Food and Beverage Analysis

A new study demonstrates that infrared spectroscopy combined with chemometric modeling offers a fast, cost-effective way to classify plant-based milk alternatives and detect compositional variability, particularly in almond beverages.

A new study highlights terahertz (THz) metamaterials as a promising non-invasive, highly sensitive technology for improving food safety testing in agriculture.

Webinar Date/Time: Wednesday, September 17, 2025 Morning Session: 10:00 AM EST | 7:00 AM PST | 3:00 PM BST | 4:00 PM CEST Afternoon Session: 1:00 PM EST | 10:00 AM PST | 6:00 PM BST | 7:00 PM CEST

Researchers at the National Institute of Technology Rourkela have developed a highly accurate machine learning-assisted FT-IR spectroscopy method to detect and quantify sawdust adulteration in coriander powder, offering a fast and scalable solution to enhance food safety and authenticity.

A new study reveals that anthocyanin-rich fruit extracts degrade rapidly under sunlight but remain most stable in cold, dark storage.

Researchers have developed a fast, chemical-free method using near-infrared spectroscopy to accurately analyze the quality of dark chocolate, offering a sustainable alternative to traditional testing techniques.

Researchers at Jiangnan University have developed a highly accurate method combining Raman spectroscopy with deep learning to monitor acid value in palm oil.

A recent study developed and tested a highly stable, nanozyme-based colorimetric sensor capable of rapidly and accurately detecting vitamin C in commercial beverages, offering a practical tool for real-time nutritional assessment.

A recent study demonstrated that portable Raman spectroscopy, combined with advanced statistical modeling, offers a reliable, non-destructive method for monitoring nitrate levels in greenhouse-grown spinach.

A new review published in Trends in Food Science & Technology highlights how advanced spectroscopy, multidimensional chromatography, artificial intelligence (AI), and novel sensors are improving food safety by enhancing sensitivity, speed, and sustainability in contaminant detection.

Researchers at INIAV in Portugal have demonstrated that near-infrared (NIR) spectroscopy combined with chemometric algorithms offers a rapid, non-destructive, and accurate method for detecting harmful fumonisins in maize, enhancing food safety monitoring.

Researchers at China Agricultural University developed a rapid and accurate spectroscopic method using NIR and FT-IR combined with PLS regression to measure protein content in rice noodles, enhancing quality control for the popular river snail rice noodle (luosifen) industry.

This study presents a new system that enables the precise detection of glucose, choline, and lactate without traditional labels or antibodies.

Researchers from China Agricultural University introduce PeaNet, promising rapid, accurate, and nondestructive protein analysis.

Researchers at the University of Belgrade have demonstrated that combining Raman and FT-IR spectroscopy with machine learning algorithms offers a highly accurate, non-destructive method for identifying seed varieties in lettuce, paprika, and tomato.

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.

Machine learning models and spectral analysis provide a scalable alternative to conventional trace metal detection.

A new study published in the journal Food Chemistry by lead authors Qian Zhao and Jun Huang from Zhejiang University of Science and Technology unveil a new data-driven framework for predicting resistant starch content in rice

Researchers at Wittenborg University of Applied Sciences have developed a non-destructive method using hyperspectral imaging combined with chemometrics and machine learning to accurately predict fat and protein content in diverse cheese types.

Researchers from the Institute of Agrifood Research and Technology (IRTA) in Catalunya, Spain used fluorescence and Raman spectroscopy to explore complex tissue changes behind wooden breast myopathy in chickens.