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Table 4 Model statistics for predicting GBS concentration on a fresh weight and dry weight bases

From: Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue

Spectral data used for model development Calibration (n = 68) Cross-validation (n = 24)
R2cala RMSECb R2CVc RMSEPd RPDe Termsf
Raw (fr wt) 0.89 40.47 0.75 35.12 2.3 10
Raw (dr wt) 0.90 2.17 0.80 2.07 2.4 10
Raw plus SNV + DT (fr wt) 0.65 53.61 0.60 39.56 2.0 2
Raw plus SNV + DT (dr wt) 0.63 3.67 0.41 3.46 1.4 2
1st derivative (fr wt) 0.89 43.29 0.76 30.44 2.6 6
1st derivative (dr wt) 0.90 2.41 0.79 2.07 2.4 6
1st derivative plus SNV + DTg (fr wt) 0.76 53.10 0.55 42.17 1.9 2
1st derivative plus SNV + DT (dr wt) 0.64 3.22 0.46 3.33 1.5 1
  1. aCoefficient of determination of the calibration
  2. bRoot mean squared error of calibration
  3. cCoefficient of determination of the cross-validation
  4. dRoot mean squared error of prediction
  5. eRatio of prediction to deviation
  6. fNumber of terms (PLS components) used in the model selected for cross-validation
  7. gStandard normal variate with detrending spectral preprocessing