Abstract: The near infrared spectra of honey samples were calculated by the method of Norris smoothing combined with first derivative. The outliers were detected by Monte Carlo cross validation (MCCV), and the variables were selected by competitive adaptive reweighted sampling (CARS). The samples were divided into calibration set and validation set by Kennard-Stone (KS) algorithm. Partial least squares (PLS) regression was applied to build a quantitative calibration model of pH and acidity. The coefficient of cross-validation of the calibration set (Rcv2) was 0.8516, and the root mean square error of cross-validation (RMSECV) was 0.1214.The coefficient of determination of the validation set (Rp2 ) was 0.8205, and the root mean square error of prediction (RMSEP) was 0.1196 for pH value. For acidity, the Rcv2, RMSECV, Rp2 and RMSEP were 0.8723, 2.1734, 0.8250 and 2.4674, respectively. The finding shows that this method is suitable for quantitative analysis of honey pH, while caution is needed for honey acidity analysis.

Key words: near infrared spectroscopy, outliers detection of Monte Carlo cross validation (MCCV), competitive adaptive reweighted sampling for variable selection, honey, pH value, acidity