Sensory Evaluation and Model Prediction of Vacuum-Packed Fresh Corn during Long-Term Storage

Li, Yilin; Zhong, Kui; Wang, Xue; Wang, Houyin; Yong-jiu, Zhang; Shi, Bolin; Luo, Huarong; Zhao, Lei; Jiang, Shilong; Wang, Sisi

doi:10.3390/foods12030478

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…It is critical to identify the key signs and develop a shelf-life prediction model. The Arrhenius equation is a typical chemical kinetic model that can predict the changes in food quality changes at various storage temperatures [15]. Many studies on the kinetic model based on the Arrhenius equation have been conducted to predict the shelf life of foods such as rabbit meat [16], beef [17], and pork sausage [18].…”

Section: Introductionmentioning

confidence: 99%

Quality Analysis and Shelf-Life Prediction of Antarctic Krill (Euphausia superba) Sauce Based on Kinetic Model and Back Propagation Neural Network Model

Chi,

Zhang,

Zhao

et al. 2024

Journal of Food Processing and Preservation

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The study is aimed at determining how the quality of Antarctic krill (Euphausia superba) sauce (AkS) changed over time, including changes in color, moisture content, acid value (AV), peroxide value (POV), thiobarbituric acid reactive substance (TBARS), aerobic plate count, and sensory score. Quality variations and shelf life of AkS were estimated using kinetic model and back propagation (BP) neural network model. The results showed that sensory score, moisture content, and a∗ values of AkS declined as storage temperature increased at 4, 25, and 37°C. In addition, the L∗ values, b∗ values, AV, POV, and TBARS of AkS increased as storage duration increased, indicating that high storage temperature of the samples accelerated quality degradation. The primary reason for AkS degradation was the oxidation of proteins and lipids. The POV, TBARS, and total sensory evaluation rating exhibited a highly significant correlation, and therefore, POV and TBARS were selected as the indicators for the two models. The BP neural network outperformed the kinetic model in predicting quality changes over the whole storage period, with relative errors of less than 10%. In terms of shelf-life prediction, the BP neural network’s relative errors were 11.76% and 13.39% in POV and TBARS, respectively. POV and TBARS had experimental shelf lengths of 119 and 142 d, respectively. Compared with the kinetic model, the BP neural network model predicted the quality changes and shelf life of AkS with greater accuracy and stability. The findings offer fundamental insights and innovative concepts for the production of high-value Antarctic krill products, as well as the exploitation of Antarctic krill resources.

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