Abstract Hyperspectral imaging in the visible and near infrared spectral range (450–1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and non-brined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957–1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450–960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSEp 1.9%, R2p 0.92) and cooked (RMSEp 2.6%, R2p 0.83) samples used the 957–1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed.

中文翻译：

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评估 Vis-NIR 高光谱成像作为一种过程分析工具，对腌制猪肉样品进行分类并预测卤制盐浓度

摘要 研究了可见光和近红外光谱范围 (450–1664 nm) 中的高光谱成像与化学计量学相结合，用于对腌制和非腌制猪腰肉进行分类并预测所采用的腌制盐浓度。获得了来自 16 只动物的对照、水浸和盐水（5、10 或 15% 盐 (w/v)）生和熟猪腰肉的高光谱图像。开发了偏最小二乘 (PLS) 判别分析模型来对腌制猪肉样品进行分类，并开发了 PLS 回归模型来预测所采用的腌制盐浓度。集成蒙特卡罗变量选择方法 (EMCVS) 用于提高所开发模型的性能。偏最小二乘 (PLS) 判别分析模型开发了正确分类的卤水和非卤水样品，原始样品的最佳分类模型 (Sen = 100%, Spec = 100%, G = 1.00) 使用 957–1664 nm 光谱范围，而熟样品的最佳分类模型 (Sen = 100%, Spec = 100%, G = 1.00) 使用 450–960 nm 光谱范围。为生（RMSEp 1.9%，R2p 0.92）和熟（RMSEp 2.6%，R2p 0.83）样品开发的最佳卤化盐浓度预测模型使用 957–1664 nm 光谱范围。这项研究证明了高光谱成像作为一种过程分析工具的巨大潜力，可以对腌制和非腌制猪腰肉进行分类并预测所采用的腌制盐浓度。为生（RMSEp 1.9%，R2p 0.92）和熟（RMSEp 2.6%，R2p 0.83）样品开发的最佳卤化盐浓度预测模型使用 957–1664 nm 光谱范围。这项研究证明了高光谱成像作为一种过程分析工具的巨大潜力，可以对腌制和非腌制猪腰肉进行分类并预测所采用的腌制盐浓度。为生（RMSEp 1.9%，R2p 0.92）和熟（RMSEp 2.6%，R2p 0.83）样品开发的最佳卤化盐浓度预测模型使用 957–1664 nm 光谱范围。这项研究证明了高光谱成像作为一种过程分析工具的巨大潜力，可以对腌制和非腌制猪腰肉进行分类并预测所采用的腌制盐浓度。