CN116165160A - Method for discriminating high-temperature Daqu by infrared spectrum technology - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 48
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- IOLCXVTUBQKXJR-UHFFFAOYSA-M potassium bromide Chemical compound [K+].[Br-] IOLCXVTUBQKXJR-UHFFFAOYSA-M 0.000 claims abstract description 8
- 238000012569 chemometric method Methods 0.000 claims abstract description 7
- 238000004566 IR spectroscopy Methods 0.000 claims abstract description 3
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- 235000013305 food Nutrition 0.000 abstract description 2
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- 239000000796 flavoring agent Substances 0.000 description 9
- 238000010239 partial least squares discriminant analysis Methods 0.000 description 9
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- 238000011156 evaluation Methods 0.000 description 3
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- 238000001228 spectrum Methods 0.000 description 3
- 240000007594 Oryza sativa Species 0.000 description 2
- 235000007164 Oryza sativa Nutrition 0.000 description 2
- 241000209140 Triticum Species 0.000 description 2
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- 238000000227 grinding Methods 0.000 description 2
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- 238000007605 air drying Methods 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
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Abstract
The application discloses a method for distinguishing high-temperature Daqu by using infrared spectrum technology in the technical field of food detection, which comprises the following steps: 1) Collecting a high-temperature Daqu typical sample, and collecting infrared spectrum data of the sample; 2) Establishing a class analysis model by combining infrared spectroscopy technology with a chemometric method, wherein the chemometric method comprises any one of partial least square method, partial least square discriminant analysis method or orthogonal partial least square discriminant analysis method; 3) And (3) collecting infrared spectrum data of the sample to be detected, and taking the infrared spectrum data into the model in the step (2) to judge the Daqu type of the sample to be detected. The Daqu category discrimination method provided by the application is quick in analysis, simple and convenient to operate, the detection time is about 10-15min, only potassium bromide is needed, other reagents are not needed, the detection process is safe, and the discrimination accuracy can reach more than 90%.
Description
Technical Field
The invention relates to the technical field of food detection, in particular to a method for distinguishing high-temperature Daqu by using an infrared spectrum technology.
Background
The Maotai-flavor white spirit is the main flavor white spirit in China, has the characteristics of mellow wine body, prominent Maotai-flavor, lasting fragrance of empty cups, long aftertaste and the like, and has unique flavor which is closely related to the special brewing process which fills in ancient charm. The soy sauce-flavor white spirit adopts high-temperature Daqu as saccharification starter, the raw material of the high-temperature Daqu is wheat, the wheat is crushed, water and yeast mother are added to step into a yeast embryo, the microorganism is grown and propagated in indoor heat preservation culture to generate each kind of flavor component required by brewing, and the high-temperature Daqu is obtained after air drying and storage. The spatial difference of environmental factors in the starter propagation process leads to the formation of different characteristic starter propagation: in the stacking process, local environments (such as temperature, moisture, oxygen concentration and the like) of different spatial positions (such as stacking positions in a yeast room and stacking inner layers and outer layers) are different, so that finished yeast with different characteristics is formed, and indexes such as microorganisms, amino acids, flavor substances, saccharification force, liquefaction force, esterification force and the like of black yeast, white yeast and Huang Qusan Daqu are different, and the quality and proportion of the three yeast are important influences on wine production through storage, mixing and grinding.
The black yeast sauce has outstanding fragrance and burnt fragrance, but low saccharification power; white yeast has higher saccharification power but insufficient sauce flavor; the indexes of yellow rice are more suitable. In the production, the black yeast is small in dosage, and the wine has pleasant burnt fragrance; if the dosage is large, the bitter taste of the finished wine paste is heavy, so that the result of Jiao Gaijiang is caused, and the style and quality of the wine are affected. The standard of the existing production requiring the delivering yeast is that yellow yeast is more than or equal to 80 percent, white yeast and black yeast are <20 percent, and white yeast is more than black yeast. In the current practical production, black-white yellow yeast for brewing is mainly prepared by a yeast with abundant experience by means of sensory (appearance, section, aroma and the like) to carry out quality grading proportion on the yeast blocks, but the method of sensory classification by the yeast is subjective, and the color of the yeast is gradually changed.
Based on this, related art, see chinese patent application No. 202010518502.9, discloses a method for identifying the quality of Daqu, which comprises determining the quality of Daqu according to the enthalpy value of Daqu, wherein the method comprises obtaining the enthalpy value of Daqu according to differential scanning calorimetric analysis, and determining the quality of Daqu according to the enthalpy value of Daqu. However, the above related art has the following drawbacks: when the differential scanning calorimeter is used for detecting the heat enthalpy value of the Daqu, the single sample detection time is long, the operation steps are complicated, and the requirement of rapid detection of a large number of samples in production is difficult to meet.
Disclosure of Invention
Aiming at the defects of the prior art, the invention designs a method capable of rapidly and accurately distinguishing large batches of yeast.
One of the purposes of the invention is to provide a method for distinguishing high-temperature Daqu by using an infrared spectrum technology, which comprises the following steps:
1) Collecting a high-temperature Daqu typical sample, and collecting infrared spectrum data of the sample;
2) Establishing a class analysis model by combining infrared spectroscopy technology with a chemometric method, wherein the chemometric method comprises any one of partial least square method, partial least square discriminant analysis method or orthogonal partial least square discriminant analysis method;
3) And (3) collecting infrared spectrum data of the sample to be detected, and taking the infrared spectrum data into the model in the step (2) to judge the Daqu type of the sample to be detected.
The working principle and the beneficial effects of the invention are as follows: 1. the method can rapidly judge the black-white yellow yeast type, further provide basis for the proportion and content of the yeast for production, and promote the original sensory judgment according to the data to control the white spirit production more scientifically and accurately. The Daqu category discrimination method provided by the application is quick in analysis, simple and convenient to operate, the detection time is about 10-15min, only potassium bromide is needed, other reagents are not needed, the detection process is safe, and the discrimination accuracy can reach more than 90%.
2. The implementation of the method ensures the accurate identification of the Daqu, the scientific proportioning and selection of the Daqu can obviously improve the production quality of the white spirit all the year round, and the complicated detection operation is avoided, so that the labor cost and the time cost can be saved; the method can be used as a general method for evaluating the quality of the Daqu, can be used for reference and application by other wine enterprises, and plays an demonstration and promotion role in the industry.
Further, in the typical sample of high temperature Daqu, black qu, white qu and yellow qu are not less than 10 in each category. Preferably, the total number of the samples is not less than 30, the more the total number of the samples is, the more the model is accurate, but the number of the samples is too large, the time and cost for establishing the model are increased, the samples are preferably less than 50 (only 31 in the embodiment of the application), and the final judging accuracy is as high as 90% after each step of modeling is optimized.
Further, step 1) pre-treating the sample: grinding Daqu sample with granularity of 15-200 meshes, and mixing small amount of sample with potassium bromide. Preferably 30-60 mesh; more preferably 60 mesh.
Further, during modeling in the step 2), the method specifically comprises the following steps:
a: preprocessing infrared spectrum data, finding out models of various types of phase aggregation, and applying the models to obtain a new data matrix;
b: assigning values to black yeast and Huang Quhe white yeast respectively, and setting the range of each type of determined interval;
c: c, taking absorbance or transmittance of each wave number on the data matrix obtained in the step a as an independent variable, taking each type of virtual value as a response value, establishing a PLS model, and examining R of the model under different data matrices 2 Selecting R with the largest value on the basis of being larger than the acceptable value 2 The model is a discrimination model of different types; if R is 2 When the values are lower than acceptable values, re-optimizing the model, and finally, R is calculated 2 The acceptable model is marked as M, and the model is a judging model of different Daqu types;
d: when the unknown sample is predicted, the sample is processed and detected according to the steps to obtain an infrared spectrogram, the infrared spectrogram is substituted into an established discrimination model, the value Y 'of the infrared spectrogram is predicted, the range of the interval where Y' falls is inspected, and the type of the infrared spectrogram is judged.
When preprocessing infrared spectrum data, a spectrogram processing mode can be adopted.
Further, when infrared spectrum data of a sample is collected, the scanning wave band is as follows: 4000-500 cm -1 Resolution of 1-16 cm -1 The number of scans was 8 to 64. Resolution is preferably 2cm -1 、4cm -1 、8cm -1m More preferably 4cm -1 The number of scans is preferably 16 or 32, more preferably 32.
Further, the mode of optimizing the model comprises spectrum processing or band selection, wherein the spectrum processing mode comprises one or a combination of a plurality of modes of smoothing (smoothen), derivative (derivative), normalization (Baseline), trending (Detrend), multiple scattering correction (multiplicative scatter correction, MSC), variable normalization (standard normalized variate, SNV) and the like. The smoothing (smoothing) may be Moving Average or SG smoothing; the derivative (Derivatives) may be a first derivative, a second derivative, a third derivative, a fourth derivative, preferably a first derivative or a second derivative; normalization may be vector normalization (Unit Vector Normalization), area normalization (Area Normalization), mean normalization (Mean Normalization), preferably vector normalization (Unit Vector Normalization). The preferred treatment is SG first order +snv.
Further, the optimal spectrogram processing mode is SG first-order+MSC.
Further, the band selection may be performed by correlation coefficient method, anova method, non-information variable elimination method (UVE), genetic Algorithm (GA), continuous projection algorithm (SPA), interval least square method (Interval PLS), or a combination thereof.
Further, R 2 Greater than 0.9.
Further, before modeling in the step 2), PCA processing is carried out on the optical data, and Hotelling's T is removed 2 Samples outside a certain confidence interval.
Chemometric methods include PLS-based methods such as Partial Least Squares (PLS), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (orthogonal partial least-squares discrimination analysis, OPLS-DA), and the like.
The modeling described above is a refinement of the PLS method.
PLS-DA or OPLS-DA method: and establishing an analysis model by taking infrared spectrum data as X and black yeast, white yeast and yellow yeast categories as Y. Investigation model R 2 Y and Q 2 ，R 2 Y reflects the fitting effect of the model, Q 2 Reflecting the prediction effect of the model, setting R 2 Y and Q 2 Is typically 0.9.
If the model does not reach the standard, the model is optimized by spectrogram processing and band selection, and the two aspects are consistent with the mode described in PLS.
Two matrices are involved in PLS-DA and OPLS-DA: the X matrix is a sample-variable observation matrix, and the Y matrix is a sample category attribution matrix. Modeling is performed by means of an X and Y matrix, i.e. a sample relationship is established by means of a sample-variable relationship. The OPLS-DA is corrected by orthogonal transformation based on the PLS-DA, noise irrelevant to classification information can be filtered, and the analysis capability and effectiveness of the model are improved.
PLS and PLS-DA, OPLS-DA methods are PLS-based, PLS is an artificial assignment to Y, and for some atypical samples, the setting of Y can be adjusted, while for predictions of the sample to be tested, a value is given, e.g. white curve=1, yellow curve=2, black curve=3, and then when the samples are predicted to be 1.8 and 2.2, both are judged to be yellow curve, but they can show the degree.
PLS-DA and OPLS-DA can better obtain inter-group difference information. PLS-DA is also a multidimensional vector analysis method based on dimension reduction, classification can be preset, and the method has the advantages that influence of uncontrolled variables on data analysis can be removed as much as possible, information in the data is further mined, and meanwhile the degree of component difference caused by characteristic compounds can be quantified.
Drawings
FIG. 1 is a technical roadmap of the invention;
FIG. 2 is a schematic illustration of modeling of the present invention;
FIG. 3 is a scatter plot of example 1;
FIG. 4 is a residual diagram of example 1;
FIG. 5 is a prediction diagram of example 1;
FIG. 6 is a scatter plot of example 2;
fig. 7 is a scatter diagram of example 3.
Detailed Description
The following is a further detailed description of the embodiments:
example 1:
1. sample collection: collecting 31 Maotai-flavor type yeast samples from Guizhou Jiu group Co., ltd and Guizhou Jiu Co., ltd, crushing Daqu, pulverizing with a pulverizer (FW-100 high-speed universal pulverizer, beijing Zhongxing Wei Jiu Co., ltd.), sieving with a 60-mesh sieve, and collecting 2g and keeping for use.
3. Spectral data inspection: hotelling's T is done for all spectral data 2 99% confidence intervals were checked and no outlier samples (sample points with very different spectra) were present.
4. Setting the category: y: white yeast=1, yellow yeast=2, black yeast=3, and spectral treatment (SG first-order+msc) is performed by taking absorbance as an independent variable, so as to establish a PLS model. Simultaneously setting the numerical range of each type: predicted value (- ≡1.5), white starter; predicted values [1.5, 2.5), as yellow rice; the predicted value of 2.5, ++ infinity a) of the above-mentioned components, is black yeast.
Fig. 3 is a scatter diagram, and it can be seen from the graph that black and white Huang Qusan has a relatively obvious clustering trend, PC1 (41%, 91%), a relatively good dimension reduction effect, and the category difference is mainly embodied on PC 1.
Fig. 4 is a residual diagram, and it can be seen from the diagram that the dimension reduction effect of the model is reflected, and PC1 and PC2 have been extracted to more than 90% of the information, so that the dimension reduction effect is better, and therefore the optimal principal component number is selected to be 2 (pc=2).
FIG. 5 is a predictive view, cal R 2 ＝0.9445，Val R 2 ＝0.9372，RMSEC＝0.1937，RMSECV＝0.2397。
5. Predicting unknown samples
Collecting 20 high-temperature Daqu samples, collecting infrared spectrograms of the samples to be tested according to an original experimental method, and carrying the infrared spectrograms into a judgment model for prediction. And please Qu Shi the sensory evaluation to judge the classification, the sensory classification is compared with the prediction classification, if the classification is consistent, the prediction is correct, and if the classification is inconsistent, the classification is wrong, and the accuracy of the process of the fermented soy sauce wine is up to 95% through statistics.
Note that: in this embodiment, the model building and spectrogram processing are all completed in The un-crambler (10.4) software, but The algorithm is not limited to this software, and several software such as MATLAB and TQ analysis can be implemented.
Example 2:
1. PLS-DA model was built in SIMCA-P software with the same samples, experiments and data as in example 1; spectral SG first-order+snv processing, principal component score a=4, sample size n=31, r 2 Y＝0.957，Q 2 ＝0.919。
From the scatter diagram of FIG. 6, the tendency of clustering of black and white and yellow can be seen, and meanwhile, R 2 Y and Q 2 All are larger than 0.9, which shows that the model fitting is better and the prediction capability is stronger.
2. And collecting 15 high-temperature Daqu samples, collecting infrared spectrograms of the samples to be tested according to an original experimental method, and carrying the infrared spectrograms into a judgment model for prediction. And please Qu Shi the sensory evaluation to judge the classification, the sensory classification is compared with the prediction classification, if the classification is consistent, the prediction is correct, and if the classification is inconsistent, the classification is wrong, and the accuracy of the process of the fermented soy sauce wine is 100% through statistics.
Example 3:
1. collecting Daqu samples of different types, and establishing an OPLS-DA model in accordance with the treatment and experimental mode of the embodiment 1; spectral SG first-order +snv treatment, n=31, r 2 Y＝0.957，Q 2 =0.918. From the scatter diagram of FIG. 7, the tendency of clustering of black and white and yellow can be seen, and meanwhile, R 2 Y and Q 2 All are larger than 0.9, which shows that the model fitting is better and the prediction capability is stronger.
2. Collecting 12 high-temperature Daqu samples, collecting infrared spectrograms of the samples to be tested according to an original experimental method, and carrying the infrared spectrograms into a judgment model for prediction. And please Qu Shi the sensory evaluation to judge the classification, the sensory classification is compared with the prediction classification, if the classification is consistent, the prediction is correct, and if the classification is inconsistent, the classification is wrong, and the accuracy of the process of the fermented soy sauce wine is judged to be 91% through statistics.
| Sample of | Prediction classification | Sensory classification | |
| Sample | |||
| 1 to be measured | White yeast | White yeast | Correct and |
| Sample | |||
| 2 to be measured | White yeast | White yeast | Correct and |
| Sample | |||
| 3 to be measured | White yeast | White yeast | Correct and |
| Sample | |||
| 4 to be measured | White yeast | White yeast | Correct and |
| Sample | |||
| 5 to be measured | Huang Qu | Huang Qu | Correct and |
| Sample | |||
| 6 to be measured | Huang Qu | Huang Qu | Correct and |
| Sample | |||
| 7 to be measured | Huang Qu | Huang Qu | Correct and |
| Sample | |||
| 8 to be measured | Huang Qu | Huang Qu | Correct and correct |
| Sample 9 to be measured | Black yeast | Black yeast | Correct and correct |
| Sample to be measured 10 | Huang Qu | Black | Errors |
| Sample | |||
| 11 to be measured | Black yeast | Black yeast | Correct and correct |
| Sample 12 to be measured | Black yeast | Black yeast | Correct and correct |
The foregoing is merely exemplary embodiments of the present invention, and specific structures and features that are well known in the art are not described in detail herein. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (10)
1. The method for distinguishing the high-temperature Daqu by using the infrared spectrum technology is characterized by comprising the following steps of:
1) Collecting a high-temperature Daqu typical sample, and collecting infrared spectrum data of the sample;
2) Establishing a class analysis model by combining infrared spectroscopy technology with a chemometric method, wherein the chemometric method comprises any one of partial least square method, partial least square discriminant analysis method or orthogonal partial least square discriminant analysis method;
3) And (3) collecting infrared spectrum data of the sample to be detected, and taking the infrared spectrum data into the model in the step (2) to judge the Daqu type of the sample to be detected.
2. The method for distinguishing high-temperature Daqu by using the infrared spectrum technology according to claim 1, which is characterized in that: and in the typical sample of the high-temperature Daqu, the number of black yeast, white yeast and yellow yeast is not less than 10.
3. The method for distinguishing high-temperature Daqu by using the infrared spectrum technology according to claim 2, which is characterized in that: step 1) pretreatment is carried out on a sample: crushing a typical sample of the high-temperature Daqu, wherein the granularity is 15-200 meshes, and uniformly mixing a small amount of sample with potassium bromide.
4. The method for discriminating high-temperature Daqu by infrared spectrum technology according to claim 1, wherein the modeling in the step 2) specifically comprises the following steps:
a: preprocessing infrared spectrum data, finding out models of various types of phase aggregation, and applying the models to obtain a new data matrix;
b: assigning values to black yeast and Huang Quhe white yeast respectively, and setting the range of each type of determined interval;
c: c, taking absorbance or transmittance of each wave number on the data matrix obtained in the step a as an independent variable, taking each type of virtual value as a response value, establishing a PLS model, and examining R of the model under different data matrices 2 Selecting R with the largest value on the basis of being larger than the acceptable value 2 The model is a discrimination model of different types; if R is 2 When the values are lower than acceptable values, re-optimizing the model, and finally, R is calculated 2 The acceptable model is marked as M, and the model is a judging model of different Daqu types;
d: when the unknown sample is predicted, the sample is processed and detected according to the steps to obtain an infrared spectrogram, the infrared spectrogram is substituted into an established discrimination model, the value Y 'of the infrared spectrogram is predicted, the range of the interval where Y' falls is inspected, and the type of the infrared spectrogram is judged.
5. The method for distinguishing high-temperature Daqu by using the infrared spectrum technology according to claim 1, which is characterized in that: when infrared spectrum data of a sample are collected, the scanning wave band is as follows: 4000-500 cm -1 Resolution of 1-16 cm -1 The number of scans was 8 to 64.
6. The method for distinguishing high-temperature Daqu by using the infrared spectrum technology according to any one of claims 1 to 5, which is characterized in that: the mode of optimizing the model comprises spectrogram processing or wave band selection, wherein the spectrogram processing mode comprises one or a combination of a plurality of modes of smoothing, derivative, standardization, baseline correction, trending, multi-element scattering correction and variable standardization; wherein smoothing includes Moving Average or SG smoothing; the derivative comprises a first derivative, a second derivative, a third derivative or a fourth derivative; normalization includes vector normalization, area normalization, and mean normalization.
7. The method for discriminating high temperature Daqu by infrared spectrum technique according to claim 6 wherein: the optimal spectrogram processing mode is SG first-order+MSC.
8. The method for distinguishing high-temperature Daqu by using the infrared spectrum technology according to any one of claims 1 to 5, which is characterized in that: the wave band selection comprises one or a combination of a plurality of correlation coefficient method, analysis of variance method, non-information variable elimination method, genetic algorithm, continuous projection algorithm and interval partial least square method.
9. The method for discriminating high temperature Daqu by infrared spectrum technology according to claim 8, wherein the method comprises the following steps: r is R 2 Greater than 0.9.
10. The method for discriminating high temperature Daqu by infrared spectrum technology according to claim 9, wherein: before modeling, step 2) PCA processing is carried out on the optical data, and Hotelling's T is removed 2 Samples outside a certain confidence interval.
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| CN117851979B (en) * | 2024-03-07 | 2024-05-03 | 常熟市宏宇钙化物有限公司 | Near infrared spectrum technology-based calcium hydroxide concentration detection method |
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