Abstract
Near-infrared (NIR) spectroscopy combined with the partial least squares (PLS) regression was successfully applied for the rapid quantitative analysis of Zn2+in soil. The models were established using an approach based on randomness and similarity to obtain objective and practical models. Sixty-three samples were randomly selected from a total of 148 samples as the validation set. The remaining 85 samples were used as the modeling set, and it was divided into similar calibration (50 samples) and prediction (35 samples) sets. The results show that the long-NIR region at 1100 nm to 2498 nm could be considered as the information waveband of Zn2+in soil. The optimal number of PLS factors was 10, and the validation root mean square error (V-SEP) and validation correlation coefficients of prediction (V-RP) were 21.817 mg kg-1and 0.849, respectively. The Zn2+prediction values of the validation samples are close to the measured values. The results provided valuable reference for designing the dedicated spectrometers.
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