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Food Chem

Title:		"Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: Comparison of different processing approaches by LDA, kNN, and SVM"
Author(s):		Gerhardt N; Schwolow S; Rohn S; Perez-Cacho PR; Galan-Soldevilla H; Arce L; Weller P;
Address:		"Institute for Instrumental Analytics and Bioanalysis, Mannheim University of Applied Sciences, 68163 Mannheim, Germany. Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, 20146 Hamburg, Germany. Laboratorio de Estudios Sensoriales (GrupoSens), Departamento de Bromatologia y Tecnologia de los Alimentos, Universidad de Cordoba. Campus de Rabanales, 14070 Cordoba, Spain. Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, University of Cordoba, Campus de Rabanales, Marie Curie Annex Building, 14071 Cordoba, Spain. Institute for Instrumental Analytics and Bioanalysis, Mannheim University of Applied Sciences, 68163 Mannheim, Germany. Electronic address: p.weller@hs-mannheim.de"
Journal Title:		Food Chem
Year:		2019
Volume:		20181122
Issue:
Page Number:		720 - 728
DOI:		10.1016/j.foodchem.2018.11.095
ISSN/ISBN:		1873-7072 (Electronic) 0308-8146 (Linking)
Abstract:		"For the first time, this study describes a HS-GC-IMS strategy for analyzing non-targeted volatile organic compounds (VOCs) profiles to distinguish between virgin olive oils of different classification. Correlations among measured flavor characteristics and sensory attributes evaluated by a test panel were determined by applying unsupervised (PCA, HCA) and supervised (LDA, kNN and SVM) chemometric techniques. PCA and HCA were applied for natural clustering of the samples and LDA, kNN, and SVM methods were used to create predictive models for olive oil classification. Identification of 26 target compounds revealed which compounds are responsible for discrimination, and how their distribution correlates with the sensory evaluation. In the investigated samples, LDA, kNN, and SVM models correctly classified 83.3%, 73.8%, and 88.1% of the samples, respectively. This suggests that mathematical correlations of HS-GC-IMS 3D fingerprints with the sensory analysis may be appropriate for calculating a good predictive value to classify virgin olive oils"
Keywords:		Cluster Analysis Discriminant Analysis Gas Chromatography-Mass Spectrometry/*methods Olive Oil/*analysis/chemistry Principal Component Analysis Support Vector Machine Temperature Volatile Organic Compounds/analysis Chemometry Classification Gas chromatogr;
Notes:		"MedlineGerhardt, Natalie Schwolow, Sebastian Rohn, Sascha Perez-Cacho, Pilar Ruiz Galan-Soldevilla, Hortensia Arce, Lourdes Weller, Philipp eng England 2018/12/26 Food Chem. 2019 Apr 25; 278:720-728. doi: 10.1016/j.foodchem.2018.11.095. Epub 2018 Nov 22"