Total Metals Analysis of Digested Plant Tissue Using an Agilent 4200 Microwave Plasma-AES

Researchers from Guangdong Polytechnic Normal University highlight how combining Raman spectroscopy with machine learning enables rapid, non-destructive, and highly accurate analysis of fruit quality, offering transformative potential for food safety and agricultural diagnostics.

Published in Food Chemistry, researchers from Jiangsu University of Science and Technology and Jimei University use near-infrared (NIR) spectroscopy and machine learning to tackle food adulteration and enhance quality control.

Researchers at Heilongjiang University have developed a rapid and accurate method for detecting sweeteners in food using Raman spectroscopy combined with a Random Forest machine learning algorithm, offering a powerful tool for improving food safety.

Researchers from institutions in Brazil harness near-infrared spectroscopy and machine learning to determine cocoa content with precision.

Researchers from Jiangsu University and Jimei University developed an advanced FT-NIR-based method for food safety monitoring, achieving over 97% accuracy in identifying multiple oil-based contaminants in peanut oil.

A new review highlights how vibrational spectroscopy techniques like FTIR, NIR, and Raman offer rapid, non-destructive tools for accurately analyzing plant-based protein content and structure.