Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Journal: Journal of food science
Published Date: Jun 1, 2024

Abstract

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

Authors

  • Yan Liang
    Department of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States.
  • Jianping Tian
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Xinjun Hu
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Yuexiang Huang
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Kangling He
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Liangliang Xie
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Haili Yang
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Dan Huang
    Department of Anesthesiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.; Department of Anesthesiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China.
  • Yifei Zhou
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
  • Yuanyuan Xia
    School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.

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