Potential of Vis-NIR to measure heavy metals in different varieties of organic-fertilizers using Boruta and deep belief network.

The quick identification of heavy metals is of major importance and is beneficial for controlling the fertilizer production process in the fertilizer industries. This work aimed to use visible and near-infrared spectroscopy (Vis-NIR), Boruta, and deep learning to establish rapid heavy metals screening methods. Boruta algorithm was used to extract appropriate wavelengths, and a deep belief network (DBN) was computed to determine the amounts of various heavy metals such as chromium (Cr), cadmium (Cd), lead (Pb), and mercury (Hg) for both the entire and selected wavelengths. To assess the model, coefficient of determination (R), root mean squared error (RMSE), and residual prediction deviation (RPD) were used to calculate the reliability of the model. The results of the selected wavelengths were excellent and much higher than the full wavelengths with Rp = 0.96, RMSEP = 0.2017 mg kg and RPDpred = 5.0 for Cr; Rp = 0.91, RMSEP = 0.2832 mg kg and RPDpred = 3.4 for Pb; Rp = 0.90, RMSEP = 0.2992 mg kg, and RPDpred = 3.3 for Hg. Descent prediction was obtained also for Cd (Rp = 0.87, RMSEP = 0.3435 mg kg, and RPDpred = 2.7). To further assess the robustness of the DBN, it was compared with conventional machine learning methods such as support vector machine for regression (SVR), k nearest neighbor (KNN), and partial least squares (PLS). The overall results indicated that the Vis-NIR technique coupled with Boruta and DBN could be reliable and accurate for screening heavy metals in organic fertilizers.