Federico Magagna

Parallel dual secondary column-dual detection: a further way of enhancing the informative potential of two-dimensional comprehensive gas chromatography.

Comprehensive two-dimensional gas chromatography (GC×GC) coupled with Mass Spectrometry (MS) is one of todays most powerful analytical platforms for detailed analysis of medium-to-high complexity samples. The column set usually consists of a long, conventional-inner-diameter first dimension ((1)D) (typically 15-30m long, 0.32-0.25mm dc), and a short, narrow-bore second dimension ((2)D) column (typically 0.5-2m, 0.1mm dc) where separation is run in a few seconds. However, when thermal modulation is used, since the columns of a set are coupled in series, a flow mismatch occurs between the two dimensions, making it impossible to operate simultaneously at optimized flow conditions. Further, short narrow-bore capillaries can easily be overloaded, because of their lower loadability, limiting the effectiveness of (2)D separation. In this study, improved gas linear velocities in both chromatographic dimensions were achieved by coupling the (1)D column with two parallel (2)D columns, having identical inner diameter, stationary phase chemistry, and film thickness. In turn, these were connected to two detectors: a fast quadrupole Mass Spectrometer (MS) and a Flame Ionization Detector (FID). Different configurations were tested and performances compared to a conventional set-up; experimental results on two model mixtures (n-alkanes and fourteen medium-to-high polarity volatiles of interest in the flavor and fragrance field) and on the essential oil of Artemisia umbelliformis Lam., show the system provides consistent results, in terms of analyte identification (reliability of spectra and MS matching) and quantitation, also affording an internal cross-validation of quantitation accuracy.

Black tea volatiles fingerprinting by comprehensive two-dimensional gas chromatography – Mass spectrometry combined with high concentration capacity sample preparation techniques: Toward a fully automated sensomic assessment

Tea prepared by infusion of dried leaves of Camellia sinensis (L.) Kuntze, is the second worlds most popular beverage, after water. Its consumption is associated with its chemical composition: it influences its sensory and nutritional quality addressing consumer preferences, and potential health benefits. This study aims to obtain an informative chemical signature of the volatile fraction of black tea samples from Ceylon by applying the principles of sensomics. In particular, several high concentration capacity (HCC) sample preparation techniques were tested in combination with GC×GC-MS to investigate chemical signatures of black tea volatiles. This platform, using headspace solid phase microextraction (HS-SPME) with multicomponent fiber as sampling technique, recovers 95% of the key-odorants in a fully automated work-flow. A group 123 components, including key-odorants, technological and botanical tracers, were mapped. The resulting 2D fingerprints were interpreted by pattern recognition tools (i.e. template matching fingerprinting and scripting) providing highly informative chemical signatures for quality assessment.

Advanced fingerprinting of high-quality cocoa: Challenges in transferring methods from thermal to differential-flow modulated comprehensive two dimensional gas chromatography

The possibility to transfer methods from thermal to differential-flow modulated comprehensive two-dimensional gas chromatographic (GC×GC) platforms opens interesting perspectives for routine analysis of complex samples. Flow modulated platforms avoid the use of cryogenics, thereby simplifying laboratory operations and analyst supervision during intensive analytical sessions. This study evaluates the feasibility of transferring a fingerprinting method capable of classifying and discriminating cocoa samples based on the volatiles fraction composition according to their origin and processing steps. Previously developed principles of GC×GC method translation are applied to an original fingerprinting method, developed for a loop-type thermal modulated GC×GC-MS system, to engineer a method for a reverse-injection differential flow modulated platform (GC×2GC-MS/FID) with a dual-parallel secondary column and dual detection. Effective method translation preserves analytes elution order, 1D resolution, and 2D pattern coherence. The experimental results confirm the feasibility of translating fingerprinting method conditions while preserving the informative power of 2D peak patterns for sample classification and discrimination. Correct translation enables effective transfer of metadata (e.g., compound names and MS fragmentation patterns) by automatic template transformation and matching from the original/reference method to its translated counterpart. Although the adoption of a narrow bore (i.e. 0.1mm dc) column in the first-dimension enabled operation under close-to-optimal conditions with the differential-flow modulation platform, due to the dual-parallel columns in the second-dimension, it resulted in lower overall method sensitivity. Nevertheless, fingerprinting accuracy was preserved and most of the key-aroma compounds and technological markers were effectively mapped, thus limiting the loss of fingerprinting information.

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.