Rapid screening of mayonnaise quality using computer vision and machine learning

Abstract

Implementing reliable, fast, and low-cost analysis is gaining popularity in the food industry. One alternative for this type of analysis is an artificial intelligence based on image analysis. This study aimed to use image analysis to develop classification models for discriminating the acceptability of mayonnaises. A semi-trained panel comprised of 8 evaluators classified 300 pictures of mayonnaises. Features extracted from the images include the mean, standard deviation, minimum and maximum intensity values, skewness, and kurtosis from Red–Green–Blue (RGB), Hue-Saturation-Value (HSV), and the Commission Internationale d’Eclairage L* a* b* (CIELab) color spaces. Haralick Features and the intensity differences between the region of interest and the background were calculated using gray-level intensity values. A Support Vector Machine (SVM), Gradient Boosting, and K-Nearest Neighbors (KNN) models were used and evaluated in terms of accuracy, precision, recall, and F1-measure with tenfold cross-validation. Color features revealed to be the most important data for the models; these models demonstrated 92.60–93.30% accuracy, 89.00–93.30% precision, 91.40–96.43% recall, and 91.90–92.30% F1-measure. Tested models showed similar results among them. Every tested model did not exhibit significant difference compared to the panel, which presented 88.33, 94.37, 93.54, and 93.75% of accuracy, precision, recall, and F1-measure, respectively. The models obtained herein, showed to be a possible approach for a fast, low-cost, and simple methodology to estimate the acceptability of mayonnaise in sensory analysis or shelf-life studies.