Researchers have developed a small near-infrared (NIR) spectrometer dedicated to achieve painless, accurate glucose measurements.
A team of scientists from the SRM Institute of Science and Technology in India has made significant strides in developing a non-invasive method for blood glucose monitoring. Their pilot study, published in IRBM, explores the potential of near-infrared (NIR) optical techniques as an alternative to traditional invasive glucose testing methods.
A Non-Invasive Alternative to Blood Glucose Monitoring
Diabetes mellitus, a chronic disease affecting millions worldwide, requires frequent blood glucose monitoring to manage the condition effectively (1). By 2045, IDF projections show that 1 in 8 adults, approximately 783 million, will be living with diabetes, an increase of 46%.
Over 90% of people with diabetes have type 2 diabetes, which is driven by socio-economic, demographic, environmental, and genetic factors (2). Traditional methods, such as finger-prick tests and continuous glucose monitors (CGMs), rely on invasive procedures that can cause discomfort and carry risks such as skin irritation and infection. In response to these challenges, researchers M. Sameera Fathimal, Janardanan Subramonia Kumar, A. Jeya Prabha, Jothiraj Selvaraj, F. Fabiola Jemmie Shilparani, and S. P. Angeline Kirubha developed a novel optical device that uses dual NIR wavelengths to estimate glucose levels non-invasively (1).
How the NIR-Based System Works
The research team designed an optical system incorporating two specific NIR wavelengths, 940 nm and 1050 nm, housed within a 3D-printed enclosure. The selection of these wavelengths was based on their ability to penetrate the skin and interact with glucose molecules, leveraging the characteristic absorption of glucose in the NIR spectral region. The system works by measuring the intensity of diffuse reflected light from a fingertip, with changes in absorption linked to blood glucose concentration (1).
Promising Results for Accuracy and Reliability
In clinical testing, the NIR-based device demonstrated impressive accuracy. The study reported that 95.6% of the predicted glucose values fell within the A+B zones of the Parkes error grid, a widely recognized standard for assessing the clinical accuracy of glucose monitoring systems. Additionally, the predictions met the accuracy requirements outlined in the blood glucose monitoring surveillance study, with a mean absolute percentage error (MAPE) of just 5.99% (1).
The researcher’s reported that statistical analysis further validated the system’s effectiveness. The paper describes how they were able to find a strong inverse correlation between blood glucose levels and photodiode voltage outputs at both 940 nm and 1050 nm wavelengths. This correlation suggests that the optical system provides a reliable means of estimating blood glucose levels, with results comparable to traditional glucose measurement methods (1).
Advancements in Spectroscopic Analysis
The manuscript highlights the advantages of spectroscopic techniques in non-invasive glucose monitoring. The researchers emphasized that glucose exhibits multiple characteristic absorption features within the NIR region, particularly in the overtone and combination bands associated with C-H, O-H, and C=O bonds. By carefully selecting optimal wavelengths, the team minimized interference from other blood constituents, thereby enhancing the system’s accuracy.
Their findings align with previous studies that have explored NIR spectroscopy for glucose monitoring. For example, prior research demonstrated that a 940 nm light source effectively reduces optical attenuation by non-glucose elements in blood. Additionally, their use of a 1050 nm wavelength aligns with findings from in-vitro studies, which reported minimal measurement errors at this wavelength (1).
Future Implications and Next Steps
The researchers believe that their dual-wavelength NIR system could serve as a viable alternative to traditional blood glucose monitors, offering a painless, cost-effective solution for diabetes management. Unlike semi-invasive CGMs that require periodic sensor replacements, this optical method presents a more convenient and sustainable option for continuous glucose monitoring.
While the initial results are promising, further clinical validation with a larger sample size is necessary before further conclusions or commercialization. The researchers aim to refine their system by integrating machine learning (ML) algorithms to enhance prediction accuracy and developing wearable prototypes for real-time glucose tracking (1).
The study conducted by the SRM Institute of Science and Technology marks a step forward in the quest for non-invasive glucose monitoring. By leveraging NIR spectroscopy, the team has demonstrated a device that not only achieves high accuracy but also addresses the limitations of existing glucose monitoring technologies. With further research and development, this innovation could transform diabetes care, offering millions of patients a more comfortable and efficient way to monitor their blood sugar levels.
References
(1) Sameera, F. M.; Kumar, J. S.; Jeya, P. A.; Selvaraj, J.; Angeline, K. S. Potential of Near-Infrared Optical Techniques for Non-invasive Blood Glucose Measurement: A Pilot Study. IRBM 2025, 46 (1), 100870. DOI: 10.1016/j.irbm.2024.100870
(2) International Diabetes Federation Facts and Figures Home Page.https://idf.org/about-diabetes/diabetes-facts-figures/(accessed 2025-01-28).
Blood-Glucose Testing: AI and FT-IR Claim Improved Accuracy to 98.8%
February 3rd 2025A 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.
NIR Spectroscopy with AI Proves to be a Powerful Combination for Tea Classification
January 29th 2025A 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.