Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy

@inproceedings{Mouazen2010ComparisonAP,
  title={Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy},
  author={Abdul Mounem Mouazen and Baoping Kuang and Josse De Baerdemaeker and H. Ramon},
  year={2010}
}
Abstract The selection of calibration method is one of the main factors influencing the measurement accuracy with visible (vis) and near infrared (NIR) spectroscopy. This paper compared the performance of three calibration methods, namely, principal component regression (PCR), partial least squares regression (PLSR) and back propagation neural network (BPNN) analyses for the accuracy of measurement of selected soil properties, namely, organic carbon (OC) and extractable forms of potassium (K… CONTINUE READING

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Estimating soil organic carbon content with visible-near-infrared (vis-NIR) spectroscopy.

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