Digital Image Analysis to Evaluate Sensory Attributes of Protein-Enriched Whole-Wheat Bread

Abstract

New food products or reformulated food products require intensive sensory assessment using a group of panelists before launching in the market. Sometimes, the sensory results obtained by the panelists are inconclusive due to their subjective scores. An indirect and accurate method to evaluate the sensory attributes using images is highly beneficial to conduct preliminary screening during product development stages. Therefore, the objective of this study was to determine the potential of red-green-blue (RGB) color images to evaluate the sensory qualities of whole wheat bread reformulated with pea and soy protein isolates as model food. In this study, reformulated whole wheat (WW) bread was used as model food to determine the potential of digital color images in assessing the selected sensory attributes. Seven types of WW bread was evaluated by ten untrained panelists. Four features (edge detection, pore numbers, pore area and Hu-moment similarity) were extracted from the images of the bread slices and compared with measured sensory scores. In general, the polynomial regression models yielded higher R2 values than linear regression models. The R2 values in polynomial regression models ranged 0.82-0.97, 0.60-0.92, 0.55-0.96, 0.77-0.99, 0.67-0.97, and 0.50-0.87 for chewiness, graininess, moistness, taste, desired aroma and overall acceptability, respectively. Hu-moment similarity provided the highest R2 values for the sensory attributes in polynomial regression models. In conclusion, although image-based sensory assessment may not substitute the current human sensory, it can provide valuable information to supplement the decision making process.