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Model of vegetable freshness perception using luminance cues
Affiliation:1. Faculty of Engineering, ICESI University, Calle 18 No. 122-135 Pance, Colombia;2. Faculty of Environment and Information Sciences, Yokohama National University, E404 General Research Building, 9-7 Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa 240-8501, Japan;3. Food Function Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12, Kannondai, Tsukuba, Ibaraki 305-8642, Japan;4. Tokyo Denki University, 2-1200 Muzai Gakuendai, Inzai-shi, Chiba 270-1382, Japan;1. Department of Urology, University of Michigan, Ann Arbor, Michigan;2. Department of Urology, Oakland University William Beaumont School of Medicine, Rochester, Michigan;1. Dept. of Computing Science, ATH 3-55, University of Alberta, Edmonton, AB, Canada T6G 2E8;2. Robotics Institute, Smith Hall 211, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;3. Dept. of Electrical & Computer Engineering and Centre of Intelligent Machines, McGill University, McConnell Engineering Building, Room 441, 3480 University Street, Montreal, QC, Canada H3A 2E9;1. CSIRO Animal, Food and Health Sciences, PO Box 10041, Adelaide, SA 5000, Australia;2. CSIRO Ecosystem Sciences, PO Box 350, Glen Osmond, SA 5064, Australia
Abstract:Freshness perception is a quality discrimination process that influences our consumer choice and eating behavior, especially of highly perishable products such as vegetables. Previous research used photographic stimuli to investigate the relationship between luminance distribution and freshness perception for a cabbage leaf (C. Arce-Lopera, Masuda, Kimura, et al., 2013) and a strawberry (Carlos Arce-Lopera, Masuda, Kimura, Wada, & Okajima, 2012). In this study, the luminance and chromatic information of the freshness degradation process of four different vegetables (cabbage, strawberry, carrot and spinach) was recorded in a temperature, humidity and light controlled environment. However, instead of a camera, a 2D luminance and chromaticity analyzer (TOPCON UA1000) was chosen as the measurement equipment. Then, using a color management system to guarantee the exact reproduction of the recorded luminance and chromatic data of the real objects, a color and a grayscale version of the stimuli was created. Subsequently, those pictures were randomly presented to subjects who had to rate their perceived freshness using a visual analog scale. The achromatic results did not differ from the chromatic ones suggesting that luminance information is sufficient to enable an accurate estimation of vegetable freshness. Additionally, the original images were digitally manipulated only by modifying their luminance distribution and keeping their color information intact. When the resulting images were presented, using the same psychophysical experimental setting, the results showed that the perceived freshness also changed concordantly with the changes on the asymmetry of the luminance distribution. Finally, a model for vegetable freshness perception that utilizes only luminance cues is presented.
Keywords:Visual freshness perception  Luminance distribution  Image analysis  Statistics  Vegetables
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