Webinar Date/Time: Tue, Apr 4, 2023 1:00 PM EDT
Experts share the best practices for safe, worry-free, and fast food sample preparation when implanting Closer to Zero.
Register Free: https://www.spectroscopyonline.com/spec_w/closer-to-zero
Event Overview:
Although microwave digestion reactors have made sample preparation of food samples faster and simpler than traditional methods, there are still significant challenges with preparing food samples for ICP-OES, ICP-MS, or AAS analysis.
Due to the typically low levels of trace metal contamination found in food samples, relatively high sample masses need to be digested in order to even measure the metal content. Additionally, the allowable levels of trace metals are in the process of being further reduced, as detailed in the FDA's 'Closer to zero' initiative. For this reason, the mass of foods samples required for testing will likely increase, thus requiring closer attention to sample reactivities during digestion, and a stronger focus on microwave operator safety.
This webinar will highlight how modern microwave digestion reactors enable fast, safe and accurate digestion of larger samples while simultaneously enabling the sensitivities needed to detect even lower levels of contamination. Best practices for safe, worry-free and fast food sample preparation will round off this practical and actionable webinar.
Key Learning Objectives:
Speakers:
Dr. Christian Trampitsch
Product Competence Expert,
Analytical and Synthetic Chemistry
Anton Paar
Dr. Christian Trampitsch obtained his degree in chemistry at the Graz University of Technology. He is currently the product competence expert in analytical and synthetic chemistry with Anton Paar GmbH, where he has worked for more than 15 years in the field of sample preparation. Dr. Trampitsch is involved in application development as well as R&D projects and has gained wide-spread experience in this topic.
Register Free: https://www.spectroscopyonline.com/spec_w/closer-to-zero
Hyperspectral Imaging for Walnut Quality Assessment and Shelf-Life Classification
June 12th 2025Researchers 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.
Researchers Use Machine Learning and Hyperspectral Imaging to Pinpoint Best Apple Bagging Techniques
June 12th 2025A 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.
Machine Learning and NMR Unite to Authenticate Wine with Near-Perfect Accuracy
June 11th 2025In 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
MIR Spectroscopy Validates Origin of Premium Brazilian Cachaças
June 11th 2025A 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.