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.

DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks.

The browning of the internal tissues of hazelnut kernels, which are visible when the nuts are cut in half, as well as the discolouration and brown spots on the kernel surface, are important defects that are mainly attributed to Diaporthe eres. The knowledge regarding the Diaporthe eres infection cycle and its interaction with hazelnut crops is incomplete. Nevertheless, we developed a mechanistic model called DEFHAZ. We considered georeferenced data on the occurrence of hazelnut defects from 2013 to 2020 from orchards in the Caucasus region and Turkey, supported by meteorological data, to run and validate the model. The predictive model inputs are the hourly meteorological data (air temperature, relative humidity, and rainfall), and the model output is the cumulative index (Dh-I), which we computed daily during the growing season till ripening/harvest time. We established the probability function, with a threshold of 1% of defective hazelnuts, to define the defect occurrence risk. We compared the predictions at early and full ripening with the observed data at the corresponding crop growth stages. In addition, we compared the predictions at early ripening with the defects observed at full ripening. Overall, the correct predictions were >80%, with <16% false negatives, which confirmed the model accuracy in predicting hazelnut defects, even in advance of the harvest. The DEFHAZ model could become a valuable support for hazelnut stakeholders.

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.

Corylus avellana L. Aroma Blueprint: Potent Odorants Signatures in the Volatilome of High Quality Hazelnuts.

The volatilome of hazelnuts (Corylus avellana L.) encrypts information about phenotype expression as a function of cultivar/origin, post-harvest practices, and their impact on primary metabolome, storage conditions and shelf-life, spoilage, and quality deterioration. Moreover, within the bulk of detectable volatiles, just a few of them play a key role in defining distinctive aroma (i.e., aroma blueprint) and conferring characteristic hedonic profile. In particular, in raw hazelnuts, key-odorants as defined by sensomics are: 2,3-diethyl-5-methylpyrazine (musty and nutty); 2-acetyl-1,4,5,6-tetrahydropyridine (caramel); 2-acetyl-1-pyrroline (popcorn-like); 2-acetyl-3,4,5,6-tetrahydropyridine (roasted, caramel); 3-(methylthio)-propanal (cooked potato); 3-(methylthio)propionaldehyde (musty, earthy); 3,7-dimethylocta-1,6-dien-3-ol/linalool (citrus, floral); 3-methyl-4-heptanone (fruity, nutty); and 5-methyl-(E)-2-hepten-4-one (nutty, fruity). Dry-roasting on hazelnut kernels triggers the formation of additional potent odorants, likely contributing to the pleasant aroma of roasted nuts. Whiting the newly formed aromas, 2,3-pentanedione (buttery); 2-propionyl-1-pyrroline (popcorn-like); 3-methylbutanal; (malty); 4-hydroxy-2,5-dimethyl-3(2H)-furanone (caramel); dimethyl trisulfide (sulfurous, cabbage) are worthy to be mentioned. The review focuses on high-quality hazelnuts adopted as premium primary material by the confectionery industry. Information on primary and secondary/specialized metabolites distribution introduces more specialized sections focused on volatilome chemical dimensions and their correlation to cultivar/origin, post-harvest practices and storage, and spoilage phenomena. Sensory-driven studies, based on sensomic principles, provide insights on the aroma blueprint of raw and roasted hazelnuts while robust correlations between non-volatile precursors and key-aroma compounds pose solid foundations to the conceptualization of aroma potential.

Ecology of Diaporthe eres, the causal agent of hazelnut defects.

Diaporthe eres has been recently reported as the causal agent of hazelnut defects, with characteristic brown spots on the kernels surface and internal fruit discoloration. Knowledge regarding the ecology of this fungus is poor but, is critical to support a rationale and effective hazelnut crop protection strategy. Therefore, a study was performed to describe and model the effect of different abiotic factors such as temperature (T, 5-35°C, step 5°C) and water activity (aw 0.83-0.99, step 0.03) regimes on D. eres mycelial growth, pycnidial conidiomata development and asexual spore production during a 60-day incubation period. Alpha conidia germination was tested in the same T range and at different relative humidities (RH = 94, 97 and 100%) over 48 h incubation period. Fungal growth was observed from the first visual observation; regarding pycnidia and cirrhi, their development started after 8 and 19 days of incubation, respectively and increased over time. The optimum T for growth was 20-25°C and for pycnidia and cirrhi development was 30°C; aw ≥ 0.98 was optimal for the tested steps of the fungal cycle. The best condition for conidial germination of D. eres was at 25°C with RH = 100%. Quantitative data obtained were fitted using non- linear regression functions (Bete, logistic and polynomial), which provided a very good fit of the biological process (R2 = 0.793-0.987). These functions could be the basis for the development of a predictive model for the infection of D. eres of hazelnuts.

An effective chromatographic fingerprinting workflow based on comprehensive two-dimensional gas chromatography - Mass spectrometry to establish volatiles patterns discriminative of spoiled hazelnuts (Corylus avellana L.).

The volatile fraction of hazelnuts encrypts information about: cultivar/geographical origin, post-harvest treatments, oxidative stability and sensory quality. However, sensory features could be buried under other dominant chemical signatures posing challenges to an effective classification based on pleasant/unpleasant notes. Here a novel workflow that combines Untargeted and Targeted (UT) fingerprinting on comprehensive two-dimensional gas-chromatographic patterns is developed to discriminate spoiled hazelnuts from those of acceptable quality. By flash-profiling, six hazelnut classes are defined: Mould, Mould-rancid-solvent, Rancid, Rancid-stale, Rancid-solvent, and Uncoded KO. Chromatographic fingerprinting on composite 2D chromatograms from samples belonging to the same class (i.e., composite class-images) enabled effective selection of chemical markers: (a) octanoic acid that guides the sensory classification being positively correlated to mould; (b) Æ´-nonalactone, Æ´-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and Æ´-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples.

Molecular Characterization of Diaporthe Species Associated With Hazelnut Defects.

Fungi of the genus Diaporthe have been reported as the main causative agent of hazelnut defects in the Caucasus area. This study aimed to define which fungal species are present in defective hazelnuts grown in Turkey and confirm the role of Diaporthe spp. Seven hazelnut orchards were selected, with each one located in a different Turkish Province (Düzce, Giresun, Ordu, Samsun, Sakarya, Trabzon, and Zonguldak), and hazelnuts were collected at early and full ripening. Fungal isolation and identification were performed at the genus level based on morphological characteristics. Several genera were isolated, with Diaporthe spp. being among the prevalent. This was the only genus with increasing incidence from early to full ripening, and incidence at full ripening was positively correlated both with internal (ρ = 0.86) and visible defects (ρ = 0.81), which confirmed its role as the key causative agent of hazelnut defects. The correlation of defect occurrence with rainfall, reported in previous study, was not confirmed, possibly due to the low defect incidence. A total of 86 Diaporthe monosporic strains isolated from Turkish hazelnut samples, together with 33 strains collected in the Caucasus region and 6 from Italy, were analyzed with a multi-locus phylogeny based on three genomic loci (ITS, EF1-α, and tub). The results showed that Diaporthe strains can be grouped into 7 distinct clades, with a majority of Turkish strains (95%) being placed into a single clade related with D. eres. These samples were organized into several sub-clades, which indicates the existence of genetically diverse sub-populations.

Effect of Drying Temperatures and Exposure Times on Aspergillus flavus Growth and Aflatoxin Production on Artificially Inoculated Hazelnuts.

ABSTRACT: Aspergillus flavus may colonize hazelnuts and produce aflatoxins in the field and during storage. The main purpose of this study was to investigate the influence of drying temperature and exposure times on the viability of A. flavus and its ability to produce aflatoxins during the drying process and storage. Hazelnuts were inoculated with A. flavus and dried at different temperatures to reach 6% moisture content and a water activity (aw) of 0.71, a commercial requirement to avoid fungal development and aflatoxin contamination. Hazelnuts were dried at 30, 35, 40, 45, and 50°C and subsequently stored at 25°C for 14 days. After drying at 30, 35, and 40°C, increased amounts of A. flavus were evident, with the highest concentration occurring after drying at 35°C ([6.1 ± 2.4] × 106A. flavus CFU/g). At these temperatures, aflatoxins were detected only at 30 and 35°C. Aflatoxins, however, were present at higher levels after drying at 30°C, with concentrations of 1.93 ± 0.77 µg/g for aflatoxin B1 (AFB1) and 0.11 ± 0.04 µg/g for aflatoxin B2 (AFB2). After 14 days of storage, the highest A. flavus concentration and the highest levels of mycotoxins were detected in samples treated at 35°C ([8.2 ± 2.1] × 107A. flavus CFU/g and 9.30 ± 1.58 µg/g and 0.89 ± 0.08 µg/g for AFB1 and AFB2, respectively). In hazelnuts dried at 45 or 50°C, no aflatoxins were found either after drying or storage, and a reduction of A. flavus viable conidia was observed, suggesting that a shorter and warmer drying is essential to guarantee nut safety. The lowest temperature that guarantees the lack of aflatoxins should be selected to maintain the organoleptic quality of hazelnuts. Therefore, 45°C should be the recommended drying temperature to limit A. flavus growth and aflatoxin contamination on hazelnuts.

Evolution of potent odorants within the volatile metabolome of high-quality hazelnuts (Corylus avellana L.): evaluation by comprehensive two-dimensional gas chromatography coupled with mass spectrometry.

Within the pattern of volatiles released by food products (volatilome), potent odorants are bio-active compounds that trigger aroma perception by activating a complex array of odor receptors (ORs) in the regio olfactoria. Their informative role is fundamental to select optimal post-harvest and storage conditions and preserve food sensory quality. This study addresses the volatile metabolome from high-quality hazelnuts (Corylus avellana L.) from the Ordu region (Turkey) and Tonda Romana from Italy, and investigates its evolution throughout the production chain (post-harvest, industrial storage, roasting) to find functional correlations between technological strategies and product quality. The volatile metabolome is analyzed by headspace solid-phase microextration combined with comprehensive two-dimensional gas chromatography and mass spectrometry. Dedicated pattern recognition, based on 2D data (targeted fingerprinting), is used to mine analytical outputs, while principal component analysis (PCA), Fisher ratio, hierarchical clustering, and analysis of variance are used to find decision makers among the most informative chemicals. Low-temperature drying (18-20 °C) has a decisive effect on quality; it correlates negatively with bacteria and mold metabolic activity, nut viability, and lipid oxidation products (2-methyl-1-propanol, 3-methyl-1-butanol, 2-ethyl-1-hexanol, 2-octanol, 1-octen-3-ol, hexanal, octanal and (E)-2-heptanal). Protective atmosphere storage (99% N2-1% O2) effectively limits lipid oxidation for 9-12 months after nut harvest. The combination of optimal drying and storage preserves the aroma potential; after roasting at different shelf-lives, key odorants responsible for malty and buttery (2- and 3-methylbutanal, 2,3-butanedione and 2,3-pentanedione), earthy (methylpyrazine, 2-ethyl-5-methyl pyrazine and 3-ethyl-2,5-dimethyl pyrazine) and caramel-like and musty notes (2,5-dimethyl-4-hydroxy-3(2H)-furanone - furaneol and acetyl pyrrole) show no significant variation. Graphical abstract Comprehensive two-dimensional gas chromatography (GC × GC) coupled with mass spectrometric detection captures hazelnut volatiles signatures while advanced fingerprinting approaches based on pattern recognition enable access to a higher level of information.