Dataset of NIR, MIR, FIR and NMR spectroscopy and GC-MS of real samples of e-liquids from Malaysia
Abstract
This dataset presents comprehensive spectroscopic and chromatographic profiling of 27 e-liquid samples including commercial formulations, a booster, and a nicotine solution (the e-liquids were collected in Ampang Jaya, Malaysia before April 2023). Fourier-transform infrared (FTIR) spectroscopy was performed across the near-, mid-, and far-infrared ranges (6000–80 cm−1), generating unique transmittance spectra for each sample. These spectra revealed vibrational bands characteristic of nicotine, propylene glycol, vegetable glycerine, and various additives, supporting rapid qualitative fingerprinting and comparison through OPUS software. 1H nuclear magnetic resonance (NMR) spectroscopy, conducted using a 600 MHz Bruker spectrometer with cryoprobe, enabled molecular-level identification of sample matrices. Signals from nicotine, propylene glycol, vegetable glycerine, and flavourings were resolved, with spectral expansion in the region of 5.5–10.5 ppm highlighting proton signals that differentiate nicotine forms and concentrations. Meanwhile, gas chromatography-mass spectrometry (GC-MS) analysis of all samples provided compound identification, detecting over 30 volatile compounds per sample including nicotine, esters, aldehydes, and nicotine-related degradation products. The results, available as chromatograms and tabulated peak profiles, highlight the presence of nicotine (including nicotine-N’-oxide), ethyl maltol, vanillin, and prohibited or potentially harmful compounds such as benzaldehyde derivatives. Collectively, these datasets offer a robust foundation for regulatory of nicotine in Malaysia, compositional fingerprinting, and substances screening of e-liquids using FTIR, GC-MS, and NMR as complementary tools.
Related
- Orthogonal phase modulation and Lissajous mode decoupling in light-induced thermoelastic spectroscopy for real-time multi-component gas sensing
- Quantifying Validation-Induced Optimism in Machine Learning for Food Spectroscopy: An Empirical Multi-Dataset Benchmark
- PySpecTrace: A Python-based Graphical User Interface for Real-Time Spectroscopy Analysis and Data Analysis [version 1; peer review: 1 approved, 3 approved with reservations]
- Metastable Phase Separation and Information Retrieval in Multicomponent Liquids
- Unified Mesoscale Framework Bridging Equilibrium Thermodynamics and Transport Properties of Ionic Liquids and Their Mixtures
BibTeX
@article{hamzah2026dataset,
title = {{Dataset of NIR, MIR, FIR and NMR spectroscopy and GC-MS of real samples of e-liquids from Malaysia}},
author = {Noor Hazfalinda Hamzah and Farah Natasha Mohd Aris and Nur Hayatna Mukhni and Stéphane Balayssac and Saïda Danoun and Véronique Gilard and Mohd Rashidi Abdull Manap},
journal = {Data in Brief},
year = {2026},
month = oct,
doi = {10.1016/j.dib.2025.111591},
url = {https://hal.science/hal-05092150v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dataset of NIR, MIR, FIR and NMR spectroscopy and GC-MS of real samples of e-liquids from Malaysia
AU - Noor Hazfalinda Hamzah
AU - Farah Natasha Mohd Aris
AU - Nur Hayatna Mukhni
AU - Stéphane Balayssac
AU - Saïda Danoun
AU - Véronique Gilard
AU - Mohd Rashidi Abdull Manap
PY - 2026
DA - 2026/10/26
JO - Data in Brief
DO - 10.1016/j.dib.2025.111591
UR - https://hal.science/hal-05092150v1
AB - This dataset presents comprehensive spectroscopic and chromatographic profiling of 27 e-liquid samples including commercial formulations, a booster, and a nicotine solution (the e-liquids were collected in Ampang Jaya, Malaysia before April 2023). Fourier-transform infrared (FTIR) spectroscopy was performed across the near-, mid-, and far-infrared ranges (6000–80 cm−1), generating unique transmittance spectra for each sample. These spectra revealed vibrational bands characteristic of nicotine, propylene glycol, vegetable glycerine, and various additives, supporting rapid qualitative fingerprinting and comparison through OPUS software. 1H nuclear magnetic resonance (NMR) spectroscopy, conducted using a 600 MHz Bruker spectrometer with cryoprobe, enabled molecular-level identification of sample matrices. Signals from nicotine, propylene glycol, vegetable glycerine, and flavourings were resolved, with spectral expansion in the region of 5.5–10.5 ppm highlighting proton signals that differentiate nicotine forms and concentrations. Meanwhile, gas chromatography-mass spectrometry (GC-MS) analysis of all samples provided compound identification, detecting over 30 volatile compounds per sample including nicotine, esters, aldehydes, and nicotine-related degradation products. The results, available as chromatograms and tabulated peak profiles, highlight the presence of nicotine (including nicotine-N’-oxide), ethyl maltol, vanillin, and prohibited or potentially harmful compounds such as benzaldehyde derivatives. Collectively, these datasets offer a robust foundation for regulatory of nicotine in Malaysia, compositional fingerprinting, and substances screening of e-liquids using FTIR, GC-MS, and NMR as complementary tools.
SN - 2352-3409
ER -