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Table 2 Classification statistics for each PCA model constructed.

From: Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

Data type Scaling Sensitivity Specificity Correctly classified Cross-validation accuracy
1D NMR, mussel muscle unscaled 0.333 0.800 16 of 27 37.04%
  autoscaled 0.083 0.933 15 of 27 33.33%
  Pareto 0.500 0.733 17 of 27 51.85%
  glog 1.000 1.000 27 of 27 100.00%
  extended glog 1.000 0.86667 25 of 27 92.60%
pJRES NMR, dog urine unscaled 0.294 0.750 20 of 37 32.43%
  autoscaled 0.824 0.850 31 of 37 83.78%
  Pareto 0.530 0.700 23 of 37 56.76%
  glog 0.824 0.850 31 of 37 83.78%
  extended glog 0.824 0.850 31 of 37 83.78%
2D JRES NMR, fish liver unscaled 1.000 0.550 29 of 38 68.42%
  autoscaled 0.944 0.800 33 of 38 63.16%
  Pareto 0.944 0.800 33 of 38 86.84%
  glog 0.889 0.850 33 of 38 86.84%
  extended glog 1.000 1.000 38 of 38 100.00%