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Plants emission of Volatile Organic Compounds (VOCs) is involved in a wide class of ecological functions, as VOCs play a crucial role in plants interactions with biotic and abiotic factors. Accordingly, they vary widely across species and underpin differences in ecological strategy. In this paper, VOCs spontaneously emitted by 109 plant species (belonging to 56 different families) have been qualitatively and quantitatively analysed in order to provide an alternative classification of plants species. In particular, by using bipartite networks methodology from Complex Network Theory, and through the application of community detection algorithms, we show that is possible to classify species according to chemical classes such as terpenes and sulfur compounds. Such complex network analysis allows to uncover hidden plants relationships related to their evolutionary and adaptation to the environment story.

Due to the globalization of coffee trade, ensuring the safety and traceability of coffee has become a critical challenge, prompting global authorities to implement new traceability systems to enhance quality identification and protect consumers from fraud. Aroma is a crucial parameter in the evaluation and differentiation of coffees, influenced by factors such as genetics, origin, post harvesting process, roast level, and brewing method. This paper provides, for the first time, a comprehensive overview of the volatile fingerprints of specialty coffees, categorized by their respective quality levels. In particular, this study aimed to evaluate the potential of volatile compounds monitored through Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS) as objective, fast, reliable and repeatable tool for tracking the quality and genetic lineage of Arabica specialty coffees. The spectra of volatile organic compounds (VOCs) were acquired from 1132 coffee samples (both specialty and non-specialty) from various varieties, origins, and processing methods. Results clearly indicate that the volatile composition of specialty coffee is predominantly influenced by its genetic lineage. Arabica coffee species belonging to Bourbon, Typica, and Ethiopian landraces showed higher total VOCs emission, while varieties related to Robusta, which are related to the Canephora one, emit less. Finally, by employing a complex network analysis approach based on headspace VOC analysis, it was possible to accurately distinguish between different categories of specialty Arabica coffee. Notably, our analysis shows that the quality of specialty coffee is not linked to the number of VOCs emitted, but rather to the level emission of some pleasant aroma compounds. These findings open new perspectives for the development of aroma profiling techniques and demonstrate the unique aroma release characteristics of specialty coffees.

In this study, for the first time, the volatile fraction from two domesticated Capsicum annuum accessions (“Paprika” and “Baklouti”) collected in Tunisia was investigated by two complementary analytical techniques, such as Solid-Phase Microextraction–Gas Chromatography/Mass Spectrometry (SPME-GC/MS) and Proton Transfer Reaction–Time-of-Flight–Mass Spectrometry (PTR-ToF-MS). The obtained results highlighted the presence of a high number of Volatile Organic Compounds (VOCs), including monoterpene and sesquiterpene compounds with α-curcumene, I-zingiberene, β-bisabolene and β-sesquiphellandrene as the major components. In addition, GC/MS was used to investigate the non-volatile chemical composition of the dried powders and their extracts, which were found to be rich in sulfur compounds, fatty acids and sugars. Eleven bacterial strains were chosen to assess the antimicrobial effectiveness of the extracts. The results showed that the extracts exhibited strain-dependent behavior, and the type strains displayed a greater susceptibility to the treatments, if compared to the wild strains, and, in particular, showed the best antimicrobial activity against Gram-positive bacteria, such as Listeria monocytogenes and Staphylococcus aureus.

Plants activate defense-related pathways in response to subtle abiotic or biotic disturbances, changing their volatile profile rapidly. How such perturbations reach and potentially affect neighboring plants is less understood. We evaluated whether brief and light touching had a cascade effect on the profile of volatiles and gene expression of the focal plant and a neighboring untouched plant. Within minutes after contact, Zea mays showed an up-regulation of certain defense genes and increased the emission of specific volatiles that primed neighboring plants, making them less attractive for aphids. Exposure to volatiles from touched plants activated many of the same defense-related genes in non-touched neighboring plants, demonstrating a transcriptional mirroring effect for expression of genes up-regulated by brief contact. Perception of so-far-overlooked touch-induced volatile organic compounds was of ecological significance as these volatiles are directly involved in plant–plant communication as an effective trigger for rapid defense synchronization among nearby plants. Our findings shed new light on mechanisms of plant responses to mechanical contact at the molecular level and on the ecological role of induced volatiles as airborne signals in plant–plant interactions.

In this study, in order to verify the effects due to the addition of spirulina (Arthrospira platensis) in a food product, a wildflower honey was analyzed in terms of chemical composition, physicochemical properties and antioxidant activity before and after the addition of the spirulina. HS-SPME/GC–MS and HPLC/UV were applied to carry out the chemical analyses. The obtained results demonstrated that the volatile profile and also the sugar content were significantly influenced by the addition of spirulina, showing significant qualitative and quantitative differences compared to honey without spirulina. The increase in HMF in honey added with spirulina was significant, demonstrating that its presence could accelerate the Maillard reaction. Electrical conductivity measured by using a conductometer was also increased while the moisture content was reduced in honey enriched with spirulina. Instead, the pH value was similar between the two samples. On the other hand, honey fortification with spirulina determined a significant increase of 12.5% in the total phenolic content (TPC), and a 56.25% increase in protein content. Further, the total antioxidant capacity (TAC) was also evaluated and a significant increase was determined as a result of the addition of spirulina. In conclusion, honey enriched with A. platensis was found to be characterized by a high pool of bioactive metabolites as well as significant changes in almost all the measurements performed.

Considering the large number of volatile molecules that characterize Cannabis sativa L., adequate investigation supported by the application of robust and effective analytical methods is essential to better understand the impact of these low- and medium-molecular-weight molecules on the entire phytocomplex. This work aimed to characterize the volatile fraction of the chemical profile of three different cultivars of Cannabis sativa L. pollen, grown in Italy, which were thoroughly investigated by the application of two complementary techniques: SPME-GC-MS and PTR-ToF-MS. Furthermore, in order to provide more information on the chemical profile of the matrices under study, the cannabinoid content of the hexane extracts was also measured by GC-MS. Until now, no similar study, in terms of survey techniques applied, has been performed on C. sativa pollen. The obtained results showed a high content of volatile molecules, which differentiated the three matrices. The data relating to the content of cannabinoids were also interesting as they showed that one of the three cultivars was richer than the others. Finally, an in-depth statistical survey was performed to better compare the investigated samples and identify the molecules that most contribute to differentiating them. The findings of this study may be useful for integrating the compositional information on C. sativa L.

The fruit of chili pepper ( Capsicum spp.) is used fresh or dried all over the world because of their unique pungency, color, taste, and aroma. Limited information is available about a hypothetical correlation between volatile organic compound (VOC) emission and spiciness of different pepper species and varieties. The aromatic profile and the pungency of 21 different Capsicum varieties, belonging to four different Capsicum species, have been investigated to evaluate a possible correlation between different VOC emission and their spiciness. The measurement of capsaicinoid concentration was performed by a high-performance liquid chromatography method while the VOCs from fresh samples were extracted using a proton-transfer-reaction time-of-flight mass spectrometer. The partial least squares regression model used for the determination of capsaicin content from VOCs reported high performances ( r = 0.84 in the test set). VOCs detected at m / z 103.075 and m / z 43.054 showed a higher contribution in the correlation with the capsaicin content. This study has provided clear and promising results, showing a real possibility to use these tools in routine operations for predicting the spiciness of fresh peppers useful for breeding programs and consumers.

Spectral and VOCs data are important to determine the genuineness of the EVOO. The aim of this work is to apply an open-source device for VIS–NIR spectrophotometric measurements and a VOCs analyzer to identify some types of sophistication. 96 samples were analyzed. Samples included pure EVOO compound olive oil pure seed oils and olive oil samples adulterated with 7 different seed oils at different ratios. An artificial intelligence model was applied to identify adulterations from the spectral and VOCs data. The models built on both spectral and VOCs data showed perfect classification of pure EVOO samples with respect to sophisticated ones in both model and test sets. The most important spectral values and VOCs were extracted. Artificial intelligence applicative models aimed not only to identify sophisticated samples but also to understand the most informative spectra and VOCs to prototype specific devices for anti-fraud control.

Mango (Mangifera indica L.) is a popular tropical fruit enjoyed worldwide, with Europe being a significant importer of this fruit. Its climacteric nature and short shelf-life pose challenges for maintaining quality, while emissions from transportation threaten the sustainability of the supply chain. This highlights the importance of low-impact logistics in maintaining fruit quality. This study aimed to evaluate the quality of fresh mangoes in Italy by comparing the different shipping systems (air, sea, and road) for seven cultivars sourced from seven countries. Quality assessment included pomological analysis, PTR-ToF-MS for volatile profiling (n = 11 cultivars × 2 years × 3 replicates), and consumer sensory analysis (n = 65 for untrained panellists in 1 year, n = 8 for trained panellists over 2 years). Results indicated that air and truck transport better preserved fruit quality compared to sea freight, primarily due to shorter transit times, which allowed for harvesting at more advanced ripeness stages. The combination of PTR-ToF-MS and PLS-DA effectively differentiated samples based on the method of transport, showcasing its potential as a quick quality monitoring tool. Mangoes transported by air showed significantly higher levels of volatile organic compounds (VOCs), a 29% greater total soluble solids (TSSs) content, and a 44% lower acidity (TA). Sensorial tests indicated that consumers preferred these mangoes. However, air transport resulted in 30 times higher CO2 emissions per kg of fruit compared to sea freight (~642,117 CO2e (kg) vs. ~19,132 CO2e (kg)), highlighting a critical dilemma between sustainability and quality. These findings provide a framework for developing hybrid logistics strategies that strike a balance between preserving quality and environmental responsibility. Additionally, they support the development of European mango cultivation, which can optimise harvest timing, reduce emissions, and enhance fruit quality.

Extra virgin olive oil (EVOO) represents one of the first-choice products made in Italy for its high quality and use in the Mediterranean diet. The aim of this study was to evaluate the effectiveness of a portable VIS–NIR open source spectroscopic system coupled with an artificial intelligence model and compared to panel tests for the determination of EVOOs quality. The quality of 203 oil samples was determined through panel tests and spectrophotometric analysis with a VIS–NIR instrument (740–1070 nm). The spectral data were processed through an artificial intelligence algorithm. On the base of the Sensory Evaluation, the oil samples were divided into two classes: “Superior” and “Standard” quality, respectively. The artificial neural network (ANN) model with an external test showed a correct classification percentage of 99%. Both tested methods MANOVA and ANN showed that the most important spectral wavelengths for quality determination reside in the range 890–980 nm. These wavelengths are closely related to the quality of the fruit being ground. As a result, a clear and explicit link between the fruit quality status and the quality of the obtained EVOO emerges. The combined use of panel testing and VIS–NIR spectroscopy could be a useful tool for determining the quality of EVOO and, in a reverse-engineer perspective, the open-source VIS–NIR device allows to predict the EVOO quality given the average fruit quality status in the field.