Agricultural Product Processing and Storage Laboratory, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China.
Anal Chim Acta. 2011 Nov 7;706(1):105-12. doi: 10.1016/j.aca.2011.08.026. Epub 2011 Aug 25.
The objective of this study was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation and distribution in leaves using hyperspectral imaging. The hyperspectral imaging data cube of cucumber (Cucumis sativus) leaves in the range of 450-850 nm was investigated and preprocessed. Sixty optical signatures or indices as a function of the associated reflectance (R(λ)) at the special wavelength (λ) nm which proposed in the literatures were used to predict the total chlorophyll content in cucumber leaves. Finally, R(710)/R(760), (R(780)-R(710))/(R(780)-R(680)), (R(750)-R(705))/(R(750)+R(705)), (R(680)-R(430))/(R(680)+R(430)), R(860)/(R(550)×R(708)), (R(695-705))(-1)-(R(750-800))(-1), and REP-LEM (a index based on red edge position and estimated with a linear extrapolation method) were identified as optimum indices. Red-edge waveband (680-780 nm) appeared in all these optimum indices, indicating the importance of REP (red edge position) in chlorophyll estimation. When (R(695-705))(-1)-(R(750-800))(-1), the best index was applied to an independent validation set, chlorophyll content (r=0.8286) were reasonably well predicted, indicating model robustness. Depending on the sample, this technique enables to identify and characterize the relative content of various chlorophyll that distribution in the cucumber leaves. The map shows a relatively low level of chlorophyll at margins. Higher level can be noticed in the regions along the main veins and in some areas exhibiting dark green tissue. Our results indicate that hyperspectral imaging has considerable promise for predicting pigments in leaves and, the pigments can be detected in situ in living plant samples non-destructively.
本研究旨在探讨反射率与叶绿素含量之间的关系的光谱行为，并利用高光谱成像技术开发一种非破坏性估计和分布叶片中叶绿素的技术。研究了黄瓜（Cucumis sativus）叶片在 450-850nm 范围内的高光谱成像数据立方体，并对其进行了预处理。使用了 60 个光学特征或指标，作为与特定波长（λ）nm 相关的反射率（R(λ)）的函数，这些特征或指标是在文献中提出的，用于预测黄瓜叶片中的总叶绿素含量。最后，R(710)/R(760)、(R(780)-R(710))/(R(780)-R(680))、(R(750)-R(705))/(R(750)+R(705))、(R(680)-R(430))/(R(680)+R(430))、R(860)/(R(550)×R(708))、(R(695-705))(-1)-(R(750-800))(-1)和 REP-LEM（一种基于红边位置的指数，用线性外推法估计）被确定为最佳指数。所有这些最佳指标都出现了红边波段（680-780nm），这表明红边位置（REP）在叶绿素估计中的重要性。当应用最佳指标 (R(695-705))(-1)-(R(750-800))(-1) 于独立验证集时，叶绿素含量（r=0.8286）得到了较好的预测，表明模型稳健性。根据样本的不同，该技术可以识别和表征分布在黄瓜叶片中的各种叶绿素的相对含量。图谱显示叶片边缘的叶绿素含量相对较低。在主叶脉沿线的区域和一些呈现深绿色组织的区域可以观察到较高的水平。我们的结果表明，高光谱成像技术在预测叶片中的色素方面具有很大的潜力，并且可以在活体植物样本中进行非破坏性的原位检测。
