Classification of local diesel fuels and simultaneous prediction of their physicochemical parameters using FTIR-ATR data and chemometrics.

Class identification and prediction of physicochemical variables of eight diesel fuel brands collected from several stations within the Atlanta metropolitan area in the State of Georgia were investigated using principal component analysis (PCA), partial least squares discriminant analysis (PLS2-DA), and partial least squares regression (PLSR) as modeling techniques. The fuels were from a common pipeline, therefore, assumed to have very similar characteristics. Ten FTIR-ATR spectra per fuel brand were collected over the 650 - 4000 cm-1 mid-infrared region, and the 80 x 3351 matrix was submitted to PCA to determine if there were any clusters. Following PCA, the 80 x 3351 matrix was split into a training matrix (56x3351) and a test matrix (24x3351). PLS2-DA models were built and evaluated for class identification using dummy variables (I,0) as input matrix. For physicochemical variable predictions, models were developed via PLSR using the FTIR-ATR spectra training matrix and physicochemical variables obtained from the Georgia Department of Agriculture Labs as input. Correlation coefficients of the eight fuels ranged from 0.9960 to 0.9998. PCA revealed all eight clusters of the diesel fuels, regardless of the tight correlation coefficients range. With a 1.0 ± 0.1 cut-off for fuel identification, the PLS2-DA models showed 100% correct predictions for four or five fuel brands, and 75% correct prediction for all eight fuel brands. PLSR predicted 100% correct physicochemical variables, with a RMSEP range of 0.019 to 1.132 for all 80 variables targeted.

Energy Harvesting by Mesoporous Reduced Graphene Oxide Enhanced the Mediator-Free Glucose-Powered Enzymatic Biofuel Cell for Biomedical Applications.

Harnessing electrochemical energy in an engineered electrical circuit from biochemical substrates in the human body using biofuel cells is gaining increasing research attention in the current decade due to the wide range of biomedical possibilities it creates for electronic devices. In this report, we describe and characterize the construction of just such an enzymatic biofuel cell (EBFC). It is simple, mediator-free, and glucose-powered, employing only biocompatible materials. A novel feature is the two-dimensional mesoporous thermally reduced graphene oxide (rGO) host electrode. An additionally novelty is that we explored the potential of using biocompatible, low-cost filter paper (FP) instead of carbon paper, a conductive polymer, or gold as support for the host electrode. Using glucose (C6H12O6) and molecular oxygen (O2) as the power-generating fuel, the cell consists of a pair of bioelectrodes incorporating immobilized enzymes, the bioanode modified by rGO-glucose oxidase (GOx/rGO), and the biocathode modified by rGO-laccase (Lac/rGO). Scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX), transmission electron microscopy, and Raman spectroscopy techniques have been employed to investigate the surface morphology, defects, and chemical structure of rGO, GOx/rGO, and Lac/rGO. N2 sorption, SEM/EDX, and powder X-ray diffraction revealed a high Brunauer-Emmett-Teller surface area (179 m2 g-1) mesoporous rGO structure with the high C/O ratio of 80:1 as well. Results from the Fourier transform infrared spectroscopy, UV-visible spectroscopy, and electrochemical impedance spectroscopy studies indicated that GOx remained in its native biochemical functional form upon being embedded onto the rGO matrix. Cyclic voltammetry studies showed that the presence of mesoporous rGO greatly enhanced the direct electrochemistry and electrocatalytic properties of the GOx/rGO and Lac/rGO nanocomposites. The electron transfer rate constant between GOx and rGO was estimated to be 2.14 s-1. The fabricated EBFC (GOx/rGO/FP-Lac/rGO/FP) using a single GOx/rGO/FP bioanode and a single Lac/rGO/FP biocathode provides a maximum power density (Pmax) of 4.0 nW cm-2 with an open-circuit voltage (VOC) of 0.04 V and remains stable for more than 15 days with a power output of ∼9.0 nW cm-2 at a pH of 7.4 under ambient conditions.

Reduction of interferences in the analysis of Children's Dimetapp using ultraviolet spectroscopy data and target factor analysis.

A calibration matrix has been developed and successfully applied to quantify actives in Children's Dimetapp®, a cough mixture whose active components suffer from heavy spectral interference. High-performance liquid chromatography/photodiode array instrument was used to identify the actives and any other UV-detectable excipients that might contribute to interferences. The instrument was also used to obtain reference data on the actives, instead of relying on the manufacturer's claims. Principal component analysis was used during the developmental stages of the calibration matrix to highlight any mismatch between the calibration and sample spectra, making certain that "apples" were not compared with "oranges". The prediction model was finally calculated using target factor analysis and partial least squares regression. In addition to the actives in Children's Dimetapp® (brompheniramine maleate, phenylephrine hydrogen chloride, and dextromethorphan hydrogen bromide), sodium benzoate was identified as the major and FD&C Blue #1, FD&C Red #40, and methyl anthranilate as minor spectral interferences. Model predictions were compared before and after the interferences were included into the calibration matrix. Before including interferences, the following results were obtained: brompheniramine maleate=481.3mgL-1±134% RE; phenylephrine hydrogen chloride=1041mgL-1±107% RE; dextromethorphan hydrogen bromide=1571mgL-1±107% RE, where % RE=percent relative error based on the reference HPLC data. After including interferences, the results were as follows: brompheniramine maleate=196.3mgL-1±4.4% RE; phenylephrine hydrogen chloride=501.3mgL-1±0.10% RE; dextromethorphan hydrogen bromide=998.7mgL-1±1.6% RE as detailed in Table 6.

Discerning some Tylenol brands using attenuated total reflection Fourier transform infrared data and multivariate analysis techniques.

Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify acetaminophen-containing medicines using their attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectra. Four formulations of Tylenol (Arthritis Pain Relief, Extra Strength Pain Relief, 8 Hour Pain Relief, and Extra Strength Pain Relief Rapid Release) along with 98% pure acetaminophen were selected for this study because of the similarity of their spectral features, with correlation coefficients ranging from 0.9857 to 0.9988. Before acquiring spectra for the predictor matrix, the effects on spectral precision with respect to sample particle size (determined by sieve size opening), force gauge of the ATR accessory, sample reloading, and between-tablet variation were examined. Spectra were baseline corrected and normalized to unity before multivariate analysis. Analysis of variance (ANOVA) was used to study spectral precision. The large particles (35 mesh) showed large variance between spectra, while fine particles (120 mesh) indicated good spectral precision based on the F-test. Force gauge setting did not significantly affect precision. Sample reloading using the fine particle size and a constant force gauge setting of 50 units also did not compromise precision. Based on these observations, data acquisition for the predictor matrix was carried out with the fine particles (sieve size opening of 120 mesh) at a constant force gauge setting of 50 units. After removing outliers, PCA successfully classified the five samples in the first and second components, accounting for 45.0% and 24.5% of the variances, respectively. The four-component PLS-DA model (R(2)=0.925 and Q(2)=0.906) gave good test spectra predictions with an overall average of 0.961 +/- 7.1% RSD versus the expected 1.0 prediction for the 20 test spectra used.