Combining AI and NIR Spectroscopy to Predict Resistant Starch (RS) Content in Rice
Hyperspectral Imaging and Machine Learning to Non-Destructively Predict Nutritional Content of Cheese
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.
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.
