A Techno-economic Analysis for Integrating an Electrochemical Reactor into a Lignocellulosic Biorefinery for Production of Industrial Chemicals and Hydrogen.

In this study, we present a techno-economic analysis for integrating an electrochemical reactor into a lignocellulosic biorefinery for the purpose of converting biorefinery lignin to higher-value industrial chemicals with co-generation of hydrogen. We consider how the electrochemical reactor impacts the manufacturing costs for producing biofuel and determine a break-even value for the lignin oxidation product stream, which is the minimum lignin conversion product stream value that renders the cost to produce biofuel the same as in the typical biorefinery concept. We conclude that at low extents of lignin conversion, the break-even product stream value is likely too high for the process to be feasible. However, at higher extents of lignin conversion, the break-even product stream value may be between $1.00 and $2.00/kg, depending on capital cost and other manufacturing costs like depreciation. Potential markets for the biomass conversion products include resin manufacturing, where the products would compete with petroleum-derived resin precursors.

Noninteger Root Transformations for Preprocessing Nanoelectrospray Ionization High-Resolution Mass Spectra for the Classification of Cannabis.

Typically, for measurements with a high dynamic range, the range is reduced by using the square root transform. By using noninteger roots coupled with systematic experimental design, improvements to the measurements may be obtained. The effect of using noninteger root transformation was evaluated using high-resolution mass spectrometry (HRMS) combined with nanoelectrospray ionization (Nano-ESI) to differentiate 23 samples of Cannabis. The mass spectra were evaluated and classified using different mass resolving powers and noninteger root transformations. Classification was achieved by super partial least-squares discriminant analysis (sPLS-DA), support vector machine (SVM), and SVM classification tree type entropy (SVMTreeH). The 2.5 root transformation gave the best overall performance at different resolving powers for chemical profiling from a multilevel factorial experimental design using 2 factors and more than 4 levels. Response surface modeling using a cubic polynomial model of the bootstrapped sPLS-DA average prediction accuracies yielded optima at 0.005 for resolving power and 2.3 for the root transformation. Root transformation is an important spectral preprocessing tool for decreasing the dynamic range so that the relative variance of smaller but more important features may be inflated. For the classification of Cannabis using Nano-ESI, the optimal ranges of root and resolution were broad. The chasing-the-optimum method has been introduced for refining the polynomial response surface model.

Strain-level Staphylococcus differentiation by CeO2-metal oxide laser ionization mass spectrometry fatty acid profiling.

BACKGROUND: The Staphylococcus genus is composed of 44 species, with S. aureus being the most pathogenic. Isolates of S. aureus are generally susceptible to ß-lactam antibiotics, but extensive use of this class of drugs has led to increasing emergence of resistant strains. Increased occurrence of coagulase-negative staphylococci as well as S. aureus infections, some with resistance to multiple classes of antibiotics, has driven the necessity for innovative options for treatment and infection control. Despite these increasing needs, current methods still only possess species-level capabilities and require secondary testing to determine antibiotic resistance. This study describes the use of metal oxide laser ionization mass spectrometry fatty acid (FA) profiling as a rapid, simultaneous Staphylococcus identification and antibiotic resistance determination method. RESULTS: Principal component analysis was used to classify 50 Staphyloccocus isolates. Leave-one-spectrum-out cross-validation indicated 100 % correct assignment at the species and strain level. Fuzzy rule building expert system classification and self-optimizing partial least squares discriminant analysis, with more rigorous evaluations, also consistently achieved greater than 94 and 84 % accuracy, respectively. Preliminary analysis differentiating MRSA from MSSA demonstrated the feasibility of simultaneous determination of strain identification and antibiotic resistance. CONCLUSION: The utility of CeO2-MOLI MS FA profiling coupled with multivariate statistical analysis for performing strain-level differentiation of various Staphylococcus species proved to be a fast and reliable tool for identification. The simultaneous strain-level detection and antibiotic resistance determination achieved with this method should greatly improve outcomes and reduce clinical costs for therapeutic management and infection control.

Comparison of Flow Injection MS, NMR, and DNA Sequencing: Methods for Identification and Authentication of Black Cohosh (Actaea racemosa).

Flow injection mass spectrometry and proton nuclear magnetic resonance spectrometry, two metabolic fingerprinting methods, and DNA sequencing were used to identify and authenticate Actaea species. Initially, samples of Actaea racemosa from a single source were distinguished from other Actaea species based on principal component analysis and soft independent modeling of class analogies of flow injection mass spectrometry and proton nuclear magnetic resonance spectrometry metabolic fingerprints. The chemometric results for flow injection mass spectrometry and proton nuclear magnetic resonance spectrometry agreed well and showed similar agreement throughout the study. DNA sequencing using DNA sequence data from two independent gene regions confirmed the metabolic fingerprinting results. Differences were observed between A. racemosa samples from four different sources, although the variance within species was still significantly less than the variance between species. A model based on the combined A. racemosa samples from the four sources consistently permitted distinction between species. Additionally, the combined A. racemosa samples were distinguishable from commercial root samples and from commercial supplements in tablet, capsule, or liquid form. DNA sequencing verified the lack of authenticity of the commercial roots but was unsuccessful in characterizing many of the supplements due to the lack of available DNA.

Characterization of near-infrared spectral variance in the authentication of skim and nonfat dry milk powder collection using ANOVA-PCA, pooled-ANOVA, and partial least-squares regression.

Forty-one samples of skim milk powder (SMP) and nonfat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of 3 days. NIR reflectance spectra (1700-2500 nm) were converted to pseudoabsorbance and examined using (a) analysis of variance-principal component analysis (ANOVA-PCA), (b) pooled-ANOVA based on data submatrices, and (c) partial least-squares regression (PLSR) coupled with pooled-ANOVA. ANOVA-PCA score plots showed clear separation of the samples with respect to milk class (SMP or NFDM), day of analysis, production site, processing temperature, and individual samples. Pooled-ANOVA provided statistical levels of significance for the separation of the averages, some of which were many orders of magnitude below 10⁻³. PLSR showed that the correlation with Certificate of Analysis (COA) concentrations varied from a weak coefficient of determination (R²) of 0.32 for moisture to moderate R² values of 0.61 for fat and 0.78 for protein for this multinational study. In this study, pooled-ANOVA was applied for the first time to PLS modeling and demonstrated that even though the calibration models may not be precise, the contribution of the protein peaks in the NIR spectra accounted for the largest proportion of the variation despite the inherent imprecision of the COA values.

Classification of cultivation locations of Panax quinquefolius L samples using high performance liquid chromatography-electrospray ionization mass spectrometry and chemometric analysis.

Panax quinquefolius L ( P. quinquefolius L) samples grown in the United States and China were analyzed with high performance liquid chromatography-mass spectrometry (HPLC-MS). Prior to classification, the two-way data sets were subjected to pretreatment including baseline correction and retention time (RT) alignment. Principal component analysis (PCA) and projected difference resolution (PDR) metrics were used to evaluate the data quality and the pretreatment effects. A fuzzy rule-building expert system (FuRES) classifier was used to classify the P. quinquefolius L samples grown in the United States and China with the optimized partial least-squares (o-PLS) classifier as the positively biased control method. A classification rate as high as 98 ± 3% with FuRES was obtained after baseline correction and RT alignment, which is equivalent to the result obtained by using the positively biased o-PLS control method (98 ± 3%). RT alignment improved the classification rates for both FuRES and o-PLS classifiers (18% improvement for the FuRES classification rate and 10% improvement for the o-PLS classification rate with baseline correction). From the rule obtained to classify the P. quinquefolius L samples grown in the United States and China, peaks were identified that can be prospective biomarkers for differentiating samples from different growth regions. HPLC-MS with chemometric analysis has the potential to be used as an authentication method for P. quinquefolius L grown in China and the United States.

Fast and selective modification of thiol proteins/peptides by N-(phenylseleno)phthalimide.

We previously reported that selenamide reagents such as ebselen and N-(phenylseleno)phthalimide (NPSP) can be used to selectively derivatize thiols for mass spectrometric analysis, and the introduced selenium tags are useful as they could survive or removed with collision-induced dissociation (CID). Described herein is the further study of the reactivity of various protein/peptide thiols toward NPSP and its application to derivatize thiol peptides in protein digests. With a modified protocol (i.e., dissolving NPSP in acetonitrile instead of aqueous solvent), we found that quantitative conversion of thiols can be obtained in seconds, using NPSP in a slight excess amount (NPSP:thiol of 1.1-2:1). Further investigation shows that the thiol reactivity toward NPSP reflects its chemical environment and accessibility in proteins/peptides. For instance, adjacent basic amino acid residues increase the thiol reactivity, probably because they could stabilize the thiolate form to facilitate the nucleophilic attack of thiol on NPSP. In the case of creatine phosphokinase, the native protein predominately has one thiol reacted with NPSP while all of four thiol groups of the denatured protein can be derivatized, in accordance with the corresponding protein conformation. In addition, thiol peptides in protein/peptide enzymatic digests can be quickly and effectively tagged by NPSP following tri-n-butylphosphine (TBP) reduction. Notably, all three thiols of the peptide QCCASVCSL in the insulin peptic digest can be modified simultaneously by NPSP. These results suggest a novel and selective method for protecting thiols in the bottom-up approach for protein structure analysis.

Classification of jet fuels by fuzzy rule-building expert systems applied to three-way data by fast gas chromatography--fast scanning quadrupole ion trap mass spectrometry.

A fast method that can be used to classify unknown jet fuel types or detect possible property changes in jet fuel physical properties is of paramount interest to national defense and the airline industries. While fast gas chromatography (GC) has been used with conventional mass spectrometry (MS) to study jet fuels, fast GC was combined with fast scanning MS and used to classify jet fuels into lot numbers or origin for the first time by using fuzzy rule-building expert system (FuRES) classifiers. In the process of building classifiers, the data were pretreated with and without wavelet transformation and evaluated with respect to performance. Principal component transformation was used to compress the two-way data images prior to classification. Jet fuel samples were successfully classified with 99.8 ± 0.5% accuracy for both with and without wavelet compression. Ten bootstrapped Latin partitions were used to validate the generalized prediction accuracy. Optimized partial least squares (o-PLS) regression results were used as positively biased references for comparing the FuRES prediction results. The prediction results for the jet fuel samples obtained with these two methods were compared statistically. The projected difference resolution (PDR) method was also used to evaluate the fast GC and fast MS data. Two batches of aliquots of ten new samples were prepared and run independently 4 days apart to evaluate the robustness of the method. The only change in classification parameters was the use of polynomial retention time alignment to correct for drift that occurred during the 4-day span of the two collections. FuRES achieved perfect classifications for four models of uncompressed three-way data. This fast GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes.

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.

Detection of cocaine and its metabolites in urine using solid phase extraction-ion mobility spectrometry with alternating least squares.

A reliable, alternative screening method for detection of cocaine and its metabolites, benzoylecgonine and cocaethylene in urine is demonstrated using solid phase extraction (SPE) coupled with ion mobility spectrometry (IMS). Data analysis with alternating least squares (ALS) is used to model IMS spectral datasets and separate the reactant ion peak from the product ion peaks. IMS has been used as a screening device for drug and explosive detection for many years. It has the advantages of atmospheric pressure operation, simple sample preparation, portability, fast analysis, and high sensitivity when compared to similar methods. Coupling SPE with IMS decreases the detection limits of drug metabolites in urine while removing salts and other polar compounds that suppress ionization during the measurement. The IMS analysis time in this experiment is 20s, much shorter than traditional chromatographic analysis. The application of ALS further increases the sensitivity and selectivity of this method. The detection limits of benzoylecgonine and cocaethylene are 10 ng/mL and 4 ng/mL, respectively. Commercial adulteration of urine specimens does not influence the ability to detect cocaine metabolites after sampling the urine with SPE. This method provides forensic chemists a viable approach for fast and simple drug screening.

Comparison of differential mobility spectrometry and mass spectrometry for gas chromatographic detection of ignitable liquids from fire debris using projected difference resolution.

The significance of forensic arson analysis accelerates the applications of new technologies in this area. Based on the previously reported application of differential mobility spectrometry (DMS) as a detection method for gas chromatography (GC) in arson analysis, the performances of DMS and mass spectrometry (MS) were compared using a novel chemometric tool, projected difference resolutions (PDRs). The PDR results show that one-way mass spectra data exhibit higher resolution than DMS data, while total ion chromatograms from GC-DMS show higher resolution than that from GC/MS for differentiating seven kinds of ignitable liquids. Combining the information from both chromatography and spectra, two-way data always have higher resolution than one-way data for these two detection methods, and GC/MS would exhibit better performance than GC-DMS according to the minimum resolution value. To verify the PDR results, a fuzzy rule-building expert system was applied for classifying these seven kinds of ignitable liquids from fire debris based on GC-DMS and GC/MS data, respectively. The prediction accuracies were consistent with PDR results, which proved that PDR is a powerful tool in comparing the performances of different analysis methods for pattern recognition.

Application of linear and nonlinear discrete wavelet transforms to MALDI-MS measurements of bacteria for classification.

The linear and nonlinear discrete wavelet transforms (DWTs) were used to compress matrix-assisted laser desorption/ionization mass spectra to address two key challenges: the relatively high noise level and the underdetermined format of the data set. By applying the DWT to MALDI-MS spectra, the spectra were simultaneously smoothed and compressed. Multivariate projected difference resolution was used to evaluate the effects of the linear and nonlinear DWT on classification. The cross-validation study using bootstrapped Latin partition and partial least-squares (PLS-2) has proved that the classification accuracy increased after data compression. The best result was obtained when using Fisher's criterion to choose wavelet coefficients for compression. With the aid of principal component analysis (PCA), different wavelet filters may provide different mathematical perspectives to visualize the clustering of bacteria. The effect of growth time was directly observed with wavelet transform, which could not be observed using the original spectra.

Discriminant analysis of fused positive and negative ion mobility spectra using multivariate self-modeling mixture analysis and neural networks.

A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).

Biomarker profiling and reproducibility study of MALDI-MS measurements of Escherichia coli by analysis of variance-principal component analysis.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has proved useful for the characterization of bacteria and the detection of biomarkers. Key challenges for MALDI-MS measurements of bacteria are overcoming the relatively large variability in peak intensities. A soft tool, combining analysis of variance and principal component analysis (ANOVA-PCA) (Harrington, P. D.; Vieira, N. E.; Chen, P.; Espinoza, J.; Nien, J. K.; Romero, R.; Yergey, A. L. Chemom. Intell. Lab. Syst. 2006, 82, 283-293. Harrington, P. D.; Vieira, N. E.; Espinoza, J.; Nien, J. K.; Romero, R.; Yergey, A. L. Anal. Chim. Acta. 2005, 544, 118-127) was applied to investigate the effects of the experimental factors associated with MALDI-MS studies of microorganisms. The variance of the measurements was partitioned with ANOVA and the variance of target factors combined with the residual error was subjected to PCA to provide an easy to understand statistical test. The statistical significance of these factors can be visualized with 95% Hotelling T2 confidence intervals. ANOVA-PCA is useful to facilitate the detection of biomarkers in that it can remove the variance corresponding to other experimental factors from the measurements that might be mistaken for a biomarker. Four strains of Escherichia coli at four different growth ages were used for the study of reproducibility of MALDI-MS measurements. ANOVA-PCA was used to disclose potential biomarker proteins associated with different growth stages.

Bootstrap classification and point-based feature selection from age-staged mouse cerebellum tissues of matrix assisted laser desorption/ionization mass spectra using a fuzzy rule-building expert system.

A bootstrap method for point-based detection of candidate biomarker peaks has been developed from pattern classifiers. Point-based detection methods are advantageous in comparison to peak-based methods. Peak determination and selection are problematic when spectral peaks are not baseline resolved or on a varying baseline. The benefit of point-based detection is that peaks can be globally determined from the characteristic features of the entire data set (i.e., subsets of candidate points) as opposed to the traditional method of selecting peaks from individual spectra and then combining the peak list into a data set. The point-based method is demonstrated to be more effective and efficient using a synthetic data set when compared to using Mahalanobis distance for feature selection. In addition, probabilities that characterize the uniqueness of the peaks are determined. This method was applied for detecting peaks that characterize age-specific patterns of protein expression of developing and adult mouse cerebella from matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) data. The mice comprised three age groups: 42 adults, 19 14-day-old pups, and 16 7-day-old pups. Three sequential spectra were obtained from each tissue section to yield 126, 57 and 48 spectra for adult, 14-day-old pup, and 7-day-old pup spectra, respectively. Each spectrum comprised 71,879 mass measurements in a range of 3.5-50 kDa. A previous study revealed that 846 unique peaks were detected that were consistent for 50% of the mice in each age group (C. Laurent, D.F. Levinson, S.A. Schwartz, P.B. Harrington, S.P. Markey, R.M. Caprioli, P. Levitt, Direct profiling of the cerebellum by MALDI MS: a methodological study in postnatal and adult mouse, J. Neurosci. Res. 81 (2005) 613-621.). A fuzzy rule-building expert system (FuRES) was applied to investigate the correlation of age with features in the MS data. FuRES detected two outlier pup-14 spectra. Prediction was evaluated using 100 bootstrap samples of 2 Latin-partitions (i.e., 50:50 split between training and prediction set) of the mice. The spectra without the outliers yielded classification rates of 99.1+/-0.1%, 90.1+/-0.8%, and 97.0+/-0.6% for adults, 14-day-old pups, and 7-day-old pups, respectively. At a 95% level of significance, 100 bootstrap samples disclosed 35 adult and 21 pup distinguishing peaks for separating adults from pups; and 8 14-day-old and 15 7-day-old predictive peaks for separating 14-day-old pup from 7-day-old pup spectra. A compressed matrix comprising 40,393 points that were outside the 95% confidence intervals of one of the two FuRES discriminants was evaluated and the classification improved significantly for all classes. When peaks that satisfied a quality criterion were integrated, the 55 integrated peak areas furnished significantly improved classification for all classes: the selected peak areas furnished classification rates of 100%, 97.3+/-0.6%, and 97.4+/-0.3% for adult, 14-day-old pups, and 7-day-old pups using 100 bootstrap Latin partitions evaluations with the predictions averaged. When the bootstrap size was increased to 1000 samples, the results were not significantly affected. The FuRES predictions were consistent with those obtained by discriminant partial least squares (DPLS) classifications.

Forensic application of gas chromatography-differential mobility spectrometry with two-way classification of ignitable liquids from fire debris.

With respect to the emerging role of forensic science for arson investigation, a low cost and promising onsite detection method for ignitable liquids is desirable. Gas chromatography-differential mobility spectrometry (GC-DMS) was investigated as a tool for analysis of ignitable liquids from fire debris. Headspace solid-phase microextraction (SPME) was applied as the preconcentration and sampling method. The combined information afforded by gas chromatography and differential mobility spectrometry provided unique two-way patterns for each sample of ignitable liquid. Two-way GC-DMS data were classified into one of seven ignitable liquids using a fuzzy rule-building expert system (FuRES). The performance of the classifier was validated using bootstrap Latin partitions (BLPs) and also compared to optimized partial least-squares (PLS) classifiers. Better prediction results can be obtained by using two-way GC-DMS data than only using one-way total ion chromatograms or integrated differential mobility spectra. FuRES models constructed with the neat ignitable liquids identified the spiked samples from simulated fire debris with 99.07 +/- 0.04% accuracy.

Fuzzy rule-building expert system classification of fuel using solid-phase microextraction two-way gas chromatography differential mobility spectrometric data.

Gas chromatography/differential mobility spectrometry (GC/DMS) has been investigated for characterization of fuels. Neat fuel samples were sampled using solid-phase microextraction (SPME) and analyzed using a micromachined differential mobility spectrometer with a photoionization source interfaced to a gas chromatograph. The coupling of DMS to GC offers an additional order of information in that two-way data are obtained with respect to compensation voltages and retention time. A fuzzy rule-building expert system (FuRES) was used as a multivariate classifier for the two-way gas chromatograms of fuels, including rocket (RP-1, RG-1), diesel, and jet (JP-4, JP-5, JP-7, JP-TS, JetA-3639, Jet A-3688, Jet A-3690, Jet A-3694, and Jet A-generic) fuels. The GC-DMS with SPME was able to produce characteristic profiles of the fuels and a classification rate of 95 +/- 0.3% obtained with a FuRES model. The classification system also had perfect classification for each fuel sample when applied one month later.

Direct detection of trimethylamine in meat food products using ion mobility spectrometry.

Biogenic amines are degradation products generated by bacteria in meat products. These amines can indicate bacterial contamination or have a carcinogenic effect to humans consuming spoiled meats; therefore, their rapid detection is essential. Trimethylamine (TMA) is a good target for the detection of biogenic amines because its volatility. TMA was directly detected in meat food products using ion mobility spectrometry (IMS). TMA concentrations were measured in chicken meat juice for a quantitative evaluation of the meat decaying process. The lowest detected TMA concentration in chicken juice was 0.6+/-0.2 ng and the lowest detected signal for TMA in a standard aqueous solution was 0.6 ng. IMS data were processed using partial least squares (PLS) and Fuzzy rule-building expert system (FuRES). Using these two chemometric methods, trimethylamine concentrations of different days of meat spoilage can be separated, indicating the decaying of meat products. Comparing the two methods, FuRES provided a better classification of different days of meat spoilage.

Thermal desorption solid-phase microextraction inlet for differential mobility spectrometry.

A splitless thermal desorber unit that interfaces a differential mobility spectrometry (DMS) sensor has been devised. This device was characterized by the detection of benzene, toluene, and xylene (BTX) in water. The detection of BTX in water is important for environmental monitoring, and ion mobility measurements are traditionally difficult for hydrocarbons in water because water competes for charge and quenches the hydrocarbon signals. This paper reports the use of a DMS with a photoionization source that is directly coupled to a solid-phase microextraction (SPME) desorber. The separation and detection capabilities of the DMS were demonstrated using BTX components. Detection limits for benzene, toluene, and m-xylene were 75, 50, and 5 microg mL(-1), respectively.