The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artificial intelligence (AI) algorithms into the instrumental data treatment software. Machine learning, deep learning, and computer vision are the most common techniques adopted to exploit the information potential of advanced analytical chemistry measures. In this paper, our primary focus is on elucidating the remarkable advantages of leveraging AI tools for comprehensive two-dimensional gas chromatography data (pre)processing. We illustrate how AI techniques can efficiently explore the complex datasets derived from multidimensional platforms combining comprehensive two-dimensional separations with mass spectrometry in the challenging application area of food-omics.

中文翻译：

---

利用人工智能促进全面的二维色谱分析：在食品组学中的应用

分析仪器的不断发展决定了数据产量的指数级增长，这反过来又带来了新的尖端分析挑战，需要将人工智能（AI）算法逐步集成到仪器数据处理软件中。机器学习、深度学习和计算机视觉是开发先进分析化学测量信息潜力的最常用技术。在本文中，我们的主要重点是阐明利用人工智能工具进行全面的二维气相色谱数据（预处理）处理的显着优势。我们说明了人工智能技术如何在食品组学这一具有挑战性的应用领域中有效地探索源自多维平台的复杂数据集，该多维平台将全面的二维分离与质谱分析相结合。