Crichton Stuart O J, Kirchner Sascha M, Porley Victoria, Retz Stefanie, von Gersdorff Gardis, Hensel Oliver, Weygandt Martin, Sturm Barbara
Postharvest Technologies and Processing Group, Department of Agricultural Engineering, University of Kassel, Witzenhausen, Germany.
School of Chemistry, University of St Andrews, St Andrews, UK; School of Physics, University of St Andrews, St Andrews, UK.
Meat Sci. 2017 Jul;129:20-27. doi: 10.1016/j.meatsci.2017.02.005. Epub 2017 Feb 8.
Consumer trust in the food industry is heavily reliant upon accurate labelling of meat products. As such, methods, which can verify whether meat is correctly labelled are of great value to producers, retailers, and consumers. This paper illustrates two approaches to classify between, fresh and frozen thawed, and in a novel manner matured and matured frozen-thawed, as well as fresh and matured beef using the 500-1010nm waveband, captured using hyperspectral imaging, and CIELAB measurements. The results show successful classification based upon CIELAB between 1) fresh and frozen-thawed (CCR=0.93), and 2) fresh and matured (CCR=0.92). With successful classification between matured and matured frozen-thawed beef using the entire spectral range (CCR=1.00). The performance of reduced spectral models is also investigated. Overall it was found that CIELAB co-ordinates can be used for successful classification for all comparisons except between matured and matured frozen-thawed. Biochemical and physical changes of the meat are thoroughly discussed for each condition.
消费者对食品行业的信任很大程度上依赖于肉类产品的准确标签。因此,能够验证肉类标签是否正确的方法对生产商、零售商和消费者都具有重要价值。本文阐述了两种利用500 - 1010纳米波段对新鲜与冷冻解冻、成熟与成熟冷冻解冻以及新鲜与成熟牛肉进行分类的方法,这些波段是通过高光谱成像和CIELAB测量获取的。结果表明,基于CIELAB能够成功进行分类,具体如下:1)新鲜与冷冻解冻(CCR = 0.93),以及2)新鲜与成熟(CCR = 0.92)。利用整个光谱范围对成熟与成熟冷冻解冻牛肉也成功进行了分类(CCR = 1.00)。还研究了简化光谱模型的性能。总体而言,发现除了成熟与成熟冷冻解冻之间的比较外,CIELAB坐标可用于所有比较的成功分类。针对每种情况,都深入讨论了肉类的生化和物理变化。