Determination of citric acid of lemon vinegar using visible/near infrared spectroscopy and least squares-support vector machine

The determination of citric acid of lemon vinegar was processed using visible and near infrared (Vis/NIR) spectroscopy combined with least squares-support vector machine (LS-SVM). Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were studied. The calibration set was consisted of 225 samples (45 samples for each level) and the remaining 75 samples for the validation set. Partial least squares (PLS) analysis was employed for the calibration models as well as extraction of certain latent variables (LVs) and effective wavelengths (EWs). Different preprocessing methods were compared in PLS models including smoothing, standard normal variate (SNV), the first and second derivative. The selected LVs and EWs were employed as the inputs to develop least square-support vector machine (LSSVM) models. The optimal prediction results were achieved by LV-LS-SVM model, and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.9990, 0.1972 and -0.0334, respectively. Moreover, the EW-LS-SVM model was also acceptable and slightly better than all PLS models. The results indicated that Vis/NIR spectroscopy could be utilized as a parsimonious and efficient way for the determination of citric acid of lemon vinegar based on LS-SVM method.