Determination of sugar content of instant milk-tea using effective wavelengths and least squares-support vector machine

Abstract

Visible and near infrared (Vis/NIR) spectroscopy combined with least squares-support vector machine (LS-SVM) was investigated to determine the sugar content of instant milk-teas. The Savitzky-Golay smoothing, standard normal variate and 1 st derivative were applied as preprocessing methods. The PLS model was developed and the optimal latent variables (LVs) and effective wavelengths (EWs) were also selected. The LV-LS-SVM model with LVs outperformed PLS models. Wavelengths at 484, 515 and 957 nm were confirmed to be EWs and the EW-SL-SVM model achieved the best performance in all developed models. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.964, 0.087 and 0.005, respectively. The results indicated that the EWs combined with LS-SVM method was successfully implemented for the prediction of sugar content of instant milk-teas, and the confirmed EWs were helpful to develop commercial instrument to progress the quality evaluation of instant milk-teas. © 2010 Springer-Verlag Berlin Heidelberg.