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Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling

机译:南非野味肉的近红外(NIR)光谱和分层建模

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摘要

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908–1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%–93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%–100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy’s potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.
机译:近红外(NIR)光谱技术与多变量数据分析技术相结合,可用于快速区分南非猎物种类,而不论治疗(新鲜或先前冷冻的)或肌肉类型如何。还使用分层建模为每个物种确定了这些单独的类别(新鲜;先前已冻结;肌肉类型)。用便携式手持分光光度计在908–1676 nm范围内收集光谱。使用偏最小二乘判别分析模型,我们可以区分精度在89.8%–93.2%之间的物种。也可以区分新鲜和先前冷冻的肉(准确度为90%–100%)。此外,可以区分鸵鸟肌肉(100%),斑马的前肢和后躯(90.3%)和跳羚(97.9%)肌肉。结果证实了近红外光谱技术作为一种快速,无损的方法,可用于物种识别,新鲜和先前冷冻的肉类鉴别以及肌肉类型确定的潜力。

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