Combining different biomarkers to distinguish Chemlali virgin olive oils from different geographical areas of Tunisia.

BACKGROUND: Despite their high potential, Tunisian virgin olive oils (VOOs) are mainly exported in bulk or blended with VOOs of other origin, hindering their international market placement. To face this situation, their valorization is needed by highlighting their unique features and by developing tools to guarantee their geographical authenticity. Compositional features of Chemlali VOOs produced in three Tunisian regions were assessed to identify suitable authenticity markers. RESULTS: Quality indices ensured the quality of the VOOs studied. Volatile compounds, total phenols, fatty acid (FA) and chlorophylls are significantly influenced by the region of origin, which was justified by the differences found in soil and climatic conditions of the three geographical regions. To explore the capabilities of these markers for the geographical authentication of Tunisian Chemlali VOOs, classification models based on partial least squares-discriminant analysis (PLS-DA) were developed by grouping the minimum number of variables allowing the highest discrimination power, minimizing in this way the analytical procedure. The PLS-DA authentication model based on combining volatile compounds with FA or with total phenols achieved a correct classification of 95.7% of the VOOs according to their origin, as assessed by 10%-out cross-validation. Sidi Bouzid Chemlali VOOs achieved 100% of correct classification, while the misclassification between Sfax and Enfidha ones did not exceed 10%. CONCLUSIONS: These results allowed to establish the most promising and affordable combination of markers for the geographical authentication of Tunisian Chemlali VOOs from distinct production regions and provide the basis to further develop authentication models based on wider datasets. © 2023 Society of Chemical Industry.

Method for the analysis of volatile compounds in virgin olive oil by SPME-GC-MS or SPME-GC-FID.

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.

Revealing adulterated olive oils by triacylglycerol screening methods: Beyond the official method.

Official control methods to detect olive oil (OO) adulteration fail to provide satisfactory consumer protection. Thus, faster and more sensitive screening tools are needed to increase their effectiveness. Here, the official method for adulterant detection in OO was compared with three untargeted screening methods based on triacylglycerol analysis using high-throughput (FIA-HESI-HRMS; HT-GC-MS; HPLC-RID) and pattern recognition techniques (PLS-DA). They were assayed on a set of genuine and adulterated samples with a high natural variability (n = 143). The sensitivity of the official method was 1 for high linoleic (HL) blends at ≥2 % but only 0.39 for high oleic (HO) blends at ≥5 %, while specificity was 0.96. The sensitivity of the screening methods in external validation was 0.90-0.99 for the detection of HL and 0.82-0.88 for HO blends. Among them, HT-GC-MS offered the highest sensitivity (0.94) and specificity (0.76), proving to be the most suitable screening tool for OO authentication.

Using fluorescence excitation-emission matrices to predict bitterness and pungency of virgin olive oil: A feasibility study.

Unlike other food products, virgin olive oil must undergo an organoleptic assessment that is currently based on a trained human panel, which presents drawbacks that might affect the efficiency and robustness. Therefore, disposing of instrumental methods that could serve as screening tools to support sensory panels is of paramount importance. The present work aimed to explore excitation-emission fluorescence spectroscopy (EEFS) to predict bitterness and pungency, since both attributes are related with fluorophore compounds, such as polar phenols. Bitterness and pungency intensities of 250 samples were provided by an official sensory panel and used to build and compare partial least squares regressions (PLSR) with the excitation-emission matrix. Both PARAFAC scores and two-way unfolded data led to successful PLSR. The most relevant PARAFAC scores agreed with virgin olive oil phenolic spectra, evidencing that EEFS would be the fit-for-purpose screening tool to support the sensory panel.

Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration.

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC-MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).

Stepwise strategy based on 1H-NMR fingerprinting in combination with chemometrics to determine the content of vegetable oils in olive oil mixtures.

1H NMR fingerprinting of edible oils and a set of multivariate classification and regression models organised in a decision tree is proposed as a stepwise strategy to assure the authenticity and traceability of olive oils and their declared blends with other vegetable oils (VOs). Oils of the 'virgin olive oil' and 'olive oil' categories and their mixtures with the most common VOs, i.e. sunflower, high oleic sunflower, hazelnut, avocado, soybean, corn, refined palm olein and desterolized high oleic sunflower oils, were studied. Partial least squares (PLS) discriminant analysis provided stable and robust binary classification models to identify the olive oil type and the VO in the blend. PLS regression afforded models with excellent precisions and acceptable accuracies to determine the percentage of VO in the mixture. The satisfactory performance of this approach, tested with blind samples, confirm its potential to support regulations and control bodies.

Different Wines from Different Yeasts? "Saccharomyces cerevisiae Intraspecies Differentiation by Metabolomic Signature and Sensory Patterns in Wine".

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.

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics.

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.

Bio-Protection as an Alternative to Sulphites: Impact on Chemical and Microbial Characteristics of Red Wines.

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.

Flash Gas Chromatography in Tandem with Chemometrics: A Rapid Screening Tool for Quality Grades of Virgin Olive Oils.

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.

Determination and Comparison of the Lipid Profile and Sodium Content of Gluten-Free and Gluten-Containing Breads from the Spanish Market.

The objective is to verify if gluten-free (GF) and gluten-containing (G) breads differ in their sodium content and lipid profile. Samples of GF (n = 20) and G (n = 14) sliced white sandwich bread of commercial brands most frequently consumed in Spain were collected. The fatty acid (FA) composition and the contents of sodium, fat, cholesterol and phytosterols were determined. Sodium, fat and cholesterol contents were significantly higher in GF bread. The FA composition also differed, while G breads declared in most instances the use of sunflower oil as fat ingredient and presented a higher polyunsaturated FA percentage; GF breads declared a wide variety of fats and oils as ingredients (coconut, palm, olive, sunflower, etc.) which was reflected in their FA profile. Cholesterol content was higher in GF bread because five samples declared the use of whole egg, while G samples did not include any egg product in their formulas. Phytosterol content was higher in G bread but its variability was greater in GF bread. In conclusion, nutritional quality of GF bread varied depending on the ingredients used and might be lower than that of G bread. However, these differences in composition could be reduced or eliminated through changes in the formulation of GF bread. Moreover, the comparison of the results obtained in our laboratory for fat and salt content with the declared contents on the labels showed a much higher deviation for GF samples and it can be concluded that the quality of the nutritional information declared was lower in GF samples.

Chemical Markers to Distinguish the Homo- and Heterozygous Bitter Genotype in Sweet Almond Kernels.

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.

Profiling versus fingerprinting analysis of sesquiterpene hydrocarbons for the geographical authentication of extra virgin olive oils.

The verification of the geographical origin of extra virgin (EVOO) and virgin olive oil (VOO) is crucial to protect consumers from misleading information. Despite the large number of studies performed, specific markers are still not available. The present study aims to evaluate sesquiterpene hydrocarbons (SHs) as markers of EVOO geographical origin and to compare the discrimination efficiency of targeted profiling and fingerprinting approaches. A prospective study was carried out on 82 EVOOs from seven countries, analyzed by Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS). Classification models were developed by Partial Least Square-Discriminant Analysis (PLS-DA) and internally validated (leave 10%-out cross-validation). The percentage of correct classification was higher for the fingerprinting (100%) than for the profiling approach (45.5-100%). These results confirm the suitability of SHs as EVOO geographical markers and establish the fingerprinting as the most efficient approach for the treatment of SH analytical data with this purpose up to date.

Influence of cell-cell contact between L. thermotolerans and S. cerevisiae on yeast interactions and the exo-metabolome.

Sequential fermentation of grape must inoculated with L. thermotolerans and then S. cerevisiae 24 h later (typical wine-making practice) was conducted with or without cell-cell contact between the two yeast species. We monitored cell viability of the two species throughout fermentation by flow cytometry. The cell viability of S. cerevisiae decreased under both conditions, but the decrease was greater if there was cell-cell contact. An investigation of the nature of the interactions showed competition between the two species for nitrogen compounds, oxygen, and must sterols. Volatile-compound analysis showed differences between sequential and pure fermentation and that cell-cell contact modifies yeast metabolism, as the volatile-compound profile was significantly different from that of sequential fermentation without cell-cell contact. We further confirmed that cell-cell contact modifies yeast metabolism by analyzing the exo-metabolome of all fermentations by FT-ICR-MS analysis. These analyses show specific metabolite production and quantitative metabolite changes associated with each fermentation condition. This study shows that cell-cell contact not only affects cell viability, as already reported, but markedly affects yeast metabolism.

Non-Saccharomyces Yeasts Nitrogen Source Preferences: Impact on Sequential Fermentation and Wine Volatile Compounds Profile.

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.