What is it about?
This chapter provides an overview of the application of different machine learning algorithms (random forest, 2D-CNNs, etc.) combined with Raman spectral techniques in the rapid analysis of chemical compounds and contaminants in foods.
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Why is it important?
Raman spectroscopy is an emerging nondestructive and rapid detection technology and has been used for food analysis. The underlying principle of Raman spectroscopy as well as the advantages and limitations of machine learning algorithms are addressed. Data analysis such as standardized protocol and methodology of data file size, partition method of training and test dataset, and cloud database and online web server for the application, particularly for online/real-time detection, are discussed.
Perspectives
This knowledge of the book chapter will fundamentally help develop rapid detection methods using the AI and machine learning–driven Raman spectroscopy technique.
Dr. Hefei Zhao
University of California Davis
Read the Original
This page is a summary of: Machine Learning-Driven Raman Spectroscopy Techniques for Rapid Detection of Chemical Compounds and Contaminants in Foods, September 2024, Taylor & Francis,
DOI: 10.1201/9781003359975-3.
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