Chemometrics in Spectroscopy Comparison of Goodness of Fit Statistics for Linear Regression, Part I (PDF)

In 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.

Researchers 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.

A new study used advanced techniques, including µ-Raman spectroscopy and machine learning, to map and predict microplastic pollution on São Paulo’s urban beaches.

A 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.

This 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.

Mississippi State University researchers show that mid-infrared (MIR), a.k.a. infrared (IR), portable spectrometers, combined with calibration transfer techniques, can match lab instruments for soil property analysis.