Best of the Week: AI, Rapid Food Analysis, Agriculture Analysis, and Soil Property Prediction
Top articles published this week include a review article on the latest research in agriculture analysis, a peer-reviewed article on near-infrared (NIR) spectroscopy, and an interview about using fluorescence spectroscopy in cheese ripening.
A Review of the Latest Spectroscopic Research in Agriculture Analysis
September 4th 2024Spectroscopic analytical techniques are crucial for the analysis of agricultural products. This review emphasizes the latest advancements in several key spectroscopic methods, including atomic, vibrational, molecular, electronic, and X-ray techniques. The applications of these analytical methods in detecting important quality parameters, adulteration, insects and rodent infestation, ripening, and other essential applications are discussed.
AI-Powered Spectroscopy Faces Hurdles in Rapid Food Analysis
September 4th 2024A recent study reveals on the challenges and limitations of AI-driven spectroscopy methods for rapid food analysis. Despite the promise of these technologies, issues like small sample sizes, misuse of advanced modeling techniques, and validation problems hinder their effectiveness. The authors suggest guidelines for improving accuracy and reliability in both research and industrial settings.
Examining the Role of ATR-FT-IR Spectroscopy and Machine Learning in Wood Forensics, Part 2
In part two of our exploration of wood forensics, a deep dive of a recent study from Panjab University explains why attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy improves on traditional methods in this space.
Examining the Role of ATR-FT-IR Spectroscopy and Machine Learning in Wood Forensics, Part 1
Wood forensics is an important field that helps authenticate wood and addresses the challenges that illegal logging brings. In this multipart article, we explore the wood forensics industry, and how spectroscopic techniques are contributing to its advancement.
Non-Linear Memory-Based Learning Advances Soil Property Prediction Using vis-NIR Spectral Data
September 3rd 2024Researchers from Zhejiang University have developed a new non-linear memory-based learning (N-MBL) model that enhances the prediction accuracy of soil properties using visible near-infrared (vis-NIR) spectroscopy. By comparing N-MBL with traditional machine learning and local modeling methods, the study reveals its superior performance, particularly in predicting soil organic matter and total nitrogen.
Examining the Cheese Ripening Process with Mid-Infrared and Synchronous Fluorescence Spectroscopy
A joint French-Canadian study examined the ripening process of commercially popular Comté and cheddar cheeses, which are widely consumed in those countries, utilizing mid-infrared (mid-IR) and synchronous fluorescence spectroscopy (SFS) in their analysis.
Measuring Muscle Oxidation in Athletes with Near-Infrared Spectroscopy
A recent article authored by scientists from the Institute of Sport and Preventive Medicine, part of the University of Saarland (Saarbrücken, Germany), discusses their investigation of the absolute and relative test-retest reliability of the Moxy Monitor, as well as their investigations into side differences of oxygen saturation at the vastus lateralis muscle of both legs in male cyclists.
Best of the Week: SMASH Conference, Forensics, Paprika and Chicken, Protein Purification
Top articles published this week include an article on the upcoming SMASH 2024 Conference, a report on handheld near-infrared (NIR) spectrophotometers, and an inside look at the paprika and poultry industries.