Artificial Intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: the challenge of quantitative volatilomics in food quality assessment.

Effective investigation of food volatilome by comprehensive two-dimensional gas chromatography with parallel detection by mass spectrometry and flame ionization detector (GC×GC-MS/FID) gives access to valuable information related to industrial quality. However, without accurate quantitative data, results transferability over time and across laboratories is prevented. The study applies quantitative volatilomics by multiple headspace solid phase microextraction (MHS-SPME) to a large selection of hazelnut samples (Corylus avellana L. n = 207) representing the top-quality selection of interest for the confectionery industry. By untargeted and targeted fingerprinting, performant classification models validate the role of chemical patterns strongly correlated to quality parameters (i.e., botanical/geographical origin, post-harvest practices, storage time and conditions). By quantification of marker analytes, Artificial Intelligence (AI) tools are derived: the augmented smelling based on sensomics with blueprint related to key-aroma compounds and spoilage odorant; decision-makers for rancidity level and storage quality; origin tracers. By reliable quantification AI can be applied with confidence and could be the driver for industrial strategies.

Integrated Strategy for Informative Profiling and Accurate Quantification of Key-Volatiles in Dried Fruits and Nuts: An Industrial Quality Control Perspective.

Edible nuts and dried fruits, usually traded together in the global market, are one of the cornerstones of the Mediterranean diet representing a source of essential nutrients and bioactives. The food industry has an interest in the selection of high-quality materials for new product development while also matching consumers' expectations in terms of sensory quality. In this study, walnuts (Juglans regia), almonds (Prunus dulcis), and dried pineapples (Ananas comosus) are selected as food models to develop an integrated analytical strategy for the informative volatile organic compounds (VOCs) quali- and quantitative profiling. The study deals with VOCs monitoring over time (12 months) and in the function of storage conditions (temperature and atmosphere).VOCs are targeted within those: (i) with a role in the product's aroma blueprint (i.e., key-aromas and potent odorants); (ii) responsible for sensory degradation (i.e., rancidity); and/or (iii) formed by lipid autoxidation process. By accurate quantitative determination of volatile lipid oxidation markers (i.e., hexanal, heptanal, octanal, nonanal, decanal, (E)-2-heptenal, (E)-2-octenal, (E)-2-nonenal) product quality benchmarking is achieved. The combination of detailed VOCs profiling by headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) and accurate quantification of rancidity markers by multiple headspace-SPME (MHS-SPME) answers many different questions about shelf-life (i.e., aroma, storage stability, impact of temperature and storage atmosphere, rancidity level), while providing reliable and robust data for long-range studies and quality controls. The quantification associated with HS-SPME profiling is demonstrated and critically commented on to help the industrial research in a better understanding of the most suitable analytical strategies for supporting primary materials selection and new product development.

Intra-specific variability of stomatal sensitivity to vapour pressure deficit in Corylus avellana L.: A candidate factor influencing different adaptability to different climates?

Stomatal conductance is regulated by many factors such as air vapour pressure deficit (D), which can be the pivotal one affecting leaf gas exchange in species particularly sensitive to D such as C. avellana. The aim of this work was to evaluate stomatal sensibility to D and to determine correlations with hydraulics characteristics of leaves in three genotypes of C. avellana selected over centuries under different climatic conditions in the Italian peninsula. Among the three varieties Tonda Gentile delle Langhe (TGL), which was the one coming from northern Italy suffered the largest stomatal limitation at increasing levels of D in comparison with the other two cultivars [Tonda Romana (TR) and Tonda di Giffoni (TG), selected in central and southern Italy, respectively]. In all genotypes, photosynthesis decreased at high D although the reduction was mostly due to the rising of the temperature as suggested by the high values of sub-stomatal concentration of CO2. Concerning the hydraulic characteristics of the leaves, TG had considerable higher bulk elasticity compared with other two cultivars. These results contribute to explain the higher adaptability to different environments of TG and TR compared with TGL. Either the lower sensitivity to D of TG and TR and higher schlerophylly of TG might allow these cultivars to suffer less gas exchange limitations in hot and dry environments usually conducive to high D. Genotypic sensitivity to D represent one of the key factors to be considered in phenotyping protocols for D-sensitive species such as hazelnut.