A smartphone-interfaced, low-cost colorimetry biosensor for selective detection of bronchiectasis via an artificial neural network.
Mizaj Shabil ShaMuni Raj MauryaMuhammad E H ChowdhuryAsan G A MuthalifSomaya Al-MaadeedKishor Kumar SadasivuniPublished in: RSC advances (2022)
Exhaled breath (EB) contains several macromolecules that can be exploited as biomarkers to provide clinical information about various diseases. Hydrogen peroxide (H 2 O 2 ) is a biomarker because it indicates bronchiectasis in humans. This paper presents a non-invasive, low-cost, and portable quantitative analysis for monitoring and quantifying H 2 O 2 in EB. The sensing unit works on colorimetry by the synergetic effect of eosin blue, potassium permanganate, and starch-iodine (EPS) systems. Various sampling conditions like pH, response time, concentration, temperature and selectivity were examined. The UV-vis absorption study of the assay showed that the dye system could detect as low as ∼0.011 ppm levels of H 2 O 2 . A smart device-assisted detection unit that rapidly detects red, green and blue (RGB) values has been interfaced for practical and real-time application. The RGB value-based quantification of the H 2 O 2 level was calibrated against NMR spectroscopy and exhibited a close correlation. Further, we adopted a machine learning approach to predict H 2 O 2 concentration. For the evaluation, an artificial neural network (ANN) regression model returned 0.941 R 2 suggesting its great prospect for discrete level quantification of H 2 O 2 . The outcomes exemplified that the sensor could be used to detect bronchiectasis from exhaled breath.
Keyphrases
- neural network
- low cost
- hydrogen peroxide
- cystic fibrosis
- label free
- machine learning
- loop mediated isothermal amplification
- nitric oxide
- real time pcr
- gold nanoparticles
- sensitive detection
- high throughput
- healthcare
- artificial intelligence
- quantum dots
- health information
- light emitting
- metabolic syndrome
- adipose tissue
- highly efficient
- current status
- skeletal muscle
- deep learning