AI and Machine Learning in Analytical Chemistry: Opportunities for Ultra-Trace Detection

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@article{1716809,
      author = {Orebiyi, Kazeem Oluwafemi, Oladimeji Tolulope Folasade; Oluwaseyi Aanuoluwapo Ebisemiju, Ahunanya, Uzoma Peter; Okesooto Marvelous Abisola, Akinwale Idayat Kikelomo; Bankole Temitope Michael, Abba Kingsley; Adewole Esther Abisola},
      title = {AI and Machine Learning in Analytical Chemistry: Opportunities for Ultra-Trace Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {4453-4463},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716809.pdf},
      abstract = {Artificial intelligence (AI) is a scientific and technological domain focused on the development of engineered systems capable of generating outputs such as content, predictions, recommendations, or decisions based on human-defined objectives. The integration of AI with analytical techniques holds significant potential to transform decision-making processes and drive innovation across multiple sectors. AI enhances data detection, segmentation, and image resolution, with convolutional neural networks (CNNs) demonstrating strong performance in the analysis of complex imaging datasets for material characterization. Machine learning is increasingly integrated with analytical methods, including gas chromatography (GC), high-performance liquid chromatography (HPLC), gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), ultraviolet (UV) and infrared (IR) spectroscopy, mass spectrometry (MS), colorimetry, and biosensing techniques. This review presents an overview of AI-driven models and sensor-based analytical systems, with particular emphasis on chemometric approaches in UV and IR spectroscopy to enhance accuracy and data interpretation. Real-time AI-assisted analysis of sensor data enables rapid and actionable insights, representing a significant advancement in fields such as environmental monitoring and pharmaceutical research. The application of AI to established techniques including spectroscopy, chromatography, and mass spectrometry modernizes analytical workflows by improving precision, efficiency, and the speed of complex compound analysis. Furthermore, AI facilitates the processing of high-dimensional sensor data that are often too complex for conventional analytical approaches, thereby enabling deeper insights and more comprehensive evaluations. Overall, this paper examines AI-based tools and sensor technologies in analytical chemistry, highlighting their contributions to error reduction, process automation, and enhanced analytical performance.},
      keywords = {Artificial Intelligence, Machine Learning, Analytical Techniques, Neural Network, Biosensors, GC-MS, CNN, Separation Techniques Etc.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716809}
  }