Analysis of switchgrass characteristics using near infrared spectroscopy
Analysis of switchgrass characteristics using near infrared spectroscopy
复制标题
利用近红外光谱分析柳枝稷特性
作者：
N. Labbe;X. Ye;J. Franklin;A. Womac;D. Tyler;T. Rials
采用色散和傅里叶变换近红外光谱仪对不同环境下生长的柳枝稷品种进行了研究。使用多变量方法分析收集的NIR光谱。更具体地说，主成分分析（PCA）和投影潜在结构（PLS）回归技术进行分类和预测柳枝稷样品的特性。多变量的结果进行了比较，通常用于研究植物的生理性能的反射率指数。从近红外光谱，成功地实现了两个生长位置之间的判别PCA。基于生态类型和施肥量的分离也可以通过光谱数据的多变量分析来实现。对于柳枝稷样品的分类/区分，由色散光谱仪和傅里叶变换光谱仪收集的近红外光谱提供了类似的结果。从这两个近红外数据集强大的模型来预测非结构性碳水化合物含量和氮施加到该领域的速度。然而，由色散光谱仪收集的光谱导致这些样品的更准确的模型。
Switchgrass varieties grown under various environments were investigated by dispersive and Fourier Transform Near-Infrared (NIR) spectrometers. The collected NIR spectra were analyzed using multivariate approaches. More specifically, principal component analysis (PCA) and projection to latent structures (PLS) regression techniques were employed to classify and predict characteristics of the switchgrass samples. The multivariate results were compared to reflectance indices that are commonly used to study the physiological performance of plants. From near infrared spectra, discrimination between the two growth locations was successfully achieved by PCA. Separation based on the ecotype and the rate of fertilizer applied to the field was also possible by the multivariable analysis of the spectral data. For the classification/ discrimination of the switchgrass samples, the near infrared spectra collected by the dispersive and the Fourier Transform spectrometers provided similar results. From the two near infrared data sets robust models were developed to predict non-structural carbohydrates content and the rate of nitrogen applied to the field. However, the spectra collected by the dispersive spectrometer resulted in more accurate models for these samples.