Infrared and Near-Infrared Spectroscopy: Recent Advances and Environmental Applications (2022–2026)

Infrared (IR) and near-infrared (NIR) spectroscopy continue to advance through developments in miniaturized instrumentation, hyperspectral imaging, chemometrics, machine learning, and artificial intelligence. This review summarizes significant innovations reported between 2022 and 2026 in Spectroscopy magazine and examines their impact on analytical and environmental applications. Major trends include portable spectrometers, AI-assisted spectral interpretation, hyperspectral imaging, and multimodal data fusion. Environmental applications have expanded rapidly, particularly in microplastic detection, biosolids monitoring, soil characterization, and contaminant assessment. Together, these advances are strengthening the role of vibrational spectroscopy as a rapid, non-destructive, and increasingly intelligent tool for environmental monitoring and sustainability.

Infrared (IR) and near-infrared (NIR) spectroscopy continue to be among the most widely used analytical techniques in modern science and industry. Their popularity stems from their ability to provide rapid, non-destructive, and chemically specific information about molecular composition without extensive sample preparation. During the past several decades, advances in detector technology, miniaturization, chemometrics, machine learning, and hyperspectral imaging have dramatically expanded the scope of IR and NIR spectroscopy. Recent developments reported in Spectroscopy and Spectroscopy Online between 2022 and 2026 demonstrate how these techniques are evolving from laboratory-based analytical methods into portable, field-deployable platforms capable of supporting environmental monitoring, process control, materials characterization, and contaminant detection.^1-8

This review summarizes recent developments in infrared and near-infrared spectroscopy and highlights emerging environmental applications that are reshaping the field.

Part I. General Developments in Infrared and Near-Infrared Spectroscopy Continued Instrument Miniaturization

One of the most important trends in recent years has been the continued miniaturization of NIR instrumentation. Historically, NIR spectrometers required large monochromators, moving optical components, and cooled detectors. Advances in microelectromechanical systems (MEMS), integrated photonics, and semiconductor detector technologies have enabled the development of compact handheld instruments that achieve analytical performance previously available only in laboratory systems.¹

Recent reports describe fully integrated sensor-chip architectures capable of operating in spectral regions extending from approximately 850 to 1700 nm. Such instruments offer improved portability while maintaining sufficient spectral resolution for many qualitative and quantitative applications. Miniaturized systems are increasingly being used for agricultural measurements, pharmaceutical analysis, food authentication, material identification, and environmental screening.¹

Growth of Hyperspectral Imaging

Hyperspectral imaging (HSI) represents another major area of growth. Unlike conventional spectroscopy, which measures a single spectrum from a sample, hyperspectral imaging simultaneously collects spectral and spatial information. Each pixel in a hyperspectral image contains a complete spectrum, creating a three-dimensional data cube composed of two spatial dimensions and one spectral dimension.

NIR hyperspectral imaging has become particularly valuable for classification and mapping applications because it allows the visualization of chemical distributions across heterogeneous samples. Recent studies reported in Spectroscopy demonstrate the increasing use of NIR-HSI for material identification, contaminant detection, and environmental characterization.² The combination of hyperspectral imaging with machine-learning algorithms has significantly improved classification performance. Advanced methods such as support vector machines (SVM), convolutional neural networks (CNN), random forests, and partial least squares-discriminant analysis (PLS-DA) are increasingly used to interpret the large datasets generated by hyperspectral instruments.²

Advances in Chemometrics and Artificial Intelligence

Modern infrared and near-infrared spectroscopy are inseparable from chemometrics. The increasing complexity of spectroscopic datasets has driven the adoption of sophisticated statistical and machine-learning approaches capable of extracting useful information from large spectral databases.

Recent publications emphasize the growing importance of artificial intelligence (AI) in spectral analysis.³ Machine-learning algorithms are now routinely applied to spectral preprocessing, calibration development, classification, anomaly detection, and quantitative prediction. AI-assisted workflows reduce operator bias and often improve analytical accuracy. One particularly important trend is the use of spectral data fusion. Multiple spectroscopic techniques, such as NIR, Raman, and FT-IR spectroscopy, can be combined to exploit complementary chemical information. Data fusion often yields predictive models that outperform models based on a single technique.⁴

Expanding Applications of Vibrational Spectroscopy

The applications of IR and NIR spectroscopy continue to expand across scientific disciplines. Vibrational spectroscopy is now routinely applied in pharmaceuticals, biotechnology, medical diagnostics, food analysis, agriculture, forensics, process analytical technology (PAT), and materials science.

Recent reports have highlighted the ability of portable NIR instruments to classify wood species, authenticate materials, monitor process streams, and support field-based analytical measurements.⁵ Such applications illustrate the increasing movement of spectroscopy from centralized laboratories into operational environments.

The continued integration of spectroscopy, chemometrics, machine learning, and imaging technologies suggests that future systems will become increasingly automated, intelligent, and accessible to non-specialist users.

Part II. Infrared and Near-Infrared Spectroscopy in Environmental Applications

Environmental Spectroscopy as a Growing Discipline

Environmental analysis has emerged as one of the fastest-growing application areas for infrared and near-infrared spectroscopy. Environmental monitoring requires analytical methods capable of rapidly identifying contaminants in complex matrices such as water, soil, sediment, biosolids, and atmospheric particulates. Traditional laboratory analyses are often expensive, labor-intensive, and time-consuming. Vibrational spectroscopy offers an attractive alternative because it provides rapid measurements with minimal sample preparation.⁶ A comprehensive review published in 2024 summarized the latest developments in environmental spectroscopy and highlighted the growing role of vibrational spectroscopic methods in contaminant monitoring, environmental classification, and sustainability initiatives.⁶

Microplastics and Nanoplastics

The dominant environmental application reported during the 2022–2026 period involved the detection and characterization of microplastics and nanoplastics.

Plastic contamination has become a global environmental concern because plastic particles are now found in drinking water, oceans, rivers, sediments, soils, biosolids, and even atmospheric environments. Identifying and quantifying these particles requires analytical methods capable of distinguishing among polymer types and particle sizes.

A major review published in Spectroscopy examined the use of spectroscopic techniques for the classification and quantification of microplastics and nanoplastics.⁷ The review evaluated FT-IR microscopy, ATR-FTIR, NIR spectroscopy, Raman spectroscopy, optical photothermal infrared spectroscopy (O-PTIR), atomic force microscopy infrared spectroscopy (AFM-IR), quantum cascade laser infrared (QCL-IR) systems, fluorescence methods, and complementary analytical technologies.

Among these approaches, FT-IR spectroscopy remains the most widely used method for polymer identification because characteristic vibrational bands provide highly specific chemical fingerprints. FT-IR microscopy has become particularly important because it allows both chemical identification and spatial localization of plastic particles.⁷

Near-infrared spectroscopy has emerged as an attractive complementary technique. Although NIR spectra are generally less chemically specific than mid-infrared spectra, NIR measurements are rapid, require little sample preparation, and are easily implemented in portable instruments. As a result, NIR spectroscopy is increasingly used for high-throughput screening applications.⁷

Biosolids and Wastewater Applications

An important environmental application of vibrational spectroscopy involves the analysis of biosolids generated during wastewater treatment. Biosolids are frequently applied to agricultural land as soil amendments, creating potential pathways for environmental contaminant transport. Recent studies using ATR-FTIR and NIR spectroscopy demonstrated the presence of polyethylene, polypropylene, and other common polymers in biosolid samples.⁸ Spectral measurements enabled researchers to identify the likely sources of microplastic contamination and assess the environmental implications of land application practices.

The ability to rapidly screen biosolids using vibrational spectroscopy provides regulators and environmental scientists with valuable tools for monitoring contaminant pathways and evaluating risk.

Soil Characterization and Contamination Monitoring

Another important application area involves soil analysis. Near-infrared spectroscopy has long been used to estimate soil organic matter, moisture content, clay content, and nutrient levels. Recent studies have expanded these capabilities to include monitoring of soil contamination and ecological indicators.⁹

Researchers have demonstrated that NIR spectroscopy can be used to assess both soil and vegetation characteristics, supporting ecosystem monitoring and conservation efforts. Because NIR measurements can be acquired rapidly and non-destructively, they are well suited for large-scale environmental surveys.⁹

Recent work has also explored the use of visible-near infrared (vis-NIR) spectroscopy for monitoring potentially toxic elements in soils and sediments.¹⁰ Although metals themselves are generally not directly detected by NIR spectroscopy, indirect relationships with soil mineralogy and organic matter often permit predictive calibration models. Such approaches may ultimately provide rapid screening tools for contaminated-site assessment.

Machine Learning and Environmental Spectroscopy

Perhaps the most significant development in environmental spectroscopy has been the widespread adoption of machine learning.

Microplastic identification, hyperspectral image analysis, soil characterization, and contaminant detection increasingly rely on advanced algorithms capable of processing large spectral datasets. Machine-learning methods have improved classification accuracy, reduced analysis time, and enabled automated interpretation of complex environmental measurements.^3,7

The integration of AI with vibrational spectroscopy represents a major technological shift. Rather than relying exclusively on expert interpretation, future environmental monitoring systems will increasingly employ automated decision-making tools capable of operating in near real time.

Future Directions

Several themes consistently emerge from the environmental spectroscopy literature published between 2022 and 2026:

• Increased deployment of portable and handheld spectrometers.
• Expansion of hyperspectral imaging technologies.
• Growth of machine-learning-assisted spectral analysis.
• Continued emphasis on microplastic and nanoplastics monitoring.
• Increased use of multimodal spectroscopy and data fusion.
• Development of automated environmental monitoring platforms.

Collectively, these trends indicate that infrared and near-infrared spectroscopy will continue to play an increasingly important role in environmental science. As instrumentation becomes smaller, faster, and more intelligent, vibrational spectroscopy is likely to become a cornerstone technology for environmental monitoring, contaminant detection, and sustainability assessment.

References

1. Ou, F.; van Klinken, A.; Hakkel, K. D.; et al. Spectral Sensing Using a Handheld NIR Module Based on a Fully Integrated Sensor Chip. Spectroscopy 2022, 37 (Suppl. 11). DOI: https://doi.org/10.56530/spectroscopy.yd5989g6.

2. Wetzel, W. Detection of Colorless Microplastics in the Environment Using NIR Spectroscopy and Machine Learning. Spectroscopy Online 2025, February 5. https://www.spectroscopyonline.com/view/detection-of-colorless-microplastics-in-the-environment-using-nir-spectroscopy-and-machine-learning (accessed 2026-06-18).

3. Workman, J. Jr. AI Developments That Changed Vibrational Spectroscopy in 2025. Spectroscopy Online 2025, December 27. https://doi.org/10.56530/spectroscopy.no4686u3.

4. Workman, J. Jr. The Top 10 Most Influential Applications of Vibrational Spectroscopy in Environmental Analysis (2024–2026). Spectroscopy Online 2026, March 3. https://www.spectroscopyonline.com/view/the-top-10-most-influential-applications-of-vibrational-spectroscopy-in-environmental-analysis-2024-2026- (accessed 2026-06-18).

5. Pan, X.; Qiu, J.; Yang, Z. Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy. Spectroscopy 2022, 37 (6), 16–23. DOI: https://doi.org/10.56530/spectroscopy.zg7089n4.

6. Workman, J. Jr. A Brief Review of the Latest Spectroscopic Research in Environmental Analysis. Spectroscopy Online 2024 June 18. DOI: https://doi.org/10.56530/spectroscopy.wd2586t9.

7. Workman, J. Jr. A Review of Spectroscopic Techniques Used for the Quantification and Classification of Microplastics and Nanoplastics in the Environment. Spectroscopy Online 2024, November 19. DOI: https://doi.org/10.56530/spectroscopy.ac7567r4.

8. Workman, J. Jr. ATR FT-IR and NIR Spectroscopy Reveal the Sources of Microplastics in Biosolids. Spectroscopy 2024, August 24. https://www.spectroscopyonline.com/view/atr-ft-ir-and-nir-spectroscopy-reveal-the-sources-of-microplastics-in-biosolids (accessed 2026-06-18).

9. Wetzel, W. Examining Soil and Leaf Properties Using NIR Spectroscopy. Spectroscopy Online 2025, December 2. https://www.spectroscopyonline.com/view/examining-soil-and-leaf-properties-using-nir-spectroscopy (accessed 2026-06-18).

10. Wetzel, W. What Is the Path Forward for Soil Contamination Monitoring? Spectroscopy Online 2026, May 14. https://www.spectroscopyonline.com/view/what-is-the-path-forward-for-soil-contamination-monitoring (accessed 2026-06-18).