September 9th 2025
Near-infrared (NIR) spectroscopy combined with aquaphotomics shows potential for a rapid, non-invasive approach to detect subtle biochemical changes in biofluids and agricultural products. By monitoring water molecular structures through water matrix coordinates (WAMACs) and visualizing water absorption spectrum patterns (WASPs) via aquagrams, researchers can identify disease biomarkers, food contaminants, and other analytes with high accuracy. This tutorial introduces the principles, practical workflow, and applications of NIR aquaphotomics for everyday laboratory use.
Smarter Spectroscopy With a New Machine Learning Approach to Estimate Prediction Uncertainty
August 27th 2025A new study demonstrates how a machine learning technique, quantile regression forest, can provide both accurate predictions and sample-specific uncertainty estimates from infrared spectroscopic data. The work was applied to soil and agricultural samples, highlighting its value for chemometric modeling.
Error Bars in Chemometrics: What Do They Really Mean?
August 25th 2025This tutorial contrasts classical analytical error propagation with modern Bayesian and resampling approaches, including bootstrapping and jackknifing. Uncertainty estimation in multivariate calibration remains an unsolved problem in spectroscopy, as traditional, Bayesian, and resampling approaches yield differing error bars for chemometric models like PLS and PCR, highlighting the need for deeper theoretical and practical solutions.
Advanced Spectroscopy Techniques Improve Microplastics Identification and Characterization
August 21st 2025Researchers from Brazil have developed an improved method combining infrared and Raman spectroscopic techniques to better identify and characterize microplastics. This integrated approach enhances accuracy in distinguishing various polymer types and provides refined spectral analysis crucial for environmental studies.
Raman Spectroscopy and Machine Learning Show Promise for PFAS Detection
August 21st 2025Raman spectroscopy, combined with computational modeling and machine learning, shows strong potential for distinguishing PFAS compounds, offering a promising new framework for environmental monitoring and contamination analysis.
New Technique Combines Raman Spectroscopy and AI to Accurately Detect Microplastics in Water
August 19th 2025Researchers have developed a novel approach to quantify microplastics in water environments by combining Raman spectroscopy with convolutional neural networks (CNN). This integrated method enhances the accuracy and speed of microplastic identification, offering a promising tool for environmental monitoring.
Machine Learning Advances Spectroscopic Imaging in Biomedical Research
August 14th 2025Researchers from Zhejiang University highlight how combining machine learning with spectroscopic imaging can transform biomedical research by enabling more precise, interpretable, and efficient analysis of complex molecular data.
Plastic in Sugar? Spectroscopy Reveals Microplastic Contamination in Beet Sugar
August 12th 2025A new study using infrared spectroscopy reveals that commercial beet sugar contains microplastic particles, raising concerns over food processing and packaging practices. Scientists identified various plastic types in sugar samples, including polyethylene and PET.
Universal Calibration: Can Models Travel Successfully Across Instruments?
August 11th 2025Inter-instrument variability is a major obstacle in multivariate spectroscopic analysis, affecting the reliability and portability of calibration models. This tutorial addresses the theoretical and practical challenges of model transfer across instruments. It covers spectral variability sources—such as wavelength shifts, resolution differences, and line shape variations—and presents key standardization techniques including direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). We discuss the underlying mathematics of these approaches using matrix notation and highlight limitations that must be considered for reliable universal calibration.
Scientists Develop Smartphone Test to Detect Pesticides and Antibiotics in Food
August 6th 2025A team of researchers from universities in China have developed a rapid, smartphone-integrated sensor system that uses uranium-based fluorescent probes to detect pesticides and antibiotics in food samples with exceptional speed and selectivity.
Scientists Use Water and Light to Uncover Honey Adulteration
July 30th 2025In a 2025 study, Indian researchers demonstrated that combining near-infrared (NIR) spectroscopy with aquaphotomics enables rapid, non-destructive detection of adulterants in honey by analyzing changes in water’s spectral behavior. Using chemometric models, they accurately identified and quantified six common adulterants, offering a powerful tool for food authenticity and quality control.
Scientists Use AI and Spectroscopy to Detect Fake Honey in Bangladesh
July 29th 2025Researchers in Bangladesh have developed a rapid, non-destructive method to detect honey adulteration using UV-Vis-NIR spectroscopy paired with machine learning. Their findings could protect consumers and support food quality enforcement.
Random Forest Algorithms Gain Ground in Biomedical Signal Analysis and Chemico-Biological Research
July 29th 2025A new review article highlights the growing use of random forest machine learning (ML) models in biomedical signal analysis, emphasizing their potential for detecting cell damage, assessing toxicity, and advancing diagnostic classification.
Near-Infrared Spectroscopy for Honey Authentication: A Practical Mini-Tutorial for Food Quality Labs
July 28th 2025This tutorial introduces how NIR spectroscopy works for honey analysis, explores practical workflows, discusses real-world applications, and outlines best practices for implementing this technique in food labs.