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(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants’ exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Mass spectrometry (MS) is an analytical technique that can be used for various applications in a number of scientific areas including environmental, security, forensic science, space exploration, agri-food, and numerous others. MS is also continuing to offer new insights into the proteomic and metabolomic fields. MS techniques are frequently used for the analysis of volatile compounds (VCs). The detection of VCs from human samples has the potential to aid in the diagnosis of diseases, in monitoring drug metabolites, and in providing insight into metabolic processes. The broad usage of MS has resulted in numerous variations of the technique being developed over the years, which can be divided into hyphenated and real-time MS techniques. Hyphenated chromatographic techniques coupled with MS offer unparalleled qualitative analysis and high accuracy and sensitivity, even when analysing complex matrices (breath, urine, stool, etc.). However, these benefits are traded for a significantly longer analysis time and a greater need for sample preparation and method development. On the other hand, real-time MS techniques offer highly sensitive quantitative data. Additionally, real-time techniques can provide results in a matter of minutes or even seconds, without altering the sample in any way. However, real-time MS can only offer tentative qualitative data and suffers from molecular weight overlap in complex matrices. This review compares hyphenated and real-time MS methods and provides examples of applications for each technique for the detection of VCs from humans.

Plants are a major atmospheric source of volatile organic compounds (VOCs). These secondary metabolic products protect plants from high-temperature stress, mediate in plant–plant and plant–insect communication, and affect our climate globally. The main challenges in plant foliar VOC research are accurate sampling, the inherent reactivity of some VOC compounds that makes them hard to detect directly, and their low concentrations. Plant VOC research relies on analytical techniques for trace gas analysis, usually based on gas chromatography and soft chemical ionization mass spectrometry. Until now, these techniques (especially the latter one) have been developed and used primarily by physicists and analytical scientists, who have used them in a wide range of scientific research areas (e.g., aroma, disease biomarkers, hazardous compound detection, atmospheric chemistry). The interdisciplinary nature of plant foliar VOC research has recently attracted the attention of biologists, bringing them into the field of applied environmental analytical sciences. In this paper, we review the sampling methods and available analytical techniques used in plant foliar VOC research to provide a comprehensive resource that will allow biologists moving into the field to choose the most appropriate approach for their studies.

High and low pathogenicity avian influenza viruses (HPAIV, LPAIV) are the primary causes of poultry diseases worldwide. HPAIV and LPAIV constitute a major threat to the global poultry industry. Therefore, early detection and well-adapted surveillance strategies are of the utmost importance to control the spread of these viruses. Volatile Organic Compounds (VOCs) released from living organisms have been investigated over the last decades as a diagnostic strategy. Mass spectrometry instruments can analyze VOCs emitted upon viral infection. Selected ion flow tube mass spectrometry (SIFT-MS) enables direct analysis of cell headspace in less than 20 min. As a proof-of-concept study, we investigated the ability of a SIFT-MS coupled sparse Partial Least Square-Discriminant Analysis analytical workflow to discriminate IAV-infected cells. Supernatants of HPAIV, LPAIV, and control cells were collected from 1 to 72 h post-infection and analyzed using our analytical workflow. At each collection point, VOCs' signatures were first identified based on four independent experiments and then used to discriminate the infectious status of external samples. Our results indicate that the identified VOCs signatures successfully discriminate, as early as 1-h post-infection, infected cells from the control cells and differentiated the HPAIV from the LPAIV infection. These results suggest a virus-dependent VOCs signature. Overall, the external samples' status was identified with 96.67% sensitivity, 100% specificity, and 97.78% general accuracy.

The human olfactory system is highly attuned to detection of a wide range of volatile organic compounds (VOCs), but the sensitivity varies considerably based on chemical functionality. Whereas most humans can appreciate the sensory properties of certain foods, beverages, and fragrances, and at times be alerted to volatile chemical hazards, many VOCs are hazardous below the human odor detection threshold. Since its introduction in the mid-1990s, selected ion flow tube mass spectrometry (SIFT-MS) has been widely applied to quantitative analysis of a broad range of VOCs in applications from food products to workplace safety to environmental monitoring, and most recently to pharmaceutical testing. This review surveys the applications of SIFT-MS in odor analysis and in workplace, environmental and consumer protection, with a particular focus on the complementarity of this real-time mass spectrometry analyzer to sensor technology and conventional laboratory techniques—in particular, gas chromatography–mass spectrometry (GC/MS).

Volatile organic compounds (VOCs) are considered crucial in determining the aroma and flavor profile of food and fermented beverages. Volatile compounds emitted from food carry invaluable information that can be used to detect subtle changes in food products and are viewed as an indicator of food quality. Chemical ionization-based direct-ionization mass spectrometry (DI-MS) techniques, which are prominently used in determining the concentration of an unknown mixture of compounds, for example, air, are reviewed. DI-MS techniques, such as proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow tube mass spectrometry (SIFT-MS) offer real-time, rapid, high-sensitivity, and online analysis of VOCs. Accurate quantification of trace gases can be achieved without instrument calibration if we know the rate coefficients of ion-molecule reactions. The rate coefficients can be used to calculate the sensitivities of VOCs as detected by chemical ionization mass spectrometry (CI-MS) methods. The neutral molecule’s electric dipole moment and polarizability are essential input parameters to compute rates using parametrized trajectory model. An application for the calculation of rates is provided (GitHub: link) under elevated energy and temperature conditions, along with a database dedicated to physical and chemical properties of most exotic VOCs linked to food and alcoholic beverages.

Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban–rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances.

Minimizing flavor variation in cheeses without perceived flavor defects in order to produce a consistent product is a challenge in the Swiss cheese industry. This study evaluated flavor variability based on correlations of volatile flavor compounds and sensory attributes. The headspace concentrations of volatile compounds were analyzed using selected ion flow tube-mass spectrometry (SIFT-MS), while the sensory attributes were evaluated using descriptive sensory analysis and consumer testing. The important discriminating volatile compounds were classified into five functional groups: sulfur-containing compounds (methyl mercaptan, hydrogen sulfide, dimethyl disulfide, dimethyl trisulfide, and methional), organic acids (propanoic acid, acetic acid, 3-methylbutanoic acid), aldehydes (3-methylbutanal, butanal, and 2-methylpropanal), a ketone (2,3-butanedione), and an ester (ethyl hexanoate). Correlations were identified among volatile compounds and between volatile compounds and sensory attributes. Only a small number of volatile compounds strongly correlated positively or negatively to a specific sensory attribute. Nutty malty, milkfat lactone, salty, umami, and sweet positively correlated to overall liking and nutty flavor liking of Swiss cheese. Evaluation of cheese flavor using correlations between volatile compounds and sensory attributes provided further understanding of the complexity of flavor and flavor variability among Swiss cheeses manufactured from different factories that can be used to improve flavor consistency of Swiss cheeses.

The C5 hemiterpenes isoprene and 2-methyl-3-buten-2-ol (MBO) are important biogenic volatiles emitted from terrestrial vegetation. Isoprene is emitted from many plant groups, especially trees such as Populus , while emission of MBO is restricted to certain North American conifers, including species of Pinus . MBO is also a pheromone emitted by several conifer bark beetles. Both isoprene and MBO have typically been measured by proton-transfer reaction mass spectrometry (PTR-MS), but this method cannot accurately distinguish between them because of their signal overlap. Our study developed a method for using selective ion flow tube mass spectrometry (SIFT-MS) that allows simultaneous on-line measurement of isoprene and MBO by employing different reagent ions. The use of m / z (NO + ) = 68 u for isoprene and m / z (O 2 + ) = 71 u for MBO gave minimal interference between the compounds. We tested the suitability of the method by measuring the emission of young trees of Populus , Picea , and Pinus . Our results largely confirm previous findings that Populus nigra , Picea glauca , and Picea abies emit isoprene and Pinus ponderosa emits MBO, but we also found MBO to be emitted by Picea abies . Thus SIFT-MS provides a reliable, easy to use, on-line measuring tool to distinguish between isoprene and MBO. The method should be of use to atmospheric chemists, tree physiologists and forest entomologists, among others.

Off-odors produced by volatile compounds remain a major barrier to consumer acceptance of plant-based proteins. This study presents a novel two-stage fermentation strategy to effectively reduce undesirable volatiles in eight plant proteins. A sequential fermentation process was developed using Lactobacillus plantarum in Stage 1 and a traditional yogurt culture, Streptococcus thermophilus, Lactobacillus delbrueckii subsp. Bulgaricus and Lactobacillus acidophilus, in Stage 2. This method was applied to solutions of 9% soy, pea, chickpea, mung bean, faba bean, rice, barley-rice, and hemp proteins. Volatile profiles were analyzed via Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) and sensory evaluation before and after fermentation. The two-stage fermentation resulted in significant deodorization, with 95–99% reduction in key odorants such as hexanal, 2-pentylfuran, methoxypyrazines, and sulfur compounds across all proteins. The sequential approach significantly outperformed a one-stage fermentation. Allulose enhanced L. plantarum activity while strawberry preserves supported traditional yogurt culture performance. Non-fermentable additives such as pectin, xanthan gum, and oil had minimal effects on volatiles. The proposed fermentation method offers an effective, scalable, and clean-label solution for mitigating off-odors in plant-based proteins. By leveraging microbial metabolism and formulation synergies, this strategy provides a foundation for developing more palatable plant-based dairy alternatives.