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Prediction of quality traits and grades of intact chicken breast fillets by hyperspectral imaging.
British Poultry Science ( IF 2 ) Pub Date : 2020-09-29 , DOI: 10.1080/00071668.2020.1817326
Y Yang 1 , W Wang 1 , H Zhuang 2 , S-C Yoon 2 , H Jiang 3
Affiliation  

ABSTRACT

1. In this study, hyperspectral imaging was evaluated for its usefulness to predict quality traits and grading of intact chicken breast fillets.

2. Lightness of colour (L*) and pH of the fillets were measured as quality traits, and samples were then selected and graded to three different quality categories, i.e., dark, firm and dry (DFD), normal (NORM), and pale, soft and exudative (PSE) based on these two quality traits. Based on the prediction performance of full wavelength partial least square regression (PLSR) models, the spectral range of visible and near-infrared (Vis-NIR) was more suitable for the evaluation of quality traits and grading than the range of near-infrared (NIR). Key wavelengths of each quality trait and grade value were selected by the regression coefficient (RC) method.

3. The new key wavelength PLSR models showed good predictive performances (Rp = 0.85 and RMSEp = 2.18 for L*, Rp = 0.84, and RMSEp = 0.13 for pH, and Rp = 0.80 and RMSEp = 0.44 for quality grading). The classification accuracy for grades was 85.71% (calibration set) and 81.82% (prediction set), respectively. Finally, distribution maps showed that quality traits and grades of samples were able to be visualised.

4. These results suggested that hyperspectral imaging has the potential for quality prediction of fresh chicken meat.



中文翻译:

通过高光谱成像预测完整鸡胸肉鱼片的质量性状和等级。

摘要

1.在这项研究中,评估了高光谱成像对预测完整鸡胸肉鱼片的品质特征和等级的有用性。

2.测量鱼片的颜色亮度(L *)和pH值作为质量特征,然后选择样品并将其分级为三个不同的质量类别,即暗,硬和干(DFD),正常(NORM)和基于这两个品质特征,肤色苍白,柔软且渗出(PSE)。基于全波长偏最小二乘回归(PLSR)模型的预测性能,可见光和近红外(Vis-NIR)的光谱范围比近红外范围(Vis-NIR)更适合评估质量特征和等级NIR)。通过回归系数(RC)方法选择每个质量性状和等级值的关键波长。

3.新的关键波长PLSR模型显示出良好的预测性能(L *的Rp = 0.85和RMSEp = 2.18,pH的Rp = 0.84和RMSEp = 0.13,质量分级的Rp = 0.80和RMSEp = 0.44)。等级的分类准确度分别为85.71%(校准集)和81.82%(预测集)。最后,分布图表明样品的质量特征和等级能够被可视化。

4.这些结果表明,高光谱成像有可能预测新鲜鸡肉的质量。

更新日期:2020-09-29
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