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Infrared milk analyzers: Milk urea nitrogen calibration
M Portnoy1, C Coon1, D M Barbano1
1Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853.
Journal of Dairy Science
|April 5, 2021
Summary
This study developed a new milk calibration set to accurately measure milk urea nitrogen (MUN). The improved method reduced prediction errors by 50% for mid-infrared models.
Area of Science:
- Dairy Science
- Analytical Chemistry
- Spectroscopy
Background:
- Accurate measurement of milk urea nitrogen (MUN) is crucial for dairy herd management and nutritional strategies.
- Existing calibration sample sets may not adequately address variations in other milk components, affecting MUN prediction accuracy.
- Mid-infrared (MIR) spectroscopy coupled with partial least squares (PLS) regression is a common method for MUN prediction, but its performance can be influenced by sample matrix effects.
Purpose of the Study:
- To redesign a milk calibration sample set for a wider, well-distributed range of milk urea nitrogen (MUN) concentrations.
- To ensure the new calibration set is orthogonal to variations in fat, protein, and lactose.
- To evaluate and enhance the performance of MIR-PLS models for MUN prediction using the redesigned calibration set.
Main Methods:
- Modified a 14-sample milk calibration set formulation and manufacturing process.
- Determined MUN reference values using a spectrophotometric enzymatic method.
- Calibrated three MIR milk analyzers with the modified samples and evaluated PLS model performance.
Main Results:
- The redesigned calibration set achieved a desired range of MUN concentrations.
- Within- and between-laboratory variations for MUN reference values were 0.43% and 0.77%, respectively.
- Model performance improved by 50% (reduced standard deviation of differences between predicted and reference MUN values) after applying intercorrection factors.
Conclusions:
- The redesigned, orthogonal milk calibration set effectively supports accurate MUN reference value determination.
- The developed method significantly improves the performance and reliability of MIR-PLS models for MUN prediction in milk.
- This approach enhances the accuracy of MUN measurements, benefiting dairy nutrition and management.
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