Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

This explainer video highlights how spectroscopy is being integrated with artificial intelligence to improve detection accuracy of microplastics.

A recent study demonstrated that UV–visible (UV-vis) spectroscopy combined with machine learning (ML) can provide a fast, cost-effective, and automated method for detecting biological contamination in microalgae cultures.

Raman 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.

A new study investigates how colorants embedded in microplastics (MPs) interfere with Raman spectroscopy, one of the key tools used to identify microplastic particles. The research details how fluorescence from these additives complicates spectral analysis, underscoring challenges in environmental microplastic detection.

A new review article highlights how Explainable Artificial Intelligence (XAI) can enhance transparency, trust, and innovation in agricultural spectroscopy, paving the way for smarter and more sustainable food quality assessment.

Researchers 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.

This tutorial investigates the persistent issue of sample heterogeneity—chemical and physical—during spectroscopic analysis. Focus will be placed on understanding how spatial variation, surface texture, and particle interactions influence spectral features. Imaging spectroscopy, localized sampling strategies, and adaptive averaging algorithms will be reviewed as tools to manage this problem, as one of the remaining unsolved problems in spectroscopy.

This tutorial guides spectroscopy practitioners through the integration of Raman spectroscopy and machine learning (ML) techniques for detecting microplastics (MPs) in aquatic and environmental samples.

A recent study presented an AI-enhanced NIRS-XRF fusion spectroscopy method that significantly improves coal classification and quality prediction for coking enterprises.

A new review in Digital Discovery by Yongqiang Cheng of MIT and Oak Ridge National Laboratory highlights how AI-driven methods are changing how we study atomic vibrations.

ATR FT-IR shows polyethylene and polypropylene particles were common in facial scrubs and creams.

Researchers 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.

The findings suggest that bottling processes and PET containers are major contributors to beverage contamination, raising concerns about food safety and environmental pollution.

A 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.

Inter-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.

A 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.

Using a custom-built 785 nm Raman instrument, a recent study identified 14 pesticides and employed multivariate and machine learning techniques—particularly Random Forests (RF)—to automate classification. Readers will learn practical steps in spectral acquisition, spectral comparison across wavelengths, data preprocessing, and implementing machine learning models for real-world chemical monitoring (1).

This Icons of Spectroscopy Series article features Charles Kenneth Mann, a pioneer of quantitative Raman spectroscopy.

Researchers at Jiangnan University have developed a highly accurate method combining Raman spectroscopy with deep learning to monitor acid value in palm oil.

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.