CN111024649A - Method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy - Google Patents
Method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy Download PDFInfo
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Abstract
The invention discloses a method for rapidly determining amylose and amylopectin in millet by using a near infrared spectroscopy, which is used for establishing a Fourier transform near infrared spectroscopy (FT-NIRS) to determine the proportion (starch straight/branched ratio) of the amylose and the amylopectin in millet. The method is characterized in that millet germplasm is used as a material, and a FT-NIRS analysis technology is adopted to establish a rapid detection model of the straight/branch ratio of starch in millet. The result shows that the calibration model of the millet starch straight chain and the millet starch branched chain is established by adopting the first derivative plus the vector normalization spectrum pretreatment. For the prediction of the straight/branch ratio of starch in millet, no obvious difference exists between the chemical method and the near infrared instrument method, and the near infrared measurement result is accurate and reliable. The FT-NIRS analysis technology is adopted to meet the requirement of detecting the straight/branched ratio of starch in the millet.
Description
Technical Field
The invention relates to the field of grain analysis and detection, in particular to a method for rapidly determining amylose and amylopectin in millet by a Fourier transform near infrared spectroscopy.
Background
The millet originates from the north of China and is one of the oldest grain crops in the world, the millet is obtained after shelling, and the millet has rich contents of starch, protein, fat, vitamins, selenium and other trace element nutrients, can balance diet and promote human health. As early as recorded in Bencao gang mu, the term "millet is salty and slightly cold in taste. It is effective in nourishing kidney qi, removing heat from stomach and spleen, and invigorating qi. The old people, with bitter taste, can treat stomach heat, quench thirst and promote urination, and the effects of nourishing kidney, tonifying qi, removing heat and quenching thirst have long been known by ancient people. The starch is the most main component in the millet, the content is generally between 60% and 70%, and the content, the structure and the form of the components can influence the eating quality and the processing quality of the millet to a certain extent. Amylopectin and amylose are important components of starch, and the ratio between them directly affects the taste quality of millet. Therefore, the determination of the starch straight/branched ratio in millet is an important quality parameter for millet processing.
The important part of the millet is starch, and the content of the starch is generally between 14% and 15%. Starch is mainly present in the endosperm, and the rice hulls, embryo and bran layers are starch-free. The starch in rice consists of amylose and amylopectin. The content and ratio of these two starches are important quality characteristics of rice, directly affecting storage, eating and processing quality, and the amylose content is the main factor affecting water absorption, swelling, solubility of solid matter in cooking, color, gloss, viscoelasticity and rice softness. The quality of millet includes nutritional quality and taste quality. In the taste quality of millet, the amylose content is the percentage of the dry weight of amylose in polished rice, and the straight-through starch content is generally classified into four types, high, medium, low and extremely low. According to research, the direct starch content is high, and the viscosity, the glossiness and the softness of the rice are poor. The food with the content of 20.0-25.0% has good taste quality. The different proportions of amylose and amylopectin affect various properties of millet, the content of amylose in the millet starch is about 27.2%, and the characteristics are directly reflected that the millet starch has good gel stability, strong water holding capacity, large hot baking change, relatively high expansibility and gelatinization temperature, but low transparency, and relatively poor freeze-thaw stability and thermal stability, and provide important basis for breeding the millet with complete nutritional value.
Important influencing factors affecting the quality of millet and the quality of the process are the structure of the rice starch and its properties. Amylose, gelatinization temperature and consistency of gum are used as main indexes in the quantitative measurement indexes of taste quality. Therefore, the establishment of a rapid and nondestructive discrimination method for the quality and character of the grains can effectively and rapidly determine the nutrient components of the grains in time, and has important significance in breeding, food processing and agricultural product trade.
At present, there are many methods for measuring the straight/branched ratio of starch in millet. It is common practice to obtain the starch straight/branched ratio indirectly by measuring the amylose and starch content. The iodine colorimetric method is a commonly used method for measuring the content of amylose and has the following principle: starch forms an iodine-starch complex with iodine and has a specific color reaction. Amylopectin and iodine form a red-brown complex, and amylose and iodine form a dark blue complex. Under the condition of constant total amount of starch, the two starch dispersions are mixed according to different proportions, and react with iodine under the condition of certain wavelength and acidity to generate a series of colors from purple red to deep blue, which represent the content proportions of different amylose and amylopectin, and the absorbance and the amylose concentration are in a linear relation according to the Lambert-beer law and can be measured by a spectrophotometry. The optical rotation method is a method for measuring the starch content and has the following principle: the starch is a poly-enamel polymer, under a certain acidic condition, a calcium chloride solution is used as a dispersion medium, the starch can be uniformly dispersed in the solution, and a stable substance with optical activity can be formed, and the size of the optical activity is in direct proportion to the content of the starch, so that the determination can be carried out by an optical rotation method. And then calculating the amylopectin content according to the contents of the millet starch and the amylose, thereby obtaining the straight/branched ratio of the millet starch. Its advantages are accurate data, and simple operation, high correctness and time and labor. The method not only needs to carry out a series of pre-treatments on the sample, has complex operation and long analysis period, but also damages the sample, can not be recycled, limits the rapid detection of the nutrition components of the grains to different degrees, influences the breeding efficiency, and is not suitable for the online rapid analysis and the process quality control.
The Megazyme kit determination is a ConA method improved by Yun and Matheson, and the principle is as follows: ConA can specifically precipitate amylopectin, and the content of amylose in the total starch is judged according to the ratio of the supernatant of a ConA precipitation sample to the absorbance light value of GOPOD (Gopod) in the total starch sample at 510nm, so that the straight/branch ratio of the millet starch is obtained. The method can measure the content of amylose and starch at one time, has high reliability and less time consumption, but has complicated measuring steps and high cost, so the method is not suitable for batch analysis and nondestructive detection of samples. In the millet quality breeding process, a large amount of germplasm resources, mutants and filial generation materials need to be rapidly identified and screened, and nondestructive detection is carried out so as to facilitate seed updating and propagation. Therefore, the invention establishes a detection model of the starch direct/branch ratio in the millet by using a BrookMPA Fourier transform spectrometer, and provides a quick, simple and convenient and nondestructive analysis method for evaluating the resource quality of millet varieties.
Fourier transform near infrared spectroscopy (FT-NIRS) has its own unique advantages over ultraviolet-visible spectrophotometry. The near infrared spectrum analysis has the advantages of high analysis speed, nondestructive detection and the like, and is widely applied. The near infrared spectrum can be used for quantitative analysis, although the accuracy and precision are slightly lower than those of chemical analysis methods, the infrared spectrum has diversity and can be flexibly applied according to the properties of a detected substance, and solid, liquid and even gaseous samples can be directly injected. Most importantly, the quantitative analysis of the infrared spectrum can establish a prediction model by directly scanning the near infrared spectrum of the sample and processing the spectrogram by computer software, thereby greatly simplifying the complicated pretreatment of the sample, saving the time and the cost and realizing the rapid and accurate detection. The near infrared spectrum analysis technology has more reports on the quality analysis of large crops such as wheat, soybean, corn and the like, and is also applied to the quality analysis of small coarse cereals, and the results show that the near infrared spectrum analysis method can meet the measurement precision required by practical application.
The content of starch is closely related to the edible quality such as viscosity, gelatinization characteristic, fluffiness, softness and the like, so that the amylose content is listed as one of indexes for evaluating the quality grade of rice in the quality standard of high-quality rice in China, scientists in China generally use the amylose content as an important evaluation index in the rice breeding process, and the defects of sample damage, complex operation, long detection time, high cost and the like exist when the millet amylose content is detected by using a traditional physicochemical method. With the development of chemometric method and molecular spectrum analysis technology, many researchers combine the two to carry out rapid detection research on amylose content.
Disclosure of Invention
The invention aims to establish a Fourier transform near infrared spectroscopy (FT-NIRS) method for determining the proportion of amylose to amylopectin (starch straight/branched ratio) in millet and provide a rapid, simple and nondestructive analysis method for identifying and screening millet variety resources.
The invention is realized by adopting the following technical scheme:
a method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy comprises the following steps:
(1) selecting M parts of millet samples as a calibration sample set, and selecting N parts of millet samples as a verification sample set;
(2) establishing of near infrared calibration model
A Fourier transform near-infrared spectrometer is utilized, a gold-plated diffuse reflector is used as a reference, a sample cup is a cylindrical quartz cup, two thirds of samples are loaded each time, and the samples are compacted and paved as required; the scanning spectral region is 3594.9cm-1~12790.3cm-1The resolution is 16cm-1Each ofCollecting the reflection intensity at intervals of 2nm, repeatedly scanning and collecting the near infrared spectrum of the sample for 64 times to form a reflection spectrum for storage; scanning each calibration sample for 3 times to obtain an average value, reloading each measurement, and scanning after shaking lightly; converting the reflection spectrum information into absorbance value for storage, optimizing and mathematically preprocessing the spectrum by using OPUS/QUANT modeling software to obtain a standard building model, performing internal cross validation on the standard building models of starch straight chain and amylopectin in millet, performing external validation on the validation sample set, and determining a coefficient R according to the cross validation2cv, cross validation standard error RMSECV, external validation decision coefficient R2val, predicting the standard error RMSEP index to determine the optimal calibration model.
The internal cross validation is that the software randomly selects 2 samples from M standard millet samples as test samples each time by using the automatic validation function of the software according to the near infrared characteristics of the standard samples, establishes a calibration model by using the other samples, predicts the test samples and automatically repeats until all the standard samples are used as the test samples.
And the external verification is that the actual prediction effect of the built model is evaluated by adopting a verification sample set which is not built in the model and has known chemical components.
According to the invention, a near infrared spectrum analysis model is established according to the chemical measured values of straight and branched starch in millet, and the model is utilized to rapidly and quantitatively detect the quality of millet. In order to establish a millet conventional quality near infrared spectrum analysis model, firstly, starch straight chain and amylopectin in M (300 in the embodiment) parts of millet are measured by a chemical measurement method, then an MPA Bruk near infrared diffuse reflectance spectrometer is utilized, an optimal spectrum pretreatment method for starch straight/branch ratio modeling in the millet is respectively screened by an automatic optimization function in OPUS/QUANT software, the mathematical pretreatment modes of the original near infrared diffuse reflectance spectrum are all a first derivative and vector normalization method, abnormal values are removed, the spectrum range is selected from 3594.9cm-1~12790.3cm-1The internal cross validation of the direct and branched starch marking model of the starch in the millet is carried out, and N (50 in the embodiment) portions are used for validationPerforming external verification and prediction, and comparing cross-verification decision coefficient (R)2cv), cross validation standard error (RMSECV), external validation decision coefficient (R)2val), prediction standard error (RMSEP) and the like, and determining an optimal calibration model. The result shows that the first derivative and the vector normalization spectrum pretreatment are adopted to establish a correction model of the millet starch straight-chain and amylopectin and the cross validation determining coefficient (R) of the model2cv) 88.76, 89.2, respectively, cross validation Standard error (RMSECV) 0.466, 1.47, respectively, and external validation determinant coefficient (R)2val) of 0.9333 and 0.9703, respectively, and the standard error of prediction (RMSEP) of 0.591 and 1.9, respectively. For the prediction of the straight/branch ratio of the starch in the millet, no obvious difference exists between the chemical method and the near infrared instrument method, the near infrared measurement result is accurate and reliable, and the FT-NIRS analysis technology can meet the requirement of detecting the straight/branch ratio of the starch in the millet.
The method is reasonable in design, takes the millet germplasm as a material, and adopts the FT-NIRS analysis technology to establish a rapid detection model of the straight/branch ratio of the starch in the millet, thereby providing a convenient and effective analysis method for the quality analysis of the millet.
Drawings
Figure 1 shows a millet near infrared scanning spectrum.
FIG. 2a shows the correlation between predicted values and chemical values of amyloamylose in millet cross-validated in the near infrared model.
FIG. 2b shows the correlation between the predicted value and the chemical value of amylopectine in millet cross-validation in the near infrared model.
FIG. 3a shows the correlation between predicted values and chemical values of amyloamylose in millet externally verified by a near infrared model.
FIG. 3b shows the correlation between the predicted value and the chemical value of amylopectine in millet externally verified by the near infrared model.
Detailed Description
The following detailed description of specific embodiments of the invention refers to the accompanying drawings.
1. Materials and instruments
Test materials: 350 parts of millet resources to be tested are provided by the research institute of crop science of Chinese academy of agricultural sciences, and have various particle shapes and different producing areas, so that the quality characteristics of the model have wide representativeness. Of these, 300 (M = 300) samples were used as the calibration sample set for establishing the concentration calibration equation, and 50 (N = 50) samples were used as the verification sample set.
Experimental equipment: ultraviolet-visible spectrophotometer, Fourier transform near infrared spectrometer, vortex instrument, electric heating constant temperature water bath, and centrifuge.
2. Chemical experimental method
2.1 chemical method for measuring straight/branch ratio of starch in millet
The starch samples were completely dissolved in dimethyl sulfoxide (DMSO) by heating. Precipitating the starch with ethanol to remove lipid, and recovering the precipitated starch. Dissolving the precipitated sample with acetate solution, adding ConA, specifically precipitating amylopectin, and centrifuging to remove the precipitate. Amylose per volume of supernatant is enzymatically hydrolyzed to D-glucose and then assayed using a glucose oxidase/peroxidase reagent. The total starch in the other volume unit of acetate solution was also enzymatically hydrolyzed to D-glucose, followed by the addition of glucose oxidase/peroxidase and colorimetric determination. And judging the content of amylose in the total starch according to the ratio of the supernatant of the ConA sediment sample to the absorbance light value of GOPOD (Gopod) in the total starch sample at 510nm, and further obtaining the straight/branch ratio of the starch in the millet.
2.2 results of chemical analysis of millet
The range of amylose in millet for constructing a calibration model is 5-17.33%, and the range of amylopectin is 22.93-76.01%. The millet has different shapes, colors and sizes, the data amplitude is larger, and the established model has better applicability. The identification and evaluation of the millet show that the millet germplasm resources have rich genetic diversity.
3. Establishment of near-infrared calibration model
3.1 MPA Fourier transform near-infrared spectrometer manufactured by Bruke Germany is utilized, a gold-plated diffuse reflector is used as a reference, and a sample cup with the volume of 25cm3Of cylindrical quartzThe cups, preferably fill two-thirds (2/3) each time, are compacted and laid flat as required to prevent light leakage. The scanning spectral region is 3594.9cm-1~12790.3cm-1The resolution is 16cm-1And collecting the reflection intensity every 2nm, and repeatedly scanning and collecting the near infrared spectrum of the sample for 64 times to form a reflection spectrum for storage. Each calibration sample is scanned for 3 times to obtain an average value, the sample is reloaded for each measurement, and the calibration sample is scanned after being shaken lightly so as to reduce the errors caused by instrument fluctuation and sample loading. Fig. 1 shows that 300 parts of millet near-infrared scanning spectra in a calibration sample set have wide range of regions, and the selected millet has obvious difference, thereby being beneficial to modeling. And converting the reflection spectrum information into absorbance values for storage, and performing optimization processing and mathematical preprocessing (mathematical analysis and regression statistics) on the spectrum by using OPUS/QUANT modeling software to obtain a calibration model. Namely, the MPA Bruk near-infrared diffuse reflection spectrometer is utilized, the optimal spectrum pretreatment method for modeling the starch direct/branch ratio in millet is respectively screened through the automatic optimization function in the OPUS/QUANT software, the mathematical pretreatment methods for obtaining the original near-infrared diffuse reflection spectrum are all the first derivative plus vector normalization methods, the abnormal values are removed, and the spectrum range is selected from 3594.9cm-1~12790.3cm-1。
Then, internal cross validation is carried out on the straight and branched starch standard building models in the millet, external validation is carried out on the positive validation sample set, and the coefficient R is determined according to the cross validation2cv, cross validation standard error RMSECV, external validation decision coefficient R2val, prediction standard error RMSEP and other indexes determine an optimal standard building model to serve as a millet quality prediction model. Correcting the coefficient of determination R for regression equations constructed for the same sample set2The larger the cal is, the lower the correction standard error RMSEE is, the more the near infrared analysis result is consistent with the chemical analysis result, and the higher the credibility is; cross validation decision coefficient R2The larger cv is, the lower the cross validation standard error RMSECV is, the closer the near-infrared predicted value obtained in the cross validation in the calibration modeling process is to the chemical analysis value is, and the higher the accuracy of the calibration model is.
Verification of the built model includes internal cross-validation and external validation.
Internal cross validation: according to the near infrared characteristics of the samples, the automatic verification function of the software is utilized, the software randomly selects 2 samples from 300 standard-establishing millet samples as test samples each time, establishes a calibration model by using the other samples, predicts the test samples and automatically repeats until all the samples are used as the test samples. The results of cross-checking amylose, and starch straight/branched ratio in millet are shown in FIGS. 2a and 2b, and the cross-checking results thereof show the determination coefficients (R)2cv) are 88.76, 89.2, respectively, and the cross-check standard error (RMSECV) is 0.466, 1.47, respectively.
External verification: the near infrared model external verification is to evaluate the actual prediction effect of the established calibration model by adopting a verification sample set which is not established in the calibration model and has known chemical components. As shown in fig. 3a and 3b, the line i and the line ii are X = Y lines, and the line i and the line ii are actual regression curves. Study the above models were examined separately with 15 samples independent of modeling, and the external validation determinant (R) of the results2val) of 0.9333 and 0.9703, respectively, the prediction standard error (RMSEP) of 0.591 and 1.9, respectively, the relative analytical error for amylose of 2.78, and the relative analytical error for amylopectin of 4.06. In addition, 20 parts of millet samples independent of modeling are used for testing the starch straight chain and the amylopectin in the millet, and the absolute error between the predicted values and the chemical values of the starch straight chain and the amylopectin is less than 0.43 percent. For the prediction of millet, no obvious difference exists between the chemical method and the near infrared instrument method, and the near infrared diffuse reflection spectrum measurement result is accurate and reliable. The detection of the conventional quality components of the millet can be satisfied by adopting the near infrared diffuse reflection spectrum analysis technology.
3.2, the method can meet the requirements of rapid and nondestructive detection of large-batch varieties in millet resource quality breeding, effectively improves the millet quality breeding efficiency, and provides a new and effective technical means for analysis and identification of starch straight/branch ratio in millet. Through the evaluation and identification of the starch straight/branch ratio in the millet, excellent resources are screened out, and more millet resources can be rapidly detected by utilizing a preliminarily established millet quality prediction model in the future, so that the millet quality prediction model is used for breeding and production services.
4. Conclusion
In this embodiment, a Bruker MPA fourier transform near-infrared spectrometer is used to perform spectral scanning on 300 millet samples, and spectrum preprocessing, mathematical operation and regression statistical analysis are performed by using quantitative spectrum analysis software, so as to obtain a near-infrared calibration model of starch direct/branch ratio in millet. Because the established millet sample is complete, the loss of water or nutrition caused by grinding into powder is avoided, and the millet grains are not damaged after scanning, the model not only meets the rapid detection requirement of resources in millet quality breeding, but also is a nondestructive detection method. Not only provides beneficial reference for millet quality breeding and resource evaluation, but also provides a very convenient test means for accelerating the research of millet variety resources.
In a word, the properties of millet starch of different varieties are different, and the millet of common variety can be classified by analyzing the properties, so that the method is favorable for finding suitable raw materials for industrial production, provides reference for breeding high-quality millet varieties, and promotes the development of millet cultivation and millet starch deep processing industry in China.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the detailed description is made with reference to the embodiments of the present invention, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which shall all fall within the protection scope of the claims of the present invention.
Claims (4)
1. A method for rapidly determining amylose and amylopectin in millet by using a near infrared spectroscopy is characterized by comprising the following steps: the method comprises the following steps:
(1) selecting M parts of millet samples as a calibration sample set, and selecting N parts of millet samples as a verification sample set;
(2) establishing of near infrared calibration model
Using a Fourier transform near-infrared spectrometer, a gold-plated diffuse reflector as a reference, and a sample cup as a cylinderTwo thirds of samples are filled each time, and the samples are compacted and paved as required; the scanning spectral region is 3594.9cm-1~12790.3cm-1The resolution is 16cm-1Collecting the reflection intensity every 2nm, and repeatedly scanning and collecting the near infrared spectrum of the sample for 64 times to form a reflection spectrum for storage; scanning each calibration sample for 3 times to obtain an average value, reloading each measurement, and scanning; converting the reflection spectrum information into absorbance value for storage, optimizing and mathematically preprocessing the spectrum by using OPUS/QUANT modeling software to obtain a standard building model, performing internal cross validation on the standard building models of starch straight chain and amylopectin in millet, performing external validation on the validation sample set, and determining a coefficient R according to the cross validation2cv, cross validation standard error RMSECV, external validation decision coefficient R2val, predicting the standard error RMSEP index to determine the optimal calibration model.
2. The method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy according to claim 1, characterized in that: the mathematical preprocessing mode is a first derivative plus vector normalization method, and abnormal values are removed.
3. The method for rapidly determining amylose and amylopectin in millet by using near-infrared spectroscopy according to claim 1 or 2, characterized in that: the internal cross validation is that the software randomly selects 2 samples from M standard millet samples as test samples each time by using the automatic validation function of the software according to the near infrared characteristics of the standard samples, establishes a calibration model by using the other samples, predicts the test samples and automatically repeats until all the standard samples are used as the test samples.
4. The method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy according to claim 3, characterized in that: and the external verification is that the actual prediction effect of the built model is evaluated by adopting a verification sample set which is not built in the model and has known chemical components.
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