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首页> 外文期刊>Analytical and bioanalytical chemistry >Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study
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Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

机译:将可见光谱和近红外光谱与化学计量学相结合,以追踪乌拉圭生产的本地猪品种的肌肉的可行性研究

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摘要

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400-2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.
机译:探索了可见光(Vis)和近红外反射(NIR)光谱与化学计量学相结合的方法,作为追踪乌拉圭土生和杂种猪肌肉的工具。肌肉来自两个品种,即Pampa-Rocha(PR)和Pampa-Rocha x Duroc(PRxD)杂种。在单色仪中在Vis和NIR区域(400-2,500 nm)中扫描碎肉的反射率。基于PCA评分的主成分分析(PCA),判别偏最小二乘回归(DPLS),线性判别分析(LDA)和基于类比的软独立建模(SIMCA)用于基于Vis和NIR识别肌肉的起源数据。开发分类模型时,将全交叉验证用作验证方法。 DPLS正确分类了87%的PR和78%的PRxD肌肉样本。 LDA校准模型分别正确地将87%和67%的肌肉分类为PR和PRxD。 SIMCA正确分类了100%的PR肌肉。结果表明,Vis和NIR光谱结合化学计量学可作为根据猪的品种鉴定和鉴定肌肉的快速方法。

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