Research on a new standardization method for milk FT-MIRS on different instruments based on agglomerative clustering and application strategies

Abstract

Fourier transform mid-infrared spectroscopy (FT-MIRS) technique has been extensively employed for performance measurement of dairy cows and dairy herd improvement (DHI), but different milk analyzers have shown significant differences in the sensitivity, laser intensity, and stability of FT-MIRS determination, which cannot be directly integrated and applied in phenotype prediction and relevant studies. Existing literature has reported several FT-MIRS calibration methods such as piecewise direct standardization (PDS) and retroactive percentile standardization (RPS), achieving good standardization results. However, these methods require to be optimized because they take no account of the collinearity and redundancy of the spectrum.

Therefore, this study established an improved agglomerative clustering piecewise direct standardization (ACPDS) method. This study used 432 standard milk samples prepared by the standard laboratory within 4 months (based on the standard sample preparation procedures in the International Dairy Federation Guidelines for the Application of Mid-infrared Spectroscopy) and carried out FT-MIRS measurements and data collection on 9 instruments in 5 DHI laboratories. Meanwhile, the new method established in this study together with the existing methods of single wavelength standardization (SWS) and PDS were adopted to standardize the spectra collected on 9 instruments. The reproducibility, computation time, memory usage, and repeatability of the milk component prediction models were verified and compared.

The results revealed that ACPDS exhibited significant advantages over SWS and PDS, with a higher level of spectral reproducibility, and there was a significant advantage in the repeatability of the milk component prediction models but no significant increase in memory usage. The impact of its application across regions, months, and years was insignificant. In addition, based on the respective characteristics of ACPDS and the existing two methods, application strategies have been proposed for these three methods, providing new technologies and laying the foundation for the FT-MIRS-based milk component prediction models, widespread performance measurement of dairy cows in different instruments and at different times, and comparative analysis on the traits and phenotypes of dairy cows as well as their joint breeding in China and even the world.