Analysis of mebendazole polymorphs by Fourier transform IR spectrometry using chemometric methods

A diffuse reflectance IR Fourier transform IR spectrometry (DRIFTS) method was developed for the rapid, direct measurement of mebendazole in drugs. Conventional KBr spectra and DRIFTS spectra were compared for the best determination of the active substance in the drug formulations. Two chemometric approaches were used in the data processing: multicomponent partial least squares (PLS2) and principal component regression. The best results were obtained with the PLS2 method.     

Using FTIR spectroscopy to model alkaline pretreatment and enzymatic saccharification of six lignocellulosic biomasses

Fourier transform infrared, attenuated total reflectance (FTIR-ATR) spectroscopy, combined with partial least squares (PLS) regression, accurately predicted solubilization of plant cell wall constituents and NaOH consumption through pretreatment, and overall sugar productions from combined pretreatment and enzymatic hydrolysis. PLS regression models were constructed by correlating FTIR spectra of six raw biomasses (two switchgrass cultivars, big bluestem grass, a low-impact, high-diversity mixture of prairie biomasses, mixed hardwood, and corn stover), plus alkali loading in pretreatment, to nine dependent variables: glucose, xylose, lignin, and total solids solubilized in pretreatment; NaOH consumed in pretreatment; and overall glucose and xylose conversions and yields from combined pretreatment and enzymatic hydrolysis. PLS models predicted the dependent variables with the following values of coefficient of determination for cross-validation (Q2): 0.86 for glucose, 0.90 for xylose, 0.79 for lignin, and 0.85 for total solids solubilized in pretreatment; 0.83 for alkali consumption; 0.93 for glucose conversion, 0.94 for xylose conversion, and 0.88 for glucose and xylose yields. The sugar yield models are noteworthy for their ability to predict overall saccharification through combined pretreatment and enzymatic hydrolysis per mass dry untreated solids without a priori knowledge of the composition of solids. All wavenumbers with significant variable-important-for-projection (VIP) scores have been attributed to chemical features of lignocellulose, demonstrating the models were based on real chemical information. These models suggest that PLS regression can be applied to FTIR-ATR spectra of raw biomasses to rapidly predict effects of pretreatment on solids and on subsequent enzymatic hydrolysis.     

Betamethasone‐based chiral electrochemical sensor coupled to chemometric methods for determination of mandelic acid enantiomers

A chiral biosensing platform was developed using betamethasone (BMZ) as chiral recognition element through multilayered electrochemical deposition of BMZ, overoxidized polypyrrole, and nanosheets of graphene (OPPy‐BMZ/GR), for enantio‐recognition of mandelic acid (MA) enantiomers. The deposited film was characterized by scanning electron microscopy, differential pulse voltammetry, cyclic voltammetry, and electrochemical impedance spectroscopy. It was shown that the chiral sensing platform can discriminate R‐ and S‐MA differential pulse voltammetry signals, at the voltages of 1.35 and 1.33 V (vs Ag/AgCl), respectively. To tackle the problem of highly overlapping peaks of these enantiomers, the partial least squares (PLS) regression and genetic algorithm‐PLS (GA‐PLS) were used for simultaneous quantification of MA enantiomers. Generally, variable selection by genetic algorithm provided an improvement in prediction results when compared to full‐voltammogram PLS. Good analytical performances were obtained despite the inherent complexity of the simultaneous determination.     

Multivariate statistical modelling of mass transfer in a membrane-supported biofilm reactor

The main objective of this work is to develop an overall mass transfer model applicable to a particular case of membrane supported biofilm, the ion-exchange membrane bioreactor (IEMB). A multivariate projection to latent structures (PLS) model of the anionic membrane transport in an IEMB was developed and analyzed to establish the mass transfer limiting variables for the removal of anionic pollutants (nitrate and perchlorate) from drinking water. The proposed PLS model accounts for the biological contribution to the mass transfer and predicts the anionic fluxes across the ion-exchange membrane with a prediction improvement of at least 50% when compared with a simplified mechanistic Donnan dialysis-based transport model. The PLS model allowed for predicting the transport of target anions using only operational physicochemical data, therefore, the use of several assumptions as in mechanistic model building was avoided as well as the need for biofilm characterization. To decrease the model complexity, several techniques which select the most informative predictors were also successfully used. The analyses of important predictors to each anionic transport model show that transport driving force related variables were the most important. Moreover, at least 30% of the model information is related with biocompartment bulk variables.     

宁夏生态足迹影响因子的偏最小二乘回归分析

生态足迹分析方法是一种度量区域生态可持续程度的有效方法,偏最小二乘回归法(PLS)能有效解决多元回归分析中变量的多重相关性问题,具有容易操作,相关分析精度高等特点。以宁夏为研究区域,在计算了宁夏2001—2010年人均生态足迹的基础上,应用偏最小二乘回归分析法,对影响宁夏生态足迹的各因子的重要程度进行了分析。通过变量投影重要性分析、特异点分析和预测分析,证明所得偏最小二乘回归模型具有较好的精度。研究结果为:2001—2010年,宁夏人均生态足迹由1.818103793 hm2上升至2.894958909 hm2,生态赤字由1.28352051 hm2上升至2.42316627 hm2,生态承载力由0.53458328 hm2下降至0.47179264 hm2;全区GDP、城镇居民人均生活消费支出、第二产业产值和第一产业产值是影响宁夏生态足迹的显著因子。     

Potentiometric bioelectronic tongue for the analysis of urea and alkaline ions in clinical samples

Urea biosensors based on urease covalently immobilized on to ammonium and hydrogen ion-selective electrodes were included in arrays together with ammonium, potassium, sodium, hydrogen and generic response to alkaline sensors. Response models based on artificial neural network (ANN) and partial least squares (PLS1) were built, tested and compared for the simultaneous determination of urea, ammonium, potassium and sodium. The results show that it is possible to obtain good ANN and PLS calibration models for simultaneous determination of these four species, but with better prediction capability when the ANN are used. The developed bioelectronic tongue was applied to multidetermination in urine samples. The ANN model showed again better agreement with reference methods, allowing a simple direct determination of urea in the real samples without the necessity of eliminating the alkaline interferences, or compensating endogenous ammonium.     

Diatom (Bacillariophyceae) assemblages in salt marshes of south‐central Chile: Relations with tidal inundation time and salinity

To reconstruct sea‐level history from changes in tidal environments using diatom assemblages, we need to better understand the relations among brackish diatom assemblages and changing environments along elevational gradients from diverse coastal sites. Our statistical analysis reveals relations between environmental variables and brackish benthic diatom assemblages in the little studied region of south‐central Chile. Along four transects across salt marshes at two sites, we identified 224 diatom taxa in 112 samples. Detrended canonical correspondence analysis showed that tidal exposure time index and salinity were appropriately regressed against the abundance of diatom species using unimodal‐based methods. Our tests of classical and inverse regressions of weighted average and weighted averaging partial least squares (WA‐PLS) showed that WA‐PLS resulted in the highest coefficient of determination and the lowest root‐mean square of the error of prediction. Our regression will be useful in reconstructing environmental variables from fossil diatom assemblages in Chile.     

Spectrofluorimetric determination of certain biologically active phenothiazines in commercial dosage forms and human plasma

A validated simple and sensitive spectrofluorimetric method was developed for the determination of chlorpromazine hydrochloride, promethazine hydrochloride, trifluperazine hydrochloride, thioridazine hydrochloride, perazine maleate and oxomemazine. The method was based on condensation of malonic acid/acetic anhydride (MAA) under the catalytic effect of the tertiary amine moiety of the studied phenothiazines to provide a deep yellow to brown colour with green florescence. Relative fluorescence intensity of the products was measured at λ_exc 398 nm and λ_em 432 nm. Different variables affecting the reaction were studied and optimized. The method was successfully applied for the determination of the studied drugs in commercial dosage forms. The lower detection limits allowed the application of this method for the determination of the compounds in plasma as an example of a biological fluid. In addition, the method was considered specific for the determination of tertiary amines in the presence of primary and secondary amines; as a result, it was deemed suitable for the determination of the cited drugs in the presence of their degradation products resulting from N‐dealkylation or oxidation of the corresponding sulphoxides or sulphones. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy

Multi-wavelength fluorescence spectroscopy was evaluated as a tool for on-line monitoring of recombinant Escherichia coli cultivations expressing human basic fibroblast growth factor (hFGF-2). The data sets for the various combinations of the excitation and emission spectra from batch cultivations were analyzed using principal component analysis. Chemometric models (the partial least squares method) were developed for correlating the fluorescence data and the experimentally measured variables such as the biomass and glucose concentrations as well as the carbon dioxide production rate. Excellent correlations were obtained for these variables for the calibration cultivations. The predictability of these models was further tested in batch and fed-batch cultivations. The batch cultivations were well predicted by the PLS models for biomass, glucose concentrations and carbon dioxide production rate (RMSEPs were respectively 5%, 7%, 9%). However, when tested for biomass concentrations in fed-batch cultivations (with final biomass three times higher than the highest calibration data) the models had good predictability at high growth rates (RMSEPs were 3% and 4%, respectively for uninduced and induced fed-batch cultivations), which was as good as for the batch cultivations used for developing the models (RMSEPs were 3% and 5%, respectively for uninduced and induced batch cultivations). The fed-batch cultivations performed at low growth rates exhibited much higher fluorescence for fluorophores such as flavin and NAD(P)H as compared to fed-batch cultivations at high growth rate. Therefore, the PLS models tended to over-predict the biomass concentrations at low growth rates. Obviously the cells changed their concentration of biogenic fluorophores depending on the growth rate. Although multi-wavelength fluorescence spectroscopy is a valuable tool for on-line monitoring of bioprocess, care must be taken to re-calibrate the PLS models at different growth rates to improve the accuracy of predictions.     

Spatial ecology of the palm-leaf skeletonizer, Homaledra sabelella (Lepidoptera: Coleophoridae)

Understanding the processes that determine the distribution of populations is a fundamental goal in ecology. In this study, I determined the relative contribution of space and the biotic and abiotic environment to the distribution of the palm-leaf skeletonizer Homaledra sabalella (PLS; Lepidoptera: Coleophoridae) among patchily distributed dwarf palmettos (Sabal minor; Arecaceae). Based on surveys conducted at two sites in the Sherburne Wildlife Management Area, Louisiana, I found that the distribution of the PLS was primarily related to local environmental conditions--number of PLS increased with palmetto height, was greater in dry versus wet habitats, and varied in an inconsistent way with the type of understory cover. Spatial structure of the forest and isolation of the host plant were of minor importance to the distribution of the PLS. Based on a series of experiments, the mechanisms underlying the effects of these environmental variables on PLS abundance were elucidated. Tall palmettos have a greater abundance of PLS because they are 2.5 times more likely to be colonized than small palmettos. Tall palmettos do not represent better hosts (in terms of PLS survival to pupation, pupal length, or risk of parasitism). Similarly, an open understory increased colonization by two-fold, relative to a shrub understory, but understory type had no effect on host quality. Wet soils greatly reduced palmetto quality as a host (survival and pupal length), but only for the smallest palmettos (<0.75 m height). Finally, corroborating the survey data, my dispersal experiment revealed that the PLS is a strong flier and that local PLS populations (i.e., infested palmettos) are likely well connected by dispersal. I conclude by discussing how landscape-level changes at Sherburne Wildlife Management Area, owing to recent hurricane activity, could affect the risk of palmetto infestation by the PLS.     

Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO

We consider the problem of predicting survival times of cancer patients from the gene expression profiles of their tumor samples via linear regression modeling of log-transformed failure times. The partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO) methodologies are used for this purpose where we first modify the data to account for censoring. Three approaches of handling right censored data-reweighting, mean imputation, and multiple imputation-are considered. Their performances are examined in a detailed simulation study and compared with that of full data PLS and LASSO had there been no censoring. A major objective of this article is to investigate the performances of PLS and LASSO in the context of microarray data where the number of covariates is very large and there are extremely few samples. We demonstrate that LASSO outperforms PLS in terms of prediction error when the list of covariates includes a moderate to large percentage of useless or noise variables; otherwise, PLS may outperform LASSO. For a moderate sample size (100 with 10,000 covariates), LASSO performed better than a no covariate model (or noise-based prediction). The mean imputation method appears to best track the performance of the full data PLS or LASSO. The mean imputation scheme is used on an existing data set on lung cancer. This reanalysis using the mean imputed PLS and LASSO identifies a number of genes that were known to be related to cancer or tumor activities from previous studies.     

In silico prediction of novel phosphodiesterase type-5 inhibitors derived from Sildenafil, Vardenafil and Tadalafil

A series of drug-like compounds derived from Sildenafil, Vardenafil and Tadalafil analogues were modelled through the MIA-QSAR (multivariate image analysis applied to quantitative structure-activity relationships) ligand-based approach. A highly predictive model was achieved and novel compounds, miscellany of substructures of these three representative phosphodiesterase type-5 (PDE-5) inhibitors were predicted using the calibration parameters obtained through partial least squares (PLS) regression. The high bioactivities of eight promising compounds were corroborated by docking evaluation. Calculated ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) profiles for such compounds suggest advantages of some of them over the currently available, most common drugs used for the treatment of erectile dysfunction.     

The reversible DNA-alkylating activity of duocarmycin and its analogues.

Intact drugs with spirocyclopropylhexadienone moieties can be regenerated from the covalent DNA adducts induced by antitumor antibiotics duocarmycin (DUM) A, SA and some DUMA analogues in neutral aqueous solution. We detected the reversible nature of DUMs by determination of the antimicrobial activity and cytotoxicity of DUM-DNA adducts. All of the adducts selectively inhibited the growth of a sensitive strain of Bacillus but not that of the wild type strain, a property of parent DUM and its analogues. Most of the DNA adducts were also cytotoxic to HeLa S3. These results suggested that active drugs can be released from their covalent DNA adducts under these biological assay conditions. Regeneration of intact drugs was quantitatively analyzed by HPLC and the amount of free drug released from DNA adducts revealed that the rate and efficiency of this reversal were dependent on structural variables among the drugs. The differences in rates of reversibility were correlated with the biological activity of DUMs. The effect of pH, temperature and salt concentration on the regeneration of drugs from their DNA adducts suggest a catalytic role of double-helical DNA on the reversal pathway.     

Multiblock PLS analysis of an industrial pharmaceutical process

The performance of an industrial pharmaceutical process (production of an active pharmaceutical ingredient by fermentation, API) was modeled by multiblock partial least squares (MBPLS). The most important process stages are inoculum production and API production fermentation. Thirty batches (runs) were produced according to an experimental planning. Rather than merging all these data into a single block of independent variables (as in ordinary PLS), four data blocks were used separately (manipulated and quality variables for each process stage). With the multiblock approach it was possible to calculate weights and scores for each independent block. It was found that the inoculum quality variables were highly correlated with API production for nominal fermentations. For the nonnominal fermentations, the manipulations of the fermentation stage explained the amount of API obtained (especially the pH and biomass concentration). Based on the above process analysis it was possible to select a smaller set of variables with which a new model was built. The amount of variance predicted of the final API concentration (cross-validation) for this model was 82.4%. The advantage of the multiblock model over the standard PLS model is that the contributions of the two main process stages to the API volumetric productivity were determined.     

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.     

FT-NIRS技术应用于稻米直链淀粉含量分析研究

运用近红外光谱快速分析技术,使用偏最小二乘法建立了近红外光谱和水稻糙米直链淀粉含量的数学模型,并进行糙米直链淀粉含量预测.结果表明糙米近红外光谱与其直链淀粉含量具有良好的相关性,决定系数r2=0.8429,最大绝对误差4.82%,平均误差2.30%.该方法在不破坏样品的前提下快速分析水稻直链淀粉含量,可用于稻种资源的快速鉴定,对于水稻优质育种及其相关研究具有重要意义.