Classification of bacteria by simultaneous methylation–solid phase microextraction and gas chromatography/mass spectrometry analysis of fatty acid methyl esters

Direct methylation and solid-phase microextraction (SPME) were used as a sample preparation technique for classification of bacteria based on fatty acid methyl ester (FAME) profiles. Methanolic tetramethylammonium hydroxide was applied as a dual-function reagent to saponify and derivatize whole-cell bacterial fatty acids into FAMEs in one step, and SPME was used to extract the bacterial FAMEs from the headspace. Compared with traditional alkaline saponification and sample preparation using liquid–liquid extraction, the method presented in this work avoids using comparatively large amounts of inorganic and organic solvents and greatly decreases the sample preparation time as well. Characteristic gas chromatography/mass spectrometry (GC/MS) of FAME profiles was achieved for six bacterial species. The difference between Gram-positive and Gram-negative bacteria was clearly visualized with the application of principal component analysis of the GC/MS data of bacterial FAMEs. A cross-validation study using ten bootstrap Latin partitions and the fuzzy rule building expert system demonstrated 87 ± 3% correct classification efficiency.      

Analysis of catechins and condensed tannins by thermally assisted hydrolysis/methylation-GC/MS and by a novel two step methylation

The objective of this study is to observe high molecular weight markers of catechins and condensed tannins by thermally assisted hydrolysis/methylation (THM)-GC/MS. Techniques for formation of intact methylated flavanols of catechins using THM in the presence of trimethylsulfonium hydroxide (TMSH), and, of a dimer marker of condensed tannins using a novel two step methylation technique, are presented. The two step methylation procedure involves pre-methylation of the sample with trimethylsilyl diazomethane (TMS-diazomethane) followed by THM. The dimer marker, a methylated product containing the C–C linkage between adjacent flavanol units, has a molecular weight of 540. Intact methylated flavanols of catechins were also successfully observed as high molecular weight compounds including partially methylated catechin and epicatechin (3-flavanol, 3′,4′,5,7-tetramethoxy, cis/trans; m/z = 346), epigallocatechin and gallocatechin (3-flavanol, 3′,4′,5,5′,7-pentamethoxy, cis/trans; m/z = 376). These techniques were successfully applied to the analysis of series of condensed tannins isolated from plants, and catechins and other phenolics present in (hot water) extracts of tea leaves. In green tea the major catechins were identified as epicatechin and epigallocatechin along with flavonols and tannin dimers.     

Determination of the carbon isotopic composition of whole/intact biological specimens using at-line direct thermal desorption to effect thermally assisted hydrolysis/methylation

In this paper, we discuss the use of a direct thermal desorption (DTD) interface as an alternative to Curie-point flash pyrolysis system as an inlet technique in gas chromatography-combustion isotope-ratio mass spectrometry (GC/C-IRMS) analysis of whole/intact phytoplankton and zooplankton specimens. The DTD in combination with a combipal auto-injector is programmed to perform the injection, evaporation of solvents, transport of capped programmed-temperature vaporizer (PTV) liners to the PTV injector and chemical derivatisation (thermally assisted hydrolysis/methylation; THM) such that a profile of a cellular fatty acids is obtained. Flow-cytometric sorted microalgae and handpicked zooplankton are used as samples with trimethylsulfonium hydroxide (TMSH) as methylating reagent. A major advantage of this novel approach over the Curie-point technique is the automation of the total procedure, which allows unattended analysis of large sample series. The profiles and delta(13)C carbon isotopic signatures of the fatty acid methyl esters (FAMEs) produced are very similar to those obtained using the Curie-point flash pyrolysis method. It is shown that algal samples must be kept no longer than 48 h in the DTD sample tray prior to the THM-analysis in order to maintain the integrity of their FAME profile.     

Magnetic recyclable eggshell-based mesoporous catalyst for biodiesel production from crude neem oil: Process optimization by central composite design and artificial neural network

A magnetically recyclable eggshell-based catalyst (MKEC) was synthesized to circumvent saponification during the conversion of neem, Jatropha, and waste cooking oils (free fatty acid, 2.3–6.6%) to biodiesel. The characterization results indicated that MKEC had a mesoporous structure with the pore width of 3.24 nm, a specific surface area of 128 m²/g, and a pore volume of 0.045 cm³/g. The results confirmed that the MKEC is more tolerant to fatty acid poisoning than calcined eggshell. The effects of process parameters for maximum fatty acid methyl ester (FAME) content were evaluated by central composite design (CCD) and artificial neural network (ANN). The experimental FAME content of 94.5% was achieved for neem oil with a standard deviation (SD) of 0.68, which was in reasonable agreement with predicted values (CCD, 96.9%; ANN, 95.9%; SD, 0.73). The reusability studies showed that the mesoporous catalyst can be reused efficiently for five cycles without much deterioration in its activity.     

Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil

In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (E)-2-decenal, (E,E)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil.     

Self-Tuning GMV Control of Glucose Concentration in Fed-Batch Baker’s Yeast Production

A detailed system identification procedure and self-tuning generalized minimum variance (STGMV) control of glucose concentration during the aerobic fed-batch yeast growth were realized. In order to determine the best values of the forgetting factor (λ), initial value of the covariance matrix (α), and order of the Auto-Regressive Moving Average with eXogenous (ARMAX) model (n _a, n _b), transient response data obtained from the real process wereutilized. Glucose flow rate was adjusted according to the STGMV control algorithm coded in Visual Basic in an online computer connected to the system. Conventional PID algorithm was also implemented for the control of the glucose concentration in aerobic fed-batch yeast cultivation. Controller performances were examined by evaluating the integrals of squared errors (ISEs) at constant and random set point profiles. Also, batch cultivation was performed, and microorganism concentration at the end of the batch run was compared with the fed-batch cultivation case. From the system identification step, the best parameter estimation was accomplished with the values λ?=?0.9, α?=?1,000 and n _a?=?3, n _b?=?2. Theoretical control studies show that the STGMV control system was successful at both constant and random glucose concentration set profiles. In addition, random effects given to the set point, STGMV control algorithm were performed successfully in experimental study.     

Upgrading of palm biodiesel fuel over supported palladium catalysts

Partial hydrogenation of palm biodiesel fuel (BDF) over 0.5wt%Pd/SBA-15 and 0.5wt%Pd/Zr-SBA-15 catalysts was examined by using a continuous fixed-bed reactor at 100 °C and 0.3 MPa under an atmosphere of H₂, in comparison to the commercial 0.5wt%Pd/γ-Al₂O₃ catalyst. The results showed that the 0.5wt%Pd/SBA-15 catalyst with high Pd dispersion and fast molecular diffusion through the short channeling pores gave the highest activity and selectivity in partial hydrogenation of polyunsaturated fatty acid methyl esters (FAME) as unstable components of palm BDF into cis-mono-unsaturated FAME as a target component of upgraded palm BDF with excellent oxidation stability and cold flow properties, which makes the addition of antioxidants unnecessary. By contrast, the 0.5wt%Pd/Zr-SBA-15 catalyst with strongly and moderately acidic sites gave low selectivity toward cis-mono-unsaturated FAME. The commercial 0.5wt%Pd/γ-Al₂O₃ catalyst displayed much lower polyunsaturated FAME conversion and cis-mono-unsaturated FAME selectivity, associated with poor Pd dispersion and slow molecular diffusion through the disordered pores.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks

A method based on laser induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification and discrimination of specific bacteria strains (Pseudomonas aeroginosa, Escherichia coli and Salmonella typhimurium). Instant identification of the samples is achieved using a spectral library, which was obtained by analysis using a single laser pulse of representative samples and treatment by neural networks. The samples used in this study were divided into three groups, which were prepared on three different days. The results obtained allow the identification of the bacteria tested with a certainty of over 95%, and show that only a difference between the bacteria can cause identification. Single-shot measurements were sufficient for clear identification of the bacterial strains studied. The method can be developed for automatic real time, fast, reliable and robust measurements and can be packaged in portable systems for non-specialist users.     

Comparative study of net analyte signal-based methods and partial least squares for the simultaneous determination of amoxycillin and clavulanic acid by stopped-flow kinetic analysis

A comparative study about advantages and limitations of net analyte signal (NAS)-based methods (NBMs) and partial least squares (PLS) calibration in kinetic analysis has been performed. The different multivariate calibration methods were applied to the determination of binary mixtures of amoxycillin and clavulanic acid, by stopped-flow kinetic analysis. The reactions of oxidation of these compounds with cerium(IV), in sulphuric acid medium, were monitored by following the changes on the fluorescence of the oxidation products, in stopped-flow mode. The differences on the kinetic profiles obtained at λ_ex=256 nm and λ_em=351 nm, were used to determine mixtures of both compounds by multivariate calibration of the kinetic data, using PLS-1, a modification of hybrid linear analysis (HLA) and net analyte pre-processing combined with classical least squares (NAP/CLS) methods. The NBMs allowed the selection of optimal time data regions by calculating the minimum error indicator function (EIF), improving the results and making NBMs very convenient for the analysis. In addition, the use of the net analyte signal concept allows the calculation of the analytical figures of merit, limit of detection (LOD), sensitivity and selectivity, for each component.     

Multivariate resolution of rank-deficient near-infrared spectroscopy data from the reaction of curing epoxy resins using the rank augmentation strategy and multivariate curve resolution alternating least squares approach

A model of the curing reaction between phenyl glycidyl ether (PGE) and aniline as the curing agent was studied isothermally at 95 °C and monitored in situ by near-infrared spectroscopy (NIR). The spectra were recorded every 5 min. The ubiquitous problem of rank deficiency in reaction network systems was solved by assembling an augmented column-wise matrix containing five process runs from different initial conditions. The data were analyzed using a two-way multivariate curve resolution alternating least squares method (MCR-ALS). Initial estimates of spectra required by MCR-ALS were given by a SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) approach. The reactants, product and intermediate spectra were successfully resolved and the concentration profiles properly represented the system studied. The performance of the model was evaluated by two parameters: ALS lack of fit (lof=0.88%) and explained variance (R²=99.99%). To validate the MCR-ALS results, the similarity coefficients (r) between the recovered spectra and the pure species spectra were calculated. These were: PGE (r=0.998), aniline (r=0.994) and tertiary amine (r=0.999).     

GC-MS-based urine metabolic profiling of autism spectrum disorders

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders resulting from multiple factors. Diagnosis is based on behavioural and developmental signs detected before 3 years of age, and there is no reliable biological marker. The purpose of this study was to evaluate the value of gas chromatography combined with mass spectroscopy (GC-MS) associated with multivariate statistical modeling to capture the global biochemical signature of autistic individuals. GC-MS urinary metabolic profiles of 26 autistic and 24 healthy children were obtained by liq/liq extraction, and were or were not subjected to an oximation step, and then were subjected to a persilylation step. These metabolic profiles were then processed by multivariate analysis, in particular orthogonal partial least-squares discriminant analysis (OPLS-DA, R ²Y(cum) = 0.97, Q ²(cum) = 0.88). Discriminating metabolites were identified. The relative concentrations of the succinate and glycolate were higher for autistic than healthy children, whereas those of hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower. Eight other metabolites, which were not identified but characterized by a retention time plus a quantifier and its qualifier ion masses, were found to differ between the two groups. Comparison of statistical models leads to the conclusion that the combination of data obtained from both derivatization techniques leads to the model best discriminating between autistic and healthy groups of children.     

Two methods for the separation of monounsaturated octadecenoic acid isomers

The identification and quantification of complex mixtures of cis and trans octadecenoic (18:1) fatty acid isomers presents a major challenge for conventional one-dimensional GC/FID analysis of their methyl esters. We have compared the use of two methods to achieve optimized separations of positional and geometrical octadecenoic fatty acid isomers—comprehensive two-dimensional gas chromatography (GC × GC), and silver ion high performance liquid chromatography interfaced to atmospheric pressure photoionization (APPI) mass spectrometry. Nine isomers of octadecenoic acid methyl ester were well separated on a single silver ion column with a mobile phase of 0.018% acetonitrile and 0.18% isopropanol in hexane. Reproducible retention times were obtained with relative standard deviations of around 1% over 5 injections. The extra selectivity and reproducibility afforded by APPI-MS, together with the wide separation of cis and trans isomers by silver ion chromatography, resulted in a promising method for measurement of octadecenoic acid FAME. The GC × GC separation was performed using various column combinations, and optimal separation was obtained by coupling an ionic liquid column (Supelco SLB-IL100 [1,9-di(3-vinyl-imidazolium) nonane bis(trifluoromethyl) sulfonyl imidate]) in the first dimension with a SGE BPX50 (50% phenyl polysilphenylene-siloxane) in the second dimension. These methods have been applied to the analysis of octadecenoic acid in milk and beef fat.     

The effect of afforestation on the soil organic carbon (SOC) of a peaty gley soil using on-line thermally assisted hydrolysis and methylation (THM) in the presence of 13C-labelled tetramethylammonium hydroxide (TMAH)

In Britain substantial areas of both deep and shallow peatland have been afforested with conifers since the 1950s. However, information about the effects of afforestation on the properties of soil organic carbon (SOC) is lacking. Investigating the geochemical changes that take place when lignin- and tannin-derived phenols are degraded and incorporated into SOC will provide us with an insight into soil carbon dynamics at the molecular level. Here we compare the phenolic distributions in two different peaty gley soil profiles using on-line thermally assisted hydrolysis and methylation (THM) in the presence of both unlabelled and ¹³C-labelled tetramethylammonium hydroxide (TMAH). The two soil profiles were beneath respectively an unforested moorland (ML) and a second rotation Sitka spruce (Picea sitchensis) afforested moorland (SS), from Harwood (Northumberland, northeast England, UK). THM of these soils in the presence of ¹³C-labelled TMAH enabled us to assess the relative contributions of lignin, demethylated lignin, and non-lignin phenolics. The lignin phenolic distributions differed in both soil profiles reflecting changing source and decay dynamics within and between the sites. A progressive degradation of syringyl (S) and guaiacyl (G) phenolics was observed in the ML soil, compared with an increase of such components in the organic/mineral horizons of the SS soil. A significant tannin input was observed, particularly in the upper horizons of the SS soil. The S/G ratio gradually decreased with increasing burial in the ML soil, whilst a change in vegetation input and land preparation was recorded by this ratio in the SS soil. Overall, this suggests that afforestation influences the phenolic compositional profiles in these peaty gley soils as a result of one or more of the following processes: changing vegetation input, horizon inversion prior to planting, root input or leaching. This highlights the potential of using lignin and tannins as molecular indicators to assess the effects of afforestation on SOC.     

Effect of temperature and composition on density,viscosity and thermal conductivity of fatty acid methyl esters from soybean,castor and Jatropha curcas oils

This work is focused on experimental determination of density, viscosity and thermal conductivity as a function of temperature and composition for fatty acid methyl esters (FAME) from soybean, castor and Jatropha curcas oils. Results show that an increase in temperature, over the range of (273 to 363) K, resulted in a decrease of all properties studied. FAME from soybean and J. curcas oils presented similar rheological behaviour, while FAME from castor oil presented higher values for density and viscosity. Density, dynamic viscosity and thermal conductivity data for all systems obtained here were correlated using empirical equations with good agreement between experimental and calculated values. Experimental data presented here may be useful as a database for specification purposes and equipment design and plant operation in the biodiesel industry.     

Application of gas-sensor array technology for detection and monitoring of growth of spoilage bacteria in milk: A model study

The aim of this study was to develop a novel, rapid system for detection and monitoring of growth of undesirable bacteria in food using gas-sensor array technology. Three spoilage bacteria isolated from a cheese-processing hall were identified as Serratia marcescens, Serratia proteamacufans and Pseudomonas putida. The growth of these bacteria in milk was investigated using a commercial solid state based gas-sensor array system. On the basis of the temporal sensor readings of the pure cultures, bacterial growth could be monitored and the individual strains identified and followed throughout the complete growth cycle in both single and mixed culture. The gas-sensor signals could be used as early indicators of the onset of bacterial growth. Start detection of volatile bacterial metabolites coincided with the start of the exponential growth phase taking place around 7 h after inoculation and corresponding to bacterial numbers of 10⁴ (cfu/ml). The results were confirmed by comparing the gas profiles with the cell counts and by headspace gas chromatography mass spectrometry (GC/MS) of volatile microbial metabolites. High correlation (r > 0.90, p < 0.001) was found between the gas-sensor readings and major secondary volatile metabolites. Using the sensor readings, cell numbers of single strain cultures could be predicted with an error of less than 5%. The results show that it is possible to monitor growth of individual strains of spoilage bacteria in a mixed culture in milk on the basis of the type and amount of volatile compounds which they produce, using a gas-sensor array system. The system thus affords possibilities for further development for quick, more accurate and full scale determinations of shelf life, the design of spoilage indicators, rapid identification of undesired microorganisms and rapid measurements of spoilage.     

Spectroscopic analysis of bacterial biological warfare simulants and the effects of environmental conditioning on a bacterial spectrum

The ability to distinguish bacteria from mixed samples is of great interest, especially in the medical and defence arenas. This paper reports a step towards the aim of differentiating pathogenic endospores in situ, to aid any required response for hazard management using infrared spectroscopy combined with multivariate analysis. We describe a proof-of-principle study aimed at discriminating biological warfare simulants from common environmental bacteria. We also report an evaluation of multiple pre-processing techniques and subsequent differences in cross-validation of two pattern recognition models (Support Vector Machines and Principal Component–Linear Discriminant Analysis) for a six-class classification (bacterial classification). These classifications were possible with an average sensitivity of 88.0 and 86.9?%, and an average specificity of 97.6 and 97.5?% for the SVM and the PC-LDA models, respectively. Most spectroscopic models are built upon spectra from bacteria that have been specifically prepared for analysis by a particular method; this paper will comment upon the differences in the bacterial spectrum that occur between specific preparations when the bacteria have spent 30?days in the simulated weather conditions of a hot dry climate.