The use of δ(2)H and δ(18)O isotopic analyses combined with chemometrics as a traceability tool for the geographical origin of bell peppers.

Two approaches were investigated to discriminate between bell peppers of different geographic origins. Firstly, δ(18)O fruit water and corresponding source water were analyzed and correlated to the regional GNIP (Global Network of Isotopes in Precipitation) values. The water and GNIP data showed good correlation with the pepper data, with constant isotope fractionation of about -4. Secondly, compound-specific stable hydrogen isotope data was used for classification. Using n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based classification, using the kernel-density smoothed data, were developed to discriminate between peppers from different origins. Both methods were evaluated using the δ(2)H values and n-alkanes relative composition as variables. Misclassification rates were calculated using a Monte-Carlo 5-fold cross-validation procedure. Comparable overall classification performance was achieved, however, the two methods showed sensitivity to different samples. The combined values of δ(2)H IRMS, and complimentary information regarding the relative abundance of four main alkanes in bell pepper fruit water, has proven effective for geographic origin discrimination. Evaluation of the rarity of observing particular ranges for these characteristics could be used to make quantitative assertions regarding geographic origin of bell peppers and, therefore, have a role in verifying compliance with labeling of geographical origin.

Determination of n-alkanes in C. annuum (bell pepper) fruit and seed using GC-MS: comparison of extraction methods and application to samples of different geographical origin.

An efficient extraction and analysis method was developed for the isolation and quantification of n-alkanes from bell peppers of different geographical locations. Five extraction techniques, i.e., accelerated solvent extraction (ASE), ball mill extraction, ultrasonication, rinsing, and shaking, were quantitatively compared using gas chromatography coupled to mass spectrometry (GC-MS). Rinsing of the surface wax layer of freeze-dried bell peppers with chloroform proved to be a relatively quick and easy method to efficiently extract the main n-alkanes C27, C29, C31, and C33. A combined cleanup and fractionation approach on Teflon-coated silica SPE columns resulted in clean chromatograms and gave reproducible results (recoveries 90-95 %). The GC-MS method was reproducible (R(2) = 0.994-0.997, peak area standard deviation = 2-5%) and sensitive (LODs, S/N = 3, 0.05-0.15 ng/µL). The total main n-alkane concentrations were in the range of 5-50 µg/g dry weight. Seed extractions resulted in much lower total amounts of extracted n-alkanes compared to flesh and surface extractions, demonstrating the need for further improvement of pre-concentration and cleanup. The method was applied to 131 pepper samples from four different countries, and by using the relative n-alkane concentration ratios, Dutch peppers could be discriminated from those of the other countries, with the exception of peppers from the same cultivar. Graphical Abstract Procedure for pepper origin determination.

Probabilistic peak detection for first-order chromatographic data.

We present a novel algorithm for probabilistic peak detection in first-order chromatographic data. Unlike conventional methods that deliver a binary answer pertaining to the expected presence or absence of a chromatographic peak, our method calculates the probability of a point being affected by such a peak. The algorithm makes use of chromatographic information (i.e. the expected width of a single peak and the standard deviation of baseline noise). As prior information of the existence of a peak in a chromatographic run, we make use of the statistical overlap theory. We formulate an exhaustive set of mutually exclusive hypotheses concerning presence or absence of different peak configurations. These models are evaluated by fitting a segment of chromatographic data by least-squares. The evaluation of these competing hypotheses can be performed as a Bayesian inferential task. We outline the potential advantages of adopting this approach for peak detection and provide several examples of both improved performance and increased flexibility afforded by our approach.

Surface granularity as a discriminating feature of illicit tablets.

In this paper we propose an innovative methodology for automated profiling of illicit tablets by their surface granularity; a feature previously unexamined for this purpose. We make use of the tiny inconsistencies at the tablet surface, referred to as speckles, to generate a quantitative granularity profile of tablets. Euclidian distance is used as a measurement of (dis)similarity between granularity profiles. The frequency of observed distances is then modelled by kernel density estimation in order to generalize the observations and to calculate likelihood ratios (LRs). The resulting LRs are used to evaluate the potential of granularity profiles to differentiate between same-batch and different-batches tablets. Furthermore, we use the LRs as a similarity metric to refine database queries. We are able to derive reliable LRs within a scope that represent the true evidential value of the granularity feature. These metrics are used to refine candidate hit-lists form a database containing physical features of illicit tablets. We observe improved or identical ranking of candidate tablets in 87.5% of cases when granularity is considered.