表面增强拉曼散射光谱结合化学计量学方法对蜂蜜中双甲脒的高效检测（英文）

Highly Effective Detection of Amitraz in Honey by Using Surface-Enhanced Raman Scattering Spectroscopy Coupled with Chemometric Methods

本文建立了一种灵敏、快速、环保的表面增强拉曼光谱(SERS)方法,以银纳米棒阵列为基底,测定了蜂蜜中微量的双甲脒.银纳米棒采用斜角沉积法制备,具有良好的SERS活性,增强因子为-10^7.利用密度泛函理论首先对双甲脒的特征峰进行了归属.进一步探测了该方法用于双甲脒检测的可行性,其对蜂蜜中双甲脒的最低检测浓度为0.08mg/kg.另一方面,采用偏最小二乘回归分析方法,对SERS全波谱进行分析并与双甲脒的浓度进行关联,建立了一种多元的双甲脒预测模型.结果表明,蜂蜜样本中双甲脒的预测浓度与实际浓度吻合较好.与传统的基于SERS单峰强度的单变量定量模型相比,本文提出的多元预测模型综合了双甲脒的所有特征峰,提高了检测精度和抗干扰能力.

As an effective and universal acaricide, amitraz is widely used on beehives against varroasis caused by the mite Varroa jacobsoni. Its residues in honey pose a great danger to human health. In this study, a sensitive, rapid, and environmentally friendly surface-enhanced Raman spectroscopy method (SERS) was developed for the determination of trace amount of amitraz in honey with the use of silver nanorod (AgNR) array substrate. The AgNR array substrate fabricated by an oblique angle deposition technique exhibited an excellent SERS activity with an enhancement factor of -10^7. Density function theory was employed to assign the characteristic peak of amitraz. The detection of amitraz was further explored and amitraz in honey at concentrations as low as 0.08 mg/kg can be identified. Specifically, partial least square regression analysis was employed to correlate the SERS spectra in full-wavelength with Camitraz to afford a multiple-quantitative amitraz predicting model. Preliminary results show that the predicted concentrations of amitraz in honey samples are in good agreement with their real concentrations. Compared with the conventional univariate quantitative model based on single peak’s intensity, the proposed multiple-quantitative predicting model integrates all the characteristic peaks of amitraz, thus offering an improved detecting accuracy and anti-interference ability.