Food and Beverage Analysis

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.

Because the United Arab Emirates is seeing an increase in pet ownership, the quality of both dry and wet pet food is undergoing greater scrutiny to ensure its safety and efficacy. Lucy Semerjian, who works as a Chair and Associate Professor in the Department of Environmental Health Science at the University of Sharjah in Sharjah, United Arab Emirates, recently explored this topic in a recent paper

A recent study conducted in the Journal of Food Composition and Analysis examined the concentrations of ten metals in 52 commercially available wet and dry cat food samples, assessing their compliance with U.S. and European pet food safety standards. The lead author of this study, Lucy Semerjian, recently sat down with Spectroscopy to discuss the findings of her study.

Researchers from Hebei University and Hebei University of Engineering have developed a hyperspectral imaging method combined with data fusion and machine learning to accurately and non-destructively assess walnut quality and classify storage periods.

A new study demonstrates that paper bagging significantly enhances Fuji apple quality and appearance. Hyperspectral imaging combined with machine learning offers a powerful, non-destructive method for evaluating fruit grown under different cultivation conditions.

In a recent study published in the journal Beverages, a team of researchers from the National Institute for Research and Development of Isotopic and Molecular Technologies and Babeș-Bolyai University explored a new way to improve wine authentication

A recent study published in the journal Food Chemistry explored Brazil’s cachaça industry, focusing on a new analytical method that can confirm the geographic origin of cachaças from the Brejo Paraibano region in Brazil.

A new study highlights how chemometrics-powered spectroscopic techniques offer a fast, non-destructive, and cost-effective method for detecting phenolics and vitamins in foods.

Researchers in China propose novel postharvest processing mode using vis-NIR spectroscopy and deep learning to accurately measure pomelo sweetness.

Researchers from Tianjin Agricultural University, Nankai University, and Zhejiang A&F University have developed a highly accurate method using near-infrared spectroscopy and machine learning to rapidly detect and classify microplastics in chicken feed.

A recent study explored new rapid screening alternatives to traditional methods for detecting pork adulteration in meatballs, aiding halal food authentication efforts.

Researchers in China have pioneered a rapid, green, and non-destructive detection system using NIR spectroscopy and machine learning to ensure yak milk powder quality.

A new study published in the Journal of Food Composition and Analysis by researchers at the University of Sharjah reveals that while most cat foods sold in Sharjah meet international safety standards, some contain elevated metal levels, prompting calls for stricter regulation and quality control to protect pet health.

Researchers at McGill University have developed a fast, eco-friendly method using portable Raman spectroscopy and deep learning to accurately assess the antioxidant content of maple syrup on-site.