Integration of non-invasive and conventional systems for quality assessment and authentication of meat stuffs: A review with bibliometric analysis.

Area of Science:

• Food Science
• Analytical Chemistry
• Spectroscopy

Background:

• Global meat demand and price volatility raise concerns about safety, adulteration, and traceability.
• Conventional meat analysis methods are slow, labor-intensive, and require reagents, hindering rapid screening.
• There is a need for advanced, non-invasive techniques for efficient meat quality assessment.

Purpose of the Study:

• To review emerging non-invasive spectroscopic techniques for meat quality and adulteration detection.
• To integrate bibliometric analysis with advanced technology evaluation, aligning with UN Sustainable Development Goals.
• To highlight the potential of hybrid systems for real-time, sustainable meat authentication.

Main Methods:

• Comprehensive literature review of non-invasive spectroscopic techniques (fluorescence, NIR, MIR, Raman).
• Bibliometric analysis to assess research trends and impact.
• Critical evaluation of spectroscopic methods combined with chemometrics and machine learning.

Main Results:

• Spectroscopic techniques show promise for rapid, non-invasive meat analysis.
• Hybrid systems integrating spectroscopy with chemometrics/machine learning offer accurate, real-time authentication.
• Identified research gaps include multi-adulterant detection, standardized validation, and open-access databases.

Conclusions:

• Non-invasive spectroscopy, enhanced by chemometrics and machine learning, presents a viable path for sustainable meat authentication.
• Addressing research gaps is crucial for developing robust, scalable solutions for future meat supply chains.
• Innovation in analytical techniques is key to ensuring meat safety and traceability in a global market.