Hamed Karami

Sesame oil is a valuable edible oil whose authenticity and quality are important concerns for consumers and producers. Reliable and rapid analytical methods are therefore required to assess the quality and authenticity of sesame oil products available on the market. The electronic nose (E-nose) is a non-destructive, portable, and non-contact tool that offers advantages such as low cost, high speed, and ease of use compared with conventional quality control methods. In this study, E-nose and GC–MS techniques were applied to evaluate pure sesame oil and four commercially available sesame oil samples. Based on E-nose data analyzed using chemometric methods, the PCA model explained 96% of the total variance, while LDA and QDA achieved 100% classification accuracy. SVM and ANN models showed accuracies of 98.67% and 98.7%, respectively. The results demonstrated a strong agreement between E-nose and GC–MS analyses and confirmed the capability of the E-nose system, combined with chemometric methods, to differentiate pure sesame oil from commercially available sesame oil samples. These findings highlight the potential of E-nose technology as a rapid and cost-effective tool for sesame oil authenticity assessment and quality control.

Oxidation is a major factor affecting the quality and shelf life of Extra Virgin Olive Oil (EVOO), leading to chemical degradation and loss of freshness. This study investigates the assessment of EVOO freshness using a peptide-based optoelectronic nose (OE-nose) system combined with signal processing and machine learning techniques. Volatile organic compound (VOC) profiles from fresh and oxidized EVOO samples were acquired using a multigas sensor array implemented on the Aryballe NeOse Advance platform. The oxidation status of the samples was validated using reference chemical quality analyses. Sensor signals were subjected to baseline correction and normalization, without the application of digital smoothing. Full-sequence analysis was employed to exploit desorption-phase kinetics as a volatility-driven, implicit preseparation mechanism, enabling robust discrimination without chromatographic steps. Exploratory and supervised models were evaluated, including principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and support vector machines (SVM). The SVM model achieved a classification accuracy of 100%, while PLS-DA reached 95.8% accuracy under strict validation conditions. Compared to conventional analytical methods, the proposed approach offers a rapid, nondestructive, and cost-effective solution for on-site EVOO freshness evaluation. To the authors’ knowledge, this work represents the first application of a peptide-based optoelectronic nose for assessing EVOO oxidation, highlighting its potential advantages over conventional MOX- and polymer-based electronic nose systems reported in previous studies.

Approximately 60% of mechanical energy consumption in mechanized agriculture is dedicated to soil tillage operations and seedbed preparation. With awareness of the adverse effects of excessive tractor traffic on farms during seedbed preparation, the issue of selecting suitable equipment and...

The occurrence of mass disasters are increasing as a result of changing climates and the growing threat of terrorist activities/conflicts. When these tragedies strike, it is critical to locate victims. While search and rescue dogs are trained to locate the living, cadaver detection dogs are trained to locate the deceased. These dogs rely on the volatile organic compounds (VOCs) emitted from the victims to do so. Knowing which dog unit to deploy can be challenging, and the victims’ makeup is likely to change following disasters in densely inhabited places, where commingling is likely to occur. The use of electronic nose technologies in forensic science is a recent breakthrough. Due to their ability to detect differing VOCs, this technology can be used to assist in the recovery of victims in disaster events. The most popular types of accessible gas sensor technologies are briefly introduced and compared in this article for their potential use to locate missing persons, both living and deceased. The current and future market needs are articulated, and the lack of enrichment of these needs is examined in relation to the capabilities of existing gas sensors. This will inform further research areas of preference to increase victim detection capabilities.

Rice is an annual plant from the family of Oryzeae, provides the main food for about 2.5 billion people. The quality of this product is under the influence of various factors. Quality control and adulteration detection are among the main issues in the rice industry for which, various methods have...

In the present study, three degrees of roasting (light, medium, and dark), which significantly differentiate the properties of coffee beans, were analyzed. Additionally, the effect of the roasting level on the profile of volatile and biologically active compounds, including chlorogenic acids,...

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.

In order to accurately determine and evaluate the odour of rice, it is necessary to identify the substances that affect that odour and to develop methods to determine their amounts. For more than three decades, researchers have been studying the factors that produce and influence the aroma of...