Development of distribution maps of spectrally similar degradation products by Raman chemical imaging microscope coupled with a new variable selection technique and SIMCA classifier

Saleh, Ahmed Abdelfattah; Hegazy, Maha A.; Abbas, Samah S.; El-Kosasy, Amira M.

doi:10.1016/j.saa.2021.120654

Cited by 5 publications

(1 citation statement)

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“…The advantage of SIMCA is its ability to handle complex datasets with multiple classes, by building separate models for each class. This approach allows for the capture of the inherent variability within each class, and for making reliable predictions or classifications based on the proximity of new samples to the existing class models [36,37].…”

Section: Soft Independent Modeling Of Class Analogy (Simca)mentioning

confidence: 99%

Spectroscopy and Chemometrics for Conformity Analysis of e-Liquids: Illegal Additive Detection and Nicotine Characterization

Akhtar,

Barhdadi,

De Braekeleer

et al. 2024

Chemosensors

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Vaping electronic cigarettes (e-cigarettes) has become a popular alternative to smoking tobacco. When an e-cigarette is activated, a liquid is vaporized by heating, producing an aerosol that users inhale. While e-cigarettes are marketed as less harmful than traditional cigarettes, there are ongoing concerns about their long-term health effects, including potential lung damage. Therefore, it is essential to closely monitor and study the composition of e-liquids. E-liquids typically consist of propylene glycol, glycerin, flavorings and nicotine, though there have been reports of non-compliant nicotine concentrations and the presence of illegal additives. This study explored spectroscopic techniques to examine the conformity of nicotine labeling and detect the presence of the not-allowed additives: the caffeine, taurine, vitamin E and cannabidiol (CBD) in e-liquids. A total of 236 e-liquid samples were carefully selected for analysis. Chemometric analysis was applied to the collected data, which included mid-infrared (MIR) and near-infrared (NIR) spectra. Supervised modeling approaches such as partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were employed to classify the samples, based on the presence of nicotine and the targeted additives. This study demonstrates the efficacy of MIR and NIR spectroscopic techniques in conjunction with chemometric methods (SIMCA and PLS-DA) for detecting specific molecules in e-liquids. MIR with autoscaling data preprocessing and PLS-DA achieved 100% classification rates for CBD and vitamin E, while NIR with the same approach achieved 100% for CBD and taurine. Overall, MIR combined with PLS-DA yielded the best classification across all targeted molecules, suggesting its preference as a singular technique.

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Section: Soft Independent Modeling Of Class Analogy (Simca)mentioning

confidence: 99%