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Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation


Lebanov, L and Paull, B, Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation, Analytical Methods, 13, (36) pp. 4055-4062. ISSN 1759-9660 (2021) [Refereed Article]

Copyright Statement

Copyright 2021 The Royal Society of Chemistry

DOI: doi:10.1039/d1ay00886b


We present a method, utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated essential oils (EOs). Firstly, the approach was evaluated for discrimination of pure EOs from those adulterated with solvent, namely benzyl alcohol. In the case of ylang-ylang EO, three different types of adulteration were examined, adulteration with solvent, cheaper vegetable oil and a lower price EO. Random Forest and partial least square discrimination analysis (PLS-DA) showed excellent performance in discriminating pure from adulterated EOs, whilst the same time identifying the type of adulteration. Also, utilising partial least squares regression analysis (PLS) all adulterants, namely benzyl alcohol, vegetable oil and lower price EO, were quantified based on spectra recorded using the smartphone Raman spectrometer, with relative error of prediction (REP) being between 2.41-7.59%.

Item Details

Item Type:Refereed Article
Keywords:infrared-spectroscopy, gas-chromatography, authenticity, NIR
Research Division:Chemical Sciences
Research Group:Analytical chemistry
Research Field:Analytical spectrometry
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the chemical sciences
UTAS Author:Lebanov, L (Mr Leo Lebanov)
UTAS Author:Paull, B (Professor Brett Paull)
ID Code:151692
Year Published:2021
Web of Science® Times Cited:2
Deposited By:Chemistry
Deposited On:2022-08-03
Last Modified:2022-09-08

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