Department of Food, Environmental, and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy.
Senior S.r.l., Via Molino 2, 21052 Busto Arsizio, Italy.
Sensors (Basel). 2019 Jul 22;19(14):3225. doi: 10.3390/s19143225.
The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers' expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an "ad hoc" designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours' algorithm and Partial Least Square-Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar's test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values ( > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours' model is considered the most convenient strategy to assess meat and fish freshness.
肉和鱼质量的评估对于确保产品安全和满足消费者的期望至关重要。本工作旨在开发一种新的低成本、便携式和简化的电子鼻系统，名为 Mastersense，用于评估肉和鱼的新鲜度。通过主成分分析选择了四个金属氧化物半导体传感器，并将其插入到一个“特别设计”的测量室中。Mastersense 系统用于在 4°C 下测试牛肉和家禽切片以及比目鱼和三文鱼鱼片在保质期内和过期后的新鲜度。对总活菌数进行了相同的测试，并使用微生物结果定义新鲜度等级，通过 K-最近邻算法和偏最小二乘判别分析开发分类模型。所有获得的模型均具有高于 83.3%和 84.0%的全局敏感性和特异性，并且进行了 McNemar 检验来比较两种分类算法的预测能力，结果表明它们具有可比性（>0.05）。因此，使用 K-最近邻模型实现的 Mastersense 原型被认为是评估肉和鱼新鲜度的最便捷策略。
