Exhaled air analysis using wideband wave number tuning range infrared laser photoacoustic spectroscopy

A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases

Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3-12.8 μm (780-1890 cm-1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.

A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor - Characterization and Multi-Component Spectra Recording in Synthetic Breath

Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO₂. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far.

Types of spectroscopy and microscopy techniques for cancer diagnosis: a review

Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis.

Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction

Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The 'LaserBreeze' laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C₅H₁₂, N₂O, NO₂, C₂H₄, CO, CO₂) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.

A review on human body fluids for the diagnosis of viral infections: scope for rapid detection of COVID-19

Introduction: The unprecedented outbreaks of corona virus disease of 2019 (COVID-19) have highlighted the necessity of readily available, reliable, precise, and faster techniques for its detection. Nasopharyngeal swab has been the gold standard for the diagnosis of COVID-19. However, it is not an ideal screening procedure for massive screening as it implicates the patient's stay in the hospital or at home until diagnosis, thus causing crowding of the specimen at the diagnostic centers. Present study deal with the exploration of potential application of different body fluids using certain highly objective techniques (Optical and e-Nose) for faster detection of molecular markers thereby diagnosing viral infections.Areas covered: This report presents an evaluation of different body fluids, and their advantages for the rapid detection of COVID-19, coupled with highly sensitive optical techniques for the detection of molecular biomarkers.Expert opinion: Tears, saliva, and breath samples can provide valuable information about viral infections. Our brief review strongly recommends the application of saliva/tears and exhaled breath as clinical samples using technics such as high-performance liquid chromatography-laser-induced fluorescence, photoacoustic spectroscopy, and e-Nose, respectively, for the fast diagnosis of viral infections.

Biomedical optics applications of advanced lasers and nonlinear optics

SIGNIFICANCE

2019 SPIE Photonics West conference hosted over 5000 presentations. Some important presentations in the Industrial Laser, Laser Source and Application (LASE) and Optoelectronics, Photonic Materials and Devices (OPTO) sections of the SPIE Photonics West conference have a risk of being overlooked by the biomedical community despite their implications for the field of biophotonics. We review some recent advances in the area of development coherent radiation sources in the infrared (IR), ultraviolet (UV), and terahertz (THz) regimes.

AIM

Recent advances in coherent radiation sources in the IR, deep UV, and THz regimes were outlined, and the importance of each presentation to one or more promising biomedical applications was assessed.

APPROACH

Presentations and proceedings from the LASE and OPTO sections were reviewed for inclusion. Emphasis was placed on talks from the Nonlinear Frequency Generation and Conversion: Materials and Devices XVIII conference, and the Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XII conference. Conference sections that directly focused on biomedical applications were excluded.

RESULTS

Enhanced IR supercontinuum generation with compact supercontinuum sources may allow for real-time biomarker detection and create new opportunities for imaging tissues using the third biological window (1600 to 1850 nm). Efficient methods to generate deep UV (200 to 260 nm) radiation allow for the study of biologically important molecules through techniques such as resonance Raman spectroscopy while avoiding fluorescence overlap. Likewise, novel and improved THz generation methods seek to bridge the "THz gap" that has previously limited biomedical applications.

CONCLUSIONS

Advances in coherent radiation sources in the IR, UV, and THz regimes have created new opportunities for biomedical optics research.

Dual-optimized adaptive Kalman filtering algorithm based on BP neural network and variance compensation for laser absorption spectroscopy

A dual-optimized adaptive Kalman filtering (DO-AKF) algorithm based on back propagation (BP) neural network and variance compensation was developed for high-sensitivity trace gas detection in laser spectroscopy. The BP neural network was used to optimize the Kalman filter (KF) parameters. Variance compensation was introduced to track the state of the system and to eliminate the variations in the parameters of dynamic systems. The proposed DO-AKF algorithm showed the best performance compared with the traditional multi-signal average, extended KF, unscented KF, KF optimized by BP neural network (BP-KF) and KF optimized by variance compensation (VC-KF). The optimized DO-AKF algorithm was applied to a QCL-based gas sensor system for an exhaled CO analysis. The experimental results revealed a sensitivity enhancement factor of 23. The proposed algorithm can be widely used in the fields of environmental pollutant monitoring, industrial process control, and breath gas diagnosis.

Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods

Pathogenesis of many diseases is associated with changes in the collagen spatial structure. Traditionally, the 3D structure of collagen in biological tissues is analyzed using histochemistry, immunohistochemistry, magnetic resonance imaging, and X-radiography. At present, multiphoton microscopy (MPM) is commonly used to study the structure of biological tissues. MPM has a high spatial resolution comparable to histological analysis and can be used for direct visualization of collagen spatial structure. Because of a large volume of data accumulated due to the high spatial resolution of MPM, special analytical methods should be used for identification of informative features in the images and quantitative evaluation of relationship between these features and pathological processes resulting in the destruction of collagen structure. Here, we describe current approaches and achievements in the identification of informative features in the MPM images of collagen in biological tissues, as well as the development on this basis of algorithms for computer-aided classification of collagen structures using machine learning as a type of artificial intelligence methods.