Prediction of Linalool Content in Osmanthus fragrans Using E-Nose Technology. (7th April 2022)

• Record Type: Journal Article
• Authors: Zhou, Chenchen; Fan, Junjun; Tan, Ruinan; Peng, Qin; Cai, Junhuo; Zhang, Wangxiang
• Other Names: Rodriguez-Mendez Maria Luz Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-04-07
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/7349030
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Linalool is a well-known volatile compound. It is usually extracted from plants or synthesized by chemical methods. However, there is still no rapid screening method for plants with high linalool content. Therefore, we aimed to establish a method determining linalool content in Osmanthus fragrans by electronic nose (E-nose). The volatile gases of three cultivars of O. fragrans were characterized by gas chromatography-mass spectrometry (GC-MS) and E-nose. Different concentrations of linalool were measured by E-nose. Then, the E-nose data were subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to establish a discriminant model for determining linalool concentrations. Prediction models based on principal component regression (PCR) and multiple linear regression (MLR) were developed from all sensors and feature sensors to determine linalool concentrations in the three cultivars of O. fragrans flowers. Finally, the linalool concentrations determined by gas chromatography were used to verify the accuracy of the prediction model. The results show that linalool had the highest abundance among the volatile compounds of O. fragrans, and the MLR prediction model had an R 2 of 0.992 in calibration sets and an R 2 of 0.895 in prediction sets using 10 sensors. This study describes a rapid and accurate method for the detection and quantification of linalool concentrations.