Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)
Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)
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DOI：
10.3390/s19143225
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发表时间：
2019-07-12
期刊：
影响因子：
3.9
通讯作者：
Buratti, Susanna
中科院分区：
文献类型：
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作者：
Grassi, Silvia;Benedetti, Simona;Buratti, Susanna
肉和鱼的质量评价是确保产品安全和满足消费者期望的关键。本工作旨在开发一种新的低成本，便携式，简化的电子鼻系统，命名为Mastersense，以评估肉类和鱼类的新鲜度。四个金属氧化物半导体传感器被选择通过主成分分析，并被插入在一个特设设计的测量室。Mastersense系统用于测试牛肉和家禽切片，以及在4摄氏度下的保质期内，从包装之日起以及超过保质期的比目鱼和鲑鱼片。对相同的样品进行总活菌计数测试，并将微生物结果用于定义新鲜度类别，以通过K-最近邻算法和偏最小二乘判别分析开发分类模型。所有得到的模型给出了全球的敏感性和特异性与预测分别高于83.3%和84.0%。此外，进行McNemar检验以比较两种分类算法的预测能力，其产生可比较的值（p > 0.05）。因此，使用K-近邻模型实现的Mastersense原型被认为是评估肉类和鱼类新鲜度的最方便策略。
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 degrees 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 (p > 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.