ArticleSensors (Basel, Switzerland)2023
Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Color Darkening and Pyrazine Accumulation Associated with Apparent Roasting Intensity and Aroma Quality in Commercial Sesame Paste.Foods (Basel, Switzerland) · 2026Article
- Image-Based Volatile Organic Compound Identification Using the Cosine Similarity Method.ACS omega · 2026Article
- Effects of differentFrontiers in nutrition · 2026Article
- Comprehensive analysis of security threats and privacy issues in indoor localization systems.Scientific reports · 2025Article
- Comparative Volatile Profiles of Plain Poached (PP) and Steamed over Water (SW) Wenchang Chicken Analyzed by GC-MS, GC-IMS, and E-Nose.Foods (Basel, Switzerland) · 2025Article
- Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions.Foods (Basel, Switzerland) · 2025Review
- Ozone-assisted cataluminescence sensor based on morphology-controlled TiOMikrochimica acta · 2025Article
- Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance.Micromachines · 2025Article
- Investigation of the differences in volatile organic compounds of sesame oil under different processing methods using GC-IMS and electronic nose.Frontiers in nutrition · 2025Article
- An Exploratory Study on the Rapid Detection of Volatile Organic Compounds in Gardenia Fruit Using the Heracles NEO Ultra-Fast Gas Phase Electronic Nose.Metabolites · 2024Article
- Complementary assessment of nano-packaged garlic properties by electronic nose.Food science & nutrition · 2024Article
- Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models.Foods (Basel, Switzerland) · 2023Article
- Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms.Foods (Basel, Switzerland) · 2023Article
- FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil.Molecules (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.
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