Enseñanza del machine learning y la quimiometría en química analítica mediante propuestas prácticas e interactivas
[EN] Despite the growing popularity of machine learning (ML), the teaching of this disruptive field in analytical chemistry is challenging due to the lack of enough programming background in both, professors, and students. Because of that, this subject is sometimes underrated or even ignored in chem...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/200550 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/200550 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprendizaje Automático Machine Learning Química Analítica Quimiometría Metodología Laboratorio Analytical Chemistry Chemometrics Methodology Laboratory Programming |
| Sumario: | [EN] Despite the growing popularity of machine learning (ML), the teaching of this disruptive field in analytical chemistry is challenging due to the lack of enough programming background in both, professors, and students. Because of that, this subject is sometimes underrated or even ignored in chemistry curriculums. In this work, we firstly surveyed the previous knowledge in multivariate analysis and programming by students enrolled in the master’s degree in chemistry. Upon recognizing a deficiency in fundamental programming and statistical principles, we carried out actions to close the gap between ML and analytical chemistry in under- and post-graduate level. Accordingly, we proposed the use of the interactive software Orange and the programming of apps with MATLAB for teaching ML in the laboratory lessons of analytical chemistry. With this approach, two laboratory lessons were designed and conducted which are focused on analysis of foodstuffs by infrared spectroscopy and using ML in daily contexts. The evaluation of the methodologies proposed indicated that the use of interactive software made ML more appealing to the students and contributed to a better understanding of ML concepts. |
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