Integration of fuzzy logic and computer vision in intelligent quality control of celiac-friendly products

Automation in food industry demands intelligent and feasible techniques to replace the human brain with machine intelligence. Quality control examines product attributes which cannot be quantified exactly and thus the relationship amongst the attributes parameters is unclear. The visual properties of the product can be more accurately and quickly examined by machine. Hence, in the present paper, a sensory evaluation was carried out on one of the main quality attributes as taste and was combined with two others as appearance and texture acquired by computer vision to determine the acceptable level of ingredients of a gluten-free cake (GFC). Analysis of samples using the aforementioned method indicated that acceptable levels of 50% purslane flour (PF) and 1% quince seed gum (QSG). Sensory evaluation indicated that the quality attributes can be ranked in a descending order as texture, taste and color. Employment of fuzzy logic and image processing was promising to indicate the optimum formulation of compounds as the top rank was found to be the third sample.