TY  - 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 . .