首页|基于可见/近红外光谱的牡丹叶片花青素含量预测

基于可见/近红外光谱的牡丹叶片花青素含量预测

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以开花初期不同品种牡丹叶片为研究对象,分析叶片花青素含量与反射光谱之间的相关关系,分别建立基于单波长、不同植被指数、相关系数大于0.52的可见光波段的叶片花青素含量预测模型.研究结果表明,牡丹叶片反射光谱与花青素含量的最大相关系数位于544 nm;以544 nm波长反射率及花青素反射指数(ARI)、调整花青素反射指数(MARI)为自变量建立的预测模型可以用于牡丹叶片花青素含量预测;以偏最小二乘回归(PLSR)构建的牡丹叶片花青素含量预测模型的建模和验模R2分别为0.873和0.811,RMSE为0.068 μmol/g,RPD为2.352,是预测牡丹叶片花青素含量的最优模型.
Prediction of Anthocyanin Content in Peony Leaves Based on Visible/Near-infrared Spectra
The anthocyanin content in leaves can provide valuable information about the physiological conditions of plants and their responses to stress.Thus,there is a need for accurate,efficient and practical methodologies to estimate the biochemical parameters of vegetation.In this study,the peony leaves of different varieties in the early flowering stage were selected as the research objects to analyze the correlation between anthocyanin content in leaves and reflectance spectra.The predictive models were established based on a single band or different vegetation indices.The PLSR (Partial least squares regression) model was constructed to estimate anthocyanin content in leaves by using the reflectance spectra with correlation coefficient more than 0.52 in visible band as independent variables.The results showed that the maximum correlation coefficient between reflectance spectra and anthocyanin content was located at 544 nm.These predictive models which used the reflectance at 544 nm,ARI (Anthocyanin reflectance index) or MARI (Modified anthocyanin reflectance index) as independent variables could be used to estimate anthocyanin content in peony leaves in fact.The calibration and validation R2 of optimum model for predicting anthocyanin content in poeny leaves established by PLSR were 0.873 and 0.811,and the RMSE and RPD were 0.068 μmol/g and 2.352,respectively.This study can provide a method for nondestructive estimation of anthocyanin content in plant leaves,and make a reference for the assessment of physiological status of plants and early stress detection.

PoenyLeafAnthocyaninVisible/Near-infrared spectraPartial least squares regressionPredictive model

刘秀英、申健、常庆瑞、严林、高雨茜、谢飞

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西北农林科技大学资源环境学院,陕西杨凌712100

河南科技大学农学院,洛阳471003

牡丹 叶片 花青素 可见/近红外光谱 偏最小二乘回归 预测模型

国家高技术研究发展计划(863计划)资助项目

2013AA102401-2

2015

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCDCSCD北大核心EI
影响因子:1.904
ISSN:1000-1298
年,卷(期):2015.46(9)
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