. 2020 May 7;11:43. doi: 10.1186/s40104-020-00455-0

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

Trait	Prediction model	Calibration		LV^c	Cross Validation		External Validation
Trait	Prediction model	R²	RMSEP^b	LV^c	R²CV	RMSECV^d	R²V	RMSEV^e
Heat production, kJ/kg BW^0.75	M1^a	0.23	99.9	14	0.25	86.7	0.18	114.1
	M2^a	0.52	93.2	4	0.55	89.4	0.48	84.0
	M3^a	0.54	91.2	5	0.57	86.5	0.47	95.5

^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

^bThe square root of the mean squared error of prediction

^cLatent variables; i.e. the partial least square regression components for the prediction model

^dRoot mean squared error of cross validation

^eRoot mean squared error of external validation