Spectral and image analysis of hyperspectral data for internal and external quality assessment of peach fruit

Hyperspectral imaging was attempted to evaluate the internal and external quality of 'Feicheng' peach by providing the spectral and spatial data simultaneously. Mask-image was created from hyperspectral image at 810 nm and used to segment the fruit region where the average spectrum, after area normalization, was obtained for soluble solids content (SSC) and firmness evaluation. Pixel size and area were used for diameter and weight estimation. Then effective wavelengths were selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), and employed to develop multiple linear regression (MLR) models. The more effective prediction performances emerged from CARS-MLR model withR(V)(2) = 0.841, RMSEV = 0.546, RPD = 2.51 for SSC andR(V)(2) = 0.826, RMSEV = 1.008, RPD = 2.401 for firmness, followed by creating pixel-wise and object-wise visualization maps for quantifying SSC and firmness. Furthermore, peach diameter was estimated by calculating the minimum bounding rectangle with an average percentage error of 1.01 %, and the MLR model for weight prediction achieved a good performance ofR(V)(2) = 0.957, RMSEV = 9.203, and RPD = 4.819. The overall results showed that hyperspectral imaging could be used as an effective and non-destructive tool for evaluating the internal and external quality attributes of 'Feicheng' peach, and provided a holistic approach to develop online grading systems for quality tiers identification. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study attempted to evaluate the internal and external quality of 'Feicheng' peach using hyperspectral imaging. By analyzing the spectral and spatial data, the study achieved the evaluation and prediction of soluble solids content, firmness, and other attributes. The results showed that hyperspectral imaging could be used as an effective and non-destructive method for evaluating fruit quality and developing online grading systems.