MahaHany¹, Shaheera Rashwan¹ and Neveen M. Abdelmotilib², ¹Informatics Research Institute, Egypt, ²Arid Lands Cultivation Research Institute, Egypt

Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques.

Food quality, Supervised feature selection, Yogurt preferences prediction, random forest classification.