CN113624715A - Method for analyzing aged starch - Google Patents
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- CN113624715A CN113624715A CN202110694116.XA CN202110694116A CN113624715A CN 113624715 A CN113624715 A CN 113624715A CN 202110694116 A CN202110694116 A CN 202110694116A CN 113624715 A CN113624715 A CN 113624715A
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Abstract
The invention relates to the field of food, in particular to an analysis method of aged starch. The method comprises the following steps: collecting spectrograms of a plurality of aged starch standard samples by using a spectroscopic technology to further obtain a spectroscopic data set; respectively obtaining the aging days, the crystallinity and the retrogradation of a plurality of aging starch standard samples; performing regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation by adopting a partial least square method to respectively obtain regression equations; collecting a spectrogram of the aged starch to be analyzed to obtain a spectral data set of the aged starch to be analyzed, and respectively calculating the aging days, the crystallinity and the retrogradation of the aged starch to be analyzed according to the established regression equation; wherein the spectroscopic technique is selected from the group consisting of near infrared spectroscopy, Raman spectroscopy and terahertz spectroscopy. The method can reduce cost, greatly reduce detection time, reduce experimental error, and realize simultaneous determination of multiple physical and chemical indexes without damaging samples.
Description
Technical Field
The invention relates to the field of food, in particular to an analysis method of aged starch.
Background
The phenomenon that starch milk is converted into paste through heating processes such as cooking, baking and the like is called gelatinization of starch. The hardening phenomenon of gelatinized starch during storage due to the constant association of hydrogen bonds between molecular chains is called retrogradation or retrogradation of starch. The completely gelatinized starch is in a thermodynamic non-equilibrium state after the temperature is reduced to a certain degree due to the shortage of molecular thermal motion energy, molecular chains attract and arrange with each other by virtue of hydrogen bonds, the free enthalpy of the system is reduced, starch molecules and water molecules are matched and rearranged with each other in spatial conformation to reach an ordered arrangement stable state of system equilibrium, and linear parts of amylose and amylopectin tend to be arranged in parallel and return to a crystal from an amorphous state. The essence is that the gelatinized starch molecules are self-aligned to form highly dense, crystallized, insoluble molecular micro-beams. Retrogradation of starch is the process by which starch molecules go from disorder to order, i.e., amylose, which is broken down during gelatinization, rejoins amylopectin and forms a more ordered structure. The degree of crystallinity and the degree of retrogradation during starch retrogradation therefore have a representative effect on the degree of starch retrogradation. The aging of starch can be divided into long-term aging and short-term aging, wherein the short-term aging is performed in a short time after gelatinization and mainly comprises recrystallization of amylose; whereas long-term aging is the recrystallization of the branch structure of amylopectin.
The traditional method mostly adopts classical chemical means for detecting the aged starch, and the methods generally have the defects of high cost, long time consumption, large human error, capability of measuring only one index each time and the like.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provide an analysis method of aged starch, which can reduce the cost, greatly reduce the detection time, reduce the experimental error and realize the simultaneous determination of a plurality of physical and chemical indexes on the premise of not damaging a sample.
In order to achieve the above object, the present invention provides a method for analyzing retrograded starch, comprising:
respectively collecting spectrograms of a plurality of aged starch standard samples by using a spectroscopic technology to further obtain a spectroscopic data set, wherein the aging treatment time of each aged starch standard sample is different;
respectively obtaining the aging days, the crystallinity and the retrogradation of the plurality of aged starch standard samples;
performing regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation degree by adopting a partial least square method to respectively obtain regression equations of the aging days, the crystallinity and the retrogradation degree and the spectrum data set;
collecting a spectrogram of the aged starch to be analyzed so as to obtain a spectral data set of the aged starch to be analyzed, and respectively calculating the aging days, the crystallinity and the retrogradation of the aged starch to be analyzed according to the established regression equation;
wherein the spectroscopic technique is selected from the group consisting of near infrared spectroscopy, Raman spectroscopy and terahertz spectroscopy.
The invention can obtain the following beneficial effects:
1. the method provided by the invention adopts a spectrum technology, does not need to carry out pretreatment on starch, can realize nondestructive detection of the starch, reduces the cost, has small analysis error and provides great convenience for production and processing.
2. The method provided by the invention can realize simultaneous determination of a plurality of physical and chemical indexes on the premise of not damaging the sample, thereby greatly reducing the detection time and reducing the time cost.
Drawings
FIG. 1 is a model diagram of the near infrared spectrum for predicting the number of days for which wheat starch is aged;
FIG. 2 is a model diagram of a near infrared spectrum for predicting crystallinity of wheat starch;
FIG. 3 is a model diagram of near infrared spectrum prediction of wheat starch retrogradation;
FIG. 4 is a model diagram of the near infrared spectrum for predicting the aging days of long-shaped rice starch;
FIG. 5 is a graph of a model for predicting the crystallinity of long-grained rice starch by near infrared spectroscopy;
FIG. 6 is a model diagram of near infrared spectrum prediction of retrogradation of long-shaped rice starch;
FIG. 7 is a model graph of the number of days of wheat starch aging predicted by Raman spectroscopy;
FIG. 8 is a model diagram of a Raman spectrum for predicting crystallinity of wheat starch;
FIG. 9 is a model diagram of a Raman spectrum model for predicting wheat starch retrogradation;
FIG. 10 is a graph of model of days of aging of long-grain rice starch predicted by Raman spectroscopy;
FIG. 11 is a model diagram of crystallinity of indica rice starch predicted by Raman spectrum;
FIG. 12 is a model diagram of the retrogradation of indica rice starch predicted by Raman spectrum;
FIG. 13(a) model graph of the refractive index spectrum in the terahertz spectrum for predicting the number of days of wheat starch aging;
FIG. 13(b) model graph of wheat starch aging days predicted by absorption coefficient spectrum in terahertz spectrum;
FIG. 14(a) model diagram of wheat starch crystallinity prediction by refractive index spectrum in terahertz spectrum;
FIG. 14(b) is a model diagram of wheat starch crystallinity prediction by absorption coefficient spectrum in terahertz spectrum;
FIG. 15(a) is a model diagram of wheat starch retrogradation degree prediction by refractive index spectrum in terahertz spectrum;
FIG. 15(b) is a model diagram of wheat starch retrogradation prediction by absorption coefficient spectrum in terahertz spectrum;
FIG. 16(a) model graph of index spectra in terahertz spectrum for predicting the number of days of aging of long-shaped rice starch;
FIG. 16(b) model diagram of number of days of aging of long-shaped rice starch predicted by absorption coefficient spectrum in terahertz spectrum;
FIG. 17(a) a model diagram of prediction of crystallinity of indica rice starch by refractive index spectrum in terahertz spectrum;
FIG. 17(b) model diagram of indica rice starch crystallinity prediction by absorption coefficient spectrum in terahertz spectrum;
FIG. 18(a) a model diagram of prediction of retrogradation of indica rice starch by refractive index spectrum in terahertz spectrum;
fig. 18(b) is a model diagram of prediction of retrogradation of indica rice starch by absorption coefficient spectrum in terahertz spectrum.
Detailed Description
The endpoints of the ranges and any values disclosed herein are not limited to the precise range or value, and such ranges or values should be understood to encompass values close to those ranges or values. For ranges of values, between the endpoints of each of the ranges and the individual points, and between the individual points may be combined with each other to give one or more new ranges of values, and these ranges of values should be considered as specifically disclosed herein.
In the present invention, "fitting" refers to knowing several discrete function values of a certain function, and by adjusting several undetermined coefficients in the function, the difference (least-squares sense) between the function and the known point set is minimized.
The invention provides an analysis method of aged starch, which comprises the following steps:
respectively collecting spectrograms of a plurality of aged starch standard samples by using a spectroscopic technology to further obtain a spectroscopic data set, wherein the aging treatment time of each aged starch standard sample is different;
respectively obtaining the aging days, the crystallinity and the retrogradation of the plurality of aged starch standard samples;
performing regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation degree by adopting a partial least square method to respectively obtain regression equations of the aging days, the crystallinity and the retrogradation degree and the spectrum data set;
collecting a spectrogram of the aged starch to be analyzed so as to obtain a spectral data set of the aged starch to be analyzed, and respectively calculating the aging days, the crystallinity and the retrogradation of the aged starch to be analyzed according to the established regression equation;
wherein the spectroscopic technique is selected from the group consisting of near infrared spectroscopy, Raman spectroscopy and terahertz spectroscopy.
The spectrum data set refers to spectrum data corresponding to a near infrared spectrum, spectrum data corresponding to a Raman spectrum or spectrum data corresponding to a terahertz spectrum, which are respectively obtained after preprocessing the near infrared spectrum, the Raman spectrum or the terahertz spectrum of starch under different aging days.
The near infrared spectrum technology is a modern molecular spectrum analysis technology for detecting the content of substances and identifying the substances, and the near infrared region refers to electromagnetic waves with the wavelength within the range of 780-2526 nm.
The Raman spectrum belongs to molecular vibration spectrum, and can detect liquid and solid. Raman spectroscopy is a scattering spectrum derived from changes in the polarizability of molecules. The change of the vibration frequency of the chemical bond related to the configuration and the crystal can be detected by using Raman spectrum.
Terahertz is an electromagnetic wave with a frequency between 0.1-10 terahertz. In the terahertz spectrum, a terahertz pulse and a sample interact to generate a terahertz electric field, then a change curve of the terahertz electric field intensity along with time is obtained, a coherent terahertz radiation source and a frequency-tunable narrow band are utilized to scan the spectrum, and the energy of terahertz waves with different frequencies is measured to obtain sample information. The internal vibration frequency of a plurality of organic molecules is positioned in the middle infrared frequency band, but intermolecular forces such as hydrogen bonds, low-frequency vibration of crystal lattices and macromolecular skeleton vibration all occur in the terahertz wave band, and the vibrations reflect molecular structure information, so that the components contained in the substance and the molecular configuration classification can be judged.
The partial least squares method is a combination of principal component analysis, multiple linear regression, and canonical correlation analysis, and can solve multiple correlations between independent variables. The inventor of the invention finds that when the spectral data set and the corresponding aging days, crystallinity and retrogradation degree are subjected to regression fitting respectively by adopting a partial least square method, the obtained regression equation can better describe the relationship between the aging days, crystallinity and retrogradation degree and the spectral data set, and the analysis error is smaller.
The regression equation is the relationship of the one-to-one mapping established between the physicochemical measured values of the aging days, the crystallinity and the retrogradation of the aged starch and the spectral data set of the aged starch.
According to the invention, preferably, when the spectroscopic technique is terahertz spectroscopy, a refractive index spectrum, an absorption coefficient spectrum, a time domain spectrum and a frequency domain spectrum of the aged starch standard sample can be collected, and more preferably, the refractive index spectrum and/or the absorption coefficient spectrum are collected.
It can be understood that in the regression fitting, a regression equation set composed of a plurality of regression equations may be obtained according to a difference in the preprocessing method. According to the invention, in order to further improve the accuracy and the representativeness of the obtained regression equation, an equation with the largest determining coefficient, the smallest standard deviation and the widest spectral range in the regression equation set obtained by fitting is selected as the regression equation.
According to the present invention, in order to further improve the accuracy and the representativeness of the spectrogram of the retrograded starch standard sample and the retrograded starch to be analyzed, it is preferable that each collection is repeated 3 to 5 times (for example, it may be 3, 4, 5 times), and the average value of the above collected values is taken as a representative value.
According to the present invention, the preparation method of the retrograded starch standard sample may be a method conventional in the art; preferably, the preparation method of the aged starch standard sample comprises the following steps: preparing starch into a starch solution, heating and gelatinizing the starch solution, and then sequentially performing aging treatment, freezing and drying;
wherein the aging treatment is low-temperature aging treatment;
wherein the low temperature is 0 to 8 ℃ (for example, may be 0, 1, 2, 3, 4, 5, 6, 7, 8 ℃), more preferably 2 to 6 ℃.
According to the present invention, in order to further improve the accuracy and the representativeness of the spectrogram of the retrograded starch standard sample and the retrograded starch to be analyzed, it is preferable that the low-temperature retrogradation treatment is performed for 0 to 50 days (for example, it may be 0, 1, 3, 5, 7, 10, 15, 20, 25, 28, 30, 35, 40, 42, 49, 50 days), and more preferably for 0 to 35 days.
According to the present invention, in order to further improve the accuracy and the representativeness of the spectrogram of the retrograded starch standard sample and the retrograded starch to be analyzed, the low-temperature retrogradation treatment time can be 1-10 days (for example, can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days) at intervals; it can be understood that the accuracy and the representativeness of the spectrogram of the retrograded starch standard sample and the retrograded starch to be analyzed are higher when the time of the low-temperature retrogradation treatment is more densely spaced. However, in order to save cost, it is preferable that samples are taken at intervals of 1 or 2 days when the time of the low-temperature aging treatment is less than 7 days, and samples are taken at intervals of 7 days when the time of the low-temperature aging treatment is more than 7 days.
According to the invention, in order to further improve the accuracy and the representation of the spectra of the retrograded starch standard and the retrograded starch to be analyzed, it is preferred that the starch solution has a concentration of 3-20g/ml (e.g. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20g/ml), more preferably 6-15 g/ml.
According to the present invention, the temperature of the heating gelatinization may be a conventional temperature in the art; preferably, the temperature for heating and pasting is 80-140 ℃ (for example, may be 80, 90, 100, 110, 120, 130, 140 ℃), more preferably 100-.
According to the invention, the heating gelatinization time can be conventional time in the field; preferably, the heating for gelatinization is 5-60min (for example, it may be 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60min), more preferably 15-40 min.
According to the invention, preferably, the temperature of the freezing is from-120 to-60 ℃ (which may be, for example, -120, -110, -100, -90, -80, -70, -60 ℃), more preferably from-90 to-80 ℃.
According to the invention, the freezing time is preferably 5-35h (e.g. 5, 8, 10, 12, 14, 15, 16, 18, 20, 22, 24, 25, 26, 30, 35h is possible), more preferably 8-24 h.
According to the invention, before fitting the regression equation, samples which obviously deviate from the measurement result are removed, noise information in the samples is removed, and the spectrograms of the standard sample of the aged starch and the aged starch to be analyzed are preprocessed to obtain a spectral data set, wherein the preprocessing method can be a conventional method in the field; preferably, in order to be able to further reduce the analysis errors, the method of processing the spectrogram of the retrograded starch standard sample and the retrograded starch to be analyzed is each independently selected from: one or more of multivariate scatter correction, vector normalization, first derivative, second derivative, detrending, smoothing algorithm, Fourier transform, and wavelet transform.
The multivariate scattering correction can remove noise caused by specular reflection and nonuniformity of samples in the spectrum, and eliminate the baseline of the spectrum and the non-repeatability of the spectrum;
the vector normalization can eliminate the influence of solid particle size, surface scattering and optical path change on near-infrared diffuse reflection light.
The method of the first derivative and the second derivative can eliminate the influence of baseline drift or gentle background interference by performing a direct difference method on the discrete spectrum, and provides higher resolution and clearer spectral profile change than the original spectrum.
The detrending method may eliminate baseline drift of the diffuse reflectance spectrum.
The smoothing algorithm can process spectral signals obtained by a spectrometer which not only contain useful information, but also are superimposed with random errors (noise), thereby improving the signal-to-noise ratio.
The Fourier transform energy-conversion decomposes the original spectrum into the superposition sum of sine waves with different frequencies, and can carry out operations such as smoothing, interpolation, filtering, fitting, resolution improvement and the like on the original spectrum data.
The wavelet transform can decompose a chemical signal into a plurality of scale components according to different frequencies, and sampling step sizes with corresponding thicknesses are adopted for the scale components with different sizes, so that any part in the signal can be focused.
According to the invention, it is preferred when the corrected standard error (RMSECV) obtained by the method of pre-processing is smaller, and/or when the coefficient of determination (R) obtained by the method of pre-processing is smaller2) Closer to 100%, and/or, when the preprocessed approach yields a larger Ratio (RPD) of the validation set standard deviation to the prediction set standard deviation, the preprocessed approach can further reduce analysis errors.
According to a preferred embodiment of the present invention, the spectral data set is obtained by preprocessing the obtained spectrogram by using any one of software of OPUS, OMNIC and tdscolle. And performing regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation by using MATLAB software and adopting a partial least square method to respectively obtain regression equations of the aging days, crystallinity and retrogradation and the spectrum data set.
According to the invention, preferably, the crystallinity is determined by a method selected from the group consisting of Differential Scanning Calorimetry (DSC), infrared spectroscopy (IR), Solid nuclear magnetic resonance (Solid State NMR), more preferably DSC.
The DSC method is indicative of differential scanning calorimetry, an absorption peak appears in the aged starch through DSC detection, and the larger the starch aging degree is, the larger the endothermic peak is.
According to the present invention, preferably, in order to further reduce the analysis error, the method for measuring the retrogradation is selected from the group consisting of an α -amylase method, X-ray diffraction, and an iodonium blue method of starch, and more preferably, the α -amylase method.
According to the invention, the starch may be a commonly used starch; preferably, the starch is selected from wheat starch and indica starch.
According to a particularly preferred embodiment of the invention, the retrograded starch is prepared in the following manner:
(1) preparation of a standard sample of aged starch: taking wheat starch and deionized water to prepare a starch solution with the concentration of 8-12g/ml, uniformly mixing, putting into a sealed sterilization pot, and carrying out sealed heating for 18-25 minutes by using steam at the temperature of 110-. And (3) quickly cooling the gelatinized sample to room temperature, sealing, and storing in a refrigerator at 3-5 ℃ for 0, 1, 3, 5, 7, 14, 21, 28 and 35 days. Taking out, freezing at-83 deg.C to-77 deg.C for 8-10 hr, drying, pulverizing, and sieving. Stored in a desiccator for later determination.
(2) The crystallinity of the aged starch standards was determined by DSC.
(3) And (3) measuring the retrogradation degree of the aged starch standard sample by adopting an alpha-amylase method.
A regression equation of the aging days, the crystallinity and the retrogradation degree and the spectrum data set is established by adopting the following method:
and (3) collecting the near infrared spectrum, the Raman spectrum or the terahertz spectrum of the aged starch standard sample, and preprocessing the spectrum by using at least one of vector normalization, first derivative, second derivative methods and multivariate scattering correction by using OPUS or OMNIC software to obtain a spectrum data set.
And performing regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation by using MATLAB software and a partial least square method, and selecting an equation with the minimum average error and the widest range from the obtained equations to respectively obtain a regression equation of the aging days, the crystallinity and the retrogradation and the near infrared spectrum data set thereof.
The aged starch was analyzed as follows:
and acquiring a spectrogram of the aged starch to be analyzed, preprocessing the spectrogram by adopting at least one of vector normalization, a first derivative, a second derivative and multivariate scattering correction, and respectively calculating the aging days, the crystallinity and the retrogradation of the aged starch to be analyzed according to the established regression equation.
The present invention will be described in detail below by way of examples.
In the following examples, the reagents used were purchased from public sources, and the equipment and apparatus used in the process were those commonly used in the art.
Preparation example 1
(1) Preparation of a standard sample of aged starch: adding 20g of wheat starch into 200mL of deionized water, uniformly mixing, putting into a sealed sterilization pot, and heating for 20 minutes in a sealed manner by using steam at 120 ℃ for gelatinization. The gelatinized sample is rapidly cooled to room temperature, sealed and stored in a refrigerator at 4 ℃ for 0, 1, 3, 5, 7, 14, 21, 28 and 35 days respectively. Taking out, freezing at-80 deg.C for 8 hr, drying, pulverizing, and sieving with 100 mesh sieve. Stored in a desiccator for later determination.
(2) The crystallinity of the aged starch standards was determined by DSC.
The specific determination method comprises the following steps: and spreading the sieved powder of the aged starch standard sample which is balanced overnight in the water environment in a groove of a sample table, and ensuring the surface to be smooth by using a glass slide. The test conditions were: the method adopts monochromatic Cu-K alpha rays with the wavelength of 0.1542nm, tube pressure of 40kV, tube flow of 40mA, a scanning area of 4-50 degrees, step width of 0.02 degree and Ni sheet filtering, wherein the scanning mode is continuous, and the repetition frequency is 1, so that the crystallinity is obtained.
(3) And (3) measuring the retrogradation degree of the aged starch standard sample by adopting an alpha-amylase method.
The specific determination method comprises the following steps: accurately weighing 25mg of sieved powder of an aged starch standard sample, adding 8mL of ultrapure water, shaking and uniformly mixing, then adding 2mL of 3.5u/mL alpha-amylase solution and 2mL of 0.1mol/L acetate buffer solution (pH value of 5.6), culturing for 10min at 37 ℃, adding 5mL of 4mol/L NaOH solution to stop enzyme reaction, adjusting the pH value of the solution to be neutral by using 4mol/L HCl, and finally fixing the volume to 100 mL. 10mL of the hydrolysate was taken out and 0.2% I was added25mL of-2% KI solution, diluting to 100mL again, standing for 20min, and measuring at 625nm with an ultraviolet-visible spectrophotometerAnd determining the light absorption value to obtain the degree of retrogradation.
Preparation example 2
Analysis method for illustrating retrograded starch provided by the present invention
(1) Preparation of a standard sample of aged starch: 5g of wheat starch is added into 200mL of deionized water, and the mixture is sealed and heated for 5 minutes at 60 ℃ for gelatinization. The gelatinized sample is rapidly cooled to room temperature, sealed and stored in a refrigerator at 9 ℃ for 0, 3, 7, 21, 28 and 35 days respectively. Taking out, freezing at-50 deg.C for 4 hr, drying, pulverizing, and sieving with 100 mesh sieve. Stored in a desiccator for later determination.
(2) And (3) determining the crystallinity of the aged starch standard sample by adopting a solid nuclear magnetic resonance method.
(3) And (3) measuring the retrogradation degree of the aged starch standard sample by adopting an X-ray diffraction method.
Example 1
Analysis method for illustrating retrograded starch provided by the present invention
And (3) acquiring a near infrared spectrum of the aged starch standard sample of the preparation example 1, and preprocessing the near infrared spectrum by using an LAB (laboratory) module of OPUS (open plant system) software and adopting a vector normalization spectrum preprocessing method to obtain a spectrum data set, wherein the spectrum data set is preprocessed near infrared spectrum data.
Using MATLAB software, respectively carrying out regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation by adopting a partial least square method, selecting an equation with the minimum average error and the widest range from the obtained equations, respectively obtaining a regression equation of the aging days, crystallinity and retrogradation and the near infrared spectrum data set thereof, and determining a coefficient R of the regression equation2Is 0.9471.
Example 2
And (3) collecting the Raman spectrum of the aged starch standard sample of the preparation example 1, and preprocessing the Raman spectrum by using OMNIC software and a first derivative spectrum preprocessing method to obtain a spectrum data set.
Using MATLAB software and adopting partial least square method to respectively carry out retrogradation on the spectrum data set and the corresponding aging days, crystallinity and retrogradationFitting, selecting the equation with the minimum average error and the widest range from the obtained equations, respectively obtaining a regression equation of the aging days, the crystallinity and the retrogradation and the Raman spectrum data set thereof, and determining a coefficient R2Is 0.9314.
Example 3
Collecting a terahertz spectrum of the aged starch standard sample of preparation example 1, and preprocessing the terahertz spectrum by using a projection module of TdsConsole software and a multivariate scattering correction spectrum preprocessing method to obtain a spectrum data set.
Using MATLAB software, respectively performing regression fitting on the spectrum data sets corresponding to the refractive index spectrum and the absorption coefficient spectrum in the terahertz spectrum and the aging days, the crystallinity and the retrogradation degree corresponding to the spectrum data sets by adopting a partial least square method, selecting the equation with the minimum average error and the widest range from the obtained equations, respectively obtaining the regression equations of the aging days, the crystallinity and the retrogradation degree and the terahertz spectrum data sets thereof, and determining a coefficient R2Is 0.9417.
Example 4
The near infrared spectra of the retrograded starch standard samples of preparation example 2 and regression fitting of the spectral data sets and their corresponding retrogradation days, crystallinity and retrogradation degrees were collected according to the method of example 1 to obtain regression equations of retrogradation days, crystallinity and retrogradation degrees and its near infrared spectral data sets, respectively, and their coefficient of determination R2Is 0.9139.
Test example 1
To examine the prediction accuracy of the regression equations obtained in examples 1 to 4
Respectively preparing aged wheat starch and aged long-shaped rice starch with the concentration of 10g/ml and the low-temperature aging treatment time of 0-35 days (one sample is taken every 1 day), respectively collecting near-infrared spectrograms of the aged wheat starch and the aged long-shaped rice starch with the low-temperature aging treatment time of 0, 1, 2, 3, 5, 7, 12, 14 and 21 days and the aged long-shaped rice starch with the low-temperature aging treatment time of 0, 1, 3, 5, 7, 10, 12, 14, 21, 28 and 35 days by adopting the method of preparation example 1, and determining the real crystallinity and retrogradation of the aged long-shaped rice starch, wherein the low-temperature aging treatment time is the real aging days. The predicted days to age, crystallinity and retrogradation were calculated separately according to the regression equation established in example 1. The results of the regression equations between the actual days of aging, crystallinity and retrogradation and the predicted days of aging, crystallinity and retrogradation, respectively, are shown in fig. 1, fig. 2, fig. 3, fig. 4, fig. 5 and fig. 6, respectively.
Respectively preparing aged wheat starch and aged long-grained nonglutinous rice starch with the concentration of 10g/ml and the low-temperature aging treatment time of 0-35 days (one sample is taken every 1 day), respectively collecting aged wheat starch and aged long-grained nonglutinous rice starch with the low-temperature aging treatment time of 0, 1, 2, 3, 5, 7, 10, 12, 14, 21, 28 and 35 days and measuring the true crystallinity and retrogradation of the aged long-grained nonglutinous rice starch, wherein the low-temperature aging treatment time is the true number of days. The predicted days to age, crystallinity and retrogradation were calculated separately according to the regression equation established in example 2. The results of the regression equations between the actual number of days aged, crystallinity and retrogradation and the predicted number of days aged, crystallinity and retrogradation, respectively, are shown in fig. 7, fig. 8, fig. 9, fig. 10, fig. 11 and fig. 12, respectively.
Respectively preparing aged wheat starch and aged long-shaped rice starch with the low-temperature aging treatment time of 0-35 days (one sample is taken every 1 day) and the concentration of 10g/ml, respectively collecting the aged wheat starch and the aged long-shaped rice starch with the low-temperature aging treatment time of 0, 1, 2, 3, 5, 7, 12, 14, 21 and 35 days and the terahertz spectrogram of the aged long-shaped rice starch with the low-temperature aging treatment time of 0, 1, 2, 3, 5, 7, 10, 12, 14, 21, 28 and 35 days and measuring the crystallinity and the retrogradation of the aged long-shaped rice starch by adopting the method of preparation example 1, wherein the low-temperature aging treatment time is the real aging days. The predicted days to age, crystallinity and retrogradation were calculated separately according to the regression equation established in example 3. The results of the regression equations between the actual days of aging, crystallinity and retrogradation and the predicted days of aging, crystallinity and retrogradation, respectively, are shown in fig. 13, fig. 14, fig. 15, fig. 16, fig. 17 and fig. 18, respectively.
The low-temperature aging treatment time is 0-35 days (one sample is taken every 1 day) and the concentration is 10g/ml) The aged wheat starch and aged long-shaped rice starch are obtained by collecting a near-infrared spectrogram and determining the real crystallinity and retrogradation by adopting the method of preparation example 2, and the low-temperature aging treatment time is the real aging days. The predicted days to age, crystallinity and retrogradation were calculated separately according to the regression equation established in example 4. Results R of regression equations between the true age days, crystallinity and retrogradation and the predicted age days, crystallinity and retrogradation, respectively2Are all between 0.82 and 0.87.
R of regression equation of aging days, crystallinity and retrogradation with its near infrared spectrum data set obtained by examples 1-42All are more than 90 percent, which shows that the fitting effect of the regression equation of the aging days, the crystallinity and the retrogradation degree obtained by the scheme provided by the invention and the near infrared spectrum data set is good, wherein, the aging days, the crystallinity and the retrogradation degree obtained by the examples 1 to 3 and the R of the regression equation of the near infrared spectrum data set are good2Closer to 100%, indicating better fitting of the regression equation obtained by the method of examples 1-3.
As can be seen from FIGS. 1 to 18, by using the method for analyzing aged starch provided by the present invention, R of the regression equation of the relationship between the actual value and the predicted value is established2The number of aging days, the crystallinity and the retrogradation degree predicted by the method are larger than 90%, and errors between the actual values and the number of aging days, the crystallinity and the retrogradation degree predicted by the method are smaller, so that the regression equation of the aging days, the crystallinity and the retrogradation degree of the aged starch standard sample and the near infrared spectrum data set of the aged starch standard sample established by the method has higher reliability. Therefore, by adopting the method provided by the invention, the starch does not need to be pretreated, the nondestructive detection of the starch can be realized, the cost is reduced, the analysis error is smaller, the simultaneous determination of a plurality of physical and chemical indexes can be realized on the premise of not damaging the sample, the detection time can be reduced, and the time cost can be reduced.
The preferred embodiments of the present invention have been described above in detail, but the present invention is not limited thereto. Within the scope of the technical idea of the invention, many simple modifications can be made to the technical solution of the invention, including combinations of various technical features in any other suitable way, and these simple modifications and combinations should also be regarded as the disclosure of the invention, and all fall within the scope of the invention.
Claims (10)
1. A method for analyzing retrograded starch, the method comprising:
respectively collecting spectrograms of a plurality of aged starch standard samples by using a spectroscopic technology to further obtain a spectroscopic data set, wherein the aging treatment time of each aged starch standard sample is different;
respectively obtaining the aging days, the crystallinity and the retrogradation of the plurality of aged starch standard samples;
performing regression fitting on the spectrum data set and the corresponding aging days, crystallinity and retrogradation degree by adopting a partial least square method to respectively obtain regression equations of the aging days, the crystallinity and the retrogradation degree and the spectrum data set;
collecting a spectrogram of the aged starch to be analyzed so as to obtain a spectral data set of the aged starch to be analyzed, and respectively calculating the aging days, the crystallinity and the retrogradation of the aged starch to be analyzed according to the established regression equation;
wherein the spectroscopic technique is selected from the group consisting of near infrared spectroscopy, Raman spectroscopy and terahertz spectroscopy.
2. The method of claim 1, wherein the retrograded starch standard is prepared by: preparing starch into a starch solution, heating and gelatinizing the starch solution, and then sequentially performing aging treatment, freezing and drying;
wherein the aging treatment is low-temperature aging treatment;
wherein the low temperature is 0-8 ℃.
3. The method of claim 2, wherein the temperature of the heating to gelatinize is 80-140 ℃.
4. The method according to claim 2 or 3, wherein the time of the low-temperature aging treatment is 0 to 50 days.
5. The method of any one of claims 2-4, wherein the freezing temperature is from-120 to-60 ℃;
preferably, the freezing time is 5-35 h.
6. The method of any of claims 1-5, wherein the method further comprises: preprocessing a spectrogram of an aged starch standard sample and an aged starch to be analyzed to obtain a corresponding spectral data set;
the method of pretreatment is selected from: one or more of multivariate scatter correction, vector normalization, first derivative, second derivative, detrending, smoothing algorithm, Fourier transform, and wavelet transform.
7. The method according to any one of claims 1 to 6, wherein the crystallinity is determined by a method selected from Differential Scanning Calorimetry (DSC), infrared spectroscopy (IR), Solid nuclear magnetic resonance (Solid state NMR);
preferably, the method for measuring the crystallinity is DSC.
8. The method according to any one of claims 1 to 7, wherein the retrogradation is determined by a method selected from the group consisting of an alpha-amylase method, an iodonium blue method of starch, and X-ray diffraction.
9. The method according to any one of claims 1 to 7, wherein the retrogradation is measured by an alpha-amylase method.
10. The method according to any one of claims 1 to 9, wherein the starch is selected from wheat starch and indica starch.
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