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Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning [An article from: Analytica Chimica Acta]
by D.I. Ellis, D. Broadhurst, R. Goodacre
Discover a cutting-edge technique for swiftly detecting and quantifying microbial spoilage in beef. Using Fourier transform infrared spectroscopy and machine learning, this article from Analytica Chimica Acta offers a reliable, non-destructive method to enhance food quality control and safety in the industry.
About This Book
This scientific article explores the application of Fourier transform infrared spectroscopy (FTIR) for the rapid detection of microbial spoilage in beef. By integrating machine learning techniques, the method allows for quantitative assessment of spoilage levels.
The approach leverages spectroscopic data to identify microbial changes without the need for time-consuming traditional culturing methods. This innovation supports efficient monitoring in food processing and quality assurance.
Published in Analytica Chimica Acta, the work by D.I. Ellis, D. Broadhurst, and R. Goodacre highlights the potential of combining analytical chemistry with computational tools for practical applications in food safety.
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