Detection of internal qualities of hami melons using hyperspectral imaging technology based on variable selection algorithms

Hyperspectral imaging technology was used to assess the soluble solids content (SSC), titrable acidity content (TAC), and firmness of hami melons. The mean spectra were extracted from the regions of interest (ROI) of the hyperspectral images of each hami melon. Spectral data were first pretreated with different preprocessing methods and analyzed using the partial least squares (PLS) method to build calibration models. However, full spectral data contain a great number of redundant and colinear variables that lead to poor predicting ability of calibration models. Three typical variable selection methods, i.e., the genetic algorithm (GA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used to extract effective wavelength variables from the full spectra for determination of SSC, TAC, and firmness of hami melon. The results showed that the SNV-CARS-PLS model based on 51 and 40 wavelength variables achieved the optimal performance for SSC and TAC compared with full spectral SNV-PLS, SNV-GA-PLS, and SNV-SPA-PLS models. The R-P, RMSEP, and RPD of SNV-CARS-PLS model for SSC were 0.9606, 0.3816, and 3.598, respectively, and 0.9125, 0.0263, and 2.445 for TAC, respectively. The best model for predicting firmness was RAW-CARS-PLS model based on 57 wavelength variables with the R-P RMSEP, and RDP values of 0.8671, 20.05, and 1.996, respectively. Overall, the results indicate that the CARS is a powerful method for selecting effective wavelength variables and demonstrated the feasibility of using hyperspectral imaging technology as a fast and nondestructive method for simultaneous detection of SSC, TAC, and firmness of hami melons. Practical applicationsThe quality of melon has a direct relationship with the soluble solids content, titrable acidity content, and texture of melon. To ensure the best taste and highest nutritional value, it is better for melons to be harvested when completely mature. Nowadays, melons were harvested according to different fruit development times after flowering. Traditional methods for detection of internal qualities and maturity of melon mainly focus on the appearance of melon and depend on the feelings of professionals, which are tedious, time-consuming, expensive, and greatly influenced by subjective factors. More than anything, Traditional methods are hard to be highly accurate. Hyperspectral imaging technology has the advantages of rapid and nondestructive. The results showed that hyperspectral imaging technology for the simultaneous detection of soluble solids content, titrable acidity content, and firmness of melon is feasible. And the results can provide a reference for detecting large fruits by using hyperspectral imaging technology