2020
DOI: 10.1002/jbio.201960159
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Turbidimetric analysis of growth kinetics of bacteria in the laboratory environment using smartphone

Abstract: For different microbiological and pathological studies, it is often required to monitor the growth of bacteria in a cultured medium in the laboratory environment. UV-VIS spectrophotometer is commonly used to estimate the growth of bacterial cell population by measuring the absorbance at 600 nm over a period of time. Colony-forming unit (CFU) is another approach, which has been routinely performed to estimate the live bacterial cells on semisolid agar plates. Herein, we demonstrate an alternative yet highly rel… Show more

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Cited by 18 publications
(3 citation statements)
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“…Regarding turbidity in kombucha analog beverages, this is associated with microbial growth. Komagataeibacter , predominant in the initial culture, induces cellulose production, thus exerting a direct influence on turbidity [ 85 , 86 ].…”
Section: Resultsmentioning
confidence: 99%
“…Regarding turbidity in kombucha analog beverages, this is associated with microbial growth. Komagataeibacter , predominant in the initial culture, induces cellulose production, thus exerting a direct influence on turbidity [ 85 , 86 ].…”
Section: Resultsmentioning
confidence: 99%
“…Applied to the monitoring of a cell therapy product, measuring the optical density spectrum allows to distinguish between an increase in optical density due to cell growth or an increase due to contamination [19]. Recent works have also shown the possibility of developing portable systems suitable for in situ measurements [20,21].…”
Section: Turbidimetrymentioning
confidence: 99%
“…In the present study, we have designed, developed, tested, and validated a smartphone-based spectroscopic device on a variety of cervical precancer and cancer patients at various hospitals in the Uttar Pradesh and Odisha states of India. Smartphone is useful in rapid data collection and image processing part [41][42][43][44][45][46]. A mutual information (MI)-based feature selection technique is employed to identify significant features from the initial spectral data, and subsequently a long short-term memory (LSTM) model is applied for classification purposes.…”
Section: Introductionmentioning
confidence: 99%