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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Characterization and Interventions for Foodborne Pathogens » Research » Publications at this Location » Publication #395765

Research Project: Development of Innovative Technologies and Strategies to Mitigate Biological, Chemical, Physical, and Environmental Threats to Food Safety

Location: Characterization and Interventions for Foodborne Pathogens

Title: Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp.

item MIN, H - Purdue University
item MINA, H - Purdue University
item DEERING, A - Purdue University
item BAE, E - Purdue University

Submitted to: Journal of Microbiological Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/14/2021
Publication Date: 7/17/2021
Citation: Min, H.J., Mina, H.A., Deering, A.J., Bae, E. 2021. Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp. Journal of Microbiological Methods.

Interpretive Summary: Lateral flow assays, like the Covid-19 home tests, are a common diagnostic method used to detect pathogens in many types of samples. In many circumstances the presence or absence of a colored line on the test is used to determine the outcome and may lead to ambiguity and lack of sensitivity when the line is very faint. The research described here uses a smartphone camera to capture an image of the lateral flow strip and then uses software based on artificial intelligence (AI) to both reduce the uncertainty when the line is faint and to improve the ability of the assay to detect more dilute samples. Although this work focuses on pathogens important to food safety, the method could also be used for medically important lateral flow assays such as those used in in-home test kits for COVID-19.

Technical Abstract: Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 104 CFU/mL commercially available lateral-flow assays.