Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/23512
Title: 
Authors: 
Subjects: 
Black cumin seed oil
Oil adulteration
Clustering
Group lasso
Elastic net
Variable selection
Penalized regression
Issue Date: 
2022
Citation: 
Zhu, Y., Zou, L., & Tan, T. L. (2022). A clustering group lasso method for quantification of adulteration in black cumin seed oil using fourier transform infrared spectroscopy. Chemometrics and Intelligent Laboratory Systems. 220, Article 104471. https://doi.org/10.1016/j.chemolab.2021.104471
Journal: 
Chemometrics and Intelligent Laboratory Systems
Abstract: 
Black cumin seed oil (BCSO) contains a large number of bioactive compounds and thus has many medicinal health benefits and uses. Its high economic profits in the market lead to the frequent occurrence of adulterating this oil with cheaper edible oils such as grape seed oil, walnut oil. It is difficult to detect adulteration as the oil adulterant has similar physical characteristic and even similar chemical composition to the authentic oil. The development of an accurate and rapid analytical method using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is of essential importance for determination of authenticity of BCSO and quantification of oil adulterants. In this study, BCSO and grape seed oil (GSO) were mixed in various ratios to mimic the adulteration. A clustering group lasso method was developed by incorporating both the high correlation structure of spectral variables and the underlying group features into the model. Instead of assuming that groups are known a priori as does ordinary group lasso, the clustering group lasso infers groups of spectral features from the data and encourages spectral variables within a group to have a shared association with the response. The model using ATR-FTIR spectroscopy proved to be a powerful tool to quantify BCSO adulteration with high accuracy and can accurately predict the quantity of adulterant at levels as low as 5%. With a substantial reduction in number of spectral features, the clustering group lasso model shows a simple regression coefficient profile with improved interpretability as compared to the ordinary group lasso model and other penalized models. The spectral regions automatically selected for quantification of BCSO adulteration can be helpful for the interpretation of the major chemical constituents of BCSO regarding its anti-cancer and anti-inflammatory effects from a chemometric perspective.
URI: 
ISSN: 
0169-7439
DOI: 
Project number: 
RI 6/14 ZY
Funding Agency: 
National Institute of Education, Singapore
File Permission: 
Embargo_20230201
File Availability: 
With file
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