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During the course of the EU H2020 OLEUM project, a harmonized method was developed to quantify volatile markers of the aroma of virgin olive oil with the aim to support the work of sensory panel test to assess the quality grade. A peer validation of this method has been carried out, with good results in terms of analytical quality parameters. The method allows the quantification of volatile compounds by SPME-GC with two possible detectors, flame ionization detector and mass spectrometry, depending on the technical facilities of the labs applying this method. The method was optimized for the quantification of 18 volatile compounds that were selected as being markers responsible for positive attributes (e.g. fruity) and sensory defects (e.g. rancid and winey-vinegary). The quantification is carried out with calibration curves corrected by the internal standards. Additionally, a protocol is provided to prepare the calibration samples. This procedure enhances reproducibility between labs since one of the main sources of errors is the application of different procedures in calibration., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Alcoholic fermentation is known to be a key stage in the winemaking process that directly impacts the composition and quality of the final product. Twelve wines were obtained from fermentations of Chardonnay must made with twelve different commercial wine yeast strains of Saccharomyces cerevisiae . In our study, FT-ICR-MS, GC-MS, and sensory analysis were combined with multivariate analysis. Ultra-high-resolution mass spectrometry (uHRMS) was able to highlight hundreds of metabolites specific to each strain from the same species, although they are characterized by the same technological performances. Furthermore, the significant involvement of nitrogen metabolism in this differentiation was considered. The modulation of primary metabolism was also noted at the volatilome and sensory levels. Sensory analysis allowed us to classify wines into three groups based on descriptors associated with white wine. Thirty-five of the volatile compounds analyzed, including esters, medium-chain fatty acids, superior alcohols, and terpenes discriminate and give details about differences between wines. Therefore, phenotypic differences within the same species revealed metabolic differences that resulted in the diversity of the volatile fraction that participates in the palette of the sensory pattern. This original combination of metabolomics with the volatilome and sensory approaches provides an integrative vision of the characteristics of a given strain. Metabolomics shine the new light on intraspecific discrimination in the Saccharomyces cerevisiae species.

The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors' workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME-GC-MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel's workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.

This research aims to develop a classification model based on untargeted elaboration of volatile fraction fingerprints of virgin olive oils ( n = 331) analyzed by flash gas chromatography to predict the commercial category of samples (extra virgin olive oil, EVOO; virgin olive oil, VOO; lampante olive oil, LOO). The raw data related to volatile profiles were considered as independent variables, while the quality grades provided by sensory assessment were defined as a reference parameter. This data matrix was elaborated using the linear technique partial least squares-discriminant analysis (PLS-DA), applying, in sequence, two sequential classification models with two categories (EVOO vs. no-EVOO followed by VOO vs. LOO and LOO vs. no-LOO followed by VOO vs. EVOO). The results from this large set of samples provide satisfactory percentages of correctly classified samples, ranging from 72% to 85%, in external validation. This confirms the reliability of this approach in rapid screening of quality grades and that it represents a valid solution for supporting sensory panels, increasing the efficiency of the controls, and also applicable to the industrial sector.

In wine, one method of limiting the addition of sulphites, a harmful and allergenic agent, is bio-protection. This practice consists of the early addition of microorganisms on grape must before fermentation. Non- Saccharomyces yeasts have been proposed as an interesting alternative to sulphite addition. However, scientific data proving the effectiveness of bio-protection remains sparse. This study provides the first analysis of the chemical and microbiological effects of a Metschnikowia pulcherrima strain inoculated at the beginning of the red winemaking process in three wineries as an alternative to sulphiting. Like sulphiting, bio-protection effectively limited the growth of spoilage microbiota and had no influence on the phenolic compounds protecting musts and wine from oxidation. The bio-protection had no effect on the volatile compounds and the sensory differences were dependent on the experimental sites. However, a non-targeted metabolomic analysis by FTICR-MS highlighted a bio-protection signature., (Copyright © 2020 Simonin, Roullier-Gall, Ballester, Schmitt-Kopplin, Quintanilla-Casas, Vichi, Peyron, Alexandre and Tourdot-Maréchal.)

Bitterness in almonds is controlled by a single gene ( Sk dominant for sweet kernel, sk recessive for bitter kernel) and the proportions of the offspring genotypes ( SkSk , Sksk , sksk ) depend on the progenitors' genotype. Currently, the latter is deduced after crossing by recording the phenotype of their descendants through kernel tasting. Chemical markers to early identify parental genotypes related to bitter traits can significantly enhance the efficiency of almond breeding programs. On this basis, volatile metabolites related to almond bitterness were investigated by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry coupled to univariate and multivariate statistics on 244 homo- and heterozygous samples from 42 different cultivars. This study evidenced the association between sweet almonds' genotype and some volatile metabolites, in particular benzaldehyde, and provided for the first time chemical markers to discriminate between homo- and heterozygous sweet almond genotypes. Furthermore, a multivariate approach based on independent variables was developed to increase the reliability of almond classification. The Partial Least Square-Discriminant Analysis classification model built with selected volatile metabolites that showed discrimination capacity allowed a 98.0% correct classification. The metabolites identified, in particular benzaldehyde, become suitable markers for the early genotype identification in almonds, while a DNA molecular marker is not yet available.

Nitrogen sources in the must are important for yeast metabolism, growth, and performance, and wine volatile compounds profile. Yeast assimilable nitrogen (YAN) deficiencies in grape must are one of the main causes of stuck and sluggish fermentation. The nitrogen requirement of Saccharomyces cerevisiae metabolism has been described in detail. However, the YAN preferences of non- Saccharomyces yeasts remain unknown despite their increasingly widespread use in winemaking. Furthermore, the impact of nitrogen consumption by non- Saccharomyces yeasts on YAN availability, alcoholic performance and volatile compounds production by S. cerevisiae in sequential fermentation has been little studied. With a view to improving the use of non- Saccharomyces yeasts in winemaking, we studied the use of amino acids and ammonium by three strains of non- Saccharomyces yeasts ( Starmerella bacillaris, Metschnikowia pulcherrima , and Pichia membranifaciens ) in grape juice. We first determined which nitrogen sources were preferentially used by these yeasts in pure cultures at 28 and 20°C (because few data are available). We then carried out sequential fermentations at 20°C with S. cerevisiae , to assess the impact of the non- Saccharomyces yeasts on the availability of assimilable nitrogen for S. cerevisiae . Finally, 22 volatile compounds were quantified in sequential fermentation and their levels compared with those in pure cultures of S. cerevisiae . We report here, for the first time, that non- Saccharomyces yeasts have specific amino-acid consumption profiles. Histidine, methionine, threonine, and tyrosine were not consumed by S. bacillaris , aspartic acid was assimilated very slowly by M. pulcherrima , and glutamine was not assimilated by P. membranifaciens . By contrast, cysteine appeared to be a preferred nitrogen source for all non- Saccharomyces yeasts. In sequential fermentation, these specific profiles of amino-acid consumption by non- Saccharomyces yeasts may account for some of the interactions observed here, such as poorer performances of S. cerevisiae and volatile profile changes.