Flow Cytometry Techniques for Milk Microbial Enumeration
Flow cytometry systems analyzing raw milk samples face significant technical hurdles due to the complex matrix of milk components. Fat globules (2-10 µm), protein aggregates, and somatic cells create signal interference that can lead to false positives and imprecise enumeration. Traditional methods struggle to differentiate between cellular and non-cellular particles, particularly in concentration ranges critical for quality control (200,000-400,000 cells/mL).
The core challenge lies in achieving accurate microbial enumeration while managing the inherent complexity of milk's biological matrix without compromising throughput or requiring extensive sample preparation.
This page brings together solutions from recent research—including microfluidic impedance analysis, laser-based optical scattering techniques, automated real-time monitoring systems, and deep learning-enhanced particle discrimination. These and other approaches focus on practical implementation in dairy production environments while maintaining the precision needed for quality control and early mastitis detection.
1. Method for Detecting Subclinical Mastitis via Neutrophil-Macrophage Ratio in Milk Using Flow Cytometry
STC ELEKTRONIK SAGLIK HIZ SAN TIC LTD STI, 2017
A method to detect subclinical mastitis in dairy animals by analyzing the ratio of neutrophils to macrophages in milk. The approach employs fluorescently labeled neutrophils and macrophages, which are then analyzed using flow cytometry to determine the ratio. This ratio is compared to established threshold values to identify animals with subclinical mastitis, which is characterized by an abnormal neutrophil to macrophage ratio. The method provides a more precise and reliable detection compared to traditional somatic cell counts, particularly in cases where the cell count alone may not accurately indicate mastitis status.
2. Microfluidic High-Frequency Impedance Analysis Method for Cell Enumeration and Discrimination in Raw Milk
AMPHASYS AG, 2015
A method for automating cell enumeration and discrimination in raw milk using high-frequency impedance analysis in a microfluidic device. The method employs a microfluidic system that automatically calibrates and processes milk samples while simultaneously analyzing their impedance properties. The system determines the milk's impedance characteristics and calibrates the analysis parameters based on the inherent properties of milk components, particularly fat vesicles. This enables accurate cell counting and discrimination of cells from lipid vesicles and other non-cellular particles in raw milk, enabling more precise somatic cell enumeration and detection of pathogenic bacteria compared to traditional Coulter counters.
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