Enhancing Food Quality Analysis: The Transformative Role of Artificial Neural Networks in Modern Analytical Techniques.

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.

Scope And Approach: Details of the latest ANN models and their contribution in food quality predictions, have been presented in this review. This systematic review has also firmly focused on various types of analytical techniques and recent applications of advanced analytical techniques combining various ANNs in food authentication. Various traditional chemometrics method and their key limitations are also discussed.

Key Findings And Conclusions: ANNs when combined with analytical techniques, provide solutions to the problem of food authenticity and quality analyses can learn representational features from the data-set during the training process and demonstrate stronger ability than traditional chemometric methods. The fusion of different types of ANNs and the development of miniaturized and portable spectrometers are becoming increasingly essential and potential candidates for future research.