Predicting pork quality using Vis/NIR spectroscopy
Predicting pork quality using Vis/NIR spectroscopy
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DOI：
10.1016/j.meatsci.2015.04.018
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发表时间：
2015-10-01
期刊：
影响因子：
7.1
通讯作者：
Figueira, Ana Cristina
中科院分区：
文献类型：
--
作者：
Balage, Juliana Monteiro;da Luz e Silva, Saulo;Figueira, Ana Cristina
利用可见光和近红外反射光谱（Vis/NIRS）预测猪肉样品的最终pH值（pHu）、颜色、肌内脂肪（IMF）和剪切力（WBSF），并建立能够根据嫩度（嫩或韧）和多汁性（多汁和干燥）对样品进行分类的分类器。光谱采集范围为400 ~ 1495 nm，每个样品产生200个数据点（n = 134）。67%的样品用于校准，33%用于验证。针对所测各特征建立了偏最小二乘（PIS）标定模型。采用决定系数（R-2）和残差预测偏差（RPD）对标定模型的精度进行评价。本研究建立的pHu和颜色预测模型符合这一分类，表明这些预测模型可以用于完整猪肉样品的质量性状预测。可见光/近红外光谱（Vis/NIRS）为正确地将猪肉最长肌分为两种嫩度和两种多汁性提供了巨大的潜力。(C) 2015 Elsevier Ltd.版权所有。
Visible and near-infrared reflectance spectroscopy (Vis/NIRS) was used to predict the ultimate pH (pHu), color, intramuscular fat (IMF) and shear force (WBSF) of pork samples and to build classifiers capable of categorizing the samples by tenderness (tender or tough) and juiciness (juicy and dry). Spectra were collected from 400 to 1495 nm, and 200 data points were generated for every sample (n = 134). Sixty-seven percent of the sample set was used for calibration, and 33% was used for validation. Partial least squares (PIS) calibration models were developed for each characteristic measured. A coefficient of determination (R-2) and residual prediction deviation (RPD) were used to evaluate the accuracy of the calibration models. The pHu and color prediction models developed in this study fit this classification, indicating that these predictive models can be used to predict quality traits of intact pork samples. The Vis/NIRS offered great potential for correctly classifying pork Longissimus into two tenderness and two juiciness classes. (C) 2015 Elsevier Ltd. All rights reserved.