## 4 Conclusions  
Algorithmic models (models built from data) exhibit high predictive performance [31], but a trustworthy model that can be used across industries for NIR quantification is difficult to build. This manuscript analyzed 29 pretreatment methods with the original spectrum reflectance dataset and 240 regression models using evaluation metrics. The results are summarized as follows:  
1. The samples and near-infrared spectral features of soil-available potassium both have a normal distribution. Therefore, the PLS and SVR algorithms need the RBF kernel function to fit the nonlinear features of the VIS-NIR, and the evaluation of accuracy and stability needs the metric of the RPIQ value.
2. Near-infrared spectral curves with different combinations of pretreatment methods had great differences for the calibration of NIR quantification. The combination of Savitzky-Golay, standard normal variate, and dislodge tendency is the best pretreatment method, and the combination of multiplicative scatter correction and second derivative being useless with or without Savitzky-Golay. The pretreatment methods for the best model of level A are dislodge tendency for PLS_RBF, Savitzky-Golay and dislodge tendency for SVR_RBF, first derivative for GBRT, and the combination of logarithmic transformation and first derivative with Savitzky-Golay for AdaBoost. Therefore, first derivative, dislodge tendency and Savitzky-Golay are the most useful to search for the best regression model of the VIS-NIR. In this study, the single and mixed pretreatment methods can both help train the regression model for the optimal prediction of the VIS-NIR.
3. The chance to build a robust and trustworthy model that can be used across industries for VIS-NIR quantification increases as the sample size grows with the use of boosting algorithms. Boosting algorithms are better than PLS and SVR algorithms, although they have the problem of overfitting. The R\({}^{2}\) values of GBRT and AdaBoost with all testing datasets were 0.939 and 0.941, respectively, while the R\({}^{2}\) values of AdaBoost with low concentration and GBRT with high concentration were 0.945 and 0.947, respectively; the RPD levels were A. The performance of the boosting algorithms is better in this study than in other expert studies on the prediction of the concentration of soil-available potassium [7; 8; 9].  
The feasibility and effectiveness of these calibration methods were verified through a series of comparative experiments, but the reliability of the regression model can be weakened because of the complicated natural samples and the actual environment. Considering this problem, the calibration methods for other soil-available nutrient elements will be greatly challenged in the natural environment. Therefore, our future study will include calibration methods for other nutrient elements and the solution to solve the problems present in the natural environment.  
Conceptualization, X.J. and S.L.; written, X.J.; software, X.J. and J.S.; Experiment J.Z.; supervision, W.Z. All authors have read and agreed to the published version of the manuscript. The research received a financial grant from the research project of Anhui Education Department (KJ2019A0212); key research and development plan project of Anhui province (1804a07020108); Project of Anhui Provincial Key laboratory of Smart Agricultural Technology and Equipment (APKLSATE2019 \(\times\) 001; APKLSATE2019 \(\times\) 005). The authors declare no conflict of interest.