Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

Scientists from China and Finland have developed an advanced method for detecting cardiovascular drugs in blood using surface-enhanced Raman spectroscopy (SERS) and artificial intelligence (AI). This innovative approach, which employs "molecular hooks" to selectively capture drug molecules, enables rapid and precise analysis, offering a potential advance for real-time clinical diagnostics.

Top articles published this week include a video interview that explores using label-free spectroscopic techniques for tumor classification, an interview discussing how near-infrared (NIR) spectroscopy can classify different types of horsetails, and a news article about detecting colorless microplastics (MPs) using NIR spectroscopy and machine learning (ML).

This new study highlights the potential of visible-near-infrared (Vis-NIR) spectroscopy for predicting phosphorus sorption parameters.

Researchers have developed a small near-infrared (NIR) spectrometer dedicated to achieve painless, accurate glucose measurements.

A research team is claiming significantly enhanced accuracy of non-invasive blood-glucose testing by upgrading Fourier transform infrared spectroscopy (FT-IR) with multiple-reflections, quantum cascade lasers, two-dimensional correlation spectroscopy, and machine learning. The study, published in Spectrochimica Acta Part A, reports achieving a record-breaking 98.8% accuracy, surpassing previous benchmarks for non-invasive glucose detection.

A team of researchers from Nankai University has developed an advanced method to classify tea types using near-infrared spectroscopy (NIRS) and artificial intelligence (AI). Their approach, involves a fine-tuned 1DResNet model, outperforms traditional methods, and offers an accurate, non-destructive, and efficient classification solution for the tea industry.

Researchers have explored the potential of combining near-infrared spectroscopy (NIRS) with machine learning (ML) to create a non-invasive, rapid diagnostic tool for liver fibrosis detection, a key factor in transplant surgery planning. These approaches could offer a more accurate and accessible alternative to traditional methods like biopsy.

Scientists demonstrate a self-supervised learning framework that dramatically improves near-infrared spectroscopy classification results, even with minimal labeled data.

A new study published in Food Control introduces an approach for assessing antioxidant levels in edible oils using artificial intelligence and spectroscopy, offering significant potential for improving food quality control.

A recent study combines hyperspectral imaging (HSI) technology with chemometrics to deliver improved quality control of black garlic.

A recent study developed an accurate, non-destructive geo-traceability method using NIR spectroscopy and machine learning to authenticate the geographic origins of Gastrodia elata Bl.

A recent study used surface-enhanced Raman spectroscopy (SERS) combined with chemometrics to assess polycyclic aromatic hydrocarbons (PAHs) in water.

A recent study examined how Raman spectroscopy, when combined with machine learning (ML), can detect and analyze fertilizer nutrients.

In this study, laser-induced breakdown spectroscopy (LIBS) was applied in conjunction with principal component analysis (PCA) to identify and classify flower species.

A recent review article explores the evolving landscape of pigment analysis in cultural heritage (CH).

Over the past two years Spectroscopy Magazine has increased our coverage of artificial intelligence (AI), deep learning (DL), and machine learning (ML) and the mathematical approaches relevant to the AI topic. In this article we summarize AI coverage and provide the reference links for a series of selected articles specifically examining these subjects. The resources highlighted in this overview article include those from the Analytically Speaking podcasts, the Chemometrics in Spectroscopy column, and various feature articles and news stories published in Spectroscopy. Here, we provide active links to each of the full articles or podcasts resident on the Spectroscopy website.

Researchers have developed a novel method combining near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy with advanced data fusion techniques to improve the accuracy of non-structural carbohydrate estimation in diverse tree tissues, advancing carbon cycle research.

A recent study presents a new technique that combines femtosecond double-pulse laser-induced breakdown spectroscopy (fs-DP-LIBS) with machine learning (ML) algorithms to significantly enhance tissue discrimination and signal quality, paving the way for more precise biomedical diagnostics.

Sirish Subash is the winner of the Young Scientist Award, presented by 3M and Discovery education. His work incorporates spectrophotometry, a nondestructive method that measures the light of various wavelengths that is reflected off fruits and vegetables.

Five invited speakers joined Joseph Smith, the 2024 Emerging Leader in Molecular Spectroscopy, on stage to speak about trends in hyperspectral imaging, FT-IR, surface enhanced Raman spectroscopy (SERS), and more during the conference in Raleigh.

A review by researchers from Curtin University comprehensively explores how chemometrics can revolutionize forensic science by offering objective and statistically validated methods to interpret evidence. The chemometrics approach seeks to enhance the accuracy and reliability of forensic analyses, mitigating human bias and improving courtroom confidence in forensic conclusions.

A recent study looks at how to improve the aging life of lithium-ion batteries.

A recent review article explored the Brazilian coffee industry and how spectroscopic- and chemometrics-based approaches are helping to ensure the authenticity and quality of Brazilian coffee.

Top articles published this week include a peer-reviewed article that discuss two multivariate calibration algorithms for the spectrophotometric analysis of a drug containing antazoline hydrochloride (AN) and naphazoline hydrochloride (NP), an article about chemometric calibrations, and a feature about the 2024 Emerging Leader in Molecular Spectroscopy awardee.

This study applied principal component regression (PCR) and partial least squares (PLS) algorithms for the spectrophotometric analysis of a drug containing antazoline hydrochloride (AN) and naphazoline hydrochloride (NP) without chemical separation. Both methods showed high accuracy and precision, with results closely matching those from a reference HPLC method, and were successfully validated for analyzing commercial pharmaceutical products.

This column is the continuation of our previous column that describes and explains some algorithms and data transforms beyond those most commonly used. We present and discuss algorithms that are rarely, if ever, seen or used in practice, despite that they have been proposed and described in the literature.

The advent of artificial intelligence (AI) and machine learning (ML) has propelled spectroscopic instrumentation to new heights.

Some of the most recent articles in data analytics, statistics, machine learning, and artificial intelligence are presented below.

A recent study in beverage analysis showcased the capability of a new electronic tongue (e-tongue) prototype in analyzing liquid samples such as coconut water.