Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

Abstract

This study was aimed at evaluating the effect of freeze-drying and lacto-fermentation on the texture parameters of images and sensory attributes of beetroots. The samples were imaged using a flatbed scanner, and textures from images converted to color channels L, a, b, R, G, B, X, Y, Z were computed. The discrimination of raw and processed beetroots was performed using models based on textures selected for each color channel. The sensory quality of processed samples was determined using the attributes related to smell, color, texture and taste. The highest discrimination accuracy of 97.25% was obtained for the model built for color channel b. The accuracies for other channels were equal to 96.25% for channel a, 95.25% for channel R, 95% for channel Y, 94.75% for channel B, 94.5% for channel X, 94% for channel L, 92.5% for channel G, 88.25% for channel Z. In the case of some models, the raw and lacto-fermented beetroots were discriminated with 100% correctness. The freeze-dried and freeze-dried lacto-fermented samples were also the most similar in terms of sensory attributes, such as off-odor, attractiveness color, beetroot color, crunchiness, hardness, bitter taste, overall quality. The results indicated that the image parameters and sensory attributes may be related.