Combining multivariate analysis and monosaccharide composition modeling to identify plant cell wall variations by Fourier Transform Near Infrared spectroscopy

被引：22

作者：

Smith-Moritz, Andreia M. ^{[1
,2
]}

Chern, Mawsheng ^{[1
,3
]}

Lao, Jeemeng ^{[1
,3
]}

Sze-To, Wing Hoi ^{[1
,3
]}

Heazlewood, Joshua L. ^{[1
,2
]}

Ronald, Pamela C. ^{[1
,2
,3
]}

Vega-Sanchez, Miguel E. ^{[1
,3
]}

机构：

[1] Univ Calif Berkeley, Lawrence Berkeley Lab, Joint BioEnergy Inst, Berkeley, CA 94720 USA

[2] Univ Calif Berkeley, Lawrence Berkeley Lab, Phys Biosci Div, Berkeley, CA 94720 USA

[3] Univ Calif Davis, Dept Plant Pathol, Davis, CA 95616 USA

来源：

PLANT METHODS | 2011年 / 7卷

关键词：

near infrared spectroscopy; cell wall; hemicellulose; multivariate analysis; mutant screen; pls modeling; FT-IR; GLUCURONOXYLAN BIOSYNTHESIS; CHEMICAL-COMPOSITION; ARABIDOPSIS; MUTANTS; CELLULOSE; IDENTIFICATION; SPECTRA; WOOD; MICROSPECTROSCOPY;

D O I：

10.1186/1746-4811-7-26

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We outline a high throughput procedure that improves outlier detection in cell wall screens using FT-NIR spectroscopy of plant leaves. The improvement relies on generating a calibration set from a subset of a mutant population by taking advantage of the Mahalanobis distance outlier scheme to construct a monosaccharide range predictive model using PLS regression. This model was then used to identify specific monosaccharide outliers from the mutant population.

引用

页数：13

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