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标题：Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks
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作者：Yoshio Makino ; Yumi Kousaka
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2020
卷号：9
期号：5
页码：558-570
DOI：10.3390/foods9050558
出版社：MDPI Publishing
摘要：Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.
关键词：Brassica oleracea var; italica ; chlorophyll; mathematical model; nondestructive analysis; shelf life; spectroscopy; statistical analysis; vegetable Brassica oleracea var; italica ; chlorophyll ; mathematical model ; nondestructive analysis ; shelf life ; spectroscopy ; statistical analysis ; vegetable