高光谱成像
叶绿素
偏最小二乘回归
波长
园艺
遥感
内容(测量理论)
叶绿素a
植物
数学
化学
生物
材料科学
统计
光电子学
地质学
数学分析
作者
Yi Sun,Yihang Wang,Hui Xiao,Xuefan Gu,Leiqing Pan,Kang Tu
标识
DOI:10.1016/j.foodchem.2017.05.064
摘要
Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617 nm, 675 nm, and 818 nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches.
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