Rapid Prediction of Microbial Load on Intact Mango Surface Using Spectroscopy

Abstract

Agriculture industries are continuously in search of new user friendly techniques for evaluating overall quality (microbial and biochemical) of fruits as per quarantine requirements. In the current study, the potential of visible and near-infrared (NIR) spectroscopy in the wavelength range of 299-1100 nm and 900-1700 nm was evaluated to determine total microbial population on the surface of seven major cultivars of mangoes collected from four states of India. NIR models were developed based on multiple-linear regression (MLR) and partial least square (PLS) regression employing pre-processing technologies (baseline correction, smoothening, multiplicative scatter correction (MSC) and second-order derivatives). Wavelength range of 299-1100 nm was found to be more suitable for determination of microbial load on the mango surface as compared to wavelength range of 900-1700 nm. PLS models were found to be the best with multiple correlation coefficients of 0.66 and 0.56, for calibration and validation, respectively, in the wavelength range 504.80-533.17 nm. The standard errors of calibration, prediction and differences in them were low, which demonstrated the potential of NIRS to predict microbial load on the surface of mango non-destructively in the wavelength range of 504.806-533.176 nm.