About this Research Topic
The escalating demands for food safety and quality are amplified by the increased health concerns of the public. Foods are exposed to a spectrum of biological and chemical hazards that can compromise their safety and quality. Traditional detection methods like enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS), although precise, are slow, costly, labor-intensive, and can degrade the food sample. There is an evident need for more rapid, non-destructive, and cost-effective methodologies to ensure real-time food safety.
This Research Topic aims to address the cutting-edge of optical sensing technologies in assessing the quality attributes of various foods and agricultural products. The integration of these technologies with contemporary data processing techniques such as advanced data mining and deep learning has ushered a new era in food safety evaluations, providing promising avenues for non-destructive testing. Contributions are expected to showcase developments in optical sensing methods and their integration with the latest computational algorithms for superior precision and efficiency in food quality control.
To foster comprehensive coverage on this evolving technology, we invite contributions within defined but broad boundaries. We welcome articles that not only develop and apply new optical sensing devices but also enhance existing techniques with innovative data processing methods. Potential topics include, but are not limited to:
· Sensors for real-time quality assessment
· Biohazard detection in food products
· Authenticity verification in food supply chains
· Nutrient and bioactive component quantification
· Advances in computer vision for food quality
· Applications for spectroscopy in food testing
· Integration of deep learning in optical sensing systems
This Research Topic aims to address the cutting-edge of optical sensing technologies in assessing the quality attributes of various foods and agricultural products. The integration of these technologies with contemporary data processing techniques such as advanced data mining and deep learning has ushered a new era in food safety evaluations, providing promising avenues for non-destructive testing. Contributions are expected to showcase developments in optical sensing methods and their integration with the latest computational algorithms for superior precision and efficiency in food quality control.
To foster comprehensive coverage on this evolving technology, we invite contributions within defined but broad boundaries. We welcome articles that not only develop and apply new optical sensing devices but also enhance existing techniques with innovative data processing methods. Potential topics include, but are not limited to:
· Sensors for real-time quality assessment
· Biohazard detection in food products
· Authenticity verification in food supply chains
· Nutrient and bioactive component quantification
· Advances in computer vision for food quality
· Applications for spectroscopy in food testing
· Integration of deep learning in optical sensing systems
Keywords: food quality, food safety, optical sensor, computer vision, machine learning, data processing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
