Rapid detecting SSC and TAC of peaches based on NIR spectroscopy

Peach is one of the most important commodities in the global fresh product market. With the development of people's living standard, consumers pay more attention to the internal quality of fruits than the appearance quality of fruits. The requirement of nondestructive analysis could be satisfied by near infrared (NIR) spectroscopy with appropriate data analysis methods. In this paper, we measured the spectra, soluble solid content (SSC) and total acid content (TAC) of peach samples, applied Principal Component Analysis (PCA) algorithm, Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to conduct SSC and TAC prediction of peach samples. Through the experiments, we could know that it is feasible to forecast the SSC and TAC values of peaches and near infrared spectroscopy technique, coupled with intelligent algorithm, could be used as a quality control method for peaches.