Measurement of yogurt internal quality through using Vis/NIR spectroscopy

A nondestructive optical method for determining the sugar content and acidity of yogurt was investigated. Three types of preprocessing, S. Golay smoothing with multiplicative scatter correction (S. Golay smoothing with MSC), S. Golay 1st-Der and wavelet package transform (WPT), were used before the data were analyzed with chemometrics methods of partial least square (PLS). Spectral data sets as the logarithms of the reflectance reciprocal were analyzed to build a best model for predicting the sugar content and acidity of yogurt. A model using preprocessing of WPT with a correlation coefficient of 0.91 and 0.90, a root mean square error of prediction (RMSEP) of 0.36 and 0.04 showed an excellent prediction performance to sugar content and acidity. S. Golay smoothing with MSC was also finer, combined with the calibration and validation results. S. Golay 1st-Der was the worse preprocessing method in this experiment. In the paper, a multivariate calibration method of principal component artificial neural network (PC-ANN) was also established. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN. After adjusting the number of input nodes (principal components), hidden nodes, as well as learning rate and momentum of the network, a model with a correlation coefficient of 0.92 and 0.91, a root mean square error of prediction (RMSEP) of 0.33 and 0.04 showed an excellent prediction performance on sugar content and acidity. At the same time, the sensitive wavelengths corresponding to the sugar content and acidity of yogurt were proposed on the basis of regression coefficients by PLS.