Enhancing Food Quality Analysis: The Transformative Role of Artificial Neural Networks in Modern Analytical Techniques.
Journal:
Critical reviews in analytical chemistry
Published Date:
May 15, 2025
Abstract
BACKGROUND: Consumers have always anticipated and requested high-quality and safe food items, which is why food quality analyses and mandatory product inspections were introduced. Various novel techniques and analytical breakthroughs have emerged to cater to the problem of food authenticity. It is difficult to interpret and analyze large and complex data with traditional machine learning algorithms. Its effectiveness is constrained when dealing with non-linearity, high-dimension data, and intricate data relationships, and it is frequently faced with struggles to capture the latent patterns and structures within such data. However, using deep learning artificial neural networks has provided solutions to these challenges. Artificial neural networks (ANNs) have propelled breakthroughs across several research areas and have continually been applied in the area of food analysis, quality, and safety. ANNs are pooled along with various analytical techniques for making food quality predictions with fast response time and short computation time.
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