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<p>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.</p>

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Electronic nose and GC–MS coupling for sesame oil authenticity assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Nadia Sadat Aghili,
  • Mansour Rasekh,
  • Hamed Karami,
  • Omid Edriss,
  • Paweł Krzaczek,
  • Marek Gancarz

摘要

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.