Intelligent Quality Control in Food and Beverage Industry

The field of food and beverage analysis is witnessing a significant shift towards the adoption of artificial intelligence and machine learning techniques. Researchers are exploring the potential of these technologies to improve quality control, detect adulteration, and enhance sustainability in various industries such as wine, honey, and coconut milk production. The use of spectral information, hyperspectral imaging, and infrared spectroscopy is becoming increasingly popular for predicting attributes, classifying origins, and detecting adulteration. Noteworthy papers in this area include:

  • A study on wine characterization using spectral information and predictive artificial intelligence, which achieved high accuracy in predicting wine attributes and origin.
  • A paper on honey adulteration detection using hyperspectral imaging and machine learning, which demonstrated an overall cross-validation accuracy of 96.39%.
  • A research on detection of adulteration in coconut milk using infrared spectroscopy and machine learning, which successfully detected adulteration with a cross-validation accuracy of 93.33%.

Sources

Wine Characterisation with Spectral Information and Predictive Artificial Intelligence

Artificial intelligence for sustainable wine industry: AI-driven management in viticulture, wine production and enotourism

Classification of Honey Botanical and Geographical Sources using Mineral Profiles and Machine Learning

A Machine Learning Approach for Honey Adulteration Detection using Mineral Element Profiles

Honey Adulteration Detection using Hyperspectral Imaging and Machine Learning

Detection of Adulteration in Coconut Milk using Infrared Spectroscopy and Machine Learning

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