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<p>Traditional “gold standard” methods for quantifying arsenic (As) in rice are not ideal for routine analysis due to their time-consuming, expensive, and complex nature. Therefore, the aim of this study was to develop a simple, rapid, and minimally destructive method applying X-ray fluorescence (XRF) spectroscopy and chemometric modeling to quantify As in rice. Milled rice was spiked with As (63.47 – 553.43&#xa0;μg&#xa0;kg<sup>−1</sup>), pelletized, and analyzed utilizing an energy-dispersive XRF spectrometer. The resultant spectra were used to generate a prediction model via partial least squares regression (PLSR), which showed strong predictive capabilities (<i>R</i><sup>2</sup> = 0.99, RMSEC = 14.3&#xa0;μg&#xa0;kg<sup>−1</sup>) and strong sensitivity (LOD = 27.76&#xa0;μg&#xa0;kg<sup>−1</sup>, LOQ = 92.52&#xa0;μg&#xa0;kg<sup>−1</sup>). Validation was conducted using a certified reference material, yielding an error of prediction of only 8.96%. Analysis of rice and rice-based foods showed strong agreement between the current study and traditional methods, demonstrating its robust capabilities for routine analysis.</p>

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Development of a Rapid, Minimally Destructive, and Accurate Method to Quantify Arsenic in Rice and Rice-Based Foods Utilizing X-ray Fluorescence Spectroscopy and Chemometrics

  • 期刊论文

  • 发表日期：

    2026年1月15日

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  • Murphy Carroll,
  • Zili Gao,
  • Lili He

摘要

Traditional “gold standard” methods for quantifying arsenic (As) in rice are not ideal for routine analysis due to their time-consuming, expensive, and complex nature. Therefore, the aim of this study was to develop a simple, rapid, and minimally destructive method applying X-ray fluorescence (XRF) spectroscopy and chemometric modeling to quantify As in rice. Milled rice was spiked with As (63.47 – 553.43 μg kg−1), pelletized, and analyzed utilizing an energy-dispersive XRF spectrometer. The resultant spectra were used to generate a prediction model via partial least squares regression (PLSR), which showed strong predictive capabilities (R2 = 0.99, RMSEC = 14.3 μg kg−1) and strong sensitivity (LOD = 27.76 μg kg−1, LOQ = 92.52 μg kg−1). Validation was conducted using a certified reference material, yielding an error of prediction of only 8.96%. Analysis of rice and rice-based foods showed strong agreement between the current study and traditional methods, demonstrating its robust capabilities for routine analysis.