Abstract：
At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorithms were used for the first time to develop a multi-objective model that can simultaneously predict the shelf-life of five marine fish species at multiple storage temperatures using 14 features such as species, temperature, total viable count, K-value, total volatile basic‑nitrogen, sensory and E-nose-GC-Ms/Ms. as inputs. Among them, the radial basis function model performed the best, and the absolute errors of all test samples were <0.5. With the optimal model as the base layer, a real-time prediction platform was developed to meet the needs of practical applications. This study successfully realized multi-objective real-time prediction with accurate prediction results, providing scientific basis and technical support for food safety and quality.
摘要：
全球每年至少有1000万吨海产品在运输或储存过程中变质或损坏。实时监测海鲜的新鲜度变得尤为重要。在这项研究中,首次使用四种机器学习算法开发了一种多目标模型，该模型可以使用14种特征（例如物种）同时预测五种海洋鱼类在多个存储温度下的保质期，温度,总可行数，K值,总挥发性碱性氮，感官和电子鼻-GC-Ms/Ms作为输入。其中,径向基函数模型表现最好，所有测试样品的绝对误差均<0.5。以最优模型为基础层，为满足实际应用的需要,开发了一个实时预测平台。本研究成功实现了多目标实时预测,预测结果准确,为食品安全和质量提供科学依据和技术支持。
