Evaluating the Color of Chocolate Chip Cookies: Comparative Performance of Instrumental Colorimetry, Digital Image...

Food color is commonly quantified using colorimeters; however, these instruments may be impractical in home-based or small-scale production settings. More accessible alternatives, such as digital image analysis and computer vision, may offer reliable color quantification when conventional instrumentation is unavailable. The objective of this study was to identify the color-measurement method and sampling strategy that most accurately and reliably predict consumers' perceived cookie color. Three approaches to characterizing the color of chocolate chip cookies were compared and evaluated for concordance with human perception: instrumental colorimetry (HunterLab colorimeter), digital image analysis (Adobe Photoshop), and computer-vision analysis (OpenCV/Python). One hundred consumers evaluated the perceived lightness, redness, and yellowness of 15 cookie samples (3 cookies × 5 commercial brands) using 15-cm line scales designed to approximate L*, a*, and b* color constructs. Instrumental and image-based measurements were collected under two sampling conditions: (1) four standardized surface locations on each cookie (selected point) and (2) whole-surface averaging (whole cookie). Across methods, selected-point sampling generally produced higher L* values than whole-cookie sampling. Multivariate analyses showed that all methods differentiated among brands. However, agreement with sensory configurations was strongest for instrumental colorimetry under selected-point sampling and for computer vision under whole-cookie sampling. Predictive modeling further indicated that selected-point instrumental colorimetry most consistently predicted perceived lightness and yellowness, whereas whole-cookie computer-vision generated the best-performing predictions of perceived redness. In conclusion, selected-point colorimetry showed the closest correspondence with perceived cookie color in this dataset, while computer vision demonstrated strong promise as a practical alternative for color quantification in resource-limited settings.