CN113075197A - Method for rapidly detecting adulteration content of corn flour in white pepper powder - Google Patents
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Abstract
The invention relates to the technical field of quantitative detection and analysis of adulteration in white pepper powder, in particular to a rapid detection method of corn flour adulteration content in white pepper powder. The method comprises the following steps: preparing white pepper powder with different corn flour adulteration ratios; collecting a Raman spectrum of the white pepper powder sample; dividing a white pepper powder sample into a modeling set and a prediction set by a random diversity method, dividing the modeling set into a training set and a verification set by one-out-of-one cross validation, establishing a partial least squares regression model, and testing the quantitative effect of the model by using the prediction set sample; establishing a model by using a smooth + SNV spectrum preprocessing method; and (3) acquiring a Raman spectrum of the white pepper sample with unknown adulteration content, and substituting the Raman spectrum into the model to obtain a detection result of the adulteration content of the corn flour in the white pepper sample. The invention adopts the portable Raman spectrum to carry out rapid quantitative analysis on the adulteration content of the corn flour in the white pepper powder; the method has the advantages of no complex pretreatment, no chemical reagent pollution in the detection process, environmental protection and low detection cost.
Description
Technical Field
The invention relates to the technical field of quantitative detection and analysis of adulteration in white pepper powder, in particular to a rapid detection method of corn flour adulteration content in white pepper powder.
Background
The white pepper powder is one of the most popular seasonings with the largest market share in the world, and has very wide market prospect. Driven by economic benefits, the problem of adulteration of white pepper is also very serious. The corn flour has a smaller particle size, is similar to white pepper in shape and color, is difficult to distinguish from sense, and has lower value, and is the most common white pepper adulterant.
The traditional pepper adulteration identification usually takes the piperine content as a standard, and the detection method of the piperine content mainly adopts traditional analysis methods such as an ultraviolet spectrophotometry method, a high performance liquid chromatography method and the like. At present, the national standard related to piperine content detection in China is high performance liquid chromatography, the pretreatment of the method needs condensation reflux, the time consumption is long, chemical reagents (methanol, ethanol and the like) are needed, and chromatographic instruments and equipment are expensive, the operation is complex, and the detection cost and the detection efficiency are low. In terms of the huge transaction amount of white pepper, the pepper market also urgently develops a quick, nondestructive white pepper adulteration detection method which does not need to consume a large amount of chemical products and has low cost, and the spectral analysis technology is combined with a chemometrics tool to just meet the requirements. The Raman spectrum technology has the advantages of no need of sample preparation, no damage, rich obtained information, high sensitivity and high selectivity, and is widely applied to the field of food science. In addition, the portable Raman spectrometer further improves the convenience and timeliness of detection on the basis of the original Raman spectrometer. The corn flour containing common adulterant substances in the white pepper is quickly detected, so that the quick and effective quality control of the pepper market is facilitated, and the rights of consumers are maintained.
Disclosure of Invention
The invention aims to provide a method for rapidly detecting the adulteration content of corn flour in white pepper powder, which adopts a portable Raman spectrometer to rapidly and quantitatively analyze the adulteration of the corn flour in the white pepper powder; and the method has the advantages of no complex pretreatment, no chemical reagent pollution in the detection process, environmental protection and low detection cost.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for rapidly detecting the adulteration content of corn flour in white pepper powder comprises the following steps:
s1, preparing white pepper powder samples with different adulteration contents of corn flour, and collecting the Raman spectrum of the white pepper powder samples;
s2, randomly dividing the white pepper powder sample into a modeling set and a prediction set by a random diversity method, selecting a training set and a verification set to build a linear partial least square regression model by reserving a cross verification method in the modeling set, firstly, optimizing correction algorithm parameters by reserving a cross verification root-mean-square error, then verifying a correction model by the verification set root-mean-square error, and finally, testing the quantitative effect of the model by using the prediction set sample; in the modeling, the training fitting degree between the adulteration content of the corn flour and the spectral information is established by using linear partial least square regression, and the level of the fitting degree is improved by using a smoothing + SNV spectral preprocessing method to obtain a partial least square regression quantitative analysis model;
s3, acquiring a Raman spectrum of the white pepper powder sample with unknown adulteration content;
and S4, inputting the Raman spectrum of the white pepper sample with unknown adulteration content into the model in S3, and outputting the detection result of the adulteration content of the corn flour in the white pepper sample.
Furthermore, in S1, the adulteration content of the corn flour is 1-70%.
Further, in S1, the instrument for collecting the raman spectrum is a portable raman spectrometer.
Further, the wave number range of the Raman spectrum is 220-3200cm-1。
Further, in S2, the partial least squares regression is calculated by using the following formula:
U＝TB
B＝(TTT)-1TTY
Yis unknown＝TIs unknownBQ
Wherein: x and Y are a spectrum matrix and a concentration matrix, tk(n × 1) is the score of the k-th main factor of the absorbance matrix X; p is a radical ofk(1 × m) is the load of the k-th main factor of the absorbance matrix; u. ofk(n × 1) is the score of the kth main factor of the concentration matrix Y; q. q.sk(1 × p) is the load of the kth main factor of the concentration array Y; f is the number of major factors, i.e.: t and U are the scoring matrices of the X and Y matrices, respectively, P and Q are the loading matrices of the X and Y matrices, respectively, EXAnd EYThe residual matrices are fitted to the PLS for X and Y, respectively.
Further, in S2, the following formula is used to calculate the root mean square error:
in the formula yiIs a reference value of the ith sample in g/100 g;
the predicted value of the ith sample is in g/100 g; n is the number of samples.
Further, the model was pre-processed by smoothing the spectrum of + SNV: concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors 7; spectral range 220-3200cm-1(ii) a Correction set R20.9868, the correlation coefficient Rc is 0.9868, and the corrected root mean square error RMSEC 2.3198%; verification set R20.8775, the correlation coefficient Rcv is 0.8742, and the cross validation root mean square error RMSECV 7.1758%; prediction set R20.8760, the correlation coefficient Rp is 0.8874, and the prediction set root mean square error RMSEP 7.4244%.
Compared with the prior art, the invention has the following beneficial effects:
1. and the detection convenience and timeliness are further improved by adopting a portable Raman spectrum instrument.
2. A quantitative analysis model is established for corn flour adulteration in the white pepper powder by adopting Raman spectroscopy, so that the corn flour adulteration phenomenon in the white pepper powder can be quickly and quantitatively analyzed;
3. the PLS regression model established through smoothing + SNV spectrum preprocessing has the best effect, and can carry out quantitative analysis on the adulteration content of the corn flour in the white pepper. The correlation coefficient (Rc) of a correction set of the model reaches 0.9868, and the corrected Root Mean Square Error (RMSEC) is 2.3198%; the correlation coefficient (Rp) of the prediction set reaches 0.8874, the Root Mean Square Error (RMSEP) of the prediction set is 7.4244%, and the requirement of rapidly detecting corn flour adulteration in white pepper in practical application can be met.
4. The method has the advantages of no complex pretreatment, very quick detection process, no chemical reagent pollution, environmental protection and low detection cost.
Drawings
FIG. 1 is an initial Raman spectrum of a sample spiked with white pepper;
FIG. 2 is a regression curve of a sample without spectral pretreatment blended with white pepper; the circle is a training set, and the triangle is a verification set;
FIG. 3 is a regression curve of the original spectrum of a sample blended with white pepper after smoothing pretreatment; the circle is a training set, and the triangle is a verification set;
FIG. 4 is a regression curve of the original spectrum of a sample doped with white pepper after first-order derivation pretreatment; the circle is a training set, and the triangle is a verification set;
FIG. 5 is a regression curve of the SNV pre-treated raw spectrum of a sample blended with white pepper; the circle is a training set, and the triangle is a verification set;
FIG. 6 is a regression curve of the original spectrum of a sample doped with white pepper after smoothing and first-order derivation pretreatment; the circle is a training set, and the triangle is a verification set;
FIG. 7 is a regression curve of the original spectrum of a sample blended with white pepper after smoothing + SNV pre-treatment; the circle is a training set, and the triangle is a verification set;
FIG. 8 is a regression curve of the original spectrum of a sample doped with white pepper after first order derivation + SNV pre-treatment; the circle is a training set, and the triangle is a verification set;
FIG. 9 is a regression curve of the original spectrum of a sample doped with white pepper after smoothing + first derivative + SNV pre-treatment; the circle is a training set, and the triangle is a verification set;
fig. 10 is a prediction of the regression curve of the spiked white pepper samples after smoothing + SNV pre-treatment based on the original spectrum.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments, but the invention should not be construed as being limited thereto. The technical means used in the following examples are conventional means well known to those skilled in the art, and materials, reagents and the like used in the following examples can be commercially available unless otherwise specified.
The following examples used the following instruments and reagents:
analytical balance: mettler Toledo (Sweden), 0.1 mg;
oven: binder (germany);
portable raman spectrometer: BWTEK-iRAMAN (u.s.);
MATLAB 7.10Version(Mathworks,U.S.)。
example 1
The invention provides a method for rapidly detecting the adulteration content of corn flour in white pepper powder, which comprises the following steps:
(1) collecting white pepper grain samples and pure corn flour, putting the dried white pepper grain samples into a high-speed pulverizer to prepare pure white pepper powder, and respectively sieving the pure white corn flour and the pure corn flour with a 40-mesh sieve;
(2) mixing the two powders to prepare a white pepper powder sample with corn flour content of 1% -70%, and subpackaging the white pepper powder sample into transparent sample bags for spectrum scanning;
(3) the portable Raman spectrometer is used for directly carrying out spectrum scanning on the transparent sample bag, and the wave number range is 220-3200cm-1Each sample ofCarrying out parallel scanning twice to obtain an average spectrum for modeling analysis;
(4) modeling: a modeling set is randomly selected from all samples, and the rest of the samples are used as prediction sets. And in the modeling set, selecting a training set and a verification set by a leave-one-out cross-validation method to establish a PLS model. First, the correction algorithm parameters are optimized by leaving a cross validation Root Mean Square Error (RMSECV), then the correction model is validated using the validation set, i.e., by the validation set Root Mean Square Error (RMSEV), and finally the model quantification effect is examined using the prediction set samples (prediction set root mean square error RMSEP). The modeling uses linear partial least squares regression (partial least squares regression) to establish the training fitting degree between the corn flour adulteration content and the spectrum information, then the level of the fitting degree is improved through different spectrum preprocessing methods, and finally the quantitative analysis of the corn flour adulteration content in the unknown sample is realized.
The partial least squares regression is calculated using the following formula:
U＝TB
B＝(TTT)-1TTY
Yis unknown＝TIs unknownBQ
Wherein: x and Y are a spectrum matrix and a concentration matrix, tk(n × 1) is the score of the k-th main factor of the absorbance matrix X; p is a radical ofk(1 × m) is the load of the k-th main factor of the absorbance matrix; u. ofk(n × 1) is the score of the kth main factor of the concentration matrix Y; q. q.sk(1 × p) is the load of the kth main factor of the concentration array Y; f is the number of major factors. Namely: t and U are the scoring matrices of the X and Y matrices, respectively, P and Q are the loading matrices of the X and Y matrices, respectively, EXAnd EYPLS fitting residues X and Y, respectivelyA difference matrix.
The calculated deviation results in the modeling process are evaluated and optimized by the following formula:
in the formula yiIs a reference value of the ith sample in g/100 g;
the predicted value of the ith sample is in g/100 g; n is the number of samples.
96 mixed white pepper samples are researched, and a Raman spectrum model of the white pepper mixed with corn flour is established. The original raman spectrum of the spiked white pepper powder sample was obtained after spectral scanning, as shown in fig. 1.
After the spectrum preprocessing methods such as smoothing, first derivative and SNV are adopted, a partial least squares regression method is adopted to obtain a model result, and the PLS model result obtained after the spectrum data is preprocessed by smoothing, first derivative, SNV, smoothing + first derivative, smoothing + SNV, first derivative + SNV, smoothing + first derivative + SNV is as follows:
model without preprocessing
Concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors 7; spectral range 220-3200cm-1(ii) a Correction set R20.9941, correlation coefficient Rc of 0.9941 and corrected root mean square error RMSEC of 1.5499 percent; verification set R20.8762, the correlation coefficient Rcv is 0.8725, and the cross validation root mean square error RMSECV 7.2137%; prediction set R20.8363, the correlation coefficient Rp is 0.8538, and the prediction set root mean square error RMSEP 8.5311%. As shown in fig. 2.
Smoothed model
The method adopts Savitzky-Golay (SG) convolution smoothing method to preprocess spectral data, SG smoothing mainly carries out least square fitting on data in a moving window through polynomial, and essentially is a weighted average method which emphasizes the central action of a central point. The average value after smoothing at wavelength k is:
in the formula, hiFor the smoothing coefficient, H is a normalization factor,
each measurement is multiplied by a smoothing factor hiThe aim is to reduce as much as possible the effect of smoothing on the useful information.
The results after treatment were as follows:
concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors 7; spectral range 220-3200cm-1(ii) a Correction set R20.9760, the correlation coefficient Rc is 0.9760, and the corrected root mean square error RMSEC 3.1285%; verification set R20.8725, the correlation coefficient Rcv is 0.8690, and the cross validation root mean square error RMSECV 7.3192%; prediction set R20.8535, the correlation coefficient Rp is 0.8728, and the prediction set root mean square error RMSEP 8.0702%. As shown in fig. 3.
Model after first derivative processing
The method adopts a Savitzky-Golay (SG) derivation method to preprocess spectral data, and obtains a derivative coefficient similar to a smoothing coefficient through least square calculation. The results after treatment were as follows:
concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors is 2; spectral range 220-3200cm-1(ii) a Correction set R20.9563, the correlation coefficient Rc is 0.9563, and the corrected root mean square error RMSEC 4.2217%; verification set R20.8698, the correlation coefficient Rcv is 0.8775, and the cross validation root mean square error RMSECV 7.3968%; prediction set R20.8480, the correlation coefficient Rp is 0.8549, and the prediction set root mean square error RMSEP 8.2223%. As shown in fig. 4.
SNV-treated models
The method adopts Standard Normal Variate transformation (SNV) to mainly eliminate the influence of the solid particle size, surface scattering and optical path change of the sample on the diffuse reflection spectrum. The SNV algorithm normalizes the spectra based on the rows of the spectral array.
Wherein the content of the first and second substances,
m is the number of wavelength points, k is 1,2, …, m.
The results after treatment were as follows:
concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; number of major factors 6; spectral range 220-3200cm-1(ii) a Correction set R20.9878, the correlation coefficient Rc is 0.9878, and the corrected root mean square error RMSEC 2.2299%; verification set R20.8649, the correlation coefficient Rcv is 0.8621, and the cross validation root mean square error RMSECV 7.5356%; prediction set R20.8479, the correlation coefficient Rp is 0.8626, and the prediction set root mean square error RMSEP 8.2235%. As shown in fig. 5.
Model after smoothing + first derivative processing
Concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors is 2; spectral range 220-3200cm-1(ii) a Correction set R20.9159, the correlation coefficient Rc is 0.9159, and the corrected root mean square error RMSEC 5.8602%; verification set R20.8604, the correlation coefficient Rcv is 0.8573, and the cross validation root mean square error RMSECV 7.6600%; prediction set R20.8001, the correlation coefficient Rp is 0.8249, and the prediction set root mean square error RMSEP 9.4293%. As shown in fig. 6.
Smoothed + SNV processed model
Concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors 7; spectral range 220-3200cm-1(ii) a Correction set R20.9868, the correlation coefficient Rc is 0.9868, and the corrected root mean square error RMSEC 2.3198%; verification set R20.8775, the correlation coefficient Rcv is 0.8742, and the cross validation root mean square error RMSECV 7.1758%; prediction set R20.8760, the correlation coefficient Rp is 0.8874, and the prediction set root mean square error RMSEP 7.4244%. As shown in fig. 7.
First derivative + SNV-processed model
Concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors is 2; spectral range 220-3200cm-1(ii) a Correction set R20.9890, the correlation coefficient Rc is 0.9890, and the corrected root mean square error RMSEC 2.1168%; verification set R20.8307, the correlation coefficient Rcv is 0.8877, and the cross validation root mean square error RMSECV 8.4348%; prediction set R20.7658, the correlation coefficient Rp is 0.8721, and the prediction set root mean square error RMSEP 10.2050%. As shown in fig. 8.
Model after smoothing + first derivative + SNV processing
Concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors is 2; spectral range 220-3200cm-1(ii) a Correction set R20.9702, the correlation coefficient Rc is 0.9702, and the corrected root mean square error RMSEC 3.4855%; verification set R20.8815, the correlation coefficient Rcv is 0.9017, and the cross validation root mean square error RMSECV 7.0575%; prediction set R20.8269, the correlation coefficient Rp is 0.8479, and the prediction set root mean square error RMSEP 8.7731%. As shown in fig. 9.
Application example 1
Accuracy verification of PLS regression model established after smoothing and SNV pretreatment of original spectrum
The white pepper grain sample and the pure corn flour were blended, the raman spectra of the samples were collected and input to the smoothed + SNV treated model, and the predicted results (shown in fig. 10) were compared with the actual adulteration values, as shown in table 1.
TABLE 1 results of quantitative estimation of corn flour adulteration in prediction set samples (part)
In conclusion, the method is feasible for establishing a quantitative analysis model for corn flour adulteration in the white pepper powder by adopting the portable Raman spectrum. And (3) establishing a Raman spectrum model for corn flour adulteration in the white pepper powder by utilizing a linear regression partial least square method, comparing the influence of different spectrum pretreatment methods on the model effect, and finally selecting an optimal PLS quantitative analysis model. Experimental results show that the PLS regression model established after the original spectrum is subjected to smoothing and SNV pretreatment has the best quantitative analysis effect on corn flour adulteration in white pepper. The method has unique advantages as a rapid, nondestructive and green detection method, and the combination of spectral analysis and chemometrics tools has great potential in the aspect of pepper adulteration detection.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A method for rapidly detecting the adulteration content of corn flour in white pepper powder is characterized by comprising the following steps:
s1, preparing white pepper powder samples with different adulteration contents of corn flour, and collecting the Raman spectrum of the white pepper powder samples;
s2, randomly dividing the white pepper powder sample into a modeling set and a prediction set by a random diversity method, selecting a training set and a verification set to build a linear partial least square regression model by reserving a cross verification method in the modeling set, firstly, optimizing correction algorithm parameters by reserving a cross verification root-mean-square error, then verifying a correction model by the verification set root-mean-square error, and finally, testing the quantitative effect of the model by using the prediction set sample; in the modeling, the training fitting degree between the adulteration content of the corn flour and the spectral information is established by using linear partial least square regression, and the level of the fitting degree is improved by using a smoothing + SNV spectral preprocessing method to obtain a partial least square regression quantitative analysis model;
s3, acquiring a Raman spectrum of the white pepper powder sample with unknown adulteration content;
and S4, inputting the Raman spectrum of the white pepper sample with unknown adulteration content into the model in S3, and outputting the detection result of the adulteration content of the corn flour in the white pepper sample.
2. The method for rapidly detecting the adulteration content of the corn flour in the white pepper powder as claimed in claim 1, wherein in S1, the mass fraction of the adulteration content of the corn flour is 1% -70%.
3. The method for rapidly detecting the adulteration content of the corn flour in the white pepper powder as claimed in claim 1, wherein in the step S1, the instrument for collecting the Raman spectrum is a portable Raman spectrometer.
4. The method as claimed in claim 1, wherein the Raman spectrum has a wavenumber range of 220--1。
5. The method for rapidly detecting the adulteration content of the corn flour in the white pepper powder as claimed in claim 1, wherein in the step S2, partial least squares regression is calculated by using the following formula:
U＝TB
B＝(TTT)-1TTY
Yis unknown＝TIs unknownBQ
Wherein: x and Y are a spectrum matrix and a concentration matrix, tkA score of the k-th main factor of the absorbance matrix X; p is a radical ofkThe load being the kth main factor of the absorbance matrix; u. ofkScore for the kth main factor of concentration matrix Y; q. q.skThe load of the kth main factor of the concentration array Y; f is the number of major factors, i.e.: t and U are the scoring matrices of the X and Y matrices, respectively, P and Q are the loading matrices of the X and Y matrices, respectively, EXAnd EYThe residual matrices are fitted to the PLS for X and Y, respectively.
6. The method for rapidly detecting the adulteration content of the corn flour in the white pepper powder as claimed in claim 1, wherein in the step S2, the following formula is adopted for calculating the root mean square error:
in the formula yiIs a reference value of the ith sample in g/100 g;
the predicted value of the ith sample is in g/100 g; n is the number of samples.
7. The method for rapidly detecting the adulteration content of corn flour in white pepper as claimed in claim 1, wherein the model is preprocessed by smoothing + SNV spectrum: concentration range: 1-70g/100 g; corrected spectrum 70, predicted spectrum 26; the number of major factors 7; spectral range 220-3200cm-1(ii) a Correction set R20.9868, the correlation coefficient Rc is 0.9868, and the corrected root mean square error RMSEC 2.3198%; verification set R20.8775, Rcv 0.8742, RMSECV 7.1758%(ii) a Prediction set R20.8760, the correlation coefficient Rp is 0.8874, and the prediction set root mean square error RMSEP 7.4244%.
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