1,721,492 research outputs found
OLEUM project. Rapid screening tool of quality grades of virgin olive oils. FGC in tandem with chemometrics
No full textThis data set contains the underlying data of the scientific publication:
Barbieri S. et al., 2020. FGC in tandem with chemometrics: a rapid screening tool of quality grades of virgin olive oils.
This publication will be submitted to a scientific journal.
This research aims to develop a classification model based on an untargeted elaboration of volatile fraction fingerprints of virgin olive oils (n=331) analyzed by Flash Gas-Chromatography in order 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 the sensory assessment were defined as reference parameter. This data matrix was elaborated using a linear technique, Partial Least Squares-Discriminant Analysis (PLS-DA), applying, in sequence, two classification models with two categories (EVOO vs noEVOO followed by VOO vs LOO and LOO vs noLOO followed by VOO vs EVOO). Results from this large set of samples provide satisfactory results in terms of percentages of correctly classified samples, ranging from 72 to 85%, in external validation. This confirms the reliability of this approach as rapid screening of quality grades and that it represents a solution for supporting the sensory panels, increasing the efficiency of the controls, applicable also to the industrial sector. This data set contains the raw data obtained from the analysis of all the sample by Flash Gas-Chromatography and used for the building of the PLS-DA data matrix
OLEUM project. Analysis of volatile compounds of extra virgin and virgin olive oil with a different geographical origin by flash gas chromatography
No full textThis data set contains the underlying data of the scientific publication:
Palagano R. et al., 2020. Compliance with EU vs. extra-EU labelled geographical provenance in virgin olive oils: A rapid untargeted chromatographic approach based on volatile compounds. LWT - Food Science and Technology, 130, 109566. https://doi.org/10.1016/j.lwt.2020.109566.
To ensure that consumers are not misled, the European Regulation n. 29/2012 establishes specific rules to report the geographical origin of extra virgin and virgin olive oils (VOOs) on the product label, but an analytical procedure to verify this information is not defined.
A flash gas chromatography and untargeted chemometric data elaborations for the rapid screening of 210 extra virgin and virgin olive oils with a different geographical provenance was applied. The analysis of volatile compounds was carried out by the Flash Gas Chromatography Electronic Nose Heracles II (Alpha MOS, Toulouse, France). The instrument was equipped with two metal capillary columns (MXT-5 and MXT-1701) working in parallel mode. At the end of each column, a FID detector was placed and the acquired signal was digitalized every 0.01 sec.
This data set contains the raw data (intensity values for each point of the chromatogram considering that the signal was digitalized every 0.01 sec) obtained from the analysis of all the sample by flash gas chromatography. Only data obtained from the column MXT-5 are reported since this column showed a higher discriminating power
Data about the bioactive compounds of Extra Virgin Olive Oils (EVOO) obtained from olives of Nostrana di Brighella cv. produced with different agronomical practices (integrated pest management and organic farming)
No full textThis data set contains the underlying data of the scientific publication: Casadei et al., 2024. Valorization of monovarietal Nostrana di Brisighella extra virgin olive oils: focus on bioactive compounds. This publication will soon be submitted to a scientific journal.
The study focuses on different agronomic variables in order to optimize the presence of bioactive components of monovarietal Nostrana di Brisighella extra virgin olive oils. In fact, fruits were obtained through integrated pest management or organic farming and picked at four increasing indices of maturity, corresponding to four successive weeks of harvesting, namely positive volatile and phenolic compounds. Especially the latter allow health claims of olive oil related to phenolic content (derivatives of hydroxytyrosol and tyrosol) and positive sensory attributes (fruity, bitter, and punget) that are known to be associated with volatile and phenolic molecules.
This research work is developed within the project funded under the PSR 2014-2020 program – Type of Operation 16.2 “INnovazione e PROmozione della filiera dell’OLIO extra vergine di oliva emiliano-romagnolo (INPRO-OLIO)” and Dr. Enrico Casadei's research activity is financed within the project funded under the National Recovery and Resilience Plan (NRRP) - NextGenerationEU “ON Foods - Research and innovation network on food and nutrition Sustainability, Safety and Security - Working ON Foods”
Data on the monitoring of volatile compounds in virgin olive oils, which was conducted over a twelve-month storage period, using a combination of targeted and untargeted analytical approaches
No full textThis data set contains data related to the evolution of volatile compounds in virgin olive oils during one year of storage. The influence of various agronomic and technological factors, including agricultural practices, olive ripeness at harvest, oil storage duration, and packaging methods, were evaluated. The data were obtained through a combined approach that encompasses sensory analysis as well as both targeted (HS-GC-IMS, SPME-GC-MS) and untargeted (FGC-Enose) analytical techniques. This research work is developed within the project funded under the PSR 2014-2020 program – Type of Operation 16.2 “INnovazione e PROmozione della filiera dell’OLIO extra vergine di oliva emiliano-romagnolo (INPRO-OLIO)” and within project OnFoods (ON Foods - Research and innovation network on food and nutrition Sustainability, Safety and Security - Working ON Foods)
Radical Scavenging Activities of peels and pulps cv. Golden Deliciuos as related to their phenolic composition
No full textOLEUM Project. Classification of virgin olive oils based on quality grades and presence of defects using HS-GC-IMS as a rapid screening tool.
No full textThis data set contains the underlying data of the scientific publication:
Valli, E.; Panni, F.; Casadei, E.; Barbieri, S.; Cevoli, C.; Bendini, A.; García-González, D.L.; Gallina Toschi, T. An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test. Foods 2020, 9(5), 657; https://doi.org/10.3390/foods9050657.
Sensory analysis, carried out by Panel test, is essential for the quality classification of virgin olive oils (VOOs). The presence and perceived intensity of fruity attribute and sensory defects are linked with the occurrence of specific volatile compounds. Instrumental screening methods based on analysis of volatiles can support the Panel test through fast pre-classification of samples with a known probability, thus increasing the efficiency of quality control.
A Headspace Gas Chromatography Ion Mobility Spectrometer (HS-GC-IMS) was used to analyze commercial VOOs by a semi-targeted approach. PLS-DA models were built by data matrices composed of 15 selected volatile compounds with which was possible to classify the samples based on quality grades and presence of defects
INTAQT. WP3. T3.3. Data about consumers’ sensory evaluation of Parmigiano Reggiano PDO cheese with milk from cows reared indoors and fed different forage sources. UNIBO
No full textThe dataset contains raw data collected in the framework of Horizon 2020 INTAQT project about the sensory evaluation of Parmigiano Reggiano PDO cheese produced with milk from cows reared indoors and fed different forage sources, i.e. dry hay and fresh forage. Two cheese samples were tested by 119 Italian consumers that then answered to a sensory evaluation questionnaire. The analysis protocol included Check-All-That-Apply (CATA) method to assess the sensory profile, Just-About-Right scale to evaluate the adequacy of attributes, and questions on liking (9-point hedonic scale). In particular, results from each consumer are collected in the file and this raw data have been used to elaborate results of the study. In particular, the file contains results related to the sensory evaluation of the two samples, as well as responses to questions related to socio-demographic and consumption habits of each consumer
OLEUM project. Revised method for determination of fatty acid ethyl esters in virgin olive oils. Data from in-house validation
No full textThis data set contains the underlying data of the scientific publication:
Palagano R. et al., 2019. Development and in-house validation of a revised method for determination of fatty acid ethyl esters in virgin olive oils.
This publication has been submitted to a scientific journal.
The content of fatty acid ethyl esters (FAEEs) is one of the quality parameters to define if an olive oil can be classified as extra virgin since these compounds are considered markers for virgin olive oils obtained from poor quality olives, but can be also indirect markers to detect soft deodorization treatment. The official protocol for determination (EU Reg. 61/2011) can be improved in terms of solvents amount and time of analysis.
An off-line HPLC-GC-FID method for FAEEs determination was developed, revising both the preparative step and the GC injector required by the official method. After optimization, the method was in-house validated by analyzing several parameters (linearity, LOD, LOQ, robustness, recovery, precision and accuracy). This data set contains the in-house validation results obtained
OLEUM Project. Sensory panels analysis of commercial virgin olive oil samples. Results from the first and second year of the OLEUM project
No full textA set of 334 commercial virgin olive oil (VOO) samples were evaluated by six sensory panels during the H2020 OLEUM project. Sensory data were elaborated with two main objectives: i) to classify and characterize samples in order to use them for possible correlations with physical-chemical data and ii) to monitor and improve the performance of panels. After revision of the IOC guidelines in 2018, this work represents the first published attempt to verify some of the recommended quality control tools, in order to increase harmonization among panels. Specifically, a new "decision tree" scheme was developed, and some IOC quality control procedures, were applied. The adoption of these tools allowed for reliable classification of 289 of 334 VOOs; for the remaining 45, misalignments between panels of first (on the category, 21 cases) or second type (on the main perceived defect, 24 cases) occurred. In these cases, a “formative reassessment” was necessary. At the end, 329 of 334 VOOs (98.5%) were classified, thus confirming the effectiveness of this approach to achieve a better proficiency. Panels showed good performance, but the need to adopt new reference materials that are stable and reproducible to improve the panel’s skills and agreement also emerged
OLEUM Project. Sensory assessment of new artificial olfactory reference materials in virgin olive oil. Performance testing
No full textThe dataset contains data that validates a procedure for sensory certification of two, recently formulated, artificial olfactory reference materials (RMs) for winey-vinegary and rancid defects of virgin olive oil named “AV8” and “R6”, respectively. Specifically, a protocol for the suitability of RMs has been defined, shared, and applied by six sensory panels involved in the H2020 OLEUM project; it consisted of three steps: i) evaluation of representativeness of RMs (compared to actual samples) and of its intensity values (named “representativeness AV8_part 1 and “representativeness R6_part 1” in the dataset); ii) determination of the panels’ detection threshold (named “threshold AV8_part 2” and “threshold R6_part 2” in the dataset); and iii) evaluation of long-term stability (named “stability AV8_part 3” and “stability R6_part 3” in the dataset)
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