural product component detection. The method comprises the following steps: (1) collecting peanuts with different seed coat colors as samples, and acquiring near infrared spectrum information; (2) determining the content of soluble sugar in peanut grains with different seed coat colors by a chemical method; (3) introducing chemical values of soluble sugar content and near infrared spectrum information into chemometric software, screening out an optimal pretreatment method after adopting different pretreatment methods, and establishing a calibration model for the pretreated near infrared spectrum values and the chemical values by a partial least square method; (4) and (3) measuring the near infrared spectrum information of the unknown peanut samples with different seed coat colors, substituting the near infrared spectrum information into the calibration model, and analyzing the soluble sugar content of the peanuts with different seed coat colors. The invention constructs a near-infrared model of the soluble sugar content of black, red and pink seed coats, and can provide technical support for efficiently breeding edible peanut germplasm.
Description
Technical Field
The invention relates to the technical field of agricultural product component detection, in particular to a method for constructing a near-infrared model of soluble sugar content of peanuts with different seed coat colors.
Background
Peanuts are important economic crops in China, the annual total yield reaches 1500 ten thousand tons, which accounts for about 40 percent of the world total yield, wherein about 60 percent of the annual yield for oil extraction is used, and about 30 percent of peanut kernels are processed into favorite snack foods or directly eaten by people. The sugar content in the peanut kernels is an important index influencing the taste and quality, and directly influences the taste of consumers and the economic value of peanuts.
Several aspects of sweetness, fragrance, brittleness, tenderness, fineness, peculiar smell and the like in the peanuts are important indexes influencing the taste of the peanuts, cellulose in total sugar influences the fineness, and soluble sugar such as glucose, fructose, sucrose and the like influences the sweetness of the peanuts. The sucrose is the main soluble sugar component in the peanut seeds, directly influences the taste of peanuts, and has better taste when the sucrose content in the peanut seeds reaches more than 6 percent. The existing research results show that the total sugar content, the soluble sugar content and the sucrose content in the peanut kernels have germplasm difference. The average sucrose content of Spanish-type peanuts and Indian peanuts is about 4.6%, and the sucrose content of 28 peanut varieties detected by high performance liquid chromatography of Liwafuo and the like is 1.14% -8.38%. The content of total sugar in the peanut germplasm 57 parts of Hebei peanut measured by Hou et al is 7.81-18.01%. By identifying the content of each type of sugar in the peanut kernels, germplasm with high sugar content can be screened and cultivated, and the requirements of consumers are met.
The most common methods for identifying the peanut sugar content are three methods, namely a colorimetric method, a near infrared spectroscopy method and a differential refraction method. The near infrared spectrum analysis method has the characteristics of high efficiency, rapidness, no damage to germplasm and the like, and is widely applied to crop breeding research. In the identification of the content of each component of the peanut seeds, a near-infrared model of the fat content, the fatty acid content and the protein content is established. 72 parts, 167 parts and 185 parts of genotypes are respectively used as materials for Qinli, Tangyueisei and Reyong, and near-infrared models suitable for measuring the sucrose content of peanut seeds of a Swedish wave-passing DA7200 near-infrared analyzer, a Matrix-I type Fourier transform near-infrared spectrometer and a Spectra Star XL near-infrared spectrometer are respectively constructed. A near-infrared model for identifying the content of soluble sugar in peanut kernels is only reported.
Most of the widely planted peanut cultivars are pink seed coats, but in order to adapt to market demands, especially the fresh peanut market, black-purple and red seed coat peanuts are favored by consumers because of the rich anthocyanin antioxidant substances. High-throughput screening of high-sugar-content peanut genotypes with different seed coat colors is important work for peanut taste quality breeding. Research shows that the appearance color of the sample is one of important factors influencing near infrared analysis, and the classification calibration (correction) according to the appearance color of the sample is more favorable for improving the prediction performance of the model. Therefore, how to construct a soluble sugar near-infrared model aiming at different seed coat colors of peanuts plays a key role in solving the problems in the prior art.
Disclosure of Invention
The invention aims to provide a method for constructing a near-infrared model of soluble sugar content of peanuts with different seed coat colors, which aims to solve the problems in the prior art, combines the seed coat color and the soluble sugar content measurement to construct a near-infrared spectrum model of soluble sugar of peanuts, and is beneficial to the cultivation of special food peanut varieties; and meanwhile, after the influence factors are eliminated, the constructed model can systematically and comprehensively reflect the content of the soluble sugar in the peanut kernels.
In order to achieve the purpose, the invention provides the following scheme:
the invention provides a method for constructing a near-infrared model of soluble sugar content of peanuts with different seed coat colors, which comprises the following steps:
(1) collecting peanuts with different seed coat colors as samples, and acquiring near infrared spectrum information;
(2) determining the content of soluble sugar in peanut grains with different seed coat colors by a chemical method;
(3) introducing the chemical value of the soluble sugar content obtained in the step (2) and the near infrared spectrum information acquired in the step (1) into chemometrics software, reducing the influence of noise and errors on the near infrared spectrum value by adopting different pretreatment methods, screening out the optimal pretreatment method after respectively carrying out single treatment and composite treatment on the pretreatment methods, and then establishing a calibration model for the pretreated near infrared spectrum value and the chemical value by using a least square method;
(4) and (4) measuring the near infrared spectrum information of the unknown peanut samples with different seed coat colors, and then substituting the near infrared spectrum information into the calibration model in the step (3) to analyze the soluble sugar content of the peanuts with different seed coat colors.
Preferably, the peanuts with different seed coat colors are full and undamaged peanuts; the different seed coat colors include black purple, red and pink.
Preferably, the content of the soluble sugar in the dark purple peanuts is 2.4% -14.32%; the content of the soluble sugar in the red peanuts is 2.94% -13.75%; the content of pink soluble sugar is 2.19-14.53%.
Preferably, the different seed coat colors are set according to a color parameter L*，a*，b*Counting the minimum value, the maximum value, the mean value and the standard deviation of the values, and calculating a comprehensive chromaticity E value so as to distinguish colors; said L*From black to white, 0-100; a is a*From green to red, -a to + a; b is*From blue to yellow, -b to + b.
Preferably, in the step (1), the near infrared spectrum information is obtained by scanning with a near infrared spectrometer, and the scanning conditions are as follows: the spectrum scanning wavelength is 950-1650nm, the ambient temperature is 24-25 ℃, and the sample is processed at the constant temperature of 24-25 ℃ for more than 48 hours.
Preferably, in step (3), the preprocessing method includes multivariate scatter correction, standard normal variable transformation, first derivative, second derivative, S-G convolution smoothing and normalization.
Preferably, in the step (3), in the process of constructing the model, abnormal samples which are far away from the model are automatically removed, then abnormal values are removed through repeated cross validation, and finally, the accuracy of the calibration model is evaluated through external validation.
Preferably, in step (3), the optimal pretreatment method is selected based on an evaluation criterion that has a high correlation coefficient and a small standard error.
Preferably, in the step (2), the soluble sugar content of peanut kernels with different seed coat colors is determined by an anthrone colorimetric method, and the kernels are peeled and degreased peanut samples.
The invention discloses the following technical effects:
the invention constructs a near-infrared model of the soluble sugar content of black, red and pink seed coats on the basis of the color difference instrument for classifying the seed coats, and the highest correlation coefficient is 0.914. After the influence factors of the color of the sample are eliminated, the constructed model can systematically and comprehensively reflect the content of soluble sugar in the peanut kernels. The invention can provide technical support for efficient breeding of edible peanut germplasm.
The soluble sugar model constructed by the invention can synchronously, quickly and nondestructively evaluate the quality of the peanut genotype integrally with the existing protein, fatty acid, oleic acid, linoleic acid and other models on the same model, and provides technical support for high-throughput peanut quality identification research.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 shows peanut kernel samples with different colors of seed coats; a: a sample of a black-purple seed coat; b: a red seed coat sample; c: a pink seed coat sample;
FIG. 2 is a scanning spectrum of a dark purple sample;
FIG. 3 is a scan spectrum of a red sample;
FIG. 4 is a scanned spectrum of a pink sample;
FIG. 5 is a calibration model of soluble sugar content of peanut kernels with different seed coat colors; a: black purple seed coat, soluble sugar content; b: red seed coat, soluble sugar content; c: pink seed coat, soluble sugar content;
FIG. 6 is a model for predicting soluble sugar content of peanut kernels with different seed coat colors; a: black purple seed coat, soluble sugar content; b: red seed coat, soluble sugar content; c: pink seed coat, soluble sugar content.
Detailed Description
Reference will now be made in detail to various exemplary embodiments of the invention, the detailed description should not be construed as limiting the invention but as a more detailed description of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Further, for numerical ranges in this disclosure, it is understood that each intervening value, between the upper and lower limit of that range, is also specifically disclosed. Every smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in a stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference herein for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the present disclosure without departing from the scope or spirit of the disclosure. Other embodiments will be apparent to those skilled in the art from consideration of the specification. The specification and examples are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.
Examples
Test materials and methods
1. Material
The invention selects 332 parts of peanut genotypes, wherein No. 1-231 is a part of family line of a recombinant inbred line group derived from four red and Jinong black No. 3 as parents, and No. 232-332 is a part of genotype in American microkernel germplasm. And randomly selecting 15 varieties of three seed coat colors in a resource library of a laboratory of the university of agriculture in Hebei by using a color difference meter for model verification. The seeds used are harvested in 9 months in 2020 and in the Aster test base of Hebei agricultural university in Hebei province.
2. Determination of chromatic value of peanut seed coat
The colorimetric values of the peanut seed coats were measured by a CR-10Plus colorimeter manufactured by KONICA MINOLTA of Japan, three full, undamaged seeds were measured three times for each peanut genotype, and the values of L, a, b were measured to take the average of the three results. The color difference meter evaluates the color of the fruit from L (from black to white, 0 to 100), a (from green to red, -a to + a), b (from blue to yellow, -b to + b)3 points.
3. Spectrum collection
Simplicity using Swedish Dowtong DA7250 near Infrared Analyzer (DA7250 Diade Array Analyzer)TMAnd (4) collecting spectral information by software. The scanning wavelength range of the spectrometer is 950-1650nm, the ambient temperature is 24-25 ℃, and the sample is placed at a constant temperature of about 25 ℃ for more than 48h so as to reduce the image of the temperature on the sampleAnd (6) sounding. After the instrument is started and preheated for 30 minutes, each sample takes uniform and plump seeds and is filled into a sample cup, so that the surface is smooth. The sample was scanned 5 times and repeated 3 times to obtain an average spectrum for modeling.
4. Determination of sugar content in peanut kernels
The sugar content was determined by crushing 100mg of a sample with a JXFSTPRP-24 (Shanghai Net letters, Inc.) prototype, degreasing three times with petroleum ether (boiling range: 60-90) as a degreased sample, and performing a sample crushing process by referring to a document published by Hou et al (Hou Ming-yu M G-J, Zhang Yong-jiang. evaluation of total volatile content and analysis of related EST-SSR in Chinese Crop cultivation gemmipramim [ J ]. Crop breaking and Applied Biotechnology,2017,17 (3)). Sugar content determination three biological replicates were performed.
The determination of the soluble sugar content was carried out by reference to the colorimetric method of Lihaxu anthrone and slightly adjusted. Taking sucrose as a standard substance, putting 100mg of prepared degreased sample into a 10mL centrifuge tube, adding 1.5mL of distilled water, shaking up, carrying out water bath at 95 ℃ for 10 minutes, cooling, centrifuging at 10000r/min for 10 minutes, taking supernatant into the 10mL centrifuge tube, fixing the volume to 10mL, and shaking up for later use.
And (3) determination: 0.08mL of sample liquid, 0.6mL of distilled water, 0.2mL of anthrone reagent and 2mL of concentrated sulfuric acid are respectively added into a 5mL centrifugal tube; setting a control group: 0mL of sample solution, 0.68mL of distilled water, 0.2mL of anthrone reagent, and 2mL of concentrated sulfuric acid. Mixing, water bathing at 95 deg.C for 10 min, cooling to room temperature, and measuring light absorption value at 620nm with enzyme labeling instrument. Control blanks were set and three biological replicates were performed, respectively.
5. Model construction and optimization
Constructing a model by adopting chemometrics software of The Unscamblebler X10.4 of The Norwegian CAMO company, introducing The measured chemical value of The sugar content and The collected near infrared spectrum into The Unscamblebler X10.4 software, and adopting Multivariate Scattering Correction (MSC), standard normal variable transformation (SNV) and first derivative (1)st Derivative，1st-der), second derivative (2)ndDerivative，2nd-der), S-G convolution smoothing, normalization and other preprocessing methods to reduceAnd the influence of noise and errors on the spectrum is reduced, the pretreatment method is respectively subjected to single treatment and composite treatment, and the optimal pretreatment method is screened out. And establishing a calibration model for the preprocessed spectrum and the chemical value by a Partial Least Squares (PLS) method. During inspection, abnormal samples with large residual values are automatically removed, then abnormal values are removed through repeated cross validation, and stability of the model is evaluated through external validation. The model evaluation parameters are mainly measured by a decision coefficient (R2) and a standard error (RMSE) to measure the prediction accuracy of the model, and the model with high correlation coefficient and small standard error has good stability.
6. External validation of models
And (3) randomly selecting 15 peanut varieties (lines) with the seed coats of the three colors, detecting the sugar content of the peanut varieties by using the established near-infrared model, and recording the predicted value of the near-infrared model and the soluble sugar content of the chemically-measured sample. And comparing the correlation and the accuracy of the near infrared model predicted value and the chemical value.
Second, test results
2.1 partitioning of peanuts with different seed coat colors
The colorimetric value of the peanut seed coat is measured by using a CR-10Plus colorimeter, and 332 parts of peanut samples are divided into seed coats and grains with three different colors, namely black purple, red and pink, as shown in figure 1. Wherein, the sample comprises 120 parts of black purple seed coat sample, 80 parts of red seed coat sample and 132 parts of pink seed coat sample. Peanut with different seed coat colors (L)*，a*，b*) Minimum, maximum, mean, and standard deviation statistics, as shown in table 1.
As can be seen from Table 1, the mean values of the integrated chromaticity E values of the different seed coat colors are 10.64 (black purple), 25.74 (red) and 29.23 (pink), respectively. With color parameter L*The average value is increased, and the color of the seed coat is brighter; with color parameter a*The average value is increased, and the color is changed from light red to dark red; with color parameter b*The increase in the mean value changes the color from low-intensity yellow to high-intensity yellow.
TABLE 1 statistical analysis of the colorimetric values of different seed coat colors
2.2 spectral Collection of peanut kernels with different seed coat colors
The near infrared spectrum of 120 parts of the collected black-purple seed coat sample is shown in figure 2, the near infrared spectrum of 80 parts of the collected red seed coat sample is shown in figure 3, the near infrared spectrum of 132 parts of the collected pink seed coat sample is shown in figure 4, and as can be seen from figures 2-4, the peanut sample has obvious absorption peaks in the range of 950 + 1650nm, each sample has a plurality of absorption peaks, and the absorption peak intensities of different samples are different. The scanned near-infrared spectrogram can be used for quantitative analysis of sugar content of peanut grains.
2.3 chemical analysis of sugar content in peanut kernels
The sugar content in 332 peanut kernels was determined colorimetrically, and the chemical assay results of the samples are shown in table 2, with the soluble sugar content between 2.19% and 14.53%, where the difference between the pink kernels was the greatest, reflecting the difference in soluble sugar content between the pink kernels. The coefficient of variation of the sugar content of the seed coats with different colors is between 36.18 and 39.89 percent. The result shows that the sugar content distribution range of the selected peanut material is wide, the variation coefficient is large, the representativeness is good, and the near infrared spectrum calibration prediction can be carried out.
TABLE 2 chemical determination of sugar content in peanut kernels with different seed coat colors
2.4 peanut kernel sugar content prediction model construction
2.4.1 selection of spectral Pre-processing methods
Commonly used preprocessing methods are Multivariate Scatter Correction (MSC), standard normal-variant transformation (SNV), first derivative (1)st Derivative，1st-der) and second derivative (2)nd Derivative，2nd-der), S-G convolution smoothing, normalization (normalization), Baseline correction (Baseline), etc., which are corrected for Baseline, scatter, smoothedIn principle, single pretreatment is respectively carried out on scale, scaling and the like, and the spectrum is treated by 120 pretreatment methods in total by two pretreatment methods and three pretreatment methods. The best treatment is selected based on the correlation coefficient (R2) and the standard error (RMSE).
The optimal spectrum pretreatment methods of 3 models built by the invention are shown in table 3, and more than two pretreatment methods are used to ensure the applicability of the models.
TABLE 3 optimal pretreatment method for sugar content spectrum values of peanut kernels with different seed coat colors
2.4.2 construction and verification of peanut kernel sugar content calibration model
Respectively carrying out fitting spectrum processing on chemical values of soluble sugar in peanut with seed coats of different colors and collected near infrared spectrum data, establishing a calibration model by adopting a chemometric method of Partial Least Squares (PLS), repeatedly adopting internal cross validation to remove abnormal values, and determining coefficients (R) of the model2) And measuring the calibration model by a calibration standard deviation (RMSE) and screening the optimal model. And respectively carrying out near infrared analysis and chemical method determination on 15 varieties of three colors without calibration model establishment, and taking the varieties as an external verification set to carry out model prediction.
Correlation coefficients of the calibration model of the samples are shown in table 4 and fig. 5, the coefficient of determination of the black-purple seed coat peanut soluble sugar model is 0.914, and the SEC is 0.704 (fig. 5A); the coefficient of determinism of the red seed coat peanut soluble sugar model is 0.897, and the SEC is 0.768 (figure 5B); the determining coefficients of the pink peanut coat soluble sugar model are 0.883, the SEC is 0.830 (figure 5C), the determining coefficients of the established model are all larger than 0.88, the standard error is small, and effective prediction can be carried out. As can be seen, the determination coefficients are all larger than 0.88, the maximum is a model of the solubility content of the black-purple seed coat, and the determination coefficient is 0.914. The model built can make effective predictions.
TABLE 4 prediction model of sugar content of peanut kernels based on PLS different seed coat colors
The correlation coefficient of the predicted value and the chemical value of each model is above 0.88 (figure 6) after the built model is externally verified, and the result shows that 3 models built by the method can be used for identifying the soluble sugar content of peanuts with different seed coat colors.
According to the method, the peanut germplasm is divided into pink, red and black-purple seed coat peanuts by using a color difference meter, and a near infrared model of soluble sugar of peanut grains is constructed, so that the method is more beneficial to cultivation of special edible peanut varieties. In addition, the invention adopts DA7250 produced by Perten company to establish a sugar content prediction model, the grading is clear, and the model has wide applicability.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (9)
1. A method for constructing near-infrared models of soluble sugar content of peanuts with different seed coat colors is characterized by comprising the following steps:
(1) collecting peanuts with different seed coat colors as samples, and acquiring near infrared spectrum information;
(2) determining the content of soluble sugar in peanut grains with different seed coat colors by a chemical method;
(3) introducing the chemical value of the soluble sugar content obtained in the step (2) and the near infrared spectrum information acquired in the step (1) into chemometrics software, reducing the influence of noise and errors on the near infrared spectrum value by adopting different pretreatment methods, screening out the optimal pretreatment method after respectively carrying out single treatment and composite treatment on the pretreatment methods, and then establishing a calibration model for the pretreated near infrared spectrum value and the chemical value by using a partial least square method;
(4) and (4) measuring the near infrared spectrum information of the unknown peanut samples with different seed coat colors, and then substituting the near infrared spectrum information into the calibration model in the step (3) to analyze the soluble sugar content of the peanuts with different seed coat colors.
2. The method for constructing the near-infrared model of soluble sugar content of peanuts with different seed coat colors according to claim 1, wherein the peanuts with different seed coat colors are full and undamaged peanuts; the different seed coat colors include black purple, red and pink.
3. The method for constructing the near-infrared model of soluble sugar content in peanuts with different seed coat colors according to claim 2, wherein the content of the black-purple peanut soluble sugar is 2.4% -14.32%; the content of the soluble sugar in the red peanuts is 2.94% -13.75%; the content of pink soluble sugar is 2.19-14.53%.
4. The method for constructing a near-infrared model of soluble sugar content in peanuts with different seed coat colors according to claim 1, wherein the different seed coat colors are set according to a color parameter L*，a*，b*Counting the minimum value, the maximum value, the mean value and the standard deviation of the values, and calculating a comprehensive chromaticity E value so as to distinguish colors; said L*From black to white, 0-100; a is a*From green to red, -a to + a; b is*From blue to yellow, -b to + b.
5. The method for constructing the near-infrared model of the soluble sugar content of peanuts with different seed coat colors according to claim 1, wherein in the step (1), the near-infrared spectrum information is obtained by scanning with a near-infrared spectrometer under the scanning conditions that: the spectrum scanning wavelength is 950-1650nm, the ambient temperature is 24-25 ℃, and the sample is processed at the constant temperature of 24-25 ℃ for more than 48 hours.
6. The method for constructing the near-infrared model of the soluble sugar content of peanuts with different seed coat colors according to claim 1, wherein in the step (3), the pretreatment method comprises multivariate scattering correction, standard normal variable transformation, first-order derivative, second-order derivative, S-G convolution smoothing and normalization.
7. The method for constructing the near-infrared model of the soluble sugar content of peanuts with different seed coat colors as claimed in claim 1, wherein in the step (3), abnormal samples which are far away from each other are automatically removed in the model constructing process, then abnormal values are removed through repeated cross validation, and finally, the accuracy of the calibration model is evaluated through external validation.
8. The method for constructing the near-infrared model of the soluble sugar content of peanuts with different seed coat colors according to claim 1, wherein in the step (3), the optimal pretreatment method is obtained by screening with an evaluation standard with high correlation coefficient and small standard error.
9. The method for constructing the near-infrared model of the soluble sugar content of the peanuts with different seed coat colors according to claim 1, wherein in the step (2), the soluble sugar content of the peanut kernels with different seed coat colors is determined by an anthrone colorimetry method, and the kernels are peeled and degreased peanut samples.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102177842A (en) * | 2010-12-06 | 2011-09-14 | 山东省花生研究所 | Breeding method for once measuring quality of peanuts selected by multiple indexes |
| CN106706553A (en) * | 2016-03-17 | 2017-05-24 | 西北农林科技大学 | Method for quick and non-destructive determination of content of amylase in corn single grains |
| CN106770019A (en) * | 2017-03-01 | 2017-05-31 | 四川农业大学 | A kind of assay method of Itanlian rye soluble sugar content |
| CN108548792A (en) * | 2018-03-12 | 2018-09-18 | 河南省农业科学院 | A kind of fast non-destructive detection method of peanut kernel soluble sugar content |
| CN110108649A (en) * | 2019-05-06 | 2019-08-09 | 北京工商大学 | The fast non-destructive detection method of oil crops quality based on terahertz light spectral technology |
| US20210311011A1 (en) * | 2018-10-30 | 2021-10-07 | Teakorigin Inc. | Techniques for scoring food specimens, and related methods and apparatus |
-
2021
- 2021-11-18 CN CN202111368076.6A patent/CN114384041A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102177842A (en) * | 2010-12-06 | 2011-09-14 | 山东省花生研究所 | Breeding method for once measuring quality of peanuts selected by multiple indexes |
| CN106706553A (en) * | 2016-03-17 | 2017-05-24 | 西北农林科技大学 | Method for quick and non-destructive determination of content of amylase in corn single grains |
| CN106770019A (en) * | 2017-03-01 | 2017-05-31 | 四川农业大学 | A kind of assay method of Itanlian rye soluble sugar content |
| CN108548792A (en) * | 2018-03-12 | 2018-09-18 | 河南省农业科学院 | A kind of fast non-destructive detection method of peanut kernel soluble sugar content |
| US20210311011A1 (en) * | 2018-10-30 | 2021-10-07 | Teakorigin Inc. | Techniques for scoring food specimens, and related methods and apparatus |
| CN110108649A (en) * | 2019-05-06 | 2019-08-09 | 北京工商大学 | The fast non-destructive detection method of oil crops quality based on terahertz light spectral technology |
Non-Patent Citations (1)
| Title |
|---|
| 李勇;魏益民,王锋: "影响近红外光谱分析结果准确性的因素" * |
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