Vibrational spectroscopy (Raman and infrared) and machine learning tools in food safety and composition

Area of Science:

• Food science and technology
• Analytical chemistry
• Data science

Background:

• Vibrational spectroscopy (NIR, MIR, Raman) provides molecular insights non-destructively.
• Machine learning (ML) methods (SVMs, PLSR, NNs) efficiently process complex spectral data.
• Current food analysis faces challenges in speed, accuracy, and comprehensive assessment.

Purpose of the Study:

• To explore the integration of ML with vibrational spectroscopy for food analysis.
• To highlight advancements in food quality, authenticity, and safety assessment.
• To address limitations and showcase the transformative potential of this synergy.

Main Methods:

• Utilizing traditional ML (SVMs, PLSR) and deep learning (NNs, CNNs, RNNs) with spectral data.
• Applying non-destructive vibrational spectroscopy techniques (NIR, MIR, Raman).
• Integrating portable spectrometers with ML algorithms for real-time analysis.

Main Results:

• Enhanced capabilities in identifying adulterants, quantifying quality indicators, and detecting contaminants.
• Successful classification, spoilage identification, and origin verification using spectral data.
• Development of real-time, on-site food analysis solutions.

Conclusions:

• The ML-spectroscopy integration revolutionizes food industry analysis.
• Overcoming challenges like data variability and interpretability through techniques like data augmentation and transfer learning.
• Driving significant improvements in global food security, quality control, and sustainability.