Department of Chemistry, Clemson University, Clemson, SC, 29634, USA.
Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo, Mendoza, Argentina.
Anal Chim Acta. 2021 May 29;1161:338403. doi: 10.1016/j.aca.2021.338403. Epub 2021 Mar 15.
The last 10 years have witnessed the growth of artificial intelligence into different research areas, emerging as a vibrant discipline with the capacity to process large amounts of information and even intuitively interact with humans. In the chemical world, these innovations in both hardware and algorithms have allowed the development of revolutionary approaches in organic synthesis, drug discovery, and materials' design. Despite these advances, the use of AI to support analytical purposes has been mostly limited to data-intensive methodologies linked to image recognition, vibrational spectroscopy, and mass spectrometry but not to other technologies that, albeit simpler, offer promise of greatly enhanced analytics now that AI is becoming mature enough to take advantage of them. To address the imminent opportunity of analytical chemists to use AI, this tutorial review aims to serve as a first step for junior researchers considering integrating AI into their programs. Thus, basic concepts related to AI are first discussed followed by a critical assessment of representative reports integrating AI with various sensors, spectroscopies, and separation techniques. For those with the courage (and the time) needed to get started, the review also provides a general sequence of steps to begin integrating AI into their programs.
过去的 10 年见证了人工智能在不同研究领域的发展，它已成为一个充满活力的学科，具有处理大量信息的能力，甚至能够直观地与人类互动。在化学领域，硬件和算法的这些创新使得有机合成、药物发现和材料设计方面的革命性方法得以发展。尽管取得了这些进展，但人工智能在支持分析目的方面的应用主要局限于与图像识别、振动光谱和质谱相关的数据密集型方法，而不适用于其他技术，尽管这些技术较为简单，但由于人工智能已经足够成熟，可以利用它们，因此现在有望极大地增强分析能力。为了利用分析化学家使用人工智能的这一迫在眉睫的机会，本教程综述旨在为考虑将人工智能纳入其研究计划的初级研究人员提供一个起点。因此，首先讨论了与人工智能相关的基本概念，然后对将人工智能与各种传感器、光谱学和分离技术相结合的有代表性的报告进行了批判性评估。对于那些有勇气（和时间）开始的人，本综述还提供了一个将人工智能纳入其研究计划的通用步骤序列。
