ArticleJournal of food science2026
Evaluating the Color of Chocolate Chip Cookies: Comparative Performance of Instrumental Colorimetry, Digital Image Analysis, and Computer Vision Relative to Human Perception.
Article in Journal of food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Wheat Flour Substitution with Sacha Inchi Tea Leaf Powder: Effects on Physicochemical Properties, Starch Digestibility, and Sensory Attributes of Cookies.Foods (Basel, Switzerland) · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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