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Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.

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Ahhhh chocolate! Who doesn't love chocolate? The analysis of volatile organic compounds (VOCs) emanating from environment, plants, food or food ingredients has been the basis for many studies aiming at categorizing samples or sites. For this purpose, global VOCs profiles may be used as fingerprints of the samples, sometimes referred to as “volatilome”. In this special feature, Jean‐Luc Le Quéré and co‐workers use proton transfer reaction mass spectrometry (PTR‐MS) coupled to a time‐of‐flight (TOF) mass analyzer to classify over 200 dark chocolate samples based on their content in VOCs. The final goal was to build a model based on the PTR‐MS monitored dark chocolates volatilome that could be used to predict the four distinct sensory poles previously defined based on the flavor of cocoa from diverse origins and cultivars. Jean‐Luc Le Quéré is a Senior Researcher at the Center for Taste and Feeding Behaviour of the Institut de Recherche Agronomique (INRA) of Bourgogne‐Franche‐Comté (France). The general objective of this center is to get a better understanding of the physicochemical, molecular, cellular, behavioral and psychological mechanisms underlying sensory perception of food, eating behavior and health consequences.

Biochemical methane potential (BMP) is essential to determine the production of methane for various substrates; literature shows important discrepancies for the same substrates. In this paper, a harmonized BMP protocol was developed and tested with two phases of BMP tests carried out by eleven French laboratories. Surprisingly, for the three same solid tested substrates (straw; raw mix and dried-shredded mix of potatoes, maize, beef meat and straw; and mayonnaise), the standard deviations of the repeatability and reproducibility inter-laboratory were not enhanced by the harmonized protocol (average of about 25% depending on the substrate), as compared to a previous step where all laboratories used their own protocols. Moreover, statistical analyses of all the results, after removal of the outliers (about 15% of all observations), did not highlight significant effect of the operational effect on BMP (stirring, automatic or manual gas quantification, use of trace metal, uses a bicarbonate buffer, inoculum to substrate ratio) at least for the tested ranges. On the other hand, the average intra-laboratory repeatability was low, about 7%, whatever the protocol, the substrate and the laboratory. It also appears that drying the SA substrate, which contained proteins, carbohydrates, lipids and fibers, does not impact its BMP.