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| Fast Prediction of Pig Blood Parameters Using Near-Infrared Spectroscopy for Discrimination of Inferior Quality Pork |
| ZOU Hao, TIAN Hanyou, LIU Fei, WANG Hui, LI Wencai, LI Jiapeng, CHEN Wenhua, QIAO Xiaoling* |
| Beijing Key Laboratory of Meat Processing Technology, China Meat Research Center, Beijing Academy of Food Sciences, Beijing 100068, China |
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Abstract In order to quickly discriminate pale, soft and exudative (PSE) pork and dark, firm, and dry (DFD) pork, 64 blood samples were collected at slaughter and tested by near-infrared (NIR) spectroscopy for the establishment of a predictive model for serum cortisol concentration, and 89 additional sample were collected and also detected by NIR spectroscopy for the development of a model to predict serum glucose concentration. Spectral information was acquired employing a portable NIR spectrometer and preprocessed using individual and combined algorithms for modeling using partial least square regression. Based on evaluation of the model parameters, the predictive model for serum glucose concentration developed by spectral pretreatment using Savitzky-Golay derivative + baseline correction had the best performance. The standard error of calibration and standard error of prediction of the model were 2.07 and 2.48, respectively and the number of principal components was 6. The correlation coefficients of calibration and prediction sets were 0.88 and 0.85, respectively. The optimal spectral pretreatment method for predictive modeling of serum cortisol concentration was autoscaling + difference derivative + Savitzky-Golay smoothing + net analyte signal. The standard error of calibration and standard error of prediction of the predictive model for serum cortisol concentration were 0.05 and 0.15, respectively and the number of principal components was 6. The correlation coefficients of calibration and prediction sets were 0.97 and 0.67, respectively. The built models were respectively used to predict serum glucose and cortisol concentrations of 25 unknown samples and consequently recognize PSE and DFD meat with an accuracy of 92% and 96%, respectively. Hence, it is feasible to predict serum glucose and cortisol concentrations of pig blood using NIR spectroscopy in order to identify PSE and DFD meat.
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