Summary
This study assessed portable spectroscopic methods (pXRF and DRIFT-MIR) combined with machine learning algorithms as a scalable approach for rapid quality assessment of organic amendments. Both spectroscopic instruments demonstrated similar broad capabilities when paired with machine learning, with pXRF particularly suitable for nutrient and contaminant analysis. The work proposes a novel system for comprehensive nutrient profiling of organic soil amendments without destructive sample preparation.
UK applicability
The methodology is directly applicable to UK organic farming and soil amendment quality assurance systems, offering a cost-effective and rapid alternative to traditional laboratory analysis. Adoption could support UK compliance with organic standards and help assess nutrient content of biosolids and compost products used in UK agriculture.
Key measures
Concentrations of total carbon, nitrogen, and elemental composition; machine learning model performance (cross-validation); suitability of pXRF and DRIFT-MIR spectroscopy for nutrient and contaminant estimation
Outcomes reported
The study evaluated portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy combined with machine learning algorithms to quantify macro- and micronutrient concentrations in organic amendments. Both forest regression and extreme gradient boosting methods were assessed for their ability to estimate nutrient content and contaminants from spectroscopic data.
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