Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology

Table 1

Classification results of PLS-DA model based on raw variables, optimal variables selected from raw spectra, and optimal variables from CWT spectra.

Model	Methods	LVs^1	Class	Calibration			Validation			Prediction
				Pre^2	Sen^3	Spe^4	Pre	Sen	Spe	Pre	Sen	Spe
PLS-DA	—	7	1	1.00	1.00	1.00	0.71	0.83	0.69	0.78	0.82	0.71
			2	1.00	1.00	1.00	0.82	0.69	0.83	0.76	0.71	0.82
	VIP	5	1	1.00	1.00	1.00	0.88	0.91	0.89	0.88	0.90	0.83
			2	1.00	1.00	1.00	0.92	0.89	0.91	0.86	0.83	0.90
	Wavelet	8	1	1.00	1.00	1.00	0.99	1.00	0.99	0.95	1.00	0.94
			2	1.00	1.00	1.00	1.00	0.99	1.00	1.00	0.94	1.00
	Wavelet-VIP	9	1	1.00	1.00	1.00	0.86	0.86	0.82	0.66	0.81	0.75
			2	1.00	1.00	1.00	0.82	0.82	0.86	0.87	0.75	0.81

Notes: 1: latent variables; 2: precision; 3: sensitivity; 4: specificity.