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基于可见-近红外高光谱无损测定菠菜叶片中的硝酸盐含量

[Nondestructive determination of nitrate content in spinach leaves with visible-near infrared high spectra].

作者信息

Xue Li-hong, Yang Lin-zhang

机构信息

Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Apr;29(4):926-30.

Abstract

The objective of the present research was to study the potential of Vis-NIR (visible-near-infrared) high spectra for nondestructive determination of nitrate content in spinach leaves. Five different nitrogen treatments were carried out to achieve a wide range of nitrate content in spinach leaves. The leaf reflectance was measured between 350 to 2,500 nm with a 1 nm step with a leaf clip by ASD Fieldspec FR spectroradiometer, and the nitrate content was measured by spectrophotometric method (National Standard Method of P. R. China). Statistical models were developed using partial least squares (PLS) and principal component regression (PCR) analysis technique, different mathematical treatments for spectra processing such as smoothing, first and second derivative analysis, baseline correction, multiplicative scatter correction (MSC), and standard normal variate correction (SNV), and different wavelength ranges were compared to determine the best model. The dataset was separated into two parts: one used for calibration (n=43), and the other used for test (n=15). First, the model was calibrated and cross-validated with the calibration dataset, then the model was validated with the test dataset to test its prediction ability. The results showed that smoothing, first derivative and second derivative analysis can improve the prediction obviously, while other spectra pre-processing technique e. g. baseline correction, MSC and SNV technique can improve the prediction little. PCR analysis could get better modeling results than PLS analysis. The best model was obtained with the spectra first processed by smoothing then by first derivative change in the full range (350-2,500 nm). Test of the best PLS model and PCR model with an independent dataset exhibited a good agreement between the predicted and observed values, with the correlation coefficient of 0.94 for PLS model and 0.95 for PCR model, and the prediction RMSE was 128.2 mg x kg(-1) for PLS model and 120.8 mg x kg(-1) for PCR model, respectively. These results indicate that visible-NIR spectra technique is a feasible, nondestructive way to predict the nitrate content in spinach leaves.

摘要

本研究的目的是探讨可见-近红外(Vis-NIR)高光谱技术用于无损测定菠菜叶片硝酸盐含量的潜力。进行了五种不同的氮处理,以使菠菜叶片中的硝酸盐含量具有较宽的范围。使用ASD Fieldspec FR光谱辐射仪,通过叶夹在350至2500nm之间以1nm步长测量叶片反射率,并用分光光度法(中国国家标准方法)测量硝酸盐含量。使用偏最小二乘法(PLS)和主成分回归(PCR)分析技术建立统计模型,比较了不同的光谱处理数学方法,如平滑处理、一阶和二阶导数分析、基线校正、多元散射校正(MSC)和标准正态变量校正(SNV),以及不同的波长范围,以确定最佳模型。数据集分为两部分:一部分用于校准(n = 43),另一部分用于测试(n = 15)。首先,用校准数据集对模型进行校准和交叉验证,然后用测试数据集对模型进行验证,以检验其预测能力。结果表明,平滑处理、一阶导数和二阶导数分析可显著提高预测效果,而其他光谱预处理技术,如基线校正、MSC和SNV技术对预测的改善作用较小。PCR分析比PLS分析能获得更好的建模结果。最佳模型是通过对光谱先进行全范围(350 - 2500nm)的平滑处理,然后进行一阶导数变换得到的。用独立数据集对最佳PLS模型和PCR模型进行测试,预测值与观测值之间具有良好的一致性,PLS模型的相关系数为0.94,PCR模型的相关系数为0.95,PLS模型的预测均方根误差为128.2mg·kg⁻¹,PCR模型的预测均方根误差为120.8mg·kg⁻¹。这些结果表明,可见-近红外光谱技术是一种预测菠菜叶片硝酸盐含量的可行的无损方法。

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