Prediction of the Alkalinity in the Feed Liquid of Processing Preserved-Eggs Using Near Infrared Spectroscopy

Near-infrared spectroscopy was used for prediction of the alkalinity in the feed liquid of processing preserved-eggs. Using the techniques of partial least square (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR), three NIRS-based models for estimating the alkalinity were established. According to the standards of coefficient of determination (R2), root mean square error of calibration (RMSEC) and root mean square error (RMSEP), a best calibration model of PCR was achieved for the prediction of the alkalinity in the feed liquid of processing preserved-eggs. R2=0.93478, RMSEC=0.0101 and RMSEP=0.0126 by PCR, and a satisfying prediction precision was achieved. The results indicated that near infrared spectroscopy could be successfully applied for the prediction of the alkalinity in the feed liquid of processing preserved-eggs and PCR model could achieve a best prediction results.