The relationship between perceived freshness and water content of cabbage leaves: A near infrared imaging survey of substance distribution underlying product appearance

• Published in: Food science & technology Vol. 139; p. 110523
• Main Authors: Luo, Xuan, Masuda, Tomohiro, Matsubara, Kazuya, Wada, Yuji, Ikehata, Akifumi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2021
• Subjects: Luminance distribution; NIR imaging; NIR spectroscopy; Visual freshness perception; Water content; Visual freshness perception; NIR imaging; NIR spectroscopy; Water content; Luminance distribution
• ISSN: 0023-6438; 1096-1127
• DOI: 10.1016/j.lwt.2020.110523

Although freshness is the most important index for agricultural products, the freshness estimation for vegetables is still manual. This is because the technique is imperfect and defining freshness is difficult and based on a subjective impression based on the appearance. In previous studies, to clarify how humans perform freshness assessments by vision, researchers examined the relationship between food image statistical parameters and visual freshness perception using psychophysical experiments. The results showed the perceived freshness of food is related to surface gloss, or in other words, the luminance distribution parameters of food images. However, the factors affecting the perceived freshness have not been reported. In this study, water content of a cabbage leaf was compared with the decisive image parameters to quantify its freshness. Consequently, cabbage water content can be a viable index for interpreting human perceived freshness. In addition, a near infrared (NIR) imaging system was also evaluated as a non-destructive method for predicting water content. Loss of the perceived freshness is caused by an increase in wrinkles associated with the release of water. Further, the water distribution related to the surface morphology was visualized by NIR imaging. •NIR can detect water content in leafy cabbages.•NIR can be an accurate quantitative automatic quality assessment method.•Water content is related to standard deviation and kurtosis of leaf images' luminance distribution.•Visible image luminance distribution and moisture distribution in cabbage leaves were compared.