Flow Cytometry Techniques for Milk Microbial Enumeration

Detection of plastic microparticles in milk-based samples using flow cytometry through machine learning-based analysis. The process employs a dedicated algorithm that recognizes unique spectral signatures of plastic microparticles from the sample, eliminating non-plastic particles while preserving the precise identification of microplastic particles. The algorithm achieves this through an unsupervised deep learning approach that automatically categorizes particles based on their distinct spectral fingerprints, eliminating the need for staining or manual particle identification.

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Real-time somatic cell count monitoring system for dairy farms during milking, enabling early detection of mastitis through continuous cell analysis. The system integrates into the milking process, employing a portable, automated somatic cell counter that continuously measures cell counts at various levels along the milking line. This real-time monitoring enables early intervention before clinical mastitis develops, significantly reducing treatment costs and improving herd health. The system operates in parallel with the milking process, with the cell counter positioned between the milk source and the collective tank, allowing continuous testing throughout the milking cycle.

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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.

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A subclinical mastitis detection system that enables accurate and rapid detection of mastitis in dairy cows through automated milk sampling. The system integrates a laser-based optical scattering analysis system with automated milk sampling and processing capabilities. It uses a microfluidic chip technology to analyze milk composition and cell characteristics without requiring manual sampling, eliminating the need for traditional cell counting methods. The system can detect subclinical mastitis at concentrations as low as 200,000 cells/mL, providing real-time alerts for dairy farms to initiate treatment.

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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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