Development of Method for Rapid Quantification of Glucose, Fructose and Sucrose in Mango Juice by Chemometric Techniques in De-noised FTIR Spectroscopic Data

The present study is aimed to develop a chemometrics assisted method for predicting simple sugars (glucose, fructose and sucrose) in mango juice by using the best calibration technique among Artificial Neural Network (ANN), Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) in de-noised data from Fourier Transformed Infrared (FTIR) spectrophotometer. Sixty four mixture solutions of eight different concentrations of sugars and fifteen commercial mango juices have been run in FTIR, and spectral data are used for development, validation and test of models. Standard Normal Variate (SNV), Savitzky-Golay (S-G) filtering and Multiplicative Scatter Correction (MSC) have been used for de-noising spectral data before calibration. Among the alternatives, the best prediction performance is noticed by ANN in spectral range 1500-952 cm-1 and S-G filtering (R2 ≈0.99). Prediction of simple sugars concentration by ANN with FTIR spectroscopic data after de-noised with S-G filtering is a cost-effective and easy method for quantification of sugars in commercial mango juice.