Non-destructive intelligent prediction of shelf life and quality of Tegillarca granosa using multilayer perceptron model.

Journal: Journal of the science of food and agriculture
Published Date: Jan 16, 2026

Abstract

BACKGROUND: Tegillarca granosa is prone to spoilage and deterioration during storage due to the action of microorganisms and enzymes. The traditional shelf-life prediction methods have problems such as strong destructiveness, long time consumption, complex operation and strict requirements for personnel. This study model constructed an intelligent prediction model of T. granosa based on a multilayer perceptron (MLP). RESULTS: Under different storage temperatures (25, 4, -18 °C), the physicochemical indicators total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), total viable bacteria count (TVC), and sensory characteristics (color, electronic nose) of T. granosa all showed a deteriorating trend over time. Shelf-life prediction model outputs the shelf life by inputting multidimensional variables such as TVB-N, TBARS, color, electronic nose and TVC. The quality prediction models include three types: predicting the TVB-N values and TBARS values by inputting storage temperature and days; predicting the TVB-N value by inputting the response value of the electronic nose sensor. All the prediction models performed outstandingly, with the coefficient of determination (R2) remaining above 0.98, the mean absolute error controlled within 0.50, and the mean square error within 0.4. CONCLUSION: The above results fully verified that the models have good prediction accuracy and stability. By applying machine learning technology, the shelf life and quality dynamic change patterns of aquatic products have been predicted rapidly, accurately, and non-destructively, which has significant practical value for the quality control of aquatic products and the reduction of economic losses. © 2026 Society of Chemical Industry.

Authors

  • Yi Yuan
    School of Business, XI'AN University of Finance and Economics, Xi'an, Shaanxi, China.
  • Jiaxin Qiang
    SKL of Marine Food Processing and Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, People's Republic of China.
  • Songyi Lin
    SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, PR China.
  • Xiuping Dong
    SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, PR China; Guangdong Engineering Technology Research Center of Aquatic Food Processing and Safety Control, Shenzhen Key Laboratory of Food Nutrition and Health, Institute for Innovative Development of Food Industry, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, PR China.
  • Bo Liu
    Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
  • Haiyou Dong
    SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, PR China.
  • Jiali Zou
    Department of Breast Surgery, Guiyang Maternal and Child Healthcare Hospital, Guiyang, 550001, China.
  • Simin Zhang
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.

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