The aim of this research was to predict quality factors of tomato fruit during storage using backscattering and multispectral imaging techniques. To gather the required information for developing prediction models, batches of 200 tomatoes (cv. Pannovy) harvested at two maturity stages, were stored at standard condition up to four weeks. During storage, the modulus of elasticity, moisture content, soluble solid content, titratable acidity, hyperspectral data, and backscattering images were acquired on 40 tomatoes at regular intervals of one week. After extracting the spectral data from 40 points on each sample, they were subjected to preprocessing operations. Several feature selection techniques, including filter (Relief F, Fisher-Score, and t-Score) and wrapper (genetic algorithm) methods were used to find the sensitive wavelengths for each fruit quality parameter. With the novel strategy used, the wavelengths found by the fusion of genetic algorithm and t-Score techniques showed good prediction performance for all considered qualitative parameters. In order to verify the usefulness of selected wavelengths, backscattering and multispectral imaging techniques were applied. The artificial neural network produced the calibration models which gave a reasonably good correlation for estimating the modulus of elasticity, soluble solid content, and titratable acidity at 660 nm and moisture content at 830 nm of tomato from backscattering images. The correlation coefficient between the multispectral and backscattering imaging prediction results and reference measurement results were 0.952 and 0.891 for modulus of elasticity, 0.727 and 0.539 for moisture content, 0.736 and 0.561 for soluble solid content, and 0.811 and 0.706 for titratable acidity, respectively.
