Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3
1. College of Computer Science and Engineering，Sichuan University of Science and Engineering，Yibin 644000，China
2. College of Physics and Electronic Engineering，Sichuan University of Science and Engineering，Yibin 644000，China
3. College of Automation and Information Engineering，Sichuan University of Science and Engineering，Yibin 644000，China
Abstract：Rapid and accurate detection of the acidity of fermented grains can significantly improve the yield of Baijiu and the quality of finished liquor. Near infrared spectroscopy (NIR) mainly contains information on octave and ensemble frequencies of molecules, i. e., the vibrations of hydrogen-containing groups (C—H, N—H, O—H) in organic matter. It is usually used for qualitative and quantitative analysis of hydrogen-containing compounds in samples. The NIR can be used to determine the acidity of fermented grains in a simple, rapid overcoming the shortcomings of traditional chemical analysis methods, such as long detection cycles, large reagent consumption, and human errors. As NIR is an indirect analysis technology, establishing a calibration model is the key to accurately detecting the acidity of fermented grains. As a typical model in deep learning, convolutional neural networks (CNN) have the advantages of local area connection and weight sharing. It can not only extract critical features from complex spectral data, but also reduce the complexity of network models. Therefore, a quantitative analysis method for the acidity of fermented grains based on CNN and NIR is proposed in this work. The research object is the spectral data of 545 fermented grains samples collected in the production line of a wine enterprise, and the original spectra are preprocessed using a combination of three algorithms: standard normal variation (SNV), Savitzky-Golay(SG) filtering and first derivative (1stD); uninformative variable elimination (UVE) is used to select the characteristic wavelength of spectral data; CNN is used to establish the acidity model of fermented grains. The results show that: (1) The pre-processed spectral data eliminated the baseline shift and noise problems in the original spectra, increased the prediction set coefficient of determination by 22.85%, and decreased the root mean square error by 0.049 5 compared with the original spectral modeling, which improved the correlation between the acidity of fermented grains and spectral reflectance. (2) The model established after wavelength screening of spectral data increased the determination coefficient of the prediction set by 2.04% and decreased the root mean square error of the prediction set by 0.004 8 compared with full-wavelength modeling. (3) The acidity prediction model based on CNN had a determination coefficient of 0.955 5 and a root mean square error of 0.039 1. Compared with the partial least squares model, the determination coefficient of the prediction set is increased by 1.03%, and the root mean square error of the prediction set is reduced by 0.097 6. Compared with the backpropagation neural network model, the determination coefficient of the prediction set is increased by 1.16%, and the root mean square error of the prediction set is reduced by 0.099 4. The research results can realize the rapid and accurate measurement of the acidity content of fermented grains and provide method support for subsequent online detection of the acidity of fermented grains.
Key words：Convolutional neural network; Near infrared spectroscopy; Acidity of fermented grains; Liquor
王琦标，何余锴，罗雨诗，王淑君，谢 波，邓 超，刘 勇，庹先国. 基于卷积神经网络和近红外光谱的酒醅酸度分析方法研究[J]. 光谱学与光谱分析, 2023, 43(12): 3726-3731.
WANG Qi-biao, HE Yu-kai, LUO Yu-shi, WANG Shu-jun, XIE Bo, DENG Chao, LIU Yong, TUO Xian-guo. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731.
