Non-destructive identification of defects and classification of Hass avocado fruits with the use of a hyperspectral image
¹Plekhanov Russian University of Economics, Faculty of Trade Economics and Commodity Science, Department of Commodity Science, Stremyanny lane 36, RU115054 Moscow, Russia
²University of Plovdiv 'Paisii Hilendarski', Faculty of chemistry, Department of Chemical Technology, 24 Tsar Assen Str., BL4000 Plovdiv, Bulgaria
Sensory analysis and instrumental analytical methods are used in determining the maturity and quality monitoring of avocado fruits, which are labor-intensive and do not allow the determination of fruit quality in real time. The use of hyperspectral imaging (HSI) methods in the range of 400–1,000 nm and of the multivariate analysis was demonstrated for a non-destructive grading of Hass avocado fruits into quality classes according to the number of hidden defects. Using the sensory analysis, avocado fruits were separated into quality classes according to the number of defects after being stored for 10 days. Development of a classification model included several steps: image recording and analysis using the ANOVA and PCA method, image segmentation (selection of ROI), pre-processing (SNV-correction, centering), selection of a multivariate classification method (PLS-DA, SIMCA) and a spectral range, model verification. The analysis of hyperspectral images of avocado fruits has detected spectral regions with the maximal variance responsible for the change of the content of pigments and moisture within the avocado fruit exocarp. Comparison of PLS-DA and SIMCA models on the basis of best accuracy and test-validation results was carried out. Comparison of models showed SIMCA model as the most efficient model for fruit classification into quality classes depending on the number of hidden defects. The implementation of the developed approach as a digital avocado fruit sorting system at different stages of the product life cycle is proposed.