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

Abstract

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.

A Conventional VOC-PID Sensor for a Rapid Discrimination among Aromatic Plant Varieties: Classification Models Fitted to a Rosemary Case-Study

“…Here, two homogeneous groups were obtained: "Erectus" and "Prostatus" varieties. Our results agree with the current literature [21,23].…”

“…In the current literature, some studies have addressed the same topic using more complex technologies. Khorramifar et al [21] reported a rapid discrimination between…”

“…In the current literature, some studies have addressed the same topic using more complex technologies. Khorramifar et al [21] reported a rapid discrimination between grape cultivars (Vitis vinifera L.) using e-nose technology with a high detection accuracy using five chemometric methods, including PCA, LDA, QDA, SVM, and ANN. In other study [22], different potato cultivars have been studied using e-nose technology with regard to VOC emission.…”

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

“…For the six-group classification, based on the type of extract, the polynomial and RBF functions had a classification accuracy of 98.9 in the C-SVM method, while in the Nu-SVM method the classification accuracy of linear functions and RBF was 100% in learning and 98.9% in validation. Similar results have been reported for other crops, such as grape leaves [ 44 ], fruit juices [ 45 ], essential oils [ 4 , 16 ], coffee bean [ 46 ], corn [ 47 ], and cucumbers [ 48 ].…”

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

“…An electronic nose (E-nose) can also play a decisive role in the quality determination of the agricultural product [ 25 ]. This device employs a different approach to classify and determine the quality of the products [ 26 , 27 ], through detection of odor and volatile compounds [ 28 , 29 ].…”