Cozzolino Daniel, Bureš Daniel, Hoffman Louwrens C
Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia.
Institute of Animal Science, 104 00 Přátelství 815, 104 00 Prague, Czech Republic.
Foods. 2023 Jan 1;12(1):182. doi: 10.3390/foods12010182.
A hand-held near infrared (NIR) spectrophotometer combined with a similarity index (SI) method was evaluated to identify meat samples sourced from exotic and traditional meat species. Fresh meat cuts of lamb (), emu (), camel (), and beef () sourced from a commercial abattoir were used and analyzed using a hand-held NIR spectrophotometer. The NIR spectra of the commercial and exotic meat samples were analyzed using principal component analysis (PCA), linear discriminant analysis (LDA), and a similarity index (SI). The overall accuracy of the LDA models was 87.8%. Generally, the results of this study indicated that SI combined with NIR spectroscopy can distinguish meat samples sourced from different animal species. In future, we can expect that methods such as SI will improve the implementation of NIR spectroscopy in the meat and food industries as this method can be rapid, handy, affordable, and easy to understand for users and customers.
对一种结合相似性指数(SI)方法的手持式近红外(NIR)分光光度计进行了评估,以识别来自外来和传统肉类品种的肉样。使用了从一家商业屠宰场获取的新鲜羊肉、鸸鹋肉、骆驼肉和牛肉切块,并使用手持式近红外分光光度计进行分析。使用主成分分析(PCA)、线性判别分析(LDA)和相似性指数(SI)对商业和外来肉样的近红外光谱进行分析。LDA模型的总体准确率为87.8%。总体而言,本研究结果表明,SI与近红外光谱相结合可以区分来自不同动物物种的肉样。未来,我们可以预期,诸如SI之类的方法将改善近红外光谱在肉类和食品行业中的应用,因为这种方法快速、便捷、经济实惠,并且用户和客户易于理解。