Improved sensitive fluorescent/visible dual detection count plate for mold and yeast in food

Xu, Linlin; Ntakatsane, Moeketsi; Wang, Lei; Meng, Xianghui; Sun, Wei; Bi, Yingpu; Chen, Ping; Ren, Dayong

doi:10.1016/j.foodcont.2021.108174

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Numerous approaches have been developed to detect and quantify yeasts. The methods based on cell cultivation [3,4] are time-consuming. Guo et al [5] employed the enzyme-linked immunosorbent assay for the detection of yeasts in food.…”

Section: Introductionmentioning

confidence: 99%

Modeling of an Impedimetric Biosensor with Ultrasonic-Assisted Cell Alignment for the Detection of Yeast

Tian

Xie

et al. 2022

Journal of Sensors

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Yeast is an integral part of our environment. The detection of yeast is of great significance in many fields. The impedance-based sensor with interdigital microelectrodes is a promising method to establish a simple and portable detection system. However, the positions of yeast cells greatly influence the impedance variation and eventually the sensitivity of detection. In this study, an impedimetric biosensor with ultrasonic-assisted cell alignment for yeast detection was proposed. In order to verify the feasibility of this biosensor, finite element modeling was conducted using the software COMSOL. The acoustic pressure field and the acoustic radiation force exerted on yeast cells were investigated. Cell positions in the microfluidic channel were determined using the fluid particle tracking module. After that, the impedance between the microelectrodes was calculated. Yeast suspensions with different cell concentrations were used as the tested samples for the simulation. The proposed sensor showed a higher sensitivity than the conventional impedimetric biosensor on which the cells were randomly located. It can be used for the detection of microorganisms. This finite element modeling provided an effective approach for the design of biosensors.

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Section: Introductionmentioning

confidence: 99%