光谱学与光谱分析, 2017, 37 (9): 2751, 网络出版: 2017-10-16
利用挥发物红外光谱鉴别牛肉变质状态
Identification of Beef Spoilage Processes Using the Infrared Spectrum of Volatiles
摘要
牛肉在运输过程中极易受到微生物的感染而变质, 对牛肉变质的监测十分重要。 我们利用长光程FTIR光谱检测牛肉变质时产生的挥发性物质。 证明了牛肉在变质过程中产生的主要挥发性物质是氨气和二氧化碳。 并定量分析牛肉变质产生的氨气和二氧化碳的变化规律, 以判断牛肉的状态。 采用主成分分析法(PCA)实现对挥发性物质的红外光谱分类, 进而可以准确的区分新鲜和变质的牛肉。 我们采用化学计量学方法软独立建模聚类分析法(SIMCA)和偏最小二乘判别分析(PLS-DA)对特征波段内的光谱进行分类, 两种方法具有很好的判断率。 结果表明长光程FTIR结合化学计量学方法能够区分新鲜和变质的牛肉。
Abstract
Beef is highly susceptible to microbial infection causing spoilage in the process of transportation, so the monitoring on beef spoilage is very important. This paper proved that beef in the process of spoilage released ammonia and carbon dioxide which were the main volatile substances. We used the long optical path FTIR spectra to detect the volatiles of beef spoilage. We quantitatively analyzed the change rule of ammonia and carbon dioxide in the process of beef spoilage to judge the state of beef. We used principal component analysis(PCA) to realize infrared spectral classification of volatile substance and accurately distinguish fresh and decayed beef. We used chemometrics methods: soft independent modeling cluster analysis(SIMCA) and partial least squares discriminant analysis(PLS-DA) to classify the characteristic spectrum of volatiles. The two methods both worked well. Results showed that the long optical path FTIR combined with chemometrics methods could distinguish fresh and decayed beef.
叶松, 张丙科, 杨辉华, 张文涛, 董大明. 利用挥发物红外光谱鉴别牛肉变质状态[J]. 光谱学与光谱分析, 2017, 37(9): 2751. YE Song, ZHANG Bing-ke, YANG Hui-hua, ZHANG Wen-tao, DONG Da-ming. Identification of Beef Spoilage Processes Using the Infrared Spectrum of Volatiles[J]. Spectroscopy and Spectral Analysis, 2017, 37(9): 2751.