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Shelf-Life Prediction and Critical Value of Quality Index of Sichuan Sauerkraut Based on Kinetic Model and Principal Component Analysis.

Jie DuMin ZhangLihui ZhangChung Lim LawKun Liu
Published in: Foods (Basel, Switzerland) (2022)
Kinetic models and accelerated shelf-life testing were employed to estimate the shelf-life of Sichuan sauerkraut. The texture, color, total acid, microbe, near-infrared analysis, volatile components, taste, and sensory evaluation of Sichuan sauerkraut stored at 25, 35, and 45 °C were determined. Principal component analysis (PCA) and Fisher discriminant analysis (FDA) were used to analyze the e-tongue data. According to the above analysis, Sichuan sauerkraut with different storage times can be divided into three types: completely acceptable period, acceptable period, and unacceptable period. The model was found to be useful to determine the critical values of various quality indicators. Furthermore, the zero-order kinetic reaction model (R 2 , 0.8699-0.9895) was fitted better than the first-order kinetic reaction model. The Arrhenius model ( E a value was 47.23-72.09 kJ/mol, k ref value was 1.076 × 10 6 -9.220 × 10 10 d -1 ) exhibited a higher fitting degree than the Eyring model. Based on the analysis of physical properties, the shelf-life of Sichuan sauerkraut was more accurately predicted by the combination of the zero-order kinetic reaction model and the Arrhenius model, while the error back propagation artificial neural network (BP-ANN) model could better predict the chemical properties. It is a better choice for dealers and consumers to judge the shelf life and edibility of food by shelf-life model.
Keyphrases
  • computed tomography
  • neural network
  • physical activity
  • machine learning
  • mass spectrometry
  • deep learning
  • quality improvement
  • artificial intelligence
  • big data
  • data analysis
  • solid phase extraction