Effectiveness of modified atmosphere and vacuum packaging in preserving the volatilome of Stelvio PDO cheese over time.

We aimed to assay the effectiveness of vacuum or modified atmosphere packaging in preserving the organoleptic characteristics of already ripened slices of Stelvio Protected Designation of Origin cheese during 3 months of storage. A multi-omics panel, including metagenomic and metabolomic analyses, was implemented together with physicochemical and sensory analyses. Among the 177 volatiles identified, 30 out of the 50 potent odorants were found to be prevalent, regardless of packaging. Isovaleric acid showed the highest relative intensity in all samples. Caproic and caprylic acids always increased during storage, while metabolites such as dodecane and 2,3-butanediol always decreased. Slow proteolysis occurred during storage, but did not differentiate cheese samples. The type of packaging differentiated the microbiota and volatile profile, with modified atmosphere packaging keeping the volatilome more stable. Out of the 50 potent odorants, 9 were relevant to sample discrimination, with 8-nonen-2-one, 2-nonanone, and caproic acid being more abundant in stored samples.

Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas' sensory features and acts as an AI smelling and taste machine suitable for quality controls.

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.

Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome.

Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data.

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

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.