Human perception of color differences using computer vision system measurements of raw pork loin
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2022
Authors
Altmann, Brianne A.Gertheiss, Jan
Tomašević, Igor

Engelkes, Christina
Glaesener, Thibaud
Meyer, Jule
Schäfera, Alina
Wiesena, Richard
Mörleina, Daniel
Article (Published version)

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In the food industry, product color plays an important role in influencing consumer choices. Yet, there remains little research on the human ability to perceive differences in product color; therefore, preference testing is subjective rather than based on quantitative colors. Using a de-centralized computer-aided systematic discrimination testing method, we ascertain consumers' ability to discern between systematically varied colors. As a case study, the colors represent the color variability of fresh pork as measured by a computer vision system. Our results indicate that a total color difference (ΔE) of approximately 1 is discriminable by consumers. Furthermore, we ascertain that a change in color along the b*-axis (yellowness) in CIELAB color space is most discernable, followed by the a*-axis (redness) and then the L*-axis (lightness). As developed, our web-based discrimination testing approach allows for large scale evaluation of human color perception, while these quantitative find...ings on meat color discrimination are of value for future research on consumer preferences of meat color and beyond.
Keywords:
Color preference / Discrimination testing / Food appearance / Meat color / Triange testSource:
Meat Science, 2022, 188, 108766-Publisher:
- Elsevier Ltd
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Poljoprivredni fakultetTY - JOUR AU - Altmann, Brianne A. AU - Gertheiss, Jan AU - Tomašević, Igor AU - Engelkes, Christina AU - Glaesener, Thibaud AU - Meyer, Jule AU - Schäfera, Alina AU - Wiesena, Richard AU - Mörleina, Daniel PY - 2022 UR - http://aspace.agrif.bg.ac.rs/handle/123456789/6043 AB - In the food industry, product color plays an important role in influencing consumer choices. Yet, there remains little research on the human ability to perceive differences in product color; therefore, preference testing is subjective rather than based on quantitative colors. Using a de-centralized computer-aided systematic discrimination testing method, we ascertain consumers' ability to discern between systematically varied colors. As a case study, the colors represent the color variability of fresh pork as measured by a computer vision system. Our results indicate that a total color difference (ΔE) of approximately 1 is discriminable by consumers. Furthermore, we ascertain that a change in color along the b*-axis (yellowness) in CIELAB color space is most discernable, followed by the a*-axis (redness) and then the L*-axis (lightness). As developed, our web-based discrimination testing approach allows for large scale evaluation of human color perception, while these quantitative findings on meat color discrimination are of value for future research on consumer preferences of meat color and beyond. PB - Elsevier Ltd T2 - Meat Science T1 - Human perception of color differences using computer vision system measurements of raw pork loin SP - 108766 VL - 188 DO - 10.1016/j.meatsci.2022.108766 ER -
@article{ author = "Altmann, Brianne A. and Gertheiss, Jan and Tomašević, Igor and Engelkes, Christina and Glaesener, Thibaud and Meyer, Jule and Schäfera, Alina and Wiesena, Richard and Mörleina, Daniel", year = "2022", abstract = "In the food industry, product color plays an important role in influencing consumer choices. Yet, there remains little research on the human ability to perceive differences in product color; therefore, preference testing is subjective rather than based on quantitative colors. Using a de-centralized computer-aided systematic discrimination testing method, we ascertain consumers' ability to discern between systematically varied colors. As a case study, the colors represent the color variability of fresh pork as measured by a computer vision system. Our results indicate that a total color difference (ΔE) of approximately 1 is discriminable by consumers. Furthermore, we ascertain that a change in color along the b*-axis (yellowness) in CIELAB color space is most discernable, followed by the a*-axis (redness) and then the L*-axis (lightness). As developed, our web-based discrimination testing approach allows for large scale evaluation of human color perception, while these quantitative findings on meat color discrimination are of value for future research on consumer preferences of meat color and beyond.", publisher = "Elsevier Ltd", journal = "Meat Science", title = "Human perception of color differences using computer vision system measurements of raw pork loin", pages = "108766", volume = "188", doi = "10.1016/j.meatsci.2022.108766" }
Altmann, B. A., Gertheiss, J., Tomašević, I., Engelkes, C., Glaesener, T., Meyer, J., Schäfera, A., Wiesena, R.,& Mörleina, D.. (2022). Human perception of color differences using computer vision system measurements of raw pork loin. in Meat Science Elsevier Ltd., 188, 108766. https://doi.org/10.1016/j.meatsci.2022.108766
Altmann BA, Gertheiss J, Tomašević I, Engelkes C, Glaesener T, Meyer J, Schäfera A, Wiesena R, Mörleina D. Human perception of color differences using computer vision system measurements of raw pork loin. in Meat Science. 2022;188:108766. doi:10.1016/j.meatsci.2022.108766 .
Altmann, Brianne A., Gertheiss, Jan, Tomašević, Igor, Engelkes, Christina, Glaesener, Thibaud, Meyer, Jule, Schäfera, Alina, Wiesena, Richard, Mörleina, Daniel, "Human perception of color differences using computer vision system measurements of raw pork loin" in Meat Science, 188 (2022):108766, https://doi.org/10.1016/j.meatsci.2022.108766 . .