Food and Beverage Analysis

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.

Researchers from Tsinghua and Hainan Universities have developed a portable, non-destructive method using NIR spectroscopy, hyperspectral imaging, and machine learning to accurately assess the quality and detect adulteration in whey protein supplements.

A new study in the Journal of Food Composition and Analysis showcases high-performance detection using artificial intelligence and spectroscopy.

New predictive models promise to revolutionize livestock feeding strategies in one of China’s most important pastoral regions.

Researchers from James Cook University highlight critical gaps and future directions for developing a large-scale, machine-learning-based satellite spectroscopy system to monitor sugarcane health and detect diseases and pests.

Webinar Date/Time: Tuesday, May 27, 2025 at 11:00 AM EST

Researchers from SGF International and Geisenheim University combine 1H NMR and HPLC-DAD to elevate juice quality assurance.

A new study published in Applied Food Research demonstrates that near-infrared spectroscopy (NIRS) can effectively detect subclinical bovine mastitis in milk, offering a fast, non-invasive method to guide targeted antibiotic treatment and support sustainable dairy practices.

Jiangxi Agricultural University researchers use AI and vis-NIRS to predict meat quality and freezing duration with high accuracy.

Researchers from Jiangsu University and Zhejiang University of Water Resources and Electric Power have developed a transfer learning approach that significantly enhances the accuracy and adaptability of NIR spectroscopy models for detecting mycotoxins in cereals.

Researchers in China have developed a lightweight deep learning system for rapid, non-destructive analysis of wheat flour composition.

A recent study demonstrates that near-infrared (NIR) spectroscopy can be used as a rapid, nondestructive method for accurately assessing sugar cane quality.

A new study published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy demonstrates that near infrared (NIR) spectroscopy is a highly accurate and reliable method for authenticating hazelnut cultivars and geographical origins.

Tianjin University researchers develop an advanced AI model to enhance food safety.

A recent study explores a novel approach for improving data interpretation to provide a better understanding of food properties.

This research investigates the application of laser-induced breakdown spectroscopy (LIBS) and machine learning (ML) for detecting elemental composition of food, using rice as an example.

A study published in the International Journal of Dairy Technology by lead author Mark A. Fenelon and his team at Teagasc Food Research Centre and University College Dublin demonstrates that ATR-FT-IR spectroscopy can effectively monitor heat-induced structural changes in milk proteins and colloidal calcium phosphate, offering valuable insights for optimizing dairy product stability and quality.

A new study published in the Journal of Dairy Science demonstrates that FT-MIR spectroscopy can effectively authenticate farming practices and dairy systems in Parmigiano Reggiano production but has limited ability to verify animal welfare parameters.

Our full-length interview with Huck covers more than just NIR spectroscopy in food and bio analysis. Spectroscopy sat down with Huck to also discuss current trends going on in spectroscopy, delving into what challenges spectroscopists face today and how they can solve these concerns.

At Pittcon, Spectroscopy sat down with Christian Huck of the University of Innsbruck to talk about how NIR and imaging spectroscopy are being used in food and bioanalysis, and where this industry is heading in the future.

Near-infrared spectroscopy was recently used to estimate sweetness and total soluble solids content in cherry tomatoes.

Hyperspectral imaging was recently used to characterize chicken breast affected by myopathies, which can affect their texture and quality.

Researchers from Jiangsu University and Jimei University have developed an AI-powered detection system using near-infrared spectroscopy and a convolutional neural network long short-term memory (CNN-LSTM) model to accurately identify petroleum contamination in edible oils for improving food safety and quality control.

A recent study published in the Journal of Food Composition and Analysis explores the potential of fluorescence anisotropy as a tool for quantifying structural anisotropy in food, offering new insights for improving plant-based alternatives and dairy product textures.