Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

Table 3

Results of PLS-DA models using different selected wavelengths.

Methods	Variables	⁢Calibration		Prediction
		Correct number	Identification accuracy/%	Correct number	Identification accuracy/%
Raw	512	449	99.8	214	95.1
SPA	5	417	92.7	210	93.3	−1.78
RC	7	413	91.8	211	93.8	−1.28
LW	8	402	89.3	199	88.4	−6.60
UVE	291	449	99.8	219	97.3	0.95
UVE-SPA	12	449	99.8	216	96.0	0.88
CARS	105	449	99.8	221	98.2	2.46
iPLS	32	430	95.6	199	88.4	−6.28
BiPLS	208	450	100	223	99.1	2.38
FiPLS	480	449	99.8	219	97.3	0.14
GA-PLS	85	446	99.1	217	96.4	1.08