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<p>The detection of melamine in milk has consistently been a crucial issue in food safety. Near-infrared spectroscopy is widely used for rapid, non-destructive quantitative analysis and adulteration detection in dairy products, as evidenced by its application in detecting components, such as moisture, fat, and protein, and identifying adulterants, such as water and starch. Despite advancements in qualitative and quantitative models using near-infrared spectroscopy for melamine detection in milk, such as those employing discriminant analysis and partial least squares methods, the technique still faces challenges due to its inherent low sensitivity and high detection limits. This paper proposes a novel method for the detection of melamine in milk based on&#xa0;surface-enhanced near-infrared absorption (SENIRA)&#xa0;spectroscopy with gold nanoparticles. Gold nanospheres were successfully fabricated as substrates for enhancing near-infrared spectroscopy signals, leveraging their unique quantum confinement effects and interactions with light. Utilizing a wavelength range of 900 ~ 1700&#xa0;nm and various chemometric methods, the concentration of melamine in milk, ranging from 0.0001 to 0.1&#xa0;mg/mL, was quantified. The modelling effects were found to be favourable, with a calibration correlation coefficient (R<sub>c</sub><sup>2</sup>) of 0.9853, a prediction correlation coefficient (R<sub>p</sub><sup>2</sup>) of 0.9837, and a root mean square error of calibration (RMSEC) of 0.0059, the root mean square error of prediction (RMSEP) was 0.0066, the relative prediction deviation (RPD) was 5.3940, and the average recovery rate was calculated to be 117%. The combination of a high correlation coefficient a low prediction error, a high RPD value, and acceptable recovery rates suggests that the established model is&#xa0;robust and has competent predictive performance. Furthermore, experiments have demonstrated the feasibility of using SENIRA in conjunction with chemometrics for the determination of melamine content in milk, a process that aligns with the successful application of HPLC and NIR spectroscopy techniques as detailed in recent studies.</p> Graphical Abstract <p></p>

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Enhanced near-infrared spectroscopy for rapid and sensitive detection of melamine in milk: a novel methodological approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Wenliang Qi,
  • Qingqing Jiang

摘要

The detection of melamine in milk has consistently been a crucial issue in food safety. Near-infrared spectroscopy is widely used for rapid, non-destructive quantitative analysis and adulteration detection in dairy products, as evidenced by its application in detecting components, such as moisture, fat, and protein, and identifying adulterants, such as water and starch. Despite advancements in qualitative and quantitative models using near-infrared spectroscopy for melamine detection in milk, such as those employing discriminant analysis and partial least squares methods, the technique still faces challenges due to its inherent low sensitivity and high detection limits. This paper proposes a novel method for the detection of melamine in milk based on surface-enhanced near-infrared absorption (SENIRA) spectroscopy with gold nanoparticles. Gold nanospheres were successfully fabricated as substrates for enhancing near-infrared spectroscopy signals, leveraging their unique quantum confinement effects and interactions with light. Utilizing a wavelength range of 900 ~ 1700 nm and various chemometric methods, the concentration of melamine in milk, ranging from 0.0001 to 0.1 mg/mL, was quantified. The modelling effects were found to be favourable, with a calibration correlation coefficient (Rc2) of 0.9853, a prediction correlation coefficient (Rp2) of 0.9837, and a root mean square error of calibration (RMSEC) of 0.0059, the root mean square error of prediction (RMSEP) was 0.0066, the relative prediction deviation (RPD) was 5.3940, and the average recovery rate was calculated to be 117%. The combination of a high correlation coefficient a low prediction error, a high RPD value, and acceptable recovery rates suggests that the established model is robust and has competent predictive performance. Furthermore, experiments have demonstrated the feasibility of using SENIRA in conjunction with chemometrics for the determination of melamine content in milk, a process that aligns with the successful application of HPLC and NIR spectroscopy techniques as detailed in recent studies.

Graphical Abstract