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Table 3 The statistics of partial least square regression approach for the milk Fourier transform mid-infrared spectrometry-based estimation model for heat production of dairy cows

From: The use of milk Fourier transform mid-infrared spectra and milk yield to estimate heat production as a measure of efficiency of dairy cows

TraitPrediction modelCalibrationLVcCross ValidationExternal Validation
R2RMSEPbR2CVRMSECVdR2VRMSEVe
Heat production, kJ/kg BW0.75M1a0.2399.9140.2586.70.18114.1
M2a0.5293.240.5589.40.4884.0
M3a0.5491.250.5786.50.4795.5
  1. aModel M1 was developed using the averaged morning and afternoon spectral data. The prediction model M2 was developed by averaging the morning and afternoon spectral data and subsequent multiplication with daily milk yield. The prediction model M3 was computed by weighted averaging, where each morning or afternoon absorption spectra was multiplied to the respective milk yield
  2. bThe square root of the mean squared error of prediction
  3. cLatent variables; i.e. the partial least square regression components for the prediction model
  4. dRoot mean squared error of cross validation
  5. eRoot mean squared error of external validation