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In order to better understand and solve nutrients’ interferences, an orthogonal experimental design has been used, based on Hoagland fertilizer solutions, a widely used complete and complex nutrient mixture. The experimental factorial design consisted of eight orthogonal levels of N, P and K rendered on 83 of different samples of Hoagland solution, each one with its own specific concentration of NPK. Concentration ranges were varied in a target analyte independent style: [N]= [103.17-554.85] ppm; [P]= [15.06-515.35] ppm; [K]= [113.78-516.45] ppm, by dilution from individual stock solutions. This strategy allowed the variation of each parameter individually, maintaining the remaining constant, enabling the individual variations as well as their correlations to be obtained. A UV-Vis-based Artificial Intelligence-enhanced (AI) system was used for quantification of NPK on the analysed samples. It featured an advanced processing algorithm named Self-Learning Artificial Intelligence (SL-AI).
From the analysis of the acquired and processed data, it was possible to understand that N spectral features are dominant, whereas P and K will behave as interferents, with information on P properties not being very evident on spectra. The obtained results allowed very good quantifications for N and K, with errors of 6.7% (0.997) and 3.8% (0.987), respectively, to be achieved. Regarding P, as expected, only satisfactory results were obtained, corresponding to a qualitative grade. The developed system can be of great potential for monitoring and quantification of NPK in hydroponic platforms.
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