Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics

Abstract

To establish a non-destructive method for prediction of mango quality attributes, near infrared reflectance spectroscopy (NIRS) combined with chemometrics was studied. NIR spectra were recorded on intact mangos (cv. Kent, n = 58) in the wavelength range of 1000–2500 nm using a Fourier transform near infrared (FT-NIR) spectrometer, followed by its quality attributes measurement. Partial least squares (PLS) and principal component regression (PCR) based on various spectral pre-treatment (MSC, SNV and first derivative) were used to develop prediction models for quality attributes such as soluble solids content (SSC), titratable acidity (TA) and ascorbic acid (AA). The models yielded satisfactory results with coefficient of determination of calibration ranging from 0.66 to 0.91 (SSC), 0.94 to 0.98 (TA) and 0.62 to 0.92 (AA) while in cross validation the coefficient ranging from 0.51 to 0.66 (SSC), 0.90 to 0.95 (TA) and 0.43 to 0.61 (AA). Standard error resulted in calibration and cross validation were low. It is concluded that NIRS and chemometrics is feasible for rapid and non-destructive prediction of mango quality.