Nondestructive Determination of Citric Acid Using Successive Projections Algorithm and Spectroscopic Techniques

Abstract

Successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.