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Dades de l'article publicat a la revista Food Chemistry, Volume 307, 1 March 2020, 125556. El podeu consultar a http://hdl.handle.net/2445/143358, Data base containing SPME-GC-MS raw analytical data obtained and used by Quintanilla-Casas et al. (Food Chemistry, 2020, 125556). Data correspond to 82 authentic and traceable olive oil samples, declared as EVOO by the suppliers obtained in the framework of OLEUM project (EC H2020 Programme 2014–2020) from seven different EU and non-EU countries: Croatia (n=11); Slovenia (SVN) (n=8); Spain (ESP) (n=17); Italy (ITA) (n=15); Greece (GRC) (n=6); Morocco (MAR) (n=15) and Turkey (TUR) (n=10). With the aim of reflecting the real production scenario, EVOO samples in this prospective study were obtained under usual production practices for commercial purposes, and thus consisted of both monovarietal oils as well as market blends of olive cultivars typical of each geographical origin. Briefly, data correspond to SPME-GC-MS scan intensities of the specific sesquiterpene hydrocarbon (SH) ions (m/z 93, 107, 119, 135, 157, 159, 161, 189 and 204) obtained from the Total Ion Current (TIC) between the 18th to the 30th minute. Thus, 2467 scans were obtained for each m/z ion implying 22203 variables per sample., SPME-GC-MS data have been obtained by researchers in the OLEUM project This work was developed in the context of the project OLEUM “Advanced solutions for assuring authenticity and quality of olive oil at global scale”, funded by the European Commission within the Horizon 2020 Program (2014–2020, grant agreement no. 635690). The information and views set out in this article are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein. The study was also supported by the Ministerio de Ciencia, Innovación y Universidades (MICINN) from Spain through the Juan de la Cierva and Ramon y Cajal programs (JCI-2012_13412 and RYC-2017-23601), and by the Ministerio de Educación, Cultura y Deporte (MECD) from Spain through the FPU pre-doctoral program (FPU16/01744).

Dades primàries de l'article publicat a la revista Food Science and Technology 121: 108936, Dataset containing SPME-GC-MS raw analytical data (total ion chromatograms, not aligned) obtained and used by Quintanilla-Casas et al. (LWT - Food Science and Technology 121: 108936). Data correspond to the volatile fingerprint of 174 authentic and traceable virgin olive oil samples previously graded by six official sensory panels (data from 2 outlier samples are not included) in the framework of OLEUM project (EC H2020 Programme 2014–2020). Briefly, data correspond to SPME-GC-MS scan intensities of the total ion chromatogram at each retention time from 5.5 to 61.96 min. These data were aligned and used under a fingerprinting approach by Quintanilla-Casas et al. to develop a classification model (PLS-DA approach) to verify the sensory quality of virgin olive oils, and it was suggested as an instrumental method to support sensory panels., SPME-GC-MS data have been obtained by researchers in the OLEUM project This work was developed in the context of the project OLEUM “Advanced solutions for assuring authenticity and quality of olive oil at global scale”, funded by the European Commission within the Horizon 2020 Program (2014–2020, grant agreement no. 635690). The information and views set out in this article are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein. The study was also supported by the Ministerio de Ciencia, Innovación y Universidades (MICINN) from Spain through the Juan de la Cierva and Ramon y Cajal programs (JCI-2012_13412 and RYC-2017-23601), and by the Ministerio de Educación, Cultura y Deporte (MECD) from Spain through the FPU pre-doctoral program (FPU16/01744).

Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179., Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179., Peer reviewed

Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179., Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179., Peer reviewed

The data folder is includes the five different data sets used in the paper along with a metadata file, where the different features are explained., This dataset contains python scripts to calibrate tropospheric ozone sensor data obtained in the H2020 Captor project using sensor fusion techniques. Four different models are implemented; Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN),Random Forest(RF) and Support Vector Regression (SVR). The methodology consists of first applying the PLS procedure to derive orthogonal components (to avoid multicollinearity problems). Afterwards, the components are used as features in the machine learning algorithms, so the models are trained. The scripts available in this repository have been used in the elaboration of the paper: "Multi-sensor data fusion calibration in IoT air pollution platforms" submitted to the IEEE Internet of Things journal., National Spanish funding; Regional Project; EU H2020 CAPTOR Project; AGAUR SGR44; 10.13039/501100011033-Agencia Estatal de Investigación; Spanish Ministry of Economy, Industry and Competitiveness, Peer reviewed

Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR)., Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR)., Peer reviewed

Content of the file OLEUM_Portable_ReferenceMaterials_VOO.xlsx/.ods: • Volatile markers: this sheet contains data of volatile markers of the virgin olive oils sensory defects winey-vinegary and rancid, sensory characteristics and corresponding odor threshold relating to an oil matrix. • Relative areas: this sheet contains data of relative areas of volatile compounds selected to emulate winey-vinegary and rancid defects in VOOs in a set of 60 samples and in RMs provided by the International Olive Council for each of the defects (RM IOC). • Formulations RM AV: this sheet contains data of the main formulations (volatile compounds and concentrations in mg/kg) emulating winey-vinegary aroma in virgin olive oil and evaluation by assessors in terms of suitability as possible RM. • Formulation RM R: this sheet contains the data of the main formulations (volatile compounds and concentrations in mg/kg) emulating rancid aroma in virgin olive oil and evaluation by assessors about their eligibility as possible RM., This data set contains the underlying data of the scientific publication: Aparicio-Ruiz R., Barbieri S., Gallina Toschi T., García-González D.L. (2020). Formulations of Rancid and Winey-Vinegary Artificial Olfactory Reference Materials (AORMs) for Virgin Olive Oil Sensory Evaluation. Foods, 9 (12), 1870, https://doi.org/10.3390/foods9121870. Panel test is the only sensory method included in international regulations of virgin olive oils and its application is compulsory. At present, there is no reference material (RM), in the strict sense of the term, to be used as a validated standard for sensory defects of virgin olive oil with which tasters can be trained. Usually, real samples of virgin olive oils assessed by many panels for the International Olive Council (IOC) ring tests are used as materials of reference in panel training and control. These data correspond to the work carried out to formulate RM that emulate rancid and winey-vinegary defects found in virgin olive oils with the aim of providing reproducible RMs that can be prepared on demand. Under the criteria of representativeness, verified with the advice of the IOC, aroma persistence, and simplicity in formulation, two RMs for winey-vinegary and rancid were obtained by diluting acetic acid and ethanol (winey-vinegary defect) and hexanal (rancid defect) together with other compounds that are used to modify aroma and avoid non-natural sensory notes., OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/., Peer reviewed, Aparicio-Ruiz, R.; Barbieri, Sara; Gallina Toschi, Tullia; García-González, Diego L.