Direct classification of related species of fungal endophytes (Epichloë spp.) using visible and near-infrared spectroscopy and multivariate analysis

The aim of this work was to investigate the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for the classification of three morphologically similar species of fungal endophytes of grasses. Vis-NIR spectra (400–2498 nm) from 34 isolates of Epichloë sylvatica , 32 of Epichloë typhina and 38 of Epichloë festucae were recorded directly from fresh mycelium growing in potato dextrose agar plates. Multivariate procedures applied to the spectral data were discriminant modified partial least squares regression, soft independent modelling of class analogy and discriminant partial least squares regressions (PLS1, PLS2). Several types of data pretreatments were tested to develop the classification models. The best predictive models were achieved with PLS2 analysis; with this method, 90% of E. typhina and 100% of E. festucae and E. sylvatica external validation samples were successfully classified. These results show the potential of Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying morphologically similar species of filamentous fungi.     

食用调和油中花生油含量的近红外光谱分析

采用偏最小二乘法(PLS)等方法建立了食用调和油中花生油含量定量分析的近红外光谱定标模型。采集食用调和油样品在4 000 cm-1~10 000 cm-1范围内的近红外漫反射光谱,光谱经一阶导数处理后,采用偏最小二乘法建立样品中花生油含量的定标模型,并用Leave-one-out内部交叉验证法对模型进行验证。模型相关系数为0.99961,校正均方根RMSEC为0.830%。比较不同光谱预处理方法对定标模型的影响,结果表明一阶导数Corr.coeff最好。采用不同的化学计量学方法建立的定标模型中以偏最小二乘回归法最理想。     

A distinct utility of the amide III infrared band for secondary structure estimation of aqueous protein solutions using partial least squares methods

Fourier transform infrared spectroscopy is becoming an increasingly important method to study protein secondary structure. The amide I region of the protein infrared spectrum is the widely used region, whereas the amide III region has been comparatively neglected due to its low signal. Since there is no water interference in the amide III region and, more importantly, the different secondary structures of proteins have more resolved differences in their amide III spectra, it is quite promising to use the amide III region to determine protein secondary structure. In our current study, a partial least squares (PLS) method was used to predict protein secondary structures from the protein IR spectra. The IR spectra of aqueous solutions of 16 different proteins of known crystal structure have been recorded, and the amide I, the amide III, and the amide I combined with the amide III region of these proteins were used to set up the calibration set for the PLS algorithm. Our results correlate quite well with the data from X-ray studies, and the prediction from the amide III region is better than that from amide I or combined amide I and amide III regions.     

Direct classification of related species of fungal endophytes (Epichlo? spp.) using visible and near-infrared spectroscopy and multivariate analysis.

The aim of this work was to investigate the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for the classification of three morphologically similar species of fungal endophytes of grasses. Vis-NIR spectra (400-2498 nm) from 34 isolates of Epichlo? sylvatica, 32 of Epichlo? typhina and 38 of Epichlo? festucae were recorded directly from fresh mycelium growing in potato dextrose agar plates. Multivariate procedures applied to the spectral data were discriminant modified partial least squares regression, soft independent modelling of class analogy and discriminant partial least squares regressions (PLS1, PLS2). Several types of data pretreatments were tested to develop the classification models. The best predictive models were achieved with PLS2 analysis; with this method, 90% of E. typhina and 100% of E. festucae and E. sylvatica external validation samples were successfully classified. These results show the potential of Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying morphologically similar species of filamentous fungi.     

FT-IR spectrophotometric analysis of coenzyme Q10 (CoQ10) and its pharmaceutical formulations

A Fourier transform infrared (FT-IR) spectrometric method was developed for the rapid, direct measurement of coenzyme Q10 (CoQ10) in different pharmaceutical products. Conventional KBr spectra were compared for the best determination of active substance in drug preparations. Lambert-Beer's law and two chemometric approaches, partial least squares (PLS) and principal component regression (PCR+) methods, were used in data processing.     

Determination of the secondary structure content of proteins in aqueous solutions from their amide I and amide II infrared bands. Comparison between classical and partial least-squares methods

A method for estimating protein secondary structure from infrared spectra has been developed. The infrared spectra of H2O solutions of 13 proteins of known crystal structure have been recorded and corrected for the spectral contribution of water in the amide I and II region by using the algorithm of Dousseau et al. [Dousseau, F., Therrien, M., & Pézolet, M. (1989) Appl. Spectrosc. 43, 538-542]. This calibration set of proteins has been analyzed by using either a classical least-squares (CLS) method or the partial least-squares (PLS) method. The pure-structure spectra calculated by the classical least-squares method are in good agreement with spectra of poly(L-lysine) in the alpha-helix, beta-sheet, and undefined conformations. The results show that the best agreement between the secondary structure determined by X-ray crystallography and that predicted by infrared spectroscopy is obtained when both the amide I and II bands are used to generate the calibration set, when the PLS method is used, and when it is assumed that the secondary structure of proteins is composed of only four types of structure: ordered and disordered alpha-helices, beta-sheet, and undefined conformation. Attempts to include turns in the secondary structure estimation have led to a loss of accuracy. The standard deviation of the difference between X-ray and infrared secondary structure estimates with this method is 4.8% for the alpha-helix, 3.7% for the beta-sheet, and 5.1% for the undefined structure, whereas the regression coefficients are 0.95, 0.96, and 0.56, respectively. The spectra of the calibration proteins were also recorded in 2H2O solution.(ABSTRACT TRUNCATED AT 250 WORDS)     

Multivariate Analysis of Phenol in Freeze-Dried and Spray-Dried Insulin Formulations by NIR and FTIR

Dehydration is a commonly used method to stabilise protein formulations. Upon dehydration, there is a significant risk the composition of the formulation will change especially if the protein formulation contains volatile compounds. Phenol is often used as excipient in insulin formulations, stabilising the insulin hexamer by changing the secondary structure. We have previously shown that it is possible to maintain this structural change after drying. The aim of this study was to evaluate the residual phenol content in spray-dried and freeze-dried insulin formulations by Fourier transform infrared (FTIR) spectroscopy and near infrared (NIR) spectroscopy using multivariate data analysis. A principal component analysis (PCA) and partial least squares (PLS) projections were used to analyse spectral data. After drying, there was a difference between the two drying methods in the phenol/insulin ratio and the water content of the dried samples. The spray-dried samples contained more water and less phenol compared with the freeze-dried samples. For the FTIR spectra, the best model used one PLS component to describe the phenol/insulin ratio in the powders, and was based on the second derivative pre-treated spectra in the 850–650 cm⁻¹ region. The best PLS model based on the NIR spectra utilised three PLS components to describe the phenol/insulin ratio and was based on the standard normal variate transformed spectra in the 6,200–5,800 cm⁻¹ region. The root mean square error of cross validation was 0.69% and 0.60% (w/w) for the models based on the FTIR and NIR spectra, respectively. In general, both methods were suitable for phenol quantification in dried phenol/insulin samples.     

Quantification of blockiness in pectins—A comparative study using vibrational spectroscopy and chemometrics

The gelling properties of pectins are related not only to the degree of esterification (DE), but also to the distribution of the ester groups. In this study, we have examined an experimentally designed series of 31 pectins originating from the same mother pectin and de-esterified using combinations of two different enzymatic mechanisms. The potential of using infrared (IR), Raman, and near infrared (NIR) spectroscopies combined with chemometrics for reliable and rapid determination of the DE and distribution patterns of methyl ester groups in a designed set of pectin powders was investigated. Quantitative calibration models using partial least squares (PLS) regression were developed and compared. The calibration models for prediction of DE obtained on extended inverse signal correction (EISC)-treated spectra of all three spectroscopic methods yielded models with cross-validated prediction errors (RMSECV) between 1.1%p and 1.6%p DE and correlation coefficients of 0.99. A calibration model predicting degree of random de-esterification (R) and block de-esterification (B) was developed for each spectroscopic method, yielding RMSECV values between 4.4 and 6.7 and correlation coefficients (r) between 0.79 and 0.92. Variable selection using interval PLS (iPLS) significantly improved the prediction of R for IR spectroscopy, yielding RMSECV of 3.5 and correlation coefficients of 0.95. All three spectroscopic methods were able to distinguish the spectral patterns of pectins with different enzyme treatments in simple classification models by principal component analysis (PCA). Extended canonical variate analysis revealed one specific signal in the Raman (1045 cm⁻¹) spectrum and one significant area (1250-1400 cm⁻¹) in the IR spectrum which are able to classify the pectin samples according to the four different enzyme treatments. In both Raman and IR spectra, the signal intensity decreased in the sequence R-B > B > B-R > R > re-methylated pectin.     

Fluorescence and infrared spectrometric study of cell walls from Candida, kluyveromyces, Rhodotorula and schizosaccharomyces yeasts in relation with their chemical composition

Composition, level, and arrangement of the structural polysaccharides determine biophysical properties of fungal cell walls. A small amount of a beta(1-->4) linear homopolymer of GlcNAc in the cell wall forms chitin. To study the components of the cell walls and to estimate the quantity of chitin for different strains, two spectroscopic methods were applied. Because chemical and enzymatic methods are destructive, long, and complex, fluorescence and infrared (IR) spectroscopies were applied on cell walls and on chitin enriched fractions. The results were compared to chemical assays. IR spectra allow identifying the structural types of polysaccharides in yeast walls. Fluorescence spectroscopy was not appropriated for a full and accurate quantitative determination of the polymers but revealed level variations similar to results obtained by chemical analytical methods. The infrared spectra, using a chemometric approach (PLS1), allowed a fairly good estimation of chitin in enriched fractions with respect to the chemical assays.     

Discriminating pulmonary hypertension caused by monocrotaline toxicity from chronic hypoxia by near-infrared spectroscopy and multivariate methods of analysis

A new method has been developed for the determination of tissue pathology caused by chronic hypoxia and monocrotaline toxicity. The method is based on the use of near-infrared (NIR) spectrophotometry to measure spectra of lung tissue from normal chronic hypoxia (CH) and monocrotaline (MCT) models of pulmonary hypertension (PH), followed by analysis using multivariate methods, that is, principal component analysis (PCA) and partial least squares (PLS). Synergistic use of NIR with the PCA/PLS method makes it possible, for the first time, not only to divide different lung tissue samples into their respective groups (normal, CH, and MCT) but also to gain insight into mechanisms of PH caused by CH and MCT toxicity. Specifically, MCT metabolites and other hypertensive conditions are known to produce subtle and minor chemical changes in the compositions of tissue (e.g., proteins, carbohydrates, lipids). Although these changes were detected by the NIR technique, they were too small to be discerned through visual inspection of the spectra. However, they can be accurately classified and properly assigned by the PCA/PLS method. The fact that different tissue types can be accurately divided into their corresponding groups by the NIR and PCA/PLS methods suggests that chemical alterations and mechanisms of pulmonary vascular remodeling and PH induced by MCT are different from those induced by CH.     

基于SVR算法的林地土壤氮含量高光谱测定

提出了一种利用高光谱技术进行杉木林土壤全氮测定的新方法。以FieldSpec®3地物光谱仪采集杉木林土壤148份, 随机分成校正集(100份)和检验集(48份)。以不同方法实现了土壤光谱的预处理, 并采用偏最小二乘回归算法(PLS)建立土壤氮含量估测模型对其进行比较分析, 发现小波除噪结合多元散射校正能最有效地消除原始光谱的噪声与背景信息, 此时PLS模型校正集与预测集R²分别为0.891与0.885。为进一步优化模型, 对经小波除噪结合多元散射校正处理后的光谱采用主成分分析法(PCA)降维, 以前4个主成份为输入变量, 采用小二乘支持向量机回归算法(LS-SVR)建立了土壤氮含量估测模型, 其校正集与预测集R²分别提高至0.921与0.917, 具有比PLS算法更高的精度。结果表明:以高光谱技术进行林地土壤氮含量快速监测是可行的, 其中小波去噪结合多元散射校正系光谱预处理的优选方法, 而LS-SVR则是建模的优选方法。     

FM signals produce robust paradoxical latency shifts in the bat’s inferior colliculus

Previous studies in echolocating bats, Myotis lucifugus, showed that paradoxical latency shift (PLS) is essential for neural computation of target range and that a number of neurons in the inferior colliculus (IC) exhibit unit-specific PLS (characterized by longer first-spike latency at higher sound levels) in response to tone pulses at the unit’s best frequency. The present study investigated whether or not frequency-modulated (FM) pulses that mimic the bat’s echolocation sonar signals were equally effective in eliciting PLS. For two-thirds of PLS neurons in the IC, both FM and tone pulses could elicit PLS, but only FM pulses consistently produced unit-specific PLS. For the remainder of PLS neurons, only FM pulses effectively elicited PLS; these cells showed either no PLS or no response, to tone pulses. PLS neurons generally showed more pronounced PLS in response to narrow-band FM (each sweeping 20 kHz in 2 ms) pulse that contained the unit’s best frequency. In addition, almost all PLS neurons showed duration-independent PLS to FM pulses, but the same units exhibited duration-dependent PLS to tone pulses. Taken together, when compared to tone pulses, FM stimuli can provide more reliable estimates of target range.     

Characterization of bright tobaccos by multivariate analysis of C CPMAS NMR spectra

Univariate and multivariate statistics were applied to characterize cured bright tobacco samples on the basis of their ¹³C CPMAS NMR spectra and leaf constituent analysis. NMR spectra were obtained for 55 samples selected from a set of 134 samples of graded bright tobacco leaves from crop year 1999. Historical leaf constituent analyses were available for total alkaloids, reducing sugars, total nitrogen, and insoluble ash. In addition, we applied HPLC to quantify the two abundant plant polyphenols, chlorogenic acid, and rutin. Principal component analysis (PCA) and partial least squares (PLS) of the NMR spectra revealed systematic relationships between groups of samples related to these substances and afforded predictive quantitative models for the analyzed constituents. Analysis of the PLS significant variables showed that leaf polysaccharides, alkaloids, and minerals are major determinants influencing the grading of cured bright tobacco leaves.     

Chemometric prediction of alginate monomer composition: A comparative spectroscopic study using IR, Raman, NIR and NMR

The potential of using infrared (IR), Raman and near infrared (NIR) spectroscopy combined with chemometrics for reliable and rapid determination of the ratio of mannuronic and guluronic acid (M/G ratio) in commercial sodium alginate powders has been investigated. The reference method for quantification of the M/G ratio was solution-state ¹H nuclear magnetic resonance (NMR) spectroscopy. For a set of 100 commercial alginate powders with a M/G ratio range of 0.5–2.1 quantitative calibrations using partial least squares regression (PLSR) were developed and compared for the three spectroscopic methods. All three spectroscopic methods yielded models with prediction errors (RMSEP) of 0.08 and correlation coefficients between 0.96 and 0.97. However, the model based on extended inverted signal corrected (EISC) Raman spectra stood out by only using one PLS component for the prediction. The results are comparable to that of the experimental error of the reference method estimated to be between 0.01 and 0.08.     

Vibrational Circular Dichroism Discrimination of Diastereomeric Cedranol Acetates

The reliability of vibrational circular dichroism (VCD) spectroscopy to discriminate four diastereomeric cedranol acetates 1 , 2 , 3 , 4 by means of their absolute configuration is examined. The usage of CompareVOA software to quantify comparisons of the measured infrared (IR) and VCD spectra with the corresponding simulated spectra at the B3LYP/DGDZVP and B3PW91/DGDZVP levels of theory for each diastereomer enabled the B3PW91 functional to be qualified as superior to the B3LYP functional for vibrational calculations of 1 , 2 , 3 , 4 . Analogously, a set of quantitative VCD spectra cross‐comparisons of 1 , 2 , 3 , 4 unambiguously distinguished the diastereomers using B3PW91 and failed using B3LYP. Remarkably, quantitative IR spectra cross‐comparisons of 1 , 2 , 3 , 4 using B3PW91 or B3LYP functionals demonstrated that the achiral spectroscopic IR technique is not able to distinguish cedranol acetate diastereomers. VCD comparisons using anisotropy g‐factor values of bands in the 1550–950 cm^‐1 region of the spectra were of aid to facilitate visual spectra matching for each diastereomer. Chirality 25:939‐951, 2013. © 2013 Wiley Periodicals, Inc.     

Near infrared spectroscopy coupled with radial basis function neural network for at-line monitoring of Lactococcus lactis subsp. fermentation

In our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation.     

Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods

Rapid methods for the characterization of biomass for energy purpose utilization are fundamental. In this work, near infrared spectroscopy is used to measure ash and char content of various types of biomass. Very strong models were developed, independently of the type of biomass, to predict ash and char content by near infrared spectroscopy and multivariate analysis. Several statistical approaches such as principal component analysis (PCA), orthogonal signal correction (OSC) treated PCA and partial least squares (PLS), Kernel PCA and PLS were tested in order to find the best method to deal with near infrared data to classify and predict these biomass characteristics. The model with the highest coefficient of correlation and the lowest RMSEP was obtained with OSC-treated Kernel PLS method.     

Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis

In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation.     

High-throughput analysis of the plasmid bioproduction process in Escherichia coli by FTIR spectroscopy

Monitoring plasmid production systems is a lab intensive task. This article proposes a methodology based on FTIR spectroscopy and the use of chemometrics for the high-throughput analysis of the plasmid bioproduction process in E. coli. For this study, five batch cultures with different initial medium compositions are designed to represent different biomass and plasmid production behavior, with the maximum plasmid and biomass concentrations varying from 11 to 95 mg L(-1) and 6.8 to 12.8 g L(-1), respectively, and the plasmid production per biomass varying from 0.4 to 5.1 mg g(-1). After a short sample processing consisting of centrifugation and dehydration, the FTIR spectra are recorded from the collected cellular biomass using microtiter plates with 96 wells. After spectral pre-processing, the predictive FTIR spectra models are derived by using partial least squares (PLS) regression with the wavenumber selection performed by a Monte-Carlo strategy. Results show that it is possible to improve the PLS models by selecting specific spectral ranges. For the plasmid model, the spectral regions between 590-1,130, 1,670-2,025, and 2,565-3,280 cm(-1) are found to be highly relevant. Whereas for the biomass, the best wavenumber selections are between 900-1,200, 1,500-1,800, and 2,850-3,200 cm(-1). The optimized PLS models show a high coefficient of determination of 0.91 and 0.89 for the plasmid and biomass concentration, respectively. Additional PLS models for the prediction of the carbon sources glucose and glycerol and the by-product acetic acid, based on metabolism-induced correlations between the nutrients and the cellular biomass are also established.     

Quantitative Determination of the Biodegradable Polymer Poly(β-hydroxybutyrate) in a Recombinant Escherichia coli Strain by Use of Mid-Infrared Spectroscopy and Multivariative Statistics

Fourier transform infrared (FTIR) spectroscopy in combination with the partial least squares (PLS) multivariative statistical technique was used for quantitative analysis of the poly(β-hydroxybutyrate) (PHB) contents of bacterial cells. A total of 237 replicate spectra from 34 samples were obtained together with gas chromatography-determined reference PHB contents. Using the PLS regression, we were able to relate the infrared spectra to the reference PHB contents, and the correlation coefficient between the measured and predicted values for the optimal model with a standard error of prediction of 1.49% PHB was 0.988. With this technique, there are no solvent requirements, sample preparation is minimal and simple, and analysis time is greatly reduced; our results demonstrate the potential of FTIR spectroscopy as an alternative to the conventional methods used for analysis of PHB in bacterial cells.