A comparative multivariate analysis of nitrilase enzymes: An ensemble based computational approach

Nitrilases, member of nitrilase superfamily catalyse the hydrolysis of different nitriles to corresponding amides and acids. In this article, we demonstrate two-fold computational comparative analysis on coding gene sequences, amino acid sequences, three-dimensional structure of the nitrilases from different species and discovered conserved motifs linked with related species. A large ensemble-based dataset was utilized from bacteria, fungi, plants and animals. Here, we used comparative genomics, motif analyses and Bayesian phylogenetic analyses in combination with structural analyses [molecular dynamics simulation, principal component analysis (PCA), dynamic cross correlation (DCCM), root mean squared inner product (RMSIP), free energy surface (FES)] to investigate the evolution, ecological relationship and structure-function association of nitrilase family. The inferred evolutionary tree displayed nitrilase gene clusters to be shared among bacteria, fungi and plants. Structural analysis revealed that the folding of catalytic sites is similar among species; however, the loop region varies. We provide evidence based on PCA that the nitrilases are clustered into different clades due to variation in side chains. Numerous of significant correlations were found between sequence clades and the structural discriminating properties of nitrilases originating from different species. The results are consistent with the hypothesis that bacterial nitrilases are in ecological and evolutionary relationships with fungi and plants during plant-pathogen interaction to large extent. This compact and detail results also open new dimensions for further studying and improvement of industrially important nitrilase enzymes.     

Rapid multivariate analysis of 3D ToF‐SIMS data: graphical processor units (GPUs) and low‐discrepancy subsampling for large‐scale principal component analysis

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth‐profiles in XPS, AES and SIMS. For large images or three‐dimensional (3D) imaging depth‐profiles, PCA has been difficult to apply until now simply because of the size of the matrices of data involved. In a recent paper, we described two algorithms, random vector 1 (RV1) and random vector 2 (RV2), that improve the speed of PCA and allow datasets of unlimited size, respectively. In this paper, we now apply the RV2 algorithm to perform PCA on full 3D time‐of‐flight SIMS data for the first time without subsampling. The dataset we process in this way is a 128 × 128 pixel depth‐profile of 120 layers, each voxel having a 70 439 value mass spectrum associated with it. This forms over a terabyte of data when uncompressed and took 27 h to process using the RV2 algorithm using a conventional windows desktop personal computer (PC). While full PCA (e.g. using RV2) is to be preferred for final reports or publications, a much more rapid method is needed during analysis sessions to inform decisions on the next analytical step. We have therefore implemented the RV1 algorithm on a PC having a graphical processor unit (GPU) card containing 2880 individual processor cores. This increases the speed of calculation by a factor of around 4.1 compared with what is possible using a fast commercially available desktop PC having central processing units alone, and full PCA is performed in less than 7 s. The size of the dataset that can be processed in this way is limited by the size of the memory on the GPU card. This is typically sufficient for two‐dimensional images but not 3D depth‐profiles without sampling. We have therefore examined efficient sampling schemes that allow a good approximate solution to the PCA problem for large 3D datasets. We find that low‐discrepancy series such as Sobol series sampling gives more rapid convergence than random sampling, and we recommend such methods for routine use. Using the GPU and low‐discrepancy series together, we anticipate that any time‐of‐flight SIMS dataset, of whatever size, can be efficiently and accurately processed into PCA components in a maximum of around 10 s using a commercial PC with a widely available GPU card, although the longer RV2 approach is still to be preferred for the presentation of final results, such as in published papers. Copyright © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd     

Evaluation of beer deterioration by gas chromatography-mass spectrometry/multivariate analysis: a rapid tool for assessing beer composition

Beer stability is a major concern for the brewing industry, as beer characteristics may be subject to significant changes during storage. This paper describes a novel non-targeted methodology for monitoring the chemical changes occurring in a lager beer exposed to accelerated aging (induced by thermal treatment: 18 days at 45 °C), using gas chromatography-mass spectrometry in tandem with multivariate analysis (GC-MS/MVA). Optimization of the chromatographic run was performed, achieving a threefold reduction of the chromatographic time. Although losing optimum resolution, rapid GC runs showed similar chromatographic profiles and semi-quantitative ability to characterize volatile compounds. To evaluate the variations on the global volatile signature (chromatographic profile and m/z pattern of fragmentation in each scan) of beer during thermal deterioration, a non-supervised multivariate analysis method, Principal Component Analysis (PCA), was applied to the GC-MS data. This methodology allowed not only the rapid identification of the degree of deterioration affecting beer, but also the identification of specific compounds of relevance to the thermal deterioration process of beer, both well established markers such as 5-hydroxymethylfufural (5-HMF), furfural and diethyl succinate, as well as other compounds, to our knowledge, newly correlated to beer aging.     

Phytochemical screening and metabolomic approach based on Fourier transform infrared (FTIR): Identification of α-amylase inhibitor metabolites in Vernonia amygdalina leaves

ContextVernonia amygdalina has been reported as a potential antidiabetic agent. One of the mechanisms in diabetes mellitus treatment is the inhibition of the α-amylase enzyme’s action.ObjectiveThis study is aimed to identify the presence of secondary metabolites in Vernonia amygdalina leaf extract, which has the potential as α-amylase inhibitors through phytochemical screening combined with metabolomic analysis.Materials and methodsMethanol extract from Vernonia amygdalina leaf was partitioned into n-hexane, dichloromethane (DCM), and ethyl acetate. From this process, methanol, hexane, DCM, and ethyl acetate extracts were obtained. These extracts were then being tested for phytochemical screening, α-amylase inhibition, and FTIR. Then FTIR data were used for metabolomic analysis (PCA and PLS).ResultsThe results of the α-amylase inhibition test showed that the ethyl acetate extract had the smallest IC50 average value, which was 3.00 μg/mL. Based on the phytochemical screening test results, the extract showed positive for the presence of compounds such as flavonoids, phenols, tannins, alkaloids, and saponins. From the PCA analysis (Bi-plot), the wavenumbers that were influential in the ethyl acetate extract were 1436 to 1681 and 3341 to 3348 cm^?1. In theory, functional groups such as CH, CC, CO, NH, and OH appeared on the absorption. From the PLS analysis, these wavenumbers affected the activity.ConclusionThe most potential extract as an α-amylase inhibitor was the ethyl acetate extract. Based on phytochemical screening tests and metabolomic analysis, it was proven that this extract contained compounds as hypoglycemic agents.     

Evaluation of doped and undoped poly (o‐anisidine) as sensing materials for a sensor array for volatile organic compounds

Poly (o‐anisidine) (PoANI) and PoANI doped with nickel oxide and zinc oxide were evaluated as sensing materials for four gas analytes (methanol, ethanol, acetone, and benzene). The sensing materials had high sensitivity (showing an affinity towards the target analytes even at low concentrations, in the range of 1‐5 ppm), but rather poor selectivity, especially when the gas analytes were in a mixture. To exploit the poor selectivity, the three sensing materials were combined into a sensor array using principal component analysis (PCA) as a sensing algorithm. It was found that using a sensor array, the four individual gases could be separated. However, when all four gases were present (in analyte mixtures), there was too much overlap in the responses to distinguish between individual gas analytes and their related mixtures.     

Pharmacophore identification by molecular modeling and chemometrics: The case of HMG-CoA reductase inhibitors

Summary A methodology based on molecular modeling and chemometrics is applied to identify the geometrical pharmacophore and the stereoelectronic requirements for the activity in a series of inhibitors of 3-hydroxy 3-methylglutaryl coenzyme A (HMG-CoA) reductase, an enzyme involved in cholesterol biosynthesis. These inhibitors present two common structural features—a 3,5-dihydroxy heptanoic acid which mimics the active portion of the natural substrate HMG-CoA and a lipophilic region which carries both polar and bulky groups. A total of 432 minimum energy conformations of 11 homologous compounds showing different levels of biological activity are calculated by the molecular mechanics MM2 method. Five atoms are selected as representatives of the relevant fragments of these compounds and three interatomic distances, selected among 10 by means of a Principal Component Analysis (PCA), are used to describe the three-dimensional disposition of these atoms. A cluster analysis procedure, performed on the whole set of conformations described by these three distances, allows the selection of one cluster whose centroid represents a geometrical model for the HMG-CoA reductase pharmacophore and the conformations included are candidates as binding conformations. To obtain a refinement of the geometrical model and to have a better insight into the requirements for the activity of these inhibitors, the Molecular Electrostatic Potential (MEP) distributions are determined by the MNDO semiempirical method.     

QSAR study and VolSurf characterization of anti-HIV quinolone library

Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.     

Development of second-generation sample enrichment probe for improved sorptive analysis of volatile organic compounds

The sample enrichment probe (SEP) has recently been introduced as a user-friendly and cost-effective method for the sorptive extraction of volatile organic analytes from gaseous and aqueous samples for GC and GC-MS analyses. In a further development of the SEP technique, thinner polydimethylsiloxane (PDMS) tubing on polyimide-coated fused silica, instead of stainless steel rods or stalks, were used to produce the second-generation SEPs. The new SEP does not require widening of the needle-guiding orifice of the septum cap and analytes are desorbed at a faster rate from the thinner sleeve, which reduces the risk of carry-over. The flowless period that was previously recommended for analyses of highly volatile analytes is made redundant by the faster desorption from the thinner sorptive medium. It was found that differences in the thermal histories of SEPs are not the cause of the high relative standard deviations (RSDs) reported in our first paper on the technique. Excellent reproducibility can be attained by careful handling and storing of loaded SEPs and by rigorously following a standardised analytical protocol.     

Determination of a wide range of volatile and semivolatile organic compounds in snow by use of solid-phase micro-extraction (SPME)

Quantification and transformation of organic compounds are pivotal in understanding atmospheric processes, because such compounds contribute to the oxidative capacity of the atmosphere and drive climate change. It has recently been recognized that chemical reactions in snow play a role in the production or destruction of photolabile volatile organic compounds (VOC). We present an environmentally friendly method for determination of VOC and semi-VOC in snow collected at three sites—remote, urban, and (sub-)arctic. A solid-phase micro-extraction (SPME) procedure was developed and (semi-)VOC were identified by gas chromatography with mass spectrometric detection (GC–MS). A broad spectrum of (semi-)VOC was found in snow samples, including aldehydes, and aromatic and halogenated compounds. Quantification was performed for 12 aromatic and/or oxygenated compounds frequently observed in snow by use of neat standard solutions. The concentrations detected were between 0.12 (styrene and ethylbenzene) and 316 μg L⁻¹ (toluene) and limits of detection varied between 0.11 (styrene) and 1.93 μg L⁻¹ (benzaldehyde). These results indicate that the SPME technique presented is a broad but selective, versatile, solvent-free, ecological, economical, and facile method of analysis for (semi-)VOC in natural snow samples.     

热解析-气相色谱法测定印刷品中总挥发性有机化合物释放量

建立用气袋法收集整册印刷品释放的挥发性有机化合物,热解析-气相色谱法检测总挥发性有机化合物(TVOC)的方法。将印刷品样品置入充满高纯氮气的密封PVF采样袋中,在35℃下放置12 h后,用采样器串联Tenax-TA采样管和采样袋,让袋内气体通过采样管,样品释放出的有机化合物被吸附到采样管的吸附剂上。用热解析-气相色谱法测试采样管中挥发性有机化合物的含量,其中苯、甲苯、邻二甲苯、间二甲苯、对二甲苯、乙苯、乙酸丁酯、苯乙烯和正十一烷按外标法定量,其它物质按甲苯标准曲线定量。该方法的加标回收率为74.4%~91.1%,测定结果的相对标准偏差不大于10%(n=9)。该方法的样品采集更贴近印刷品的实际生活场景,可以为评价印刷品挥发性有机物释放对人体健康的影响提供数据基础,为相关评价提供新思路。     

顶空气相色谱-质谱法测定玩具中的10种挥发性有机物

建立了检测玩具中10种挥发性有机物(VOC)残留量的顶空气相色谱-质谱(HS-GC-MS)方法。样品经140 ℃、45 min静态顶空后,通过DB-624色谱柱分离和质谱检测,外标法定量。该方法对于不同VOC的定量限(LOQ)均在0.66 mg/kg以下,线性范围为0.001～2.0 μg,平均回收率在79%～106%之间,相对标准偏差(RSD)在0.4%～5.6%之间。该方法具有准确灵敏、简单快速等特点,将其应用于实际玩具样品的检测取得了良好效果。     

热脱附-气相色谱-三重四极杆串联质谱法测定环境空气中的挥发性有机物

采用热脱附(TD)结合气相色谱-三重四极杆串联质谱(GC-MS/MS)建立了环境空气中23种挥发性有机物(VOCs)同时检测的分析方法。空气样品通过主动采样的方式富集到装有Tenax-TA填料的热脱附管中,热解吸后在选择反应监测(SRM)模式下用GC-MS/MS进行检测,内标法定量。结果表明,23种VOCs在0.01~1 ng和1~100 ng低、高两个范围内线性关系良好,相关系数(r²)均大于0.99,方法定量限为0.00008~1 μ g/m³。加标水平为2、10和50 ng时,23种VOCs的平均回收率为77%~124%。除了最低加标水平的氯苯,相对标准偏差(RSD, n=6)均小于20%。对市内3个采样点的环境空气进行测定,其中苯、甲苯、乙苯、二甲苯、苯乙烯、1,2,4-三甲基苯和六氯丁二烯均有检出。实验证明,该TD和GC-MS/MS相结合的检测方法具有准确、可靠、灵敏度高等优点,适用于环境空气中VOCs的同时测定。     

Electrical sensing of volatile organic compounds in exhaled breath for disease diagnosis

In recent years, electrical sensors toward breath volatolomics have attracted increasing interest owing to their wide feasibility in noninvasive disease diagnostics. In this article, the working principles of active nanomaterials (e.g. metal oxides, polymers, and nanocarbon) toward volatile organic compounds are presented, with a special focus on the influence of surface chemistry and structural feature of these nanomaterials on the sensing performance. The latest and representative achievements on the direct analysis of three typical exhaled volatile organic compounds, including acetone, ammonia, and hydrogen sulfide, that are recognized as important disease biomarkers, are highlighted, indicating the capability of the electrical sensors in enabling noninvasive diagnosis and real-time monitoring. The opportunities and challenges in this field are provided in the end, with an emphasis on the background interference and data recognition which are key factors in developing prospective electrical sensors toward volatolomics analysis.     

Determination of volatile organic compounds in human breath for Helicobacter pylori detection by SPME-GC/MS

Helicobacter pylori living in the human stomach release volatile organic compounds (VOCs) that can be detected in expired air. The aim of the study was the application of breath analysis for bacteria detection. It was accomplished by determination of VOCs characteristic for patients with H. pylori and the analysis of gases released by bacteria in suspension. Solid-phase microextraction was applied as a selective technique for preconcentration and isolation of analytes. Gas chromatography coupled with mass spectrometry was used for the separation and identification of volatile analytes in breath samples and bacterial headspace. For data calculation and processing, discriminant and factor analyses were used. Endogenous substances such as isobutane, 2-butanone and ethyl acetate were detected in the breath of persons with H. pylori in the stomach and in the gaseous mixture released by the bacteria strain but they were not identified in the breath of healthy volunteers. The canonical analysis of discrimination functions showed a strong difference between the three examined groups. Knowledge of substances emitted by H. pylori with the application of an optimized breath analysis method might become a very useful tool for noninvasive detection of this bacterium.     

Application of mid-infared spectroscopy with multivariate analysis for the discrimination of toxic plant,Gelsemium elegans

Gelsemium elegans is a commonly used herb to treat different kinds of diseases. However, the indole alkaloid present in the plant might cause serious side effects. In this research, the infrared spectroscopic identification approach including Fourier transform infrared spectroscopy (FT-IR), Second derivative infrared spectra (SD-IR) and two-dimensional correlation infrared spectra (2D-IR) was used to develop a simple and rapid method to discriminate the stem, leaf and root of the Gelsemium elegans plant. This is because the stem, leaf and root contained different amount of indole alkaloid that contributed to the toxicity. Through this study, all the three parts were successfully identified and discriminated through the infrared spectroscopic identification method. The identification approach was also validated by comparing the samples of the mixture of both stem and root (SR) to the stem and root, respectively and also by comparing different plants with Gelsemium elegans plant. Besides that, all the samples of different parts of the Gelsemium elegans were analyzed with the Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) pattern recognition technique to test and verify the experimental results. The SIMCA model was validated by comparing 70 standard herbs to the model. From the results, macroscopic IR fingerprint method and the classification analysis successfully discriminate not only between Gelsemium elegans samples and standard herbs but also successfully distinguished the three different parts of Gelsemium elegans plant.     

Determination of phenolic compounds and authentication of PDO Lambrusco wines by HPLC-DAD and chemometric techniques

This work proposes a fast and simple method for detection and quantification of phenolic compounds in PDO Lambrusco wines using HPLC-DAD and chemometric techniques. Samples belonging to three different varieties of Lambrusco (Grasparossa, Salamino and Sorbara) were analyzed to provide a methodology appropriate for routine analysis. Given the high complexity of the sample and the coelution among chromatographic peaks, the use of chemometric techniques to extract the information of the individual eluting compounds was needed. Multivariate curve resolution-alternating least squares (MCR-ALS) allowed the resolution of the chromatographic peaks obtained and the use of this information for the quantification of the phenolic analytes in the presence of interferences. Use of multiset analysis and local rank/selectivity information was proven to be crucial for the correct resolution and quantification of compounds. The quantitative data provided by MCR-ALS about the phenolic targets and additional compounds present in the samples analyzed provided wine composition profiles, which were afterwards used to distinguish among wine varieties. Principal component analysis applied to the wine profiles allowed characterizing the wines according to their varieties.     

Development of certified reference materials of high-purity volatile organic compounds: purity assay by the freezing-point depression method

For accurate measurement of concentrations of substances by instrumental analysis, reliable calibration standards are needed. In Japan, national reference materials are supplied under the national standards dissemination system named the Japan Calibration Service System (JCSS). In JCSS, calibration standards for the analysis of environmental pollutants are supplied. For the traceability to the SI of reference materials for calibration in JCSS, the National Metrology Institute of Japan (NMIJ) is developing high-purity reference materials of volatile organic compounds (VOCs) as NMIJ CRMs. The freezing-point depression method, which has potential as a primary method of measurement, is employed for the determination of property value. In this paper, a development scheme of certified reference materials of high-purity VOCs is described. Presented at BERM-11, October 2007, Tsukuba, Japan.     

Lichens biomonitoring as feasible methodology to assess air pollution in natural ecosystems: Combined study of quantitative PAHs analyses and lichen biodiversity in the Pyrenees Mountains

The air quality in the Aragón valley, in the central Pyrenees, has been assessed by evaluation of lichen biodiversity and mapped by elaboration of the Index of Air Purity (IAP) based on observations of the presence and abundance of eight kinds of lichen with different sensitivity to air pollution. The IAP values obtained have been compared with quantitative analytical measures of 16 PAHs in the lichen Evernia prunastri, because this species was associated with a wide range of traffic exposure and levels of urbanization. Analyses of PAHs were carried out by the DSASE method followed by an SPE clean-up step and GC–MS analysis. The concentration of total PAHs found in lichen samples from the Aragón valley ranged from 692 to 6420 ng g⁻¹ and the PAHs profile showed predominance of compounds with three aromatic rings. The influence of the road traffic in the area has been shown because values over the median concentration of PAHs (>1092 ng g⁻¹), percentage of combustion PAHs (>50%), and equivalent toxicity (>169) were found in lichens collected at places exposed to the influence of traffic. The combination of both methods suggests IAP as a general method for evaluating the air pollution referenced to PAHs because it can be correlated with the content of combustion PAHs and poor lichen biodiversity can be partly explained by the air pollution caused by specific PAHs. Figure Map of the air pollution level in the Aragón valley (Pyrenees) based on the Index of Air Purity (IAP) calculation (lichen biodiversity)