- Open Access
Combining multivariate analysis and monosaccharide composition modeling to identify plant cell wall variations by Fourier Transform Near Infrared spectroscopy
© Smith-Moritz et al; licensee BioMed Central Ltd. 2011
- Received: 22 June 2011
- Accepted: 18 August 2011
- Published: 18 August 2011
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.
- near infrared spectroscopy
- cell wall
- multivariate analysis
- mutant screen
- pls modeling
Plant cell walls are a complex mixture of polysaccharides, proteins and the phenolic polymer lignin that have been recently targeted as possible sources of fermentable sugars for the production of biofuels and other bio-materials . The development of a lignocellulose biomass-based biofuels industry is partly dependent on genetic engineering and breeding of the next generation of crops containing, among other traits, easily extractable cell wall sugars. Thus, a better understanding of how plants synthesize, deposit and modify their cell walls is necessary for the selection of traits important for biofuel crop improvement .
The identification of plants with altered cell wall composition or structure can prove useful in the discovery of novel genes involved in the biosynthesis and modification of the cell wall. Such plants can be isolated using genome-wide association mapping of diverse populations or can be isolated from forward genetic screens, where a subset sample population with the desired traits is selected from a large pool of mutagenized individuals. However, the identification of these select samples requires a well-constructed screening process that is both robust and, due to the large sample population, high-throughput. Several successful plant cell wall mutant screens have been described over the years that make use of different screening methodologies. These include: acid hydrolysis and monosaccharide composition using gas-liquid chromatography , microscopic observation of xylem stem sections [4, 5], seedling growth on medium containing cell wall hydrolyzing enzymes  and Fourier-Transform Infrared (FT-IR) microspectroscopy [7, 8]. Most of these approaches either required at least some kind of sample processing or were not amenable to high-throughput screening, especially when dealing with, in some cases, thousands of mutagenized plant samples. In addition, most of these screens have been performed on the model species Arabidopsis thaliana, a dicot, which is known to have a different cell wall type than grasses .
Recently various infrared spectroscopy techniques such as Fourier Transform Mid-Infrared (FT-MIR) have been used to characterize plant cell wall model compounds and mutants [7, 8, 10–16]. Due to the chemical specificity of this infrared region (400 to 4000 cm-1), one can directly identify certain peaks related to cell wall components. However, the use of FT-MIR in these studies involved careful plant cell wall extraction and/or probing of individual plant cells with a FT-MIR microscopy objective. Though very effective and informative, the use of FT-MIR as a high throughput cell wall screening technique for a large population is not practical due to the need for meticulous sample handling.
Significantly, another region of the infrared spectrum, the near-infrared (NIR), has shown promise in the classification and characterization of plant material in a more rapid manner. In contrast to MIR, the NIR region (12000 to 4000 cm-1) does not reveal discrete signature peaks, but it excites several harmonic overtones of methyl, aromatic CH-OH, with minor features in methoxy and carbonyl CH bonds, generating spectra that have no easily distinguishing chemical features . However, with the help of multivariate analysis to deconvolve the spectrum, FT-NIR has been successfully applied to rapidly quantify and classify numerous known components in complex mixtures [18–20]. In this manner, cell wall components such as carbohydrates, ash content, and lignin have been successfully modeled and cross-validated from a defined plant set of various tissue types [21–27]. In order to correlate NIR spectra to chemical features and eventually quantify individual components in a mixture, a robust training set containing NIR spectra of a range of known concentrations is required. Using Partial Least Squares (PLS) regression, a model can then be developed to determine the concentration of these components in unknown mixtures, within the same range, by using NIR spectra alone . Successful applications of FT-NIR techniques for fast chemical characterization involve acquiring accurate sample spectra, applying robust chemometric/multivariate analysis for spectra processing and obtaining reliable calibration sets for modeling. Recently, FT-NIR and linear discriminate analysis (Mahalanobis distance) were used to screen a mutant maize population to identify putative mutants [29, 30]. In this study, approximately 1.8% of the samples were identified as putative mutants and 6 of these (17% validation rate) were confirmed by pyrolysis-molecular beam mass spectrometry. While highlighting the effectiveness of FT-NIR analysis in the discrimination of plant samples, the procedures outlined in these publications [30, 31] were limited in application details and no chemometric analysis (e.g. PLS modeling) were performed.
The non-destructive, fast and quantitative nature of NIR spectroscopy makes it a very attractive option to use for screening samples in large plant populations. This study outlines a detailed process for the application of fast scanning of intact plant leaves by NIR spectroscopy followed by an outlier detection scheme combining linear discriminate analysis and PLS modeling. The approach was validated on known cell wall mutants of rice and Arabidopsis and then applied to a rice mutant collection consisting of thousands of uncharacterized samples. The technique involves first nonspecific outlier detection using Mahalanobis distance analysis of NIR spectra followed by the development of a predictive model that could be readily implemented for a variety of analyses and applied to any collection of plant mutants or variants. We show that this approach significantly improves outlier detection over the Mahalanobis distance alone, as well as allowing the identification of specific cell wall variants in the mutant population.
FT-NIR analysis of Arabidopsis cell wall mutants
Arabidopsis thaliana mutants used in this study.
irregular xylem, xylan-deficiency
primary cell wall cellulose deficiency
pectic arabinan deficiency
Principal component analysis (PCA) has been widely demonstrated to be an effective data compression technique where a new basis set (principal components axes) representing the maximum variance across the whole sample set is calculated. For data compression, PCA was performed on pre-processed and area-normalized NIR spectra of Arabidopsis WT (Col-0) and cell wall mutants irregular xylem 10-Like (irx10-L), irregular xylem 14 (irx14) and radially swollen 1 (rsw1). This was followed by calculation of the Mahalanobis distance, a linear discriminate analysis (LDA) technique, to identify outliers when compared to the WT background Col-0. The 10 principal component scores (accounting for 90% of the variability in the entire population) for the Col-0 rosettes were used as the reference set to calculate a single Mahalanobis distance for cell wall mutant rosettes (Figure 1a). An analysis of the cell wall mutants arabinan deficient 1 (arad1) and xylogalacturonan deficient 1 (xgd1-1) in the quartet (qrt) background was also undertaken using the same process outlined above (Figure 1b). In both these examples, any data point greater than the largest Mahalanobis distance for the corresponding WT background was considered outside the biological variance and therefore identified as an outlier. This process demonstrates that, although we had randomly scanned whole rosettes comprising of various leaf developmental stages, half of the measurements from known cell wall mutants were identified as outliers. We surmised that even though only a portion of the mutants were identified, it was possible to use NIR and subsequent multivariate data analysis of unprocessed plant material as a first pass outlier detector scheme in a rapid manner.
In order to correlate the FT-NIR analysis with actual changes in the cell wall, we performed monosaccharide composition analysis of extracted cell wall material following trifluoroacetic acid (TFA) hydrolysis and high performance anion exchange chromatography (HPAEC). The TFA treatment mainly hydrolyses the matrix polysaccharides in the plant cell wall (pectin and hemicelluloses). We must point out here that the HPAEC protocol that we have used fails to resolve xylose and mannose efficiently. For Arabidopsis, mannose represents between 5-10% of the primary cell wall  and thus we have labeled it as the mannose/xylose value in the figure. When we refer to mol% values, these only represent the TFA hydrolysate component of the cell wall (hemicelluloses and pectin fractions).
Cell wall mutants irx14 (Additional file 1: Figure S1a), arad1 and xgd1-1 (Additional file 1: Figure S1b) clearly showed a decrease in xylose, arabinose and xylose, respectively, as has been reported previously [31–33]. Although not showing significant differences in matrix polysaccharide sugar composition, cell wall mutants rsw1 which is impaired in cellulose accumulation  and irx10-L[35, 36] can also be identified as outliers using FT-NIR and multivariate analysis (compare Figures 1 and Additional file 1: Figure S1). FT-NIR analysis coupled to Mahalanobis distance analysis thus shows that it can discern more than just differences in cell wall composition.
FT-NIR analysis of rice mutants
Utilizing the Mahalanobis distance to determine outliers in a rice mutant population
Using the criteria defined above, we analyzed 11 batches in this manner to serve as our pilot study. In summary, the pilot study consisted of 550 mutant lines (3590 leaf samples), resulting in a set of 235 leaf samples that were determined to be Mahalanobis outliers. A total of 145 of these outliers and 73 references (Mahalanobis distance references as well as more arbitrarily chosen samples) were analyzed for cell wall monosaccharide composition. The reference samples were used to define cell wall percentage monosaccharide variation in the population relative to their batch. Unlike Arabidopsis, the xylose detection is not a problem with rice samples since we know that mannose is not detected in rice leaves at that stage of development (Ronald et al, unpublished results). Across references from the 11 batches, the relative percent variability for major cell wall monosaccharides was found to be 3.3 ± 2.3% for Arabinose (Ara), 12.5 ± 8.5% for Galactose (Gal), 14.2 ± 9.2% for Glucose (Glc) and 4.8 ± 3.0% for Xylose (Xyl). Minor cell wall monosaccharides (Rhamnose [Rha], Fucose [Fuc], Glucuronic Acid [GlcA], and Galacturonic Acid [GalA]) were excluded from further analyses due to large variations. In order to determine significant changes in cell wall sugar composition, we calculated the relative percent monosaccharide differences outside 4 standard deviations (μ ± 4σ; 99.99% confidence interval) for each of the major sugars based on the references and used these values as monosaccharide outlier identification criteria. Of the 145 outliers analyzed by HPAEC, a total of 48 (33% validation rate) had a significant sugar composition difference (> 4σ) in one or more of the major cell wall monosaccharides (Additional file 2: Table S1). Significant variations range from a single sugar difference (e.g. 0373-3, 0826-7, 1533-4 and 1784-8) up to variations in 3 major monosaccharides (e.g. 0230-3, 0352-2, 1536-1, 2258-2). These variations encompass both deficiencies as well as relative abundance changes among the monosaccharides analyzed.
Modeling monosaccharide composition from FT-NIR spectra
A major advantage in using FT-NIR is the ability to derive quantitative information by means of PLS modeling. This is done by correlating known biochemical values (e.g. monosaccharide composition) of a calibration set (e.g. cell wall mutants) with the respective FT-NIR spectra. However, with the exception of Arabidopsis, no extensive and readily available collection of well documented cell wall mutants exists that could be used to develop a calibration set of varying biochemical characteristics. Consequently, for our rice mutant population, we reasoned that the set of Mahalanobis outliers that have been already identified and characterized in the pilot study would constitute a robust calibration set. Because these outliers span multiple batches grown at different times, they provide a range of cell wall monosaccharide compositions. Additionally, modeling of monosaccharides can allow us to make targeted detections of specific cell wall changes, which is not possible by using the Mahalanobis distance approach alone.
Based on the PLS model constructed from the calibration set, the cell wall sugar composition for all the samples in the pilot set (550 lines) was predicted. The differences in predicted Ara, Xyl, Glc and Gal, representing the most abundant monosaccharides, were then used as new criteria for a re-analysis of outlier detection in the pilot study. For a given batch, the averages for the major sugars were determined based on predicted values for all the samples in the batch. Next, percent differences for an individual sample in the batch were calculated based on these batch averages and the predicted sugars for that particular sample. We employed the confidence interval threshold defined above (±4 σ; >99.99% confidence interval) for determining predicted significant sugar composition variants (Ara; ≥ Δ 9.1%, Gal; ≥ Δ 34.0%, Glc; ≥ Δ 36.6%, and Xyl; ≥Δ12.1%). Based on these criteria, 75 samples were predicted to have changes in cell wall composition and a randomly selected subset of 30 was analyzed for monosaccharide composition. A total of 18 samples were experimentally confirmed with significant sugar composition differences (Additional file 4: Table S2). This constitutes a 60% validation rate for the PLS model with regard to sugar composition. The model identified an additional seven outliers, bringing the total number to 55 total outliers out of a population of 3590 samples. This constitutes a rate of 1.3% outlier confirmation rate for the pilot study of 550 lines when both the Mahalanobis distance and model outliers are considered. Out of these 55, 11 were identified by both the Mahalanobis distance and the model.
Outlier detection by Mahalanobis distance
While data acquisition is straight forward, the quantitative examination of the FT-NIR spectra can only be achieved using robust multivariate analysis such as PCA and Mahalanobis distance. In previous studies with FT-MIR microspectroscopy, the Mahalanobis distance was successfully applied to the identification of putative cell wall mutants in flax  and in Arabidopsis. Although a recent report briefly mentions the use of FT-NIR and Mahalanobis distance for the identification of maize cell wall mutants , neither validation nor a detailed analysis on the application of the method is provided. Although PCA by itself can be used to show clustering or outlier identification , this can only be done when the majority of the variances (90% or more) can be accounted for by the first few principal component scores, and if prior knowledge is available to determine which scores represent the variation of interest. In our study, we were unable to see any discrete clustering of mutants by plotting the first 2 principal component scores (e.g. Arabidopsis cell wall mutants Additional file 5: Figure S3). This was most likely due to the large biological variation between samples where more than 10 principal component scores were required to account for 90% of the variation. Consequently, in our study, PCA was only used as a data compression technique and required a linear discriminate technique (Mahalanobis distance) to serve as a metric to determine outlier classification from a reference. A requirement for using the Mahalanobis distance after PCA is that a reference set is needed to account for biological and technical variability to identify outliers from a defined population.
The applicability of the FT-NIR and Mahalanobis distance approach to identity cell wall composition differences was initially demonstrated using known cell wall mutants in both Arabidopsis and rice. While this initial study showed the validity of the approach, application of this technique is problematic when faced with biological variation prevalent in large-scale screening and with no defined reference set. Because plants grown at different times have been exposed to differing growth conditions, these would likely be reflected in the structure and composition of the cell wall. Therefore each batch in the pilot study using the rice population was treated separately and reference sets specific to the batch were required to account for variability unique to that batch.
Using this approach, we demonstrated that it is possible to identify outliers with one FT-NIR scan. Not all outliers identified by the Mahalanobis distance analysis proved to show significant monosaccharide composition differences discernible by HPAEC analysis of TFA hydrolysates. These most likely represent outliers for different reasons, for example changes in lignin, starch, cellulose content or could constitute developmental stage differences. This has been shown in previous studies that utilize Mahalanobis distance as the outlier detecting scheme. In a forward-genetic screen of maize mutants, 33 out of the 39 NIR Mahalanobis distance outliers showed no differences in cell wall composition and were identified as nir mutants with "invisible" phenotypes . This was highlighted in our study of the Arabidopsis cell wall mutant analysis where irx10-L, a known xylan biosynthesis mutant that fails to show a clear morphological or sugar phenotype [35, 36], but was an FT-NIR outlier in the Mahalanobis analysis. Similarly, the Arabidopsis cellulose deficient mutant rsw1 and the rice brittle culm mutant (deficient in secondary cell wall cellulose deposition) were also found as outliers in the Mahalanobis distance analysis without showing changes in matrix polysaccharide sugar composition. It is known that rsw1 is a temperature-conditional mutant . We grew rsw1 at the non-permissible temperature and still showed that it could be identified as an outlier compared to WT Col-0. We measured cellulose content in rsw1, irx10-L and irx14 and we could not find significant differences between wild type Col-0 and these mutants (Additional file 6: Figure S4). This underscores the possibility that additional changes, not previously reported in rsw1, account for the spectral differences shown here. While the correlation of FT-NIR with monosaccharide composition in this study only provides an insight into matrix polysaccharides, this approach could easily be broadened by correlating other cell wall components with FT-NIR spectra . Therefore, other robust biochemical methods that can probe the content of other components in the sample could help to account for a proportion of other outliers we identified. It is then clear that FT-NIR can identify a range of changes in biological samples, in addition to variations in cell wall composition. In support of this hypothesis, a combination of FT-NIR and GC-TOF/MS profiling was recently applied to identify Arabidopsis mutants with changes in seed metabolite fingerprints .
Predictive modeling of sugar composition
By incorporating predictive modeling of monosaccharide composition in a mutant screen, a more targeted outlier detection scheme can be implemented; however this can only be achieved after a robust calibration set is obtained. To generate calibration sets that encompass large variability for modeling, previous applications for biomass characteristics using NIR have used various plant tissue types [23, 40]. That approach to modeling is not feasible in a mutant screen dealing with variability associated with a single tissue type. In order to derive a calibration set spanning a varied range of cell wall sugar compositions, we used the set of outliers determined by Mahalanobis distance analysis of the rice mutant population. We constructed a PLS model from the calibration set to correlate FT-NIR spectra with sugar composition, which allowed the prediction of sugar composition of every leaf sample (3590) that was scanned by FT-NIR. The power of this technique in a large population screen is that the model can be continuously improved upon as more candidates are identified and added to the calibration set. We chose to scan a rice leaf only once and therefore only a single scan is correlated to experimental monosaccharide composition. This initial step could be improved by undertaking multiple scans at various locations on the leaf that could improve both the Mahalanobis and PLS model outlier validation rate but will be time-consuming. In addition, other improvements in the model can be made by how the spectra is preprocessed . The modeling process is limited by the quality of the biochemical method, thus if monosaccharide composition analysis by HPAEC contains inaccuracies, the prediction model error will also increase. For these reasons, low abundant sugars in the cell walls of rice samples such have Fuc, Rha, GalA and GlcA had a higher degree of associated prediction error. Consequently, for this study we focused our comparative analyses on Xyl, Ara, Gal and Glc.
Even though not all of the predicted Xyl, Ara, Glu and Gal differences correlated with experimental values for some of the mutants we discovered, at least one of the predicted monosaccharide changes from each sample would identify it as an outlier for further analysis. This is evident with mutant 2073-3 which was set aside as Gal outlier based on sugar prediction, but experimental monosaccharide composition analysis showed it to have significant changes in other sugars as well (Figure 5 and Additional file 4: Table S2). Once measured and confirmed, these samples would then serve as additional data points in the calibration set, and an improved model can be developed.
This study has demonstrated a robust high-throughput application for FT-NIR on a single tissue type to identify cell wall composition changes. This approach is applicable to large-scale mutant and population analyses, as it requires minimal sample handling and additional well-established methods for data processing. The procedure can be done in a two-step process by first identifying outliers by Mahalanobis distance analysis, followed by a more targeted screen using a PLS model for monosaccharide composition. Additionally, this procedure can be continually improved upon during the screening process when more candidates are identified and confirmed. We identified 55 confirmed outliers with significant cell wall monosaccharide composition changes in this pilot study using a subset of a rice mutant population. After screening the entire population by Mahalanobis distance analysis or by sugar modeling of NIR spectra, all candidates will need to be validated by detailed segregation analyses in subsequent segregating generations.
Fourier Transform Near-Infrared Spectroscopy
A MPA FT-NIR Spectrometer (Bruker Optics) was used to measure samples. Spectral absorbance covering a range from 38000 to 12000 cm-1 was taken at a spectral resolution of 8 cm-1. Spectra were collected in diffuse reflectance mode. A total of 32 scans were taken and co-added for each sample (10 seconds). Whole and dried Arabidopsis rosettes and rice leaves were placed directly on the sampling window for measurements.
Preprocessing of absorption spectra was done using Opus software (Bruker Optics). Absorption spectra were first cropped to 3800 cm-1 to 9000 cm-1, smoothed using 25 points then baseline corrected.
Statistical analysis and modeling
Statistical analysis was undertaken using the Statistical toolbox in Matlab (Mathworks). After pre-processing of the spectra, the data set was area-normalized then mean centered. Principal component analysis was used for data compression . Mahalanobis distance is expressed as where and are the group means for 2 groups and S is the covariance matrix. A mathematical constraint in calculating the Mahalanobis distance forces that the number of variables (pc scores) cannot equal or exceed the number of observations (controls). Taking the inverse of a covariance matrix with fewer observations than the number of variables is not recommended therefore forcing the constraint that there cannot be more principal component scores (variables) than references (observations) when calculating the Mahalanobis distance. Calibration set: 12 samples were dropped from the 218 samples that were measured by HPAEC due to error in the FT-NIR spectra upon closer inspection. The remaining 206 files were subsequently used as the calibration set. PLS was performed using in-house programming using the plsregress function in matlab. Each monosaccharide was modeled individually with different number of fitting components to avoid overfitting of data (Arabinose: 8 components, Galactose: 7 components, Glucose: 11 components, Xylose: 10 components). A K-fold cross validation was used to validate our sugar prediction model and involves removing a randomly selected subset of data and assigning it as a test group then creating a model based on the remaining data. Values of the test group are then predicted and compared to real values . This is repeated K times (K = 10 times) and a Root Mean Square Error of Prediction (RMSEP) was calculated for all 10 times and used as a metric to refine the partial least squares model and determine fitting parameters.
Arabidopsis and rice growth conditions
Arabidopsis plants were grown in a growth chamber maintained at 22°C with 8 h photoperiod for 4 weeks after 2 days stratification at 4°C. Whole rosettes were harvested, sandwiched between filter paper (Whatman) and immediately placed in a 40°C oven to dry for 2 days. A total of 5 to 8 biological replicates were used for each analysis. Wild type plants were Col-0 or qrt, depending on the mutant background. Rice plants were grown as described in .
Monosaccharide composition of cell wall material
Plant material (approximately 60 mg) was oven dried at 40°C and ground in a bead beater (Retsch) to a fine powder. Preparation and hydrolysis of alcohol-insoluble residues were prepared from five to eight replicates from Arabidopsis rosettes and individual rice leaves according to previous procedures . Monosaccharide composition was measured by high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) (Dionex) using a CarboPac PA20 column using established procedures .
Cellulose content determination
We used the Updegraff method for cellulose content estimation . Briefly, samples are first hydrolyzed with acetic acid/nitric acid solution to remove matrix polysaccharides and amorphous cellulose. The remaining sample is digested with 67% sulfuric acid and glucose content is measured using the anthrone reagent method .
We wish to thank Gabriela Diaz and Nathan Kane for assistance with sample analysis and preparation for FT-NIR. We thank the Sundaresan laboratory (UC Davis) for providing a transposon insertion line in the rice CesA7 gene (also known as a brittle culm mutant) and Henrik V. Scheller (LBNL/UC Berkeley) for providing Arabidopsis cell wall mutants and critical examination of the manuscript. This work conducted by the Joint BioEnergy Institute was supported by the Office of Science, Office of Biological and Environmental Research, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
- Yuan JS, Tiller KH, Al-Ahmad H, Stewart NR, Stewart CN: Plants to power: bioenergy to fuel the future. Trends Plant Sci. 2008, 13: 421-429. 10.1016/j.tplants.2008.06.001.View ArticlePubMedGoogle Scholar
- Vega-Sánchez ME, Ronald PC: Genetic and biotechnological approaches for biofuel crop improvement. Curr Opin Biotechnol. 2010, 21: 218-224. 10.1016/j.copbio.2010.02.002.View ArticlePubMedGoogle Scholar
- Reiter WD, Chapple C, Somerville CR: Mutants of Arabidopsis thaliana with altered cell wall polysaccharide composition. Plant J. 1997, 12: 335-345. 10.1046/j.1365-313X.1997.12020335.x.View ArticlePubMedGoogle Scholar
- Turner SR, Somerville CR: Collapsed xylem phenotype of Arabidopsis identifies mutants deficient in cellulose deposition in the secondary cell wall. Plant Cell. 1997, 9: 689-701.PubMed CentralView ArticlePubMedGoogle Scholar
- Turner SR, Taylor N, Jones L: Mutations of the secondary cell wall. Plant Mol Biol. 2001, 47: 209-219. 10.1023/A:1010695818416.View ArticlePubMedGoogle Scholar
- Gille S, Hansel U, Ziemann M, Pauly M: Identification of plant cell wall mutants by means of a forward chemical genetic approach using hydrolases. Proc Natl Acad Sci USA. 2009, 106: 14699-14704. 10.1073/pnas.0905434106.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen LM, Carpita NC, Reiter WD, Wilson RH, Jeffries C, McCann MC: A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra. Plant J. 1998, 16: 385-392. 10.1046/j.1365-313x.1998.00301.x.View ArticlePubMedGoogle Scholar
- Mouille G, Robin S, Lecomte M, Pagant S, Hofte H: Classification and identification of Arabidopsis cell wall mutants using Fourier-Transform InfraRed (FT-IR) microspectroscopy. Plant J. 2003, 35: 393-404. 10.1046/j.1365-313X.2003.01807.x.View ArticlePubMedGoogle Scholar
- Vogel J: Unique aspects of the grass cell wall. Curr Opin Plant Biol. 2008, 11: 301-307. 10.1016/j.pbi.2008.03.002.View ArticlePubMedGoogle Scholar
- Sene CFB, McCann MC, Wilson RH, Grinter R: Fourier-transform raman and fourier-transform infrared-spectroscopy - an investigation of 5 higher-plant cell-walls and their components. Plant Physiol. 1994, 106: 1623-1631.PubMed CentralPubMedGoogle Scholar
- McCann MC, Defernez M, Urbanowicz BR, Tewari JC, Langewisch T, Olek A, Wells B, Wilson RH, Carpita NC: Neural network analyses of infrared spectra for classifying cell wall architectures. Plant Physiol. 2007, 143: 1314-1326. 10.1104/pp.106.093054.PubMed CentralView ArticlePubMedGoogle Scholar
- Dokken KM, Davis LC, Marinkovic NS: Use of infrared microspectroscopy in plant growth and development. Appl Spectrosc Rev. 2005, 40: 301-326. 10.1080/05704920500230898.View ArticleGoogle Scholar
- Kacurakova M, Wellner N, Ebringerova A, Hromadkova Z, Wilson RH, Belton PS: Characterisation of xylan-type polysaccharides and associated cell wall components by FT-IR and FT-Raman spectroscopies. Food Hydrocolloid. 1999, 13: 35-41. 10.1016/S0268-005X(98)00067-8.View ArticleGoogle Scholar
- Kacurakova M, Capek P, Sasinkova V, Wellner N, Ebringerova A: FT-IR study of plant cell wall model compounds: pectic polysaccharides and hemicelluloses. Carbohydr Polym. 2000, 43: 195-203. 10.1016/S0144-8617(00)00151-X.View ArticleGoogle Scholar
- Alonso-Simon A, Encina AE, Garcia-Angulo P, Alvarez JM, Acebes JL: FTIR spectroscopy monitoring of cell wall modifications during the habituation of bean (Phaseolus vulgaris L.) callus cultures to dichlobenil. Plant Sci. 2004, 167: 1273-1281. 10.1016/j.plantsci.2004.06.025.View ArticleGoogle Scholar
- Robin S, Lecomte M, Hofte H, Mouille G: A procedure for the clustering of cell wall mutants in the model plant Arabidopsis based on Fourier-transform infrared (FT-IR) spectroscopy. J Appl Stat. 2003, 30: 669-681. 10.1080/0266476032000053745.View ArticleGoogle Scholar
- Coates JP: The interpretation of infrared spectra: Published reference sources. Appl Spectrosc Rev. 1996, 31: 179-192. 10.1080/05704929608000568.View ArticleGoogle Scholar
- Watson CA: Near IR reflectance spectrophotometric analysis of agricultural products. Anal Chem. 1977, 49: A835-10.1021/ac50017a002.View ArticleGoogle Scholar
- Wetzel DL: Near-infrared reflectance analysis - sleeper among spectroscopic techniques. Anal Chem. 1983, 55: 1165-10.1021/ac00258a042.View ArticleGoogle Scholar
- Norris KH, Williams PC: Optimization of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat.I. Influence of particle-size. Cereal Chem. 1984, 61: 158-165.Google Scholar
- Yeh TF, Yamada T, Capanema E, Chang HM, Chiang V, Kadla JF: Rapid screening of wood chemical component variations using transmittance near-infrared spectroscopy. J Agric Food Chem. 2005, 53: 3328-3332. 10.1021/jf0480647.View ArticlePubMedGoogle Scholar
- Tsuchikawa S: A review of recent near infrared research for wood and paper. Appl Spectrosc Rev. 2007, 42: 43-71. 10.1080/05704920601036707.View ArticleGoogle Scholar
- Ye XP, Liu L, Hayes D, Womac A, Hong KL, Sokhansanj S: Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques. Bioresour Technol. 2008, 99: 7323-7332. 10.1016/j.biortech.2007.12.063.View ArticleGoogle Scholar
- Sanderson MA, Agblevor F, Collins M, Johnson DK: Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy. Biomass Bioenerg. 1996, 11: 365-370. 10.1016/S0961-9534(96)00039-6.View ArticleGoogle Scholar
- Gierlinger N, Schwanninger M, Hinterstoisser B, Wimmer R: Rapid determination of heartwood extractives in Larix sp by means of Fourier transform near infrared spectroscopy. J Near Infrared Spectrosc. 2002, 10: 203-214. 10.1255/jnirs.336.View ArticleGoogle Scholar
- Jin SY, Chen HZ: Near-infrared analysis of the chemical composition of rice straw. Ind Crop Prod. 2007, 26: 207-211. 10.1016/j.indcrop.2007.03.004.View ArticleGoogle Scholar
- Nkansah K, Dawson-Andoh B: Rapid characterization of biomass using fluorescence spectroscopy coupled with multivariate data analysis. I. Yellow poplar (Liriodendron tulipifera L.). J Renew Sustain Energy. 2010, 2: 12-Google Scholar
- Geladi P, Kowalski BR: Partial least-squares regression: a tutorial. Anal Chim Acta. 1986, 185: 1-17. 10.1016/0003-2670(86)80028-9.View ArticleGoogle Scholar
- Vermerris W, Saballos A, Ejeta G, Mosier NS, Ladisch MR, Carpita NC: Molecular breeding to enhance ethanol production from corn and sorghum stover. Crop Sci. 2007, 47: S142-S153.View ArticleGoogle Scholar
- Penning BW, Hunter CT, Tayengwa R, Eveland AL, Dugard CK, Olek AT, Vermerris W, Koch KE, McCarty DR, Davis MF: Genetic resources for maize cell wall biology. Plant Physiol. 2009, 151: 1703-1728. 10.1104/pp.109.136804.PubMed CentralView ArticlePubMedGoogle Scholar
- Jensen JK, Sorensen SO, Harholt J, Geshi N, Sakuragi Y, Moller I, Zandleven J, Bernal AJ, Jensen NB, Sorensen C: Identification of a xylogalacturonan xylosyltransferase involved in pectin biosynthesis in Arabidopsis. Plant Cell. 2008, 20: 1289-1302. 10.1105/tpc.107.050906.PubMed CentralView ArticlePubMedGoogle Scholar
- Harholt J, Jensen JK, Sorensen SO, Orfila C, Pauly M, Scheller HV: ARABINAN DEFICIENT 1 is a putative arabinosyltransferase involved in biosynthesis of Pectic Arabinan in Arabidopsis. Plant Physiol. 2006, 140: 49-58.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown DM, Goubet F, Vicky WWA, Goodacre R, Stephens E, Dupree P, Turner SR: Comparison of five xylan synthesis mutants reveals new insight into the mechanisms of xylan synthesis. Plant J. 2007, 52: 1154-1168. 10.1111/j.1365-313X.2007.03307.x.View ArticlePubMedGoogle Scholar
- Peng L, Hocart CH, Redmond JW, Williamson RE: Fractionation of carbohydrates in Arabidopsis root cell walls shows that three radial swelling loci are specifically involved in cellulose production. Planta. 2000, 211: 406-414. 10.1007/s004250000301.View ArticlePubMedGoogle Scholar
- Brown DM, Zhang ZN, Stephens E, Dupree P, Turner SR: Characterization of IRX10 and IRX10-like reveals an essential role in glucuronoxylan biosynthesis in Arabidopsis. Plant J. 2009, 57: 732-746. 10.1111/j.1365-313X.2008.03729.x.View ArticlePubMedGoogle Scholar
- Wu AM, Rihouey C, Seveno M, Hornblad E, Singh SK, Matsunaga T, Ishii T, Lerouge P, Marchant A: The Arabidopsis IRX10 and IRX10-LIKE glycosyltransferases are critical for glucuronoxylan biosynthesis during secondary cell wall formation. Plant J. 2009, 57: 718-731. 10.1111/j.1365-313X.2008.03724.x.View ArticlePubMedGoogle Scholar
- Tanaka K, Murata K, Yamazaki M, Onosato K, Miyao A, Hirochika H: Three distinct rice cellulose synthase catalytic subunit genes required for cellulose synthesis in the secondary wall. Plant Physiol. 2003, 133: 73-83. 10.1104/pp.103.022442.PubMed CentralView ArticlePubMedGoogle Scholar
- Bailleres H, Davrieus F, Pichavant FH: Near infrared analysis as a tool for rapid screening of some major wood characteristics in a eucalyptus breeding program. Ann For Sci. 2002, 59: 479-490. 10.1051/forest:2002032.View ArticleGoogle Scholar
- Suzuki M, Kusano M, Takahashi H, Nakamura Y, Hayashi N, Kobayashi M, Ichikawa T, Matsui M, Hirochika H, Saito K: Rice-Arabidopsis FOX line screening with FT-NIR-based fingerprinting for GC-TOF/MS-based profiling. Metabolomics. 2010, 6: 137-145. 10.1007/s11306-009-0182-2.View ArticleGoogle Scholar
- Liu L, Ye XP, Womac AR, Sokhansanj S: Variability of biomass chemical composition and rapid analysis using FT-NIR techniques. Carbohydr Polym. 2010, 81: 820-829. 10.1016/j.carbpol.2010.03.058.View ArticleGoogle Scholar
- Andersen CM, Bro R: Variable selection in regression-a tutorial. J Chemometr. 2010, 24: 728-737. 10.1002/cem.1360.View ArticleGoogle Scholar
- Krzanowski WJ: Principles of multivariate analysis: a user's perspective. 2000, Oxford: Oxford University Press, RevisedGoogle Scholar
- Stone M: Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B-Methodol. 1974, 36: 111-147.Google Scholar
- Bart RS, Chern M, Vega-Sanchez ME, Canlas P, Ronald PC: Rice Snl6, a cinnamoyl-CoA reductase-like gene family member, is required for NH1-mediated immunity to Xanthomonas oryzae pv. oryzae. PLoS Genet. 2010, 6:Google Scholar
- Updegraf.Dm: Semimicro Determination of Cellulose in Biological Materials. Anal Biochem. 1969, 32: 420-10.1016/S0003-2697(69)80009-6.View ArticleGoogle Scholar
- Scott TA, Melvin EH: Determination of dextran with anthrone. Anal Chem. 1953, 25: 1656-1661. 10.1021/ac60083a023.View ArticleGoogle Scholar
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