Articles producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

Classification of essential oil composition in Rosa damascena Mill. genotypes using an electronic nose

  • Identification data

    Identifier: imarina:9285496
    Authors:
    Gorji-Chakespari ANikbakht AMSefidkon FGhasemi-Varnamkhasti MValero EL
    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
  • Others:

    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: 2024-09-07
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S2214786116300328
    Licence document URL: https://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
  • Keywords:

    Drug Discovery,Plant Science,Plant Sciences
    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
    Plant sciences
    Plant science
    Drug discovery
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