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<p>Severe threats to food safety and human health are posed by Hg<sup>2</sup>⁺, thereby necessitating the development of rapid, sensitive, and portable on-site detection methods. In the present study, the aspartic acid-functionalized gold nanoparticles (Asp-AuNPs) were prepared via the one-step green synthesis strategy. The structural characteristics of Asp-AuNPs and their Hg<sup>2</sup>⁺-sensing properties were systematically characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared (FT-IR) spectroscopy, and zeta potential measurements. The Asp-AuNPs-based colorimetric sensing platform was developed for the detection of Hg<sup>2</sup>⁺, achieving a limit of detection (LOD) of 2.6&#xa0;nM and a linear detection range from 0.005 to 1.0&#xa0;µM. Concurrently, a smartphone-assisted color feature analysis platform was constructed, which exhibited two distinct linear segments with high correlation coefficients for the quantitative assay of Hg<sup>2</sup>⁺. When applied to real samples, including pears, grapes, potatoes, and Chinese medicinal herbs (<i>Selaginella</i> and <i>Corydalis yanhusuo</i>), this smartphone-integrated system afforded satisfactory recoveries ranging from 90.39 to 109.16%, with relative standard deviations of less than 6.15%. Notably, the analytical results obtained via the smartphone-based platform were found to be in excellent agreement with those derived from the Asp-AuNPs colorimetric method. The principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) were applied for machine learning analysis of colorimetric images. After reducing the high‑dimensional color features with t‑SNE, a k‑nearest neighbors (k‑NN) classifier was used to recognize different Hg<sup>2</sup>⁺ concentration levels, achieving an accuracy of 91.41% with favorable repeatability. The fabricated Asp-AuNPs-based sensing system exhibited excellent sensitivity, selectivity and portability, thereby providing a reliable technical strategy for the on-site detection of Hg<sup>2</sup>⁺ in food and herbal matrices.</p> Graphical Abstract <p></p>

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Highly Sensitive and Selective Colorimetric Sensing of Hg2+ Using Aspartic Acid Functionalized Gold Nanoparticles and Portable Smartphone-Assisted Machine Learning Analysis

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Ling Kang,
  • Qi-Qi Cui,
  • Jing-Jing Chen,
  • Xiao-Bei Shi,
  • Hui Yan,
  • Li-Lin Ge,
  • Tian-Shu Wang,
  • Gui-Sheng Zhou

摘要

Severe threats to food safety and human health are posed by Hg2⁺, thereby necessitating the development of rapid, sensitive, and portable on-site detection methods. In the present study, the aspartic acid-functionalized gold nanoparticles (Asp-AuNPs) were prepared via the one-step green synthesis strategy. The structural characteristics of Asp-AuNPs and their Hg2⁺-sensing properties were systematically characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared (FT-IR) spectroscopy, and zeta potential measurements. The Asp-AuNPs-based colorimetric sensing platform was developed for the detection of Hg2⁺, achieving a limit of detection (LOD) of 2.6 nM and a linear detection range from 0.005 to 1.0 µM. Concurrently, a smartphone-assisted color feature analysis platform was constructed, which exhibited two distinct linear segments with high correlation coefficients for the quantitative assay of Hg2⁺. When applied to real samples, including pears, grapes, potatoes, and Chinese medicinal herbs (Selaginella and Corydalis yanhusuo), this smartphone-integrated system afforded satisfactory recoveries ranging from 90.39 to 109.16%, with relative standard deviations of less than 6.15%. Notably, the analytical results obtained via the smartphone-based platform were found to be in excellent agreement with those derived from the Asp-AuNPs colorimetric method. The principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) were applied for machine learning analysis of colorimetric images. After reducing the high‑dimensional color features with t‑SNE, a k‑nearest neighbors (k‑NN) classifier was used to recognize different Hg2⁺ concentration levels, achieving an accuracy of 91.41% with favorable repeatability. The fabricated Asp-AuNPs-based sensing system exhibited excellent sensitivity, selectivity and portability, thereby providing a reliable technical strategy for the on-site detection of Hg2⁺ in food and herbal matrices.

Graphical Abstract