激光诱导击穿光谱（LIBS）和波长色散 X 射线荧光（WDXRF）数据融合预测豆种样品中 K、Mg 和 P 的浓度。
Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples.
机构信息
Group of Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, P.O. Box 676, São Carlos, São Paulo State 13565-905, Brazil.
Laboratory of Toxicant and Drug Analyses (LATF), Federal University of Alfenas (Unifal), Alfenas, MG 37130-000, Brazil.
出版信息
Food Res Int. 2020 Jun;132:109037. doi: 10.1016/j.foodres.2020.109037. Epub 2020 Jan 28.
The present study aims to develop a fast and simple method for the determination of potassium (K), magnesium (Mg) and phosphor (P) in bean seed samples employing a data fusion strategy in the low-level with laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) techniques combined with direct solid sample analysis. Univariate and multivariate (multiple linear regression, MLR) calibration and leave-one-out cross validation strategies were evaluated to build the calibration models correlated with reference values obtained by inductively coupled plasma optical emission spectrometry (ICP OES) after microwave-assisted acid digestion. The proposed calibration models for WDXRF and LIBS were tested using 14 samples, where the best results were obtained using the data fusion of both techniques. The standard error of cross validation (SECV) obtained were: 0.12% for K, 0.019% for Mg and 0.10% for P, and the trueness ranged between 89 and 124% for K, 82 to 125% for Mg and 73 to 128% for P. These values showed a good accuracy, precise and robustness of the method and a greater reliability of the results. In addition, the predicted concentrations ranged from 0.97 to 1.55% for K, 0.14 to 0.28% for Mg, and 0.27 to 0.82% for P.
本研究旨在开发一种快速简便的方法，用于测定豆种样品中的钾（K）、镁（Mg）和磷（P），采用激光诱导击穿光谱（LIBS）和波长色散 X 射线荧光（WDXRF）技术与直接固体样品分析相结合的数据融合策略。采用单变量和多变量（多元线性回归，MLR）校准和留一法交叉验证策略来建立与微波辅助酸消解后电感耦合等离子体发射光谱（ICP OES）获得的参考值相关的校准模型。使用 14 个样本对 WDXRF 和 LIBS 的建议校准模型进行了测试，结果表明，两种技术的数据融合效果最佳。获得的交叉验证标准误差（SECV）分别为：K 为 0.12%，Mg 为 0.019%，P 为 0.10%，K 的准确度在 89%至 124%之间，Mg 在 82%至 125%之间，P 在 73%至 128%之间。这些值表明该方法具有良好的准确性、精密度和稳健性，结果更可靠。此外，预测浓度的范围为 K 为 0.97%至 1.55%，Mg 为 0.14%至 0.28%，P 为 0.27%至 0.82%。
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