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<p>Ensuring quality milk is of utmost importance to the public health sector and the dairy industry at large, but the prevailing methods for detecting contamination—chemical analysis and chromatography—are time-consuming, expensive, and impractical for use in real-time monitoring. These often fail to give comprehensive detection of multiple contaminants, including urea, moisture content, and other adulterants, due to their reliance on isolated measurements. This work introduces a novel multi-methodology framework for precise and real-time measurement of milk quality using a fringing-field interdigital capacitive sensor. The hybrid sensor design employed by this work uses a multi-layer architecture with nested electrode arrays to allow selective sensing of contaminants by adjusting electric field penetration depths. This design guarantees high sensitivity to urea at 0.01% w/v, resolution ± 0.005%, and to moisture at accuracy ± 0.1%. This framework uses broadband dielectric spectroscopy for the extraction of the dispersion signatures of impurities within a large frequency range for multi-component analysis with a 95% accuracy rate. This is enhanced through integration with machine learning using convolutional neural networks (CNNs) on the converted 2D heatmaps of the dielectric spectra, and yields a 98% classification accuracy. Moreover, inline monitoring microfluidics is dynamic real-time sensing in a production line, having a resolution of the time instance set for 1&#xa0;s for measurement. Furthermore, the use of surface-engineered sensor coatings improves the specificity and sensitivity of targeted adsorption mechanisms. Therefore, this proposed model gives the high precision real-time milk quality estimation solution scalable and cost-effective for different operations. This would revolutionize contamination monitoring and ensure that consumers are protected while fulfilling regulatory requirements by improving efficiency in dairy operations.</p>

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Design of an Improved Method for Milk Quality Assessment Using Hybrid Capacitive Sensor and Broadband Dielectric Spectroscopy

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Divya Palakaluri,
  • Praveen Maurya

摘要

Ensuring quality milk is of utmost importance to the public health sector and the dairy industry at large, but the prevailing methods for detecting contamination—chemical analysis and chromatography—are time-consuming, expensive, and impractical for use in real-time monitoring. These often fail to give comprehensive detection of multiple contaminants, including urea, moisture content, and other adulterants, due to their reliance on isolated measurements. This work introduces a novel multi-methodology framework for precise and real-time measurement of milk quality using a fringing-field interdigital capacitive sensor. The hybrid sensor design employed by this work uses a multi-layer architecture with nested electrode arrays to allow selective sensing of contaminants by adjusting electric field penetration depths. This design guarantees high sensitivity to urea at 0.01% w/v, resolution ± 0.005%, and to moisture at accuracy ± 0.1%. This framework uses broadband dielectric spectroscopy for the extraction of the dispersion signatures of impurities within a large frequency range for multi-component analysis with a 95% accuracy rate. This is enhanced through integration with machine learning using convolutional neural networks (CNNs) on the converted 2D heatmaps of the dielectric spectra, and yields a 98% classification accuracy. Moreover, inline monitoring microfluidics is dynamic real-time sensing in a production line, having a resolution of the time instance set for 1 s for measurement. Furthermore, the use of surface-engineered sensor coatings improves the specificity and sensitivity of targeted adsorption mechanisms. Therefore, this proposed model gives the high precision real-time milk quality estimation solution scalable and cost-effective for different operations. This would revolutionize contamination monitoring and ensure that consumers are protected while fulfilling regulatory requirements by improving efficiency in dairy operations.