Food and Beverage Analysis

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.

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.

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.