Electronic Nose Analysis and Statistical Methods for Investigating Volatile Organic Compounds and Yield of Mint Essential Oils Obtained by Hydrodistillation

• Published in: Chemosensors Vol. 10; no. 11; p. 486
• Main Authors: Zorpeykar, Sepideh, Mirzaee-Ghaleh, Esmaeil, Karami, Hamed, Ramedani, Zeynab, Wilson, Alphus Dan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Artificial neural networks; Chromatography; Discriminant analysis; Distilled water; Electronic noses; Emissions; essential oil; Essential oils; Herbal medicine; Leaves; Mass spectrometry; Medicinal plants; mint; Neural networks; olfactory apparatus; Plants; Principal components analysis; Quality assessment; Quality control; Scientific imaging; Sensors; Statistical analysis; Statistical methods; Trends; VOCs; Volatile organic compounds; United States
• ISSN: 2227-9040; 2227-9040
• DOI: 10.3390/chemosensors10110486

A major problem associated with the development of medicinal plant products is the lack of quick, easy, and inexpensive methods to assess and monitor product quality. Essential oils are natural plant-derived volatile substances used worldwide for numerous applications. The important uses of these valuable products often induce producers to create fraudulent or lower quality products. As a result, consumers place a high value on authentic and certified products. Mint is valued for essential oil used in the food, pharmaceutical, cosmetic, and health industries. This study investigated the use of an experimental electronic nose (e-nose) for the detection of steam-distilled essential oils. The e-nose was used to evaluate and analyze VOC emissions from essential oil (EO) and distilled water extracts (DWEs) obtained from mint plants of different ages and for leaves dried in the shade or in the sun prior to hydrodistillation. Principal component analysis (PCA), linear discriminant analysis (LDA), and artificial neural networks (ANN) were performed on electrical signals generated from electronic nose sensors for the classification of VOC emissions. More accurate discriminations were obtained for DWEs sample VOCs than for EO VOCs. The electronic nose proved to be a reliable and fast tool for identifying plant EO. The age of plants had no statistically significant effect on the EO concentration extracted from mint leaves.