A multi-channel smartphone-based spectroscopic system for high-throughput biosensing in low-resource settings. Issue 13 (31st May 2022)

• Record Type: Journal Article
• Authors: Hussain, Iftak; Locke, Andrea; Kight, Emily; Malone, Joseph D.; Haselton, Frederick; Bowden, Audrey K.
• Is Part Of: Analyst. Volume 147:Issue 13(2022)
• Page Start: 3007
• Page End: 3016
• Publication Date: 2022-05-31
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/d2an00597b
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Simultaneous detection of multiple samples can improve the effectiveness of the point-of-care strategies. We have demonstrated a novel optical system that allows detection of multiple analytes at the same time using a smartphone spectrometer. Abstract : Primary healthcare centers (PHC) are the first point of contact for people in low-resource settings, and laboratory services play a critical role in early diagnosis of any disease. In recent years, several smartphone-based spectroscopic systems have been demonstrated to translate lab-confined healthcare applications into point-of-care environments to improve their accessibility. Due to constraints, such as the low availability of skilled personnel and consumables in a PHC, batch processing would be ideal for a large number of samples. Therefore, high-throughput and multi-channel detection is equally critical as affordability and portability. To date, most point-of-care systems are designed to perform a single type of analysis at a time. Herein, we introduce a smartphone-based spectroscopic system based on the use of line-beam illumination to achieve high-throughput sensing (15 channels simultaneously) within a 3d-printed microfluidic device. We also developed a smartphone application to process the spectral data and provide the results in real-time. Bland-Altman analysis revealed that the proposed device performs similarly to a laboratory spectrophotometer. The availability of the developed system will enable detection of multiple samples rapidly in low-resource settings with the existing limited manpower and infrastructures. The fast turnaround time may eventually help in timely diagnosis of patients during situations of high sample load, such as during disease outbreaks.