Makino Yoshio, Kousaka Yumi
Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.
Foods. 2020 May 2;9(5):558. doi: 10.3390/foods9050558.
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 mg·g·d. 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.
本研究尝试开发一种非侵入性技术来估算收获后西兰花的褪绿（绿色褪去）速度。收获后的西兰花头的绿色褪去是不均匀发生的。因此，本研究采用了能将光谱反射率与空间信息一同存储的高光谱成像技术。利用人工神经网络（ANN），我们证明了西兰花头某一部位叶绿素的减少速度与405至960nm的15个波长处光谱反射率数据的二阶导数相关。使用人工神经网络模型预测减少速度，相关系数为0.995，预测标准误差为5.37×10mg·g·d。估计的减少速度对于预测西兰花芽在储存7天内的叶绿素浓度有效，7天被确定为保持可销售性的最长时间。该技术可能有助于对西兰花头的货架期进行无损预测。
