Oil content distribution in tortillas chips during frying and their prediction by image analysis using computer vision.

Abstract

The increasing consumption worldwide of tortilla chips make relevant to design and optimize their industrial quality analysis. Surface, structural and total oil content during frying of tortilla chips fried at 160, 175, 190ºC for different times were analyzed. The aim was to obtain a relationship between oil content and features from their digital images. The results showed a high linear correlation (R>0.90) between oil content with image features at each frying temperature, indicating that trustable models can be developed, allowing the prediction of oil content of tortilla chips by using selected features extracted from their digital images, without the necessity of measuring them. Cross-validation technique demonstrated the repeatability of each model and their good performance (>90%).