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The lack of stringent regulations regarding raw materials for herbal supplements used for medicinal purposes has been a constant challenge in the industry. Ginkgo biloba L. leaf extracts attract consumers because of the supposed positive effect on mental performance and memory. Supplements are produced using dried leaf materials and standardized leaf extracts such as EGb 761. Adulteration of Ginkgo biloba L. plants and extracts are becoming more and more common practice due to economically driven motivation from increasing demand in the market and the high cost of raw materials and production. Reinforcement in quality control (QC) to avoid adulterations is necessary to ensure the efficacy of the supplements. In this study, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was used with principal component analysis (PCA) as an unsupervised exploratory method to analyze, identify, and evaluate the adulterated Ginkgo biloba L. plant materials and dried leaf extracts using the PCA scores and loadings obtained and compound identification.

Requirements for blends of drop-in petroleum/bio-derived fuels with specific thermophysical and thermochemical properties highlights the need for chemometric models that can predict these properties. Multivariate calibration methods were evaluated using the measured thermograms (i.e., change in temperature with time) of 11 diesel/biodiesel fuel blends (including four repeated runs for each fuel blend). Two National Institute of Standards and Technology Standard Reference Material ® (SRM ® ) pure fuels were blended by serial dilution to produce fuels having diesel/biodiesel volumetric fractions between (0 to 100) %. The fuels were evaluated for the prepared fuel-blend volume fraction and total specific energy release (heating value), using a laser-driven calorimetry technique, termed 'laser-driven thermal reactor'. The experimental apparatus consists of a copper sphere-shaped reactor (mounted at the center of a stainless-steel chamber) that is heated by a high-power continuous wave Nd:YAG laser. Prior to heating by the laser, liquid sample is injected onto a copper pan substrate that rests near the center of the reactor and is in contact with a fine-wire thermocouple. A second thermocouple is in contact with the sphere-reactor inner surface. The thermograms are then used to evaluate for the thermochemical characteristic of interest. Partial least squares (PLS) and support vector machine (SVM) models were constructed and evaluated for SRM-fuel-blend quantification, and determination of prepared fuel-blend volume fraction and heating value. Quantification of the fuel-blend thermograms by the SVM method was found to better correlate with the experimental results than PLS. The combination of laser-driven calorimetry and multivariate calibration methods has demonstrated the potential application of using thermograms for fuels quantification and analysis of fuel-blend properties.

The physicochemical properties of a substance, such as a fuel, can vary significantly with composition. Determining these properties with ASTM standard methods is both expensive and time-consuming, which has led to a desire to use chemometric modeling as an alternative. In this study, we compare the accuracy and robustness of two chemometric models, partial least squares (PLS) regression and support vector machine (SVM) with uncertainty estimation to determine how the physicochemical properties depend on the composition. A set of hydrocarbon mixtures, including crude oil, oil, gasoline, and biofuel/biodiesel, were collected. GC-MS data were taken, and physicochemical properties were measured for these mixtures using ASTM standard methods. PLS and SVM were used to develop predictive models of the physicochemical properties. Uncertainty in the estimated property values was estimated using a bootstrapping technique. With this uncertainty estimate, it is possible to assess the trustworthiness of any prediction, which ensures that the chemometric models can be applied for general purposes. SVM was found to be generally better for predicting the physicochemical properties, although we expect that with a more comprehensive data set the performance of the PLS models can be improved. We show in this work that PLS and SVM can be used to generate a predictive model of physicochemical properties based on GC-MS data. Combined with uncertainty analysis, these models provide robust predictions that can be used for regulatory, economic, and safety purposes.

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically-obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory's individual performance.

Using Brazilian banknotes as a test case, forensic examination and identification of Rhodamine B dye anti-theft device (ATD) staining on banknotes were performed. Easy ambient sonic spray ionization mass spectrometry (EASI-MS) was used since it allows fast and simple analysis with no sample preparation providing molecular screening of the surface with direct desorption and ionization of the security dye. For a more accurate molecular characterization of the ATD dye, Q Exactive Orbitrap™ Fourier transform (tandem) mass spectrometry using eletrospray ionization (ESI-HRMS/MS) was also applied., (Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.)

Using a desorption/ionization technique, easy ambient sonic-spray ionization coupled to mass spectrometry (EASI-MS), documents related to the 2nd generation of Brazilian Real currency (R$) were screened in the positive ion mode for authenticity based on chemical profiles obtained directly from the banknote surface. Characteristic profiles were observed for authentic, seized suspect counterfeit and counterfeited homemade banknotes from inkjet and laserjet printers. The chemicals in the authentic banknotes' surface were detected via a few minor sets of ions, namely from the plasticizers bis(2-ethylhexyl)phthalate (DEHP) and dibutyl phthalate (DBP), most likely related to the official offset printing process, and other common quaternary ammonium cations, presenting a similar chemical profile to 1st-generation R$. The seized suspect counterfeit banknotes, however, displayed abundant diagnostic ions in the m/z 400-800 range due to the presence of oligomers. High-accuracy FT-ICR MS analysis enabled molecular formula assignment for each ion. The ions were separated by 44 m/z, which enabled their characterization as Surfynol® 4XX (S4XX, XX=40, 65, and 85), wherein increasing XX values indicate increasing amounts of ethoxylation on a backbone of 2,4,7,9-tetramethyl-5-decyne-4,7-diol (Surfynol® 104). Sodiated triethylene glycol monobutyl ether (TBG) of m/z 229 (C10H22O4Na) was also identified in the seized counterfeit banknotes via EASI(+) FT-ICR MS. Surfynol® and TBG are constituents of inks used for inkjet printing., (Copyright © 2014. Published by Elsevier Ireland Ltd.)