Non-destructive prediction of quality factors in apples using VIS and NIR reflectance spectroscopy

Abstract

Abstract. Predicting maturity of fruits nondestructively is a challenging problem. In this study, spectroscope reflectance data in the visual (VIS) and near-infra red (NIR) regions was used to predict maturity, sweetness, acidity, and polyphenol content of apples. The sample consisted of 200 Jonathan apples harvested at two different locations and at five harvest dates which were approximately 10 days apart. The nondestructive measures were comprised of VIS and NIR reflectance spectral data in the 400nm –2600 nm band and the destructive measures were comprised of soluble solid content (SSC), pH value, and total polyphenol content (TPC). Partial least squares regression (PLS) and principal component regression (PCR) techniques were used to correlate reflectance spectra with physicochemical data. The results show that for SSC, pH and TPC, the PLS model gave coefficient of determination values of 0.90, .93 and 0.92, respectively, and a standard error of prediction (SEP) of less than 1%. It could thus be concluded that VIS and NIR reflectance spectroscopy in conjunction with multivariate regression is an efficient method for predicting certain quality factors in apples. The method developed in this study can be used by horticulturists to decide harvest time of fruits, by fruit packers to grade harvested fruits in automated packaging lines, and by breeders to breed fruits based on quality factors.