Journal of Spectroscopy / 2015 / Article / Tab 4 / Research Article
Quantitative Determination of Germinability of Puccinia striiformis f. sp. tritici Urediospores Using Near Infrared Spectroscopy Technology Table 4 The prediction results of the optimal SVR models using different preprocessing methods of the near infrared spectra for quantitative determination of germinability of Puccinia striiformis f. sp. tritici urediospores.
Preprocessing method The ratio of training set to testing set Spectral region/cm−1 Optimal parameters Training set Testing set MSE MSE No preprocessing 3 : 1 4000–12000 3.0314 5.278 0.9475 0.002277 0.8996 0.005421 Level 1 decomposition denoising using db2 wavelet 3 : 1 4000–12000 3.0314 5.278 0.9464 0.002322 0.8996 0.005422 Level 2 decomposition denoising using db2 wavelet 3 : 1 4000–12000 3.0314 5.278 0.9429 0.002474 0.8978 0.005508 Level 3 decomposition denoising using db2 wavelet 3 : 1 4000–12000 3.0314 5.278 0.9393 0.002630 0.8954 0.005574 Euclidean normalization 3 : 1 7000–12000 84.4485 147.0334 0.9047 0.004231 0.8956 0.005408 Multiplication scatter correction 3 : 1 7000–12000 48.5029 1 0.8925 0.004805 0.8754 0.007024 Standard normalized variate transform 3 : 1 6000–9000 84.4485 0.0359 0.8531 0.006267 0.9065 0.007758 Vector normalization 2 : 1 4000–12000 147.0334 0.3299 0.9340 0.003024 0.9034 0.004604 5 : 1 8000–11000 27.8576 16 0.9461 0.002464 0.9262 0.003488 3 : 1 8000–12000 256 0.3299 0.9320 0.003128 0.9129 0.007432 5 : 1 8000–12000 147.0334 0.5743 0.9462 0.002639 0.9259 0.004726 S-G first derivative transform 3 : 1 5000–9000 147.0334 256 0.9324 0.002947 0.8817 0.007193 S-G second derivative transform 5 : 1 8000–10000 256 256 0.9099 0.004248 0.8519 0.010270