WAVELENGTH SELECTION FOR PREDICTING PHYSICOCHEMICAL PROPERTIES OF APPLE FRUIT BASED ON NEAR‐INFRARED SPECTROSCOPY

Instrumental evaluation tools for fruit quality monitoring are important in the production and postharvest processes as well as in marketing. In the present study, near-infrared spectroscopy (600–1,100 nm) was applied to study the correlation with fruit soluble solid content (SSC ), fruit flesh firmness and water content of apples (cv. “Fuji”). Genetic algorithm and correlation coefficient (r) method were used to select the most sensitive wavelength combinations, and partial least squares regression analysis was applied to calibrate fruit quality parameter. The validation of models based on the most sensitive wavelengths gave good predictions with an r value of 0.94 and a standard error of cross validation (SECV) of 0.85°Brix for SSC; r = 0.89 and SECV = 7.54 N/cm2 for firmness; and r = 0.96 and SECV = 0.92% for water content. The reduced data set of sensitive wavelengths were found feasible for predicting internal fruit quality. PRACTICAL APPLICATIONS Soluble solid content, firmness and water content are important quality attributes of apples. A nondestructive measurement technique will be valuable for monitoring and sorting apple fruit so that high quality, uniform fresh products can be delivered to the marketplace. In the present study, fruit analyses using the entire near-infrared fruit spectra or a reduced data set of sensitive wavelengths were compared. The results demonstrate that the selected combinations of sensitive wavelengths were feasible for measuring apple quality properties. The recent research findings provide researchers and instrumentation engineers with information on the performance of different methods to select appropriate wavelengths for reducing the amount of data, e.g., in developing portable or online sensing systems.