期刊
FOODS
卷 9, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/foods9091305
关键词
Brassica oleraceavar; italica; computer vision; machine learning; evaluation; shelf life; statistical analysis; vegetable; image analysis
资金
- National Agriculture and Food Research Organization, Smart Agriculture Demonstration Project [20317773]
- Kieikai Research Foundation
Yellowing of green vegetables due to chlorophyll decomposition is a phenomenon indicating serious deterioration of freshness, and it is evaluated by measuring color space values. In contrast, mass reduction due to water loss is a deterioration of freshness observed in all horticultural crops. Therefore, in this study, we propose a novel freshness evaluation index for green vegetables that combines the degree of greenness and mass loss. The green color retention rate was measured using a computer vision system, and the mass retention rate was measured by weighing. Linear discriminant analysis (LDA) was performed using both variables (greenness and mass) as covariates to obtain a single freshness evaluation value (first canonical variable). The correct classification of storage period length by LDA was 96%. Green color retention alone allowed for classification of storage durations between 0 day and 10 days, whereas LDA could classify storage durations between 0 day and 12 days. The novel freshness evaluation index proposed by this research, which integrates greenness and mass, has been shown to be more accurate than the conventional evaluation index that uses only greenness.
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