Author, as appears in the article.: Gorji-Chakespari A; Nikbakht AM; Sefidkon F; Ghasemi-Varnamkhasti M; Valero EL
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Llobet Valero, Eduard
Keywords: Tridecane Support vector machine Rosa damascene Rosa damascena Priority journal Principal component analysis Petal Nonhuman Nerol Model Measurement accuracy Mass fragmentography Hydrodistillation Hexadecanol Geraniol Genotype Gas chromatography Farnesol Essential oil Electronic nose Drug classification Classifier Classification Citronellol Citral Chemometrics Chemical composition Article Aromatic plants Alcohol
Abstract: One of the major problems in the industry of medicinal and aromatic plants (MAPs) is the absence of a quick, easy and inexpensive method for controlling the quality of these plants. Rosa damascena Mill., is an aromatic plant which is cultivated for its high-value essential oil in the world. In this study, essential oils were extracted from nine genotypes of Rosa, and their main components were identified by GC and GC–MS. Then, the samples from different genotypes were grouped in three classes (C1, C2, C3) based on their total percentage of the six most important constituents, which have a major role in the quality of essential oil (i.e., phenyl ethyl alcohol, trans rose oxide, citronellol, nerol, geraniol, geranial). An electronic nose (EN) system was designed based on metal oxide semiconductor (MOS) sensors, and trained to identify the categories to which samples of essential oils could be classified. The response patterns of the sensors were recorded and further analyzed by chemometrics methods. Based on the results, principal components analysis (PCA) and linear discrimination analysis (LDA) showed that 85% and 99% of sample variance could be explained by the first two principal components (PC1, PC2) and two linear discrimination axis (LD1, LD2), respectively. LDA was performed on sensor response variables by cross- validated dataset (5- fold) and the classification accuracy was 95%. Finally, an error-correcting output codes (ECOC) classifier as a multiclass model for support vector machines (SVM) was considered and the classification accuracy was increased to 99%. These results reveal that an EN can be used as a quick, easy, accurate and inexpensive system for the classification of essential oil composition in Rosa damascena Mill. © 2016 Elsevier GmbH
Thematic Areas: Plant sciences Plant science Drug discovery
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: eduard.llobet@urv.cat
Author identifier: 0000-0001-6164-4342
Record's date: 2023-02-19
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S2214786116300328
Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Journal Of Applied Research On Medicinal And Aromatic Plants. 4 (1): 27-34
APA: Gorji-Chakespari A; Nikbakht AM; Sefidkon F; Ghasemi-Varnamkhasti M; Valero EL (2017). Classification of essential oil composition in Rosa damascena Mill. genotypes using an electronic nose. Journal Of Applied Research On Medicinal And Aromatic Plants, 4(1), 27-34. DOI: 10.1016/j.jarmap.2016.07.004
Article's DOI: 10.1016/j.jarmap.2016.07.004
Entity: Universitat Rovira i Virgili
Journal publication year: 2017
Publication Type: Journal Publications
Repositori URV