Identification of Tilletia foetida, Ustilago tritici, and Urocystis tritici Based on Near-Infrared Spectroscopy

Table 8

Pathogen identification results of the SVM models built with different modeling ratios in each modeling spectral region when Data treatment method 2 was used.

Spectral region (cm^−1)	Modeling ratio of training set to testing set	Training sets		Testing sets
		Average identification accuracy for the 10 data subsets (%)	Standard deviation of the identification accuracies for the 10 data subsets	Average identification accuracy for the 10 data subsets (%)	Standard deviation of the identification accuracies for the 10 data subsets
4000–12000	3 : 1	99.92	0.002280	99.78	0.006660
4000–12000	4 : 1	99.79	0.003254	99.17	0.02499
4000–12000	5 : 1	99.86	0.002720	100	0
4000–5000	3 : 1	100	0	99.78	0.006660
4000–5000	4 : 1	100	0	99.72	0.008340
4000–5000	5 : 1	100	0	99.67	0.009990
4000–6000	3 : 1	100	0	99.56	0.008880
4000–6000	4 : 1	99.86	0.004290	99.72	0.008340
4000–6000	5 : 1	100	0	99.67	0.009990
4000–7000	3 : 1	100	0	99.11	0.01776
4000–7000	4 : 1	100	0	99.72	0.008340
4000–7000	5 : 1	99.93	0.002040	100	0
4000–8000	3 : 1	100	0	99.11	0.01473
4000–8000	4 : 1	100	0	99.72	0.008340
4000–8000	5 : 1	100	0	99.67	0.009990
4000–9000	3 : 1	100	0	99.56	0.008880
4000–9000	4 : 1	100	0	98.89	0.02545
4000–9000	5 : 1	99.66	0.01026	98.67	0.03055
4000–10000	3 : 1	100	0	99.56	0.008880
4000–10000	4 : 1	99.86	0.002840	99.17	0.02499
4000–10000	5 : 1	99.73	0.006261	98.67	0.03055
5000–6000	3 : 1	99.70	0.009150	99.33	0.01421
5000–6000	4 : 1	99.86	0.004290	100	0
5000–6000	5 : 1	100	0	99.67	0.009990
5000–7000	3 : 1	100	0	99.33	0.01421
5000–7000	4 : 1	100	0	99.72	0.008340
5000–7000	5 : 1	100	0	99.67	0.009990
5000–8000	3 : 1	100	0	99.11	0.01088
5000–8000	4 : 1	100	0	98.89	0.01362
5000–8000	5 : 1	99.86	0.002720	98.67	0.02211
5000–9000	3 : 1	100	0	99.56	0.008880
5000–9000	4 : 1	100	0	99.72	0.008340
5000–9000	5 : 1	99.59	0.01233	98.67	0.03055
5000–10000	3 : 1	100	0	99.56	0.008880
5000–10000	4 : 1	99.86	0.002840	98.89	0.03333
5000–10000	5 : 1	100	0	99.67	0.009990
6000–7000	3 : 1	99.85	0.003040	99.34	0.01017
6000–7000	4 : 1	99.86	0.002840	98.33	0.03333
6000–7000	5 : 1	99.80	0.003116	99.00	0.01526
6000–8000	3 : 1	99.92	0.002280	98.89	0.01110
6000–8000	4 : 1	100	0	99.17	0.01274
6000–8000	5 : 1	99.93	0.002040	98.67	0.01631
6000–9000	3 : 1	99.92	0.002280	99.11	0.01473
6000–9000	4 : 1	99.86	0.002840	98.61	0.03345
6000–9000	5 : 1	99.86	0.002720	99.00	0.01526
6000–10000	3 : 1	99.92	0.002280	99.56	0.008880
6000–10000	4 : 1	99.86	0.002840	98.61	0.03345
6000–10000	5 : 1	99.93	0.002040	99.67	0.009990
7000–8000	3 : 1	100	0	99.11	0.01088
7000–8000	4 : 1	99.72	0.004734	98.89	0.02545
7000–8000	5 : 1	100	0	99.33	0.01332
7000–9000	3 : 1	99.77	0.006870	99.11	0.01088
7000–9000	4 : 1	99.86	0.002840	98.61	0.02241
7000–9000	5 : 1	100	0	99.33	0.01333
7000–10000	3 : 1	99.85	0.004590	99.33	0.01017
7000–10000	4 : 1	99.86	0.002840	98.89	0.01844
7000–10000	5 : 1	99.86	0.004110	99.33	0.01332
8000–9000	3 : 1	99.77	0.004893	98.00	0.01554
8000–9000	4 : 1	99.86	0.002840	98.33	0.02224
8000–9000	5 : 1	99.73	0.006261	98.33	0.02236
8000–10000	3 : 1	99.92	0.002280	99.56	0.01332
8000–10000	4 : 1	99.79	0.004573	98.61	0.03345
8000–10000	5 : 1	99.80	0.004381	99.00	0.02135
9000–10000	3 : 1	99.70	0.005065	98.67	0.02266
9000–10000	4 : 1	99.57	0.005715	98.06	0.03298
9000–10000	5 : 1	99.52	0.005344	98.67	0.01631