Non-volatile and volatile composition of West African bulk and Ecuadorian fine-flavor cocoa liquor and chocolate.

Cocoa products are obtained from the seeds of Theobroma cacao L. In this research, cocoa liquor and chocolate produced from cocoa beans from West Africa (Forastero, "bulk" cacao) and Ecuador (Nacional variety, "fine-flavor" cacao), were investigated, using a novel approach in which various analytical techniques are combined in order to obtain in-depth knowledge of the studied cocoa samples. The levels of various classes of primary metabolites were determined and a wide range of secondary metabolites, including volatile organic acids, aldehydes, esters, pyrazines, polyphenols, methylxanthines and biogenic amines, were identified and/or quantified by HS-SPME GC-MS (headspace-solid phase microextraction gas chromatography - mass spectrometry). and UPLC-HRMS (ultra-performance liquid chromatography - high resolution mass spectrometry). Odor Activity Values (OAV) were calculated to assess the contribution of individual volatiles on the final aroma. Various volatile aroma compounds were more abundant in the West African cocoa liquor and chocolate, while the Ecuadorian samples were richer in most quantified non-volatile metabolites. Principal component analysis (PCA) confirmed that the four samples can be clearly distinguished. Alcohols, pyrazines, amino acids and biogenic amines were found to be highly influential in causing this differentiation. The proposed approach can be useful in future studies on more extensive cocoa sample collections, in order to highlight similarities and pinpoint typical differences in chemical composition among these samples.

Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis.

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

Phytochemical characterization and comparative studies of four Cecropia species collected in Panama using multivariate data analysis.

Plant species of the genus Cecropia (Urticaceae) are used as traditional medicine in Latin-America, and are commercially available as food supplements. The aim of this study was to characterize and compare the phytochemical constituents of four Cecropia species collected in Panama. The structures of 11 compounds isolated from leaves of C. obtusifolia were elucidated based on high resolution mass spectrometry (HRMS) and nuclear magnetic resonance (NMR) spectroscopic analysis; the polyphenolic constituents of leaves of all four Cecropia species and commercial products were characterized using high performance liquid chromatography-diode array detection-quadrupole time of flight-tandem high resolution mass spectrometry (HPLC-DAD-QTOF). Forty-seven compounds were fully identified or tentatively characterized. Thirty-nine of these have not been previously reported for the species under investigation. Multivariate analysis revelead that C. obtusifolia and C. insignis are the most related species, while C. hispidissima is the most segregated one. Considering the importance of the description of novel chemical entities and the increasing interest and use of natural products, this study may be of great help for chemotaxonomic purposes, the interpretation of medicinal properties and for quality assessment of herbal supplements containing Cecropia leaves.

Testing of complementarity of PDA and MS detectors using chromatographic fingerprinting of genuine and counterfeit samples containing sildenafil citrate.

Counterfeit medicines are a global threat to public health. High amounts enter the European market, which is why characterization of these products is a very important issue. In this study, a high-performance liquid chromatography-photodiode array (HPLC-PDA) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) method were developed for the analysis of genuine Viagra®, generic products of Viagra®, and counterfeit samples in order to obtain different types of fingerprints. These data were included in the chemometric data analysis, aiming to test whether PDA and MS are complementary detection techniques. The MS data comprise both MS1 and MS2 fingerprints; the PDA data consist of fingerprints measured at three different wavelengths, i.e., 254, 270, and 290 nm, and all possible combinations of these wavelengths. First, it was verified if both groups of fingerprints can discriminate between genuine, generic, and counterfeit medicines separately; next, it was studied if the obtained results could be ameliorated by combining both fingerprint types. This data analysis showed that MS1 does not provide suitable classification models since several genuines and generics are classified as counterfeits and vice versa. However, when analyzing the MS1_MS2 data in combination with partial least squares-discriminant analysis (PLS-DA), a perfect discrimination was obtained. When only using data measured at 254 nm, good classification models can be obtained by k nearest neighbors (kNN) and soft independent modelling of class analogy (SIMCA), which might be interesting for the characterization of counterfeit drugs in developing countries. However, in general, the combination of PDA and MS data (254 nm_MS1) is preferred due to less classification errors between the genuines/generics and counterfeits compared to PDA and MS data separately.

Physical profiling and IR spectroscopy: simple and effective methods to discriminate between genuine and counterfeit samples of Viagra® and Cialis®.

Counterfeit medicines are a global threat to public health. High amounts enter the European market, enforcing the need for simple techniques to help customs detect these pharmaceuticals. This study focused on physical profiling and IR spectroscopy to obtain a prime discrimination between genuine and illegal Viagra® and Cialis® medicines. Five post-tableting characteristics were explored: colour, mass, long length, short length, and thickness. Hypothesis testing showed that most illegal samples (between 60 and 100%) significantly differ from the genuine medicines, in particular for mass and long length. Classification and Regression Trees (CART) analysis resulted in a good discrimination between genuine and illegal medicines (98.93% correct classification rate for Viagra®, 99.42% for Cialis®). Moreover, CART confirmed the observation that mass and long length are the key physical characteristics which determine the observed discrimination. IR analysis was performed on tablets without blister and on tablets in intact blister. These data were analyzed using Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares - Discriminant Analysis (PLS-DA). Supervised techniques needed to be applied since Principal Component Analysis (PCA) was not able to generate the desired discrimination. Our study shows that a perfect discrimination between genuine and illegal medicines can be made by both SIMCA and PLS-DA without removing the tablets from the blister. This approach has the advantage of keeping the blister intact. Our study demonstrates that these user friendly techniques are reliable methods to aid customs to obtain a prime distinction between genuine and illegal samples on the spot. Copyright © 2015 John Wiley & Sons, Ltd.

Identification of (antioxidative) plants in herbal pharmaceutical preparations and dietary supplements.

The standard procedures for the identification, authentication, and quality control of medicinal plants and herbs are nowadays limited to pure herbal products. No guidelines or procedures, describing the detection or identification of a targeted plant or herb in pharmaceutical preparations or dietary supplements, can be found. In these products the targeted plant is often present together with other components of herbal or synthetic origin. This chapter describes a strategy for the fast development of a chromatographic fingerprint approach that allows the identification of a targeted plant in herbal preparations and dietary supplements. The strategy consists of a standard chromatographic gradient that is tested for the targeted plant with different extraction solvents and different mobile phases. From the results obtained, the optimal fingerprint is selected. Subsequently the samples are analyzed according to the selected methodological parameters, and the obtained fingerprints can be compared with the one obtained for the pure herbal product or a standard preparation. Calculation of the dissimilarity between these fingerprints will result in a probability of presence of the targeted plant. Optionally mass spectrometry can be used to improve specificity, to confirm identification, or to identify molecules with a potential medicinal or antioxidant activity.