Institute of Nuclear Science & Technology, University of Nairobi, P.O. Box 30197-00100 Nairobi, Kenya.
Anal Chim Acta. 2012 Jun 4;729:21-5. doi: 10.1016/j.aca.2012.04.007. Epub 2012 Apr 21.
Precision agriculture depends on the knowledge and management of soil quality (SQ), which calls for affordable, simple and rapid but accurate analysis of bioavailable soil nutrients. Conventional SQ analysis methods are tedious and expensive. We demonstrate the utility of a new chemometrics-assisted energy dispersive X-ray fluorescence and scattering (EDXRFS) spectroscopy method we have developed for direct rapid analysis of trace 'bioavailable' macronutrients (i.e. C, N, Na, Mg, P) in soils. The method exploits, in addition to X-ray fluorescence, the scatter peaks detected from soil pellets to develop a model for SQ analysis. Spectra were acquired from soil samples held in a Teflon holder analyzed using (109)Cd isotope source EDXRF spectrometer for 200 s. Chemometric techniques namely principal component analysis (PCA), partial least squares (PLS) and artificial neural networks (ANNs) were utilized for pattern recognition based on fluorescence and Compton scatter peaks regions, and to develop multivariate quantitative calibration models based on Compton scatter peak respectively. SQ analyses were realized with high CMD (R(2)>0.9) and low SEP (0.01% for N and Na, 0.05% for C, 0.08% for Mg and 1.98 μg g(-1) for P). Comparison of predicted macronutrients with reference standards using a one-way ANOVA test showed no statistical difference at 95% confidence level. To the best of the authors' knowledge, this is the first time that an XRF method has demonstrated utility in trace analysis of macronutrients in soil or related matrices.
精准农业依赖于土壤质量 (SQ) 的知识和管理,这需要经济实惠、简单快捷但准确的生物可利用土壤养分分析方法。传统的 SQ 分析方法繁琐且昂贵。我们展示了我们开发的一种新的化学计量学辅助能量色散 X 射线荧光和散射 (EDXRFS) 光谱方法在直接快速分析土壤中痕量“生物可利用”大量营养素(即 C、N、Na、Mg、P)方面的应用。该方法除了 X 射线荧光之外,还利用土壤颗粒中检测到的散射峰来开发 SQ 分析模型。使用 (109)Cd 同位素源 EDXRF 光谱仪从置于聚四氟乙烯 (Teflon) 支架中的土壤样品中采集光谱,分析时间为 200 秒。基于荧光和康普顿散射峰区域,利用主成分分析 (PCA)、偏最小二乘法 (PLS) 和人工神经网络 (ANNs) 等化学计量技术进行模式识别,并基于康普顿散射峰分别开发多元定量校准模型。基于荧光和 Compton 散射峰区域,利用主成分分析 (PCA)、偏最小二乘法 (PLS) 和人工神经网络 (ANNs) 等化学计量技术进行模式识别,并基于 Compton 散射峰分别开发多元定量校准模型。通过使用单向方差分析 (ANOVA) 测试对预测的大量营养素与参考标准进行比较,在 95%置信水平下没有统计学差异。据作者所知,这是首次证明 XRF 方法在土壤或相关基质中痕量分析大量营养素方面具有实用性。