Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy

A comparative study and trace- level detection of volatile organic biomarkers using UV-IR-THz sources based high -Q helmholtz photoacoustic sensor

This paper reports the trace-level detection of volatile organic compounds (VOCs) like methanol, ethanol, and isopropanol, which are biomarkers for various diseases like diabetes, breast cancer, lung cancer, chronic pulmonary diseases, squamous cancer, cystic fibrosis, chronic liver diseases, chronic kidney diseases and so on. Here, the photoacoustic spectroscopy technique was used for the trace-level detection of these biomarkers using an indigenously designed tunable frequency (1.4 to 4 kHz range) Helmholtz photoacoustic (PA) cell. The study was carried out with UV (266 nm), Mid IR (5.4-7.3 µm) and THz (0.11 THz) range sources to explore and compare the PA signal generated by mentioned samples for different electronic vibrational and rotational level excitations. We achieved a low detection limit (LoD) of the order of 39.3 ppbV, 29.7 ppbV, and 11.6 ppbV for methanol, ethanol, and isopropanol, respectively using this non-invasive cost-effective, and fast technique. In addition, THz-based PA signal for these samples is reported for the first time.

Exploring the Molecular Structure and Treatment Dynamics of Cellulose Fibres with Photoacoustic and Reversed Double-Beam Spectroscopy

In this study, we explored the structural and chemical modifications of cellulose fibres subjected to chemical and mechanical treatments through an innovative analytical approach. We employed photoacoustic spectroscopy (PAS) and reversed double-beam photoacoustic spectroscopy (RDB-PAS) to examine the morphological changes and the chemical integrity of the treated fibres. The methodology provided enhanced sensitivity and specificity in detecting subtle alterations in the treated cellulose structure. Additionally, we applied Coifman wavelet transformation to the PAS signals, which facilitated a refined analysis of the spectral features indicative of chemical and mechanical modifications at a molecular level. This advanced signal processing technique allowed for a detailed decomposition of the PAS signals, revealing hidden characteristics that are typically overshadowed in raw data analyses. Further, we utilised the concept of energy trap distribution to interpret the wavelet-transformed data, providing insights into the distribution and density of energy states within the fibres. Our results indicated significant differences in the energy trap spectra between untreated and treated fibres, reflecting the impact of chemical and mechanical treatments on the fibre's physical properties. The combination of these sophisticated analytical techniques elucidated the complex interplay between mechanical and chemical treatments and their effects on the structural integrity and chemical composition of cellulose fibres.

Non-invasive glucose prediction and classification using NIR technology with machine learning

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R 2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 ( m g / d l ) 2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

Review of Noninvasive Continuous Glucose Monitoring in Diabetics

For diabetics, taking regular blood glucose measurements is crucial. However, traditional blood glucose monitoring methods are invasive and unfriendly to diabetics. Recent studies have proposed a biofluid-based glucose sensing technique that creatively combines wearable devices with noninvasive glucose monitoring technology to enhance diabetes management. This is a revolutionary advance in the diagnosis and management of diabetes, reflects the thoughtful modernization of medicine, and promotes the development of digital medicine. This paper reviews the research progress of noninvasive continuous blood glucose monitoring (CGM), with a focus on the biological liquids that replace blood in monitoring systems, the technical principles of continuous noninvasive glucose detection, and the output and calibration of sensor signals. In addition, the existing limits of noninvasive CGM systems and prospects for the future are discussed. This work serves as a resource for further promoting the development of noninvasive CGM systems.

Photoacoustic based evaluation of viscoelastic properties of Gram-negative and Gram-positive bacterial colonies

Mechanical properties of bacterial colonies are crucial considering both addressing their pathogenic effects and exploring their potential applications. Viscoelasticity is a key mechanical property with major impacts on the cell shapes and functions, which reflects the information about the cell envelope constituents. Hereby, we have proposed the application of photoacoustic viscoelasticity (PAVE) for studying the rheological properties of bacterial colonies. In this regard, we employed an intensity-modulated laser beam as the excitation source followed by the phase delay measurement between the generated PA signal and the reference for the characterization of colonies of two different types of Gram-positive and Gram-negative bacteria. The results of our study show that the colony of Staphylococcus aureus as Gram-positive bacteria has a significantly higher viscoelasticity ratio compared to that value for Acinetobacter baumannii as Gram-negative bacteria (77% difference). This may be due to the differing cell envelope structure between the two species, but we cannot rule out effects of biofilm formation in the colonies. Furthermore, a lumped model has been provided for the mechanical properties of bacterial colonies.

Sensor using a photo-acoustic absorption cell with two perpendicular acoustic resonators to analyze multiple molecules

An ultra-high sensitivity multi-molecule sensor based on a photo-acoustic cell with two perpendicular acoustic resonators and a common microphone has been reported. In this work, a 4.5 µm distributed-feedback quantum cascade laser and a 1.5 µm external cavity diode laser (EC-DL) were used as optical excitation sources. Considering the spectral ranges of the lasers used, it is possible to analyze eight molecules (Q C L:N 2 O and C O 2, EC-DL: H 2 O, H 2 S, N H 3, CO, C H 4, and C 2 H 2). The N 2 O molecule was used to evaluate the performance of the photo-acoustic spectroscopy (PAS)-based sensor. A sensitivity of 0.073 V/ppm and a linearity of 0.99 were found by analyzing the PAS signal as a function of N 2 O concentration at 2237.656c m -1. The long-term performance of the sensor was determined by performing an Allan deviation analysis. A minimum detection limit of 9.8 ppb for 90 s integration time was achieved. The simultaneous multi-trace gas detection capability was verified by measurement of N 2 O, C O 2, and H 2 O. Depending on the coarse/fine-tuning ranges of the lasers used, the number of molecules analyzed can be further increased. Such a sensor could provide simultaneous diagnosis of many diseases through an analysis of breath air and simultaneous monitoring of the most important greenhouse gases.

A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management

The current standard of diabetes management depends upon the invasive blood pricking techniques. In recent times, the availability of minimally invasive continuous glucose monitoring devices have made some improvements in the life of diabetic patients however it has its own limitations which include painful insertion, excessive cost, discomfort and an active risk due to the presence of a foreign body under the skin. Due to all these factors, the non-invasive glucose monitoring has remain a subject of research for the last two decades and multiple techniques of non-invasive glucose monitoring have been proposed. These proposed techniques have the potential to be evolved into a wearable device for non-invasive diabetes management. This paper reviews research advances and major challenges of such techniques or methods in recent years and broadly classifies them into four types based on their detection principles. These four methods are: optical spectroscopy, photoacoustic spectroscopy, electromagnetic sensing and nanomaterial based sensing. The paper primarily focuses on the evolution of non-invasive technology from bench-top equipment to smart wearable devices for personalized non-invasive continuous glucose monitoring in these four methods. With the rapid evolve of wearable technology, all these four methods of non-invasive blood glucose monitoring independently or in combination of two or more have the potential to become a reality in the near future for efficient, affordable, accurate and pain-free diabetes management.