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The brown marmorated stink bug ( Halyomorpha halys ) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

This paper presents current developments and future perspectives on the spread of advanced analytical tasks in the field of food volatile analysis. The topics outlined comprise (a) recent advances on miniaturized sampling techniques; (b) the potential and challenges of multidimensional gas chromatography coupled with mass spectrometric detection for volatile identification and quantitation in samples with complex matrices;, ((c) the potential of comprehensive two-dimensional gas chromatography in fingerprinting studies, in particular for classifying complex samples in routine analysis; and (d) the key role of dedicated software tools for data elaboration with comprehensive two-dimensional separations.)

This study describes a non-separative headspace solid phase microextraction-mass spectrometry (HS-SPME-MS) approach, in view of its application to online monitoring of a roasting process. The system can quickly provide representative and diagnostic fingerprints of the volatile fraction of samples and, in combination with appropriate chemometric pattern recognition and regression techniques, can successfully be applied to characterize, discriminate, and/or correlate patterns with the roasting process. Eighty coffee samples of different varieties, geographical origins, and blends were analyzed. The experimental HS-SPME-MS results show that the TIC fingerprint can be used to discriminate the degree of roasting; diagnostic ion abundance(s) or ratios were closely correlated with the roasting process; both could successfully be used as markers or analytical decision makers, to monitor roasting processes online, and to define quality and safety of roasted coffee.

Stir bar sorptive extraction (SBSE) in combination with GC-ECD/FPD analysis is here applied to the determination of the residues of 11 pesticides (hexachlorobenzene, lindane, chlorothalonil, parathion methyl, parathion ethyl, fenitrothion, malathion, dieldrin, alpha- and beta-endosulfan, and tetradifon) in herbal teas prepared with Passiflora alata Dryander spiked leaves. The method was optimized using spiked herbal teas in a range from 0.05 to 1 pg/microL for organochlorine pesticides and from 0.15 to 3 pg/microL for organophosphorus pesticides. The method is reproducible and repeatable with recoveries calculated from herbal teas prepared with spiked plant material versus spiked herbal teas, varying from about 30% for tetradifon to about 90% for parathion methyl and malathion. The limits of quantitation (LOQs) ranged from 0.017 pg/microL for lindane to 0.117 pg/microL for malathion.