World Agroforestry (ICRAF), Nairobi, Kenya.
Department of Anthropology, University of New Mexico, Albuquerque, NM, United States of America.
PLoS One. 2020 Dec 10;15(12):e0242821. doi: 10.1371/journal.pone.0242821. eCollection 2020.
Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total elemental composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments.
便携式 X 射线荧光（pXRF）和漫反射傅里叶变换中红外（DRIFT-MIR）光谱是用于材料特性分析的快速且具有成本效益的分析工具。在这里，我们评估了这些方法对于分析有机肥料中总碳、氮和多种元素的总元素组成的适用性。我们开发了机器学习方法来快速定量样品中宏量和微量元素的浓度，并提出了一种有机肥料质量评估的新系统。使用来自 pXRF 和 DRIFT-MIR 光谱的数据，我们使用了两种类型的机器学习方法，森林回归和极端梯度提升。我们进行了交叉验证试验来评估在每种仪器上生成的模型的泛化能力。这两种方法都表现出了使用机器学习来估计养分的相似的广泛能力，pXRF 适用于营养物和污染物。结果表明，便携式光谱仪与机器学习相结合是一种可扩展的解决方案，可以为有机肥料提供全面的养分分析。
