Grape Cultivar Identification and Classification by Machine Olfaction Analysis of Leaf Volatiles

• Published in: Chemosensors Vol. 10; no. 4; p. 125
• Main Authors: Khorramifar, Ali, Karami, Hamed, Wilson, Alphus Dan, Sayyah, Amir Hosein Afkari, Shuba, Anastasiia, Lozano, Jesús
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Agricultural products; Authentication; chemometrics; Classification; Crop production; Crops; cultivar identification; Cultivars; Discrimination; electronic aroma detection; Electronic noses; Emissions; Fruits; grape classification; Grapes; Harvesting; Leaves; machine olfaction; Metal oxide semiconductors; Olfaction; Organic compounds; Principal components analysis; product authentication; Quality assurance; Sensor arrays; Sensors; Statistical methods; VOCs; Volatile compounds; Volatile organic compounds; Volatiles; Wines; Iran
• ISSN: 2227-9040; 2227-9040
• DOI: 10.3390/chemosensors10040125

Development of electronic technologies for precise identification of fruit crop cultivars in agricultural production provides an effective means for assuring product quality and authentication. The capabilities of discriminating between grape (Vitis vinifera L.) cultivars is essential for assuring certification of varieties sold in world markets. Machine olfaction, based on electronic-nose (e-nose) technologies, is readily available for rapid identification of fruit and vegetative agricultural products. This technology relies on detection of and discrimination between volatile organic compound (VOC) emissions from plant parts. It may be used in all stages of agricultural production to facilitate crop maintenance, cultivation, and harvesting decisions prior to marketing. An experimental e-nose device was constructed and tested in combination with five chemometric methods, including PCA, LDA, QDA, SVM, and ANN, as rapid, non-destructive tools for identification and classification of grape cultivars. An e-nose instrument equipped with nine metal oxide semiconductor (MOS) sensors was utilized to identify and classify five grape cultivars based on leaf VOC emissions using supervised and non-supervised methods. Grape leaf samples were first identified as belonging to specific cultivar types using PCA analyses, which are non-supervised classification methods, with the first two principal components (PC-1 and PC-2) accounting for 89% of the total variance. Four supervised statistical methods were further tested, including DA, QDA, SVM, and ANN, and provided effective discrimination accuracies of 98%, 99%, 92%, and 99%, respectively. These findings confirmed the suitable applicability of an MOS e-nose sensor array with supervised methods for accurate identification of grape cultivars, which is useful for authentication of vine cultivar types for commercial markets.