Literature DB >> 31820949

Label-Free Detection of Bacteria in Fruit Juice by Nano-Flow Cytometry.

Shengbin He1, Xinyi Hong1, Miaomiao Zhang1, Lina Wu1, Xiaomei Yan1.   

Abstract

Rapid quantification of microbial contamination in fruit juice is highly desired for food safety control. Yet, the complex sample matrix and the diversity of bacterial contaminants present a great challenge. Employing a laboratory-built nano-flow cytometer (nFCM), here we report the development of a label-free approach for the detection of bacteria population in fruit juice. The weak autofluorescence of bacterial cells was used as a hallmark for the identification of bacteria. The sample pretreatment protocol was optimized to reduce fluorescence background, lyse residual plant cells and debris, and attain a good recovery of bacteria from juice samples. It was demonstrated that the nFCM was able to enumerate individual bacteria of very weak autofluorescence, and a clear differentiation from residual juice particulates was achieved. For bacteria spiked in the orange juice, the recovery rate was around 95% and a linear correlation between nFCM analysis and plate counting was acquired in the range of 3 × 104 to 3 × 108 cfu/mL. The assay, including sample pretreatment and instrument analysis, can be accomplished within 1 h, which is far more efficient than plate counting. Using a 40 mL sample volume, the detection limit in apple juice was ∼102 cells/mL. The as-developed method was successfully applied to bacterial measurement of freshly made orange juice and apple juice purchased from grocery stores. We believe it could also have potential practical application in microbial control analysis of other juices and water.

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Year:  2019        PMID: 31820949     DOI: 10.1021/acs.analchem.9b01869

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  2 in total

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Authors:  Peipei Qiu; Ping Yuan; Zhichen Deng; Zhengquan Su; Yan Bai; Jincan He
Journal:  Mikrochim Acta       Date:  2021-09-06       Impact factor: 5.833

2.  Analysis of pathogens, drug resistance, sensitive antibiotic treatment and risk factors of early-onset sepsis in very low birth weight infants.

Authors:  Yingying Yu; Qikun Huang; Anchang Liu
Journal:  Am J Transl Res       Date:  2021-11-15       Impact factor: 4.060

  2 in total

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