Qualitative and quantitative evaluation of corn syrup as a potential added sweetener in apple fruit juices using mid-infrared spectroscopy assisted chemometric modeling

Abstract

The study was designed to detect and quantify corn syrup adulterations (1%–16%) in apple juices with the aid of Fourier transform infrared spectroscopy (FTIR) based chemometric modeling. Total of 252 samples were analyzed. The acquired mid-infrared (MIR) spectra were pretreated (baseline correction, standard normal variate) and the fingerprint region (1200 cm−1-900 cm−1) was selected. The whole chemometric analysis was performed on raw and 1st derivative data to compare and obtain best performing model. Principal component analysis (PCA) allowed us to reduce the dimension of the spectral data set. Soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were used as classification methods. Both methods performed equally well with 100% classification efficiencies, sensitivity, and specificity. These models were able to classify the prediction set as low as 1% (lowest) adulterated samples. Partial least squares regression (PLS-R), and principal component regression (PCR) models were used for the quantification of corn syrup in apple juices. Best optimized model (Raw PLS-R) was selected based on parameters like R2 (Cross- Val: 0.9991), root mean square error (RMSE; Cross- Val: 0.159% v/v), relative prediction error (RE%; Cross- Val: 1.7% v/v), residual predictive deviation (RPD; 49.0) and limit of detection (LOD; 0.477% v/v).