Near Infrared (NIR) Spectroscopy

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.

Despite its widespread adoption in food quality analysis, near-infrared (NIR) spectroscopy lags behind in regulatory recognition. A study led by researchers from Italy and Spain highlights the disparity between its scientific applications and official methods, urging standardized regulations to fully leverage NIR's sustainability benefits.

Researchers in China have developed a novel workflow for near-infrared reflectance spectroscopy (NIRS or NIR) that enhances the detection of low-level petroleum hydrocarbon pollution in soils, revealing new diagnostic features and significantly improving sensitivity for environmental monitoring.

Microplastics (MPs) and nanoplastics (NPs) are emerging contaminants requiring robust analytical techniques for identification and quantification in diverse environmental and biological matrices. This review highlights various spectroscopy methods, such as Raman, FT-IR, NIR, ICP-MS, Fluorescence, X-ray, and NMR detailing their methodologies, sample handling, and applications for characterizing MPs and NPs.

A recent study out of Russia introduced a new method for identifying plant-based oils and adulterated dairy products.

Researchers from the University of Cordoba have validated a novel spectroscopy technique to help distinguish between extra virgin and virgin olive oils. This approach could support existing panel-based tests, which are often slow, costly, and subjective, by providing a faster, non-destructive screening option.

A new review highlights the promising role of non-destructive spectroscopy techniques in enhancing olive and extra virgin olive oil (EVOO) quality assessments. By combining spectroscopy with imaging, researchers uncover innovative ways to determine product authenticity and improve quality control in olive oil production.

A recent study published in Frontiers in Aging Neuroscience by Wenyu Jiang and colleagues in China found that patients with mild cognitive impairment (MCI) exhibit abnormal functional connectivity in the right prefrontal cortex as revealed by fNIRS, highlighting potential cognitive implications and the protective role of education.

A team from Auburn University has developed an innovative ultrabroadband near-infrared (NIR) transient absorption (TA) spectrometer capable of detecting across a wide spectral range of 900–2350 nm in a single experiment. This advancement improves the study of ultrafast processes in low-bandgap materials and opens doors to new insights in photochemistry and charge dynamics.

A recent study showcases the potential of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) in detecting raw material defects in hazelnuts caused by improper storage conditions. FT-NIR spectroscopy proved especially effective, while SORS offered complementary insights in certain scenarios. These spectroscopic methods could modernize the speed and accuracy of hazelnut inspections in the food industry.

A new review highlights the use of ultraviolet–visible–near infrared (UV–vis–NIR) absorption spectroscopy in studying catalytic processes. The research discusses how this technique uncovers reaction mechanisms, structural properties, and reaction kinetics, particularly in heterogeneous and photocatalysis, and explores its potential for broader applications.

A recent study looked at how near-infrared (NIR) spectroscopy can evaluate the chemical composition of pet food and safeguard pet health.

Hyperspectral imaging (HSI) is revolutionizing fields such as agriculture, food safety, and medical analysis by providing high-resolution spectral data. This emerging technology is proving invaluable in diverse applications, including plant stress detection, weed discrimination, and flood management. A new review explores HSI’s fundamental principles, applications, and future research directions.

Recent James Webb Space Telescope (JWST) observations have revealed intricate details about the surface compositions of dwarf planets Sedna, Gonggong, and Quaoar. Using spectroscopy, researchers have detected a rich variety of ices and organic compounds, shedding light on the effects of size and orbit on these distant solar system bodies.

A team of researchers has developed a new machine learning (ML) method to classify asteroid spectra by analyzing meteorite spectroscopic data. Using logistic regression, the model accurately grouped meteorites into eight categories, helping to better understand the distribution of asteroid compositions in the asteroid belt. The study, published in Icarus, opens new avenues for predicting asteroid composition using spectroscopy.

NASA’s OSIRIS-REx spacecraft embarks on a new mission—OSIRIS-APEX—to study asteroid Apophis after its anticipated close flyby of Earth on Friday, April 13, 2029. Utilizing advanced spectroscopy and other instruments, the mission will provide unprecedented insights into how planetary encounters reshape small bodies in our solar system.

A recent study examined how near-infrared (NIR) spectroscopy can estimated the water content in recycled construction materials.

A recent study explores the effectiveness of near-infrared (NIR) and ultraviolet-visible (UV-vis) spectroscopy in determining the time since deposition (TSD) of bloodstains, a critical aspect of forensic investigations. By comparing these two methods, researchers aim to improve the accuracy and reliability of bloodstain dating, with potential implications for real-world forensic applications.

Functional near-infrared spectroscopy (fNIRS) has emerged as a vital tool in brain imaging over the past decade, offering noninvasive, real-time insights into brain function. A recent review study presents a comprehensive bibliometric analysis, revealing the global trends, research hotspots, and future potential of fNIRS in clinical applications, particularly in neurology, psychiatry, pediatric medicine, and sports science.

A recent study published in Neuroscience & Biobehavioral Reviews examined functional near-infrared spectroscopy (fNIRS) hyperscanning studies to learn more about how the interpersonal neural synchronization (INS) affects close relationships.

A recent study explores the effectiveness of Near-infrared (NIR) and ultraviolet-visible (UV-vis) spectroscopy in determining the time since deposition (TSD) of bloodstains, a critical aspect of forensic investigations. By comparing these two methods, researchers aim to improve the accuracy and reliability of bloodstain dating, with potential implications for real-world forensic applications.

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.

Spectroscopic 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.

A 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.

This study aimed to establish a fast, accurate method for quality evaluation of herbal medicine using NIR and chemometrics with ultraviolet-visible spectrophotometry (UV-vis) as a standard method to determine the total flavonoids content.

Researchers 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.

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.

Researchers from the University of Minnesota and Nanjing Agricultural University have highlighted the potential of deep learning to significantly improve the accuracy and robustness of fruit quality assessments using visible-near-infrared spectroscopy.

In the second part of this article, we dive into the methodology conducted by researchers from Yunnan Agricultural University in China that successfully used near-infrared (NIR) to analyze the protein content in chickens.