Non-destructive identification of defects and classification of Hass avocado fruits with the use of a hyperspectral image

إنجليزي. Received: January 15th, 2022 ; Accepted: April 19th, 2022 ; Published: May 2nd, 2022 ; Correspondence: [email protected]

إنجليزي. Sensory analysis and instrumental analytical methods are used in determining thematurity and quality monitoring of avocado fruits, which are labor-intensive and do not allow thedetermination of fruit quality in real time. The use of hyperspectral imaging (HSI) methods in therange of 400–1,000 nm and of the multivariate analysis was demonstrated for a non-destructivegrading 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 thenumber of defects after being stored for 10 days. Development of a classification model includedseveral steps: image recording and analysis using the ANOVA and PCA method, imagesegmentation (selection of ROI), pre-processing (SNV-correction, centering), selection of amultivariate classification method (PLS-DA, SIMCA) and a spectral range, model verification.The analysis of hyperspectral images of avocado fruits has detected spectral regions with themaximal variance responsible for the change of the content of pigments and moisture within theavocado fruit exocarp. Comparison of PLS-DA and SIMCA models on the basis of best accuracyand test-validation results was carried out. Comparison of models showed SIMCA model as themost efficient model for fruit classification into quality classes depending on the number ofhidden defects. The implementation of the developed approach as a digital avocado fruit sortingsystem at different stages of the product life cycle is proposed.