首页> 外文期刊>Journal of near infrared spectroscopy >Near infrared reflectance spectroscopy of oil in intact canola seed (Brassica napus L.). II. Association between principal components and oil content
【24h】

Near infrared reflectance spectroscopy of oil in intact canola seed (Brassica napus L.). II. Association between principal components and oil content

机译:完整油菜籽(甘蓝型油菜)中油的近红外反射光谱。二。主成分与含油量之间的关联

获取原文
获取原文并翻译 | 示例
       

摘要

This work reports that the measurements of the likeness or the uniqueness of the 1100-2500 nm reflectance spectra of intact canola seed determined from principal component analysis (PCA) approximated spectra with global H or neighbourhood H statistics were not associated with oil concentration within the seed. The absence of stability in association between the H measurements and oil content was related to inconsistency in the amount and distribution (between principal components) of the spectral variation correlated to the oil content within and between different batches of canola seed. PCA was used to approximate variation in the 1100-2500 nm, second order derivative, reflectance spectra of intact canola seed, acquired from 15 batches of seed samples. The first eight principal components (PCs) captured 97.14% to 99.35% of the total variance in the spectra. The amount of variation captured by individual components was independent of the number of samples in the batch and oil content within the seed. The pattern of variance distribution among principal components was inconsistent and highlighted the uniqueness of the origin of the spectral variation in each batch of canola seed. In this study, the strength of correlation between oil content and principal components was used as a measure of component significance to the analysis of oil in the intact canola seed. In the examined sets of spectra, oil content was correlated to the low order components, PC1 to PC4. In the 15 files of spectra, oil content showed the strongest correlation to PC2 in eight sets of data, to PC3 in four sets of data and to PC1 in three sets of data. The strength of association between oil and the individual components varied considerably in magnitude among examined files of spectra; r{sup}2=0.28-0.81 for the" first strongly correlated component, r{sup}2=0.05-0.29 for the second and r{sup}2=0.02-0.19 for the third. The position of the PCs in the correlation sequence was inconsistent and underlined differences of oil signal/spectral data interactions in the individual sets of data. Examination of principal component loadings showed that in the reported files of spectra, principal components correlated to the oil content frequently captured variance at segments, which denote absorptions specific and accidental to canola oil. The outline of the loadings did not conform to a single, regular pattern common to all sets of data. The reported results are in disagreement with rationale of the methodology, which involves the spectra matching techniques for validating the predictive efficiency of near infrared (NIR) calibrations. The reported results highlighted that the reliable NIR quantification of oil content from reflectance spectra of intact canola seed would require an independent validation for every acquired set of spectra.
机译:这项工作报告说,通过主成分分析(PCA)近似光谱和整体H或邻域H统计数据确定的完整油菜籽的1100-2500 nm反射光谱的相似性或唯一性与种子中的油浓度无关。 H值测量值与油含量之间缺乏关联的稳定性与光谱变化的数量和分布(主要成分之间)不一致有关,该光谱变化与不同批次的油菜籽内部和之间的油含量相关。 PCA用于估算从15批次种子样品中获得的完整油菜籽在1100-2500 nm处的二阶导数,反射光谱的变化。前八个主成分(PC)占光谱总方差的97.14%至99.35%。单个成分捕获的变异量与批次中的样品数量和种子中的油含量无关。主成分之间方差分布的模式不一致,并突出了每批油菜籽中光谱变化的起源的独特性。在这项研究中,油含量与主要成分之间的相关强度被用作衡量完整油菜籽中油含量的重要成分。在检查的光谱集中,油含量与低阶成分PC1至PC4相关。在15个光谱文件中,含油量与八组数据中的PC2,四组数据中的PC3和三组数据中的PC1关联最强。在检查的光谱文件中,油与各个成分之间的缔合强度在大小上有很大差异;对于“第一个高度相关的组件”,r {sup} 2 = 0.28-0.81,对于第二个组件,r {sup} 2 = 0.05-0.29,对于第三个组件,r {sup} 2 = 0.02-0.19。相关序列不一致,并在各组数据中强调了油信号/光谱数据相互作用的差异,对主成分载荷的检查表明,在所报告的光谱文件中,与油含量相关的主成分经常捕获各段的方差,表明芥花籽油具有特定的吸收性和偶然性,装载量的轮廓与所有数据集均不遵循单一规则的模式,报告的结果与方法学的原理不符,后者涉及用于验证质谱数据的光谱匹配技术报告结果强调指出,从完整油菜籽的反射光谱中对油含量进行可靠的NIR定量分析将需要对每个采集的光谱进行独立验证。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号