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| Discriminative Study on Huzhu Qingke Liquor by Back Propagation
Neural Network Combined With Ultraviolet-Near Infrared Fusion
Spectroscopy |
| ZHAO Yu-xia1, ZHANG Ming-jin1, 3*, WANG Ru1, ZHANG Shi-zhi2, YIN Bo1, 3 |
1. College of Chemistry and Chemical Engineering, Qinghai Normal University, Xining 810016, China
2. College of Chemistry and Chemical Engineering, Qinghai Minzu University, Xining 810016, China
3. Qinghai Key Laboratory of Advanced Technology and Application of Environmentally Functional Materials, Xining 810016, China
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Abstract Chinese Huzhu Qingke Liquor is a protected geographical indication product, and it is of great significance for its accurate evaluation and classification. Due to the advantages of ultraviolet (UV) and near-infrared (NIR) spectroscopy, such as fast, accurate, non-destructive detection and no sample pretreatment, are widely used in food and other fields. In this study, a fast, nondestructive, and efficient discriminative classification model for Huzhu Qingke Liquor was established based on UV, NIR, and UV-NIR intermediate data fusion spectroscopy (UV-NIR) combined with theback-propagation neural network (BPNN) method. Since the unoptimized spectra are affected by noise and baseline drift due to the superimposed interference of spectral eigenpeaks, the spectra are denoised using four preprocessing methods, namely, standard normal variable transform (SNV), Savitzky-Golay smoothing (SG), first-order derivative (1D) and second-order derivative (2D). Further, relative to a single spectrum, the fused spectrum can complement the diversified spectroscopic information and improve the performance of the classification model, so the feature variables are selected by five variable screening methods, namely, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), principal component analysis (PCA), variable projection importance analysis (VIP), and variable combinatorial clustering analysis (VCPA) to achieve the optimization of model performance and the purpose of fusing the effective information of two spectra. Finally, the best method for establishing the BPNN model for single and fused spectra was selected. The results show that the classification model established by selecting 30 feature variables by SPA after SNV preprocessing for UV spectra has the best recognition effect, with a classification accuracy of 100%. The MSE value, R2P, R(Train), R(Validation), R(Test) and R(All) were 0.018 0, 1, 0.928 3, 0.958 7, 0.913 0, and 0.929 7, respectively; PCA selected the NIR and UV-NIR after SG preprocessing with 84 and 106 The classification model built by feature variables had the best recognition effect, and the NIR spectral classification accuracy was 100%, with MSE value, R2P, R(Train), R(Validation), R(Test)and R(All)of 0, 1.000, 1.000, 1.000，1.000 and 1.000. respectively, UV-NIR spectral classification accuracy was 100%, MSE, R2P, R(Train), R(Validation), R(Test), and R(All) were 0.005 7, 1.000, 1.000, 0.987 1, 0.991 3 and 0.996 4, respectively; the fusion spectra can significantly improve the predictive ability and robustness of the classification model compared with the single-spectrum modeling, thus realizing the rapid and non-destructive analysis of Huzhu Qingke Liquor.
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Received: 2024-06-05
Accepted: 2024-08-14
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Corresponding Authors:
ZHANG Ming-jin
E-mail: zhangmingjin@qhnu.edu.cn
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