Rapid screening of benzyl dodecyl dimethyl ammonium bromide co-metabolic degrading bacteria based on NIR hyperspectral imaging technology.

As a typical disinfectant, the use of benzyl dodecyl dimethyl ammonium bromide (BDAB) has dramatically increased since the emergence of SARS-CoV-2, posing a threat to environmental balance and human health. Screening BDAB co-metabolic degrading bacteria is required for efficient microbial degradation. Conventional methods for screening co-metabolic degrading bacteria are laborious and time-consuming, especially when the number of strains is large. This study aimed to develop a novel method for the rapid screening of BDAB co-metabolic degrading bacteria from the cultured solid medium using near-infrared hyperspectral imaging (NIR-HSI) technology. Based on NIR spectra, the concentration of BDAB in the solid medium can be well predicted by partial least squares regression (PLSR) models, non-destructively and rapidly, with Rc2 > 0.872 and Rcv2 > 0.870. The results show that the predicted BDAB concentrations decrease after degrading bacteria utilization, comparing with the regions where no degrading bacteria grew. The proposed method was applied to directly identify the BDAB co-metabolic degrading bacteria cultured on the solid medium, and two kinds of co-metabolic degrading bacteria RQR-1 and BDAB-1 were correctly identified. This method provides a high-efficiency method for screening BDAB co-metabolic degrading bacteria from a large number of bacteria.

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degradation make them huge sources of micro- and nanoplastic particles. To better understand the consequences of the direct influence of microplastic pollution on biota, there is an urgent need to develop a reliable and high-throughput analytical tool for sub-micrometre plastic identification and visualisation in environmental and biological samples. This study evaluated the application of a combined technique based on dark-field enhanced microscopy and hyperspectral imaging augmented with deep learning data analysis for the visualisation, detection and identification of microplastic particles released from commercially available medical masks after 192 hours of UV-C irradiation. The analysis was performed using a separated blue-coloured spunbond outer layer and white-coloured meltblown interlayer that allowed us to assess the influence of the structure and pigmentation of intact and UV-exposed samples on classification performance. Microscopy revealed strong fragmentation of both layers and the formation of microparticles and fibres of various shapes after UV exposure. Based on the spectral signatures of both layers, it was possible to identify intact materials using a convolutional neural network successfully. However, the further classification of UV-exposed samples demonstrated that the spectral characteristics of samples in the visible to near-infrared range are disrupted, causing a decreased performance of the CNN. Despite this, the application of a deep learning algorithm in hyperspectral analysis outperformed the conventional spectral angle mapper technique in classifying both intact and UV-exposed samples, confirming the potential of the proposed approach in secondary microplastic analysis.

Delineation of the Arm Blood Vessels Utilizing Hyperspectral Imaging to Assist with Phlebotomy for Exploiting the Cutaneous Tissue Oxygen Concentration.

SIGNIFICANCE: The estimation of tissue oxygenation is vital in the diagnosis and therapeutic evaluation of a huge assortment of diseases. The hyperspectral (HS) imaging system is a rising innovation that can be utilized to build a highly sensitive, non-invasive, and tissue hemoglobin immersion map. OBJECTIVE: As a result of the urgent need to design and implement early detection devices and applications for the COVID-19 pandemic, we propose building a non-invasive custom optical imaging system to assist with phlebotomy and vascular approach to survey the reliability of blood oxygen saturation (SpO2) levels recovered from spectral images. MATERIALS AND METHODS: HS images were gathered from 15 healthy subjects without previous medical history complications and with an average age range of 20 to 38 years, who were undergoing phlebotomy. The forearm was vigorously illuminated utilizing an HS camera with polychromatic source light of spectrum range (400∼980 nm). Spectroscopic reflectance images were caught by a focal plane exhibit for the region of interest (ROI). Then the custom algorithm comprising normalization and moving average filtering for noise removal was applied, followed by K-mean clustering for image segmentation to visualize and highlight the arteries and the veins in the investigated forearm. RESULTS: The investigations show that after normalization of the recorded signal from the HS camera of the participating subjects it was noticed that at wavelength of 460 nm the oxygenated arteries had a stronger signal than the de-oxygenated veins, and at a wavelength of 750 nm the de-oxygenated veins had a stronger signal than the oxygenated arteries. Thus, the ideal wavelength to reveal the oxygenated arteries was 460 nm, and the ideal wavelength to reveal the de-oxygenated veins was 750 nm. CONCLUSIONS: HSI is a prospective technique to assist with phlebotomy and non-contact oxygen saturation approach. Additionally, it may permit future surgical or pharmacological intercessions that titrate or limit ischemic injury continuously.