| Title: | Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil |
| Author(s): | Aghili NS; Rasekh M; Karami H; Edriss O; Wilson AD; Ramos J; |
| Address: | "Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran. Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq. Department of Computer, Rafsanjan Branch, Islamic Azad University, Rafsanjan 77181-84483, Iran. Southern Hardwoods Laboratory, Pathology Department, Center for Forest Health & Disturbance, Forest Genetics & Ecosystems Biology, Southern Research Station, USDA Forest Service, 432 Stoneville Road, Stoneville, MS 38776-0227, USA. College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| 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" |
| Keywords: | "*Sesame Oil/analysis/chemistry Gas Chromatography-Mass Spectrometry/methods *Volatile Organic Compounds/analysis Electronic Nose Neural Networks, Computer chemometrics edible oils gas sensors machine learning mass spectroscopy oil adulteration detection v;" |
| Notes: | "MedlineAghili, Nadia Sadat Rasekh, Mansour Karami, Hamed Edriss, Omid Wilson, Alphus Dan Ramos, Jose eng Switzerland 2023/07/29 Sensors (Basel). 2023 Jul 11; 23(14):6294. doi: 10.3390/s23146294" |
