Standardization method, testing scenario, and accuracy of the infrared prediction model affect the standardization accuracy of milk mid-infrared spectra.

The widespread use of milk mid-infrared (MIR) spectroscopy for phenotype prediction has urged the application of prediction models across regions and countries. Spectra standardization is the most effective way to reduce the variability in the spectral signal provided by different instruments and labs. This study aimed to develop different standardization models for MIR spectra collected by multiple instruments, across 2 provinces of China, and investigate whether the standardization method (piecewise direct standardization, PDS, and direct standardization, DS), testing scenario (standardization of spectra collected on the same day or after 7 mo), infrared prediction model accuracy (high or low), and instrument (6 instruments from 2 brands) affect the performance of the standardization model. The results showed that the determination coefficient (R) between absorbance values at each wavenumber provided by the primary and the secondary instruments increased from less than 0.90 to nearly 1.00 after standardization. Both PDS and DS successfully reduced spectra variation among instruments, and performed significantly better than nonstandardization. However, DS was more prone to overfitting than PDS. Standardization accuracy was higher when tested using spectra collected on the same day compared with those collected 7 mo after, but great improvement in model transferability was obtained for both scenarios compared with the nonstandardized spectra. The less accurate infrared prediction model (for C8:0 and C10:0 content) benefited the most from spectra standardization compared with the more accurate model (for total fat and protein content). For spectra collected 7 mo after standardization, after PDS the root mean square error between predictions obtained by different machines decreased on average by 86% and 94% compared with the values before standardization for C8:0 and C10:0, respectively. The secondary instrument had no significant effect on the R between predictions. The variation in the spectral signal provided by different instruments was successfully reduced by standardization across 2 provinces in China. This study lays the foundations for developing a national MIR spectra database to provide consistent predictions across provinces to be used in dairy farm management and breeding programs in China. Additionally, this provides opportunities for data exchange and cooperation at international levels.