Integrating artificial intelligence with non-destructive experimental methods for effective food analysis: a case...

Area of Science:

• Food Science
• Analytical Chemistry
• Computational Science

Background:

• Spices are vital for culinary, medicinal, and economic purposes, but lack sufficient identification and quality assessment data.
• Current spice processing and authentication methods face challenges, necessitating digital solutions.
• Food computing and non-invasive techniques offer promising avenues for spice analysis and quality control.

Purpose of the Study:

• To explore the integration of artificial intelligence (AI) and non-destructive experimental methods for spice authentication and quality assessment.
• To review how advanced computational techniques can improve the analysis of complex spice compositions.
• To highlight AI's potential in addressing current challenges in spice quality analysis.

Main Methods:

• Review of AI, machine learning, and deep learning applications in spice analysis.
• Integration of non-destructive techniques: Near-infrared spectroscopy (NIRS), Fourier transform infrared spectroscopy (FTIR), and Hyper Spectral Imaging (HSI).
• Utilizing food computing for processing large datasets from spectroscopy, chromatography, and sensory evaluation.

Main Results:

• AI-integrated non-destructive methods provide comprehensive, efficient, and accurate spice component analysis.
• Food computing effectively processes and interprets complex datasets for spice classification and authentication.
• Demonstrated potential of AI and spectroscopy for reliable spice quality assessment.

Conclusions:

• AI-powered non-destructive approaches are crucial for ensuring spice authenticity, safety, and quality.
• This integration supports industry efforts to improve product quality and reduce waste.
• Advanced computational techniques offer innovative solutions for spice authentication and meet consumer demands.