Color is an important quality attribute for nearly every agricultural product. Consumers may perceive color as an indicator of freshness and wholesomeness, and color may affect their final decision to accept/reject food. A better understanding of human perception of colors in food would be beneficial to increase the consistency and quality of food products. The quantification of color is becoming more important due to an emphasis on international trade and implementation of Hazard Analysis Critical Control Points (HACCP) requiring record keeping. Thus, it is important to provide the agricultural industry with methods to quantify and correlate sensory and instrumental evaluations of foods.

Machine vision imitates human visual perception by using a camera and a computer with software capable to generate precise, consistent, and cost-effective color measurement. The comparison and correlation of instrumental and visual color analysis has been performed in many uniformly colored agricultural products such as meat, bakery and seafood. Generally, there is a close relationship between sensory and instrumental color analysis of homogenous foods. However, comparison and correlation of non-homogeneous color measurements in foods is more challenging and has not been thoroughly studied.

Machine vision was used to quantify the degree of color uniformity of mangos and nectarines using the number of color blocks and color primitives. The use of color primitives provided a more accurate method to measure color uniformity of mangos and nectarines. Three reference color bars (8, 12 and 16 colors) were created from color analysis of the fruits. A sensory panel (n=80) visually evaluated mangos and nectarines in two presentations: screen images captured by machine vision and fruits placed in trays. Panelists attempted to quantify color by selecting (2, 4 or 6 colors) from the reference color bars and compare the colors in the reference bars with those of the fruit surfaces. There were a total of 9 sessions at different days using different panelists.

Sensory and machine vision evaluations were compared using the absolute ΔE value. ΔE measures total color change by accounting for combined changes in L*a*b values. The concept of the best possible ΔE or best performance under given circumstances was also evaluated. It was apparent that the number of reference colors and color selections had an impact on the error made by panelists. More color selections reduced the ΔE values of the visual evaluations. Statistical analysis described significant differences between the number of reference colors, the number of selections, presentation, and the interaction between the reference colors and the selections. The 8 and 16 reference colors bar provided less error compared to the 12 reference colors bar, quantified by both ΔE for both mangos and nectarines. The 12 reference colors bar gave the most error. Two color selections provided the highest mean values. The screen images in general had lower mean values than the fruit trays. This study provided a better understanding of the way panelists perceive non-uniform colors. It also suggested a new formulation of consumer panel studies involving non-uniform visual attributes of foods..