Table 7: Detailed partial least squares regression model results for soil total carbon () prediction from the subsets of mid-infrared diffuse reflectance spectra based on spectral classification with -means cluster analysis. The range of values reflects the results of 10 random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full sample set models with no subsetting for comparison. For models with full cross validation (i.e., leave-one-out cross validation), the same samples used to calibrate the model were used to validate the model.

Calibration

Validation

RMSE (%)^{c}

RMSE (%)

RPD^{d}

RPIQ^{e}

Cluster 0

96

0.78–0.96

1.49–4.07

41

0.55–0.91

2.08–4.67

1.13–3.20

1.77–5.65

(0.90)

(2.45)

(0.81)

(3.43)

(2.34)

(3.31)

Cluster 1

38

0.98

1.89

Full cross validation

0.86

5.19

2.62

3.93

Cluster 2

92

0.88–0.99

0.15–0.58

40

0.77–0.90

0.39–0.82

1.50–2.84

1.30–3.33

(0.95)

(0.33)

(0.85)

(0.56)

(2.36)

(2.33)

Full sample set

215

0.93–0.98

1.68–3.61

92

0.92–0.95

2.94–3.78

3.48–4.68

2.61–4.61

(0.95)

(2.98)

(0.94)

(3.38)

(4.03)

(3.82)

Full sample set

307

0.95

3.12

Full cross validation

0.94

3.52

3.94

3.68

^{
a}Number of samples. ^{
b}Coefficient of determination. ^{
c}Root mean squared error. ^{
d}Residual prediction deviation. ^{
e}Ratio of performance to interquartile distance.