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Table 2 Prediction results of the pre-processing models constructed by partial least-squares regression (PLSR) and principal component multiple linear regression (PC-MLR) for lignocellulose components of biomass pellets

From: Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

Indices Model type Pre-processing Para Calibration set Prediction set
\(R_{\text{p}}^{2}\) RMSEC (%) \(R_{\text{p}}^{2}\) RMSEP (%)
Cellulose PC-MLR Raw 29 0.93 2.21 0.91 2.49
SNV 21 0.86 3.19 0.87 2.95
2nd 45 0.93 2.26 0.79 3.82
MSC 21 0.84 3.39 0.85 3.27
PLSR Raw 10 0.91 2.64 0.91 2.51
SNV 11 0.84 3.36 0.83 3.39
2nd 2 0.61 5.23 0.61 5.23
MSC 10 0.83 3.56 0.81 3.63
Hemicellulose PC-MLR Raw 21 0.83 1.54 0.79 1.68
SNV 16 0.81 1.61 0.83 1.53
2nd 45 0.87 1.36 0.78 1.74
MSC 18 0.80 1.66 0.78 1.73
PLSR Raw 12 0.82 1.54 0.80 1.86
SNV 10 0.81 1.60 0.82 1.58
2nd 9 0.83 1.56 0.76 1.83
MSC 7 0.72 1.95 0.59 2.67
Lignin PC-MLR Raw 38 0.88 1.19 0.75 1.75
SNV 21 0.71 1.90 0.60 2.23
2nd 32 0.85 1.30 0.74 1.78
MSC 13 0.61 2.23 0.52 2.44
PLSR Raw 13 0.86 1.31 0.74 1.79
SNV 8 0.61 2.20 0.46 2.58
2nd 16 0.82 1.47 0.71 1.87
MSC 6 0.58 2.28 0.45 2.61
  1. a Model parameters indicate the optimal number of latent variables for establishing the PLSR calibration model and optimal number of principal components for PC-MLR; \(R_{\text{c}}^{2}\) and \(R_{\text{p}}^{2}\), coefficients of determination for calibration and prediction sets, respectively; RMSEC and RMSEP, root mean square errors of calibration and prediction sets, respectively; SNV, standard normal variate; 2nd, second derivative; MSC, multiplicative scatter correction