提出了将螯合树脂作为一种新型固相基底来富集水溶液中的Cu元素和Mn元素,之后进行激光诱导击穿光谱检测的方法。选取Cu I 324.75 nm和Mn II 257.61 nm作为特征分析谱线,优化了一系列参数,包括激光能量、延迟时间、激光聚焦位置、样品溶液流速及溶液pH值。在最佳实验参数条件下,建立了Cu元素和Mn元素的定标曲线,Cu元素和Mn元素的检出限分别为0.03 mg·L ^-1和0.098 mg·L ^-1。采用所提方法采集自然水样进行检测,计算得出Cu和Mn的回收率范围分别为93.88%~108.09%和91.99%~103.88%。该方法检测灵敏度较高,也可用于自然水体的检测,在水体重金属检测领域具有广阔的应用前景。

In this work, multivariate detection limits (MDL) estimator was obtained based on the microelectro- mechanical systems–near infrared (MEMS–NIR) technology coupled with two sampling accessories to assess the detection capability of four quality parameters (glycyrrhizic acid, liquiritin, liquiritigenin and isoliquiritin) in licorice from different geographical regions. 112 licorice samples were divided into two parts (calibration set and prediction set) using Kennard– Stone (KS) method. Four quality parameters were measured using high-performance liquid chromatography (HPLC) method according to Chinese pharmacopoeia and previous studies. The MEMS–NIR spectra were acquired from fiber optic probe (FOP) and integrating sphere, then the partial least squares (PLS) model was obtained using the optimum processing method. Chemometrics indicators have been utilized to assess the PLS model performance. Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels, which indicated relatively low sensitivity for low-content analytes (below 1000 parts per million (ppm)). Therefore, MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels. The result suggested that MEMS– NIR technology coupled with fiber optic probe (FOP) and integrating sphere was able to detect minor analytes. The result further demonstrated that integrating sphere mode (i.e., MDL_0.05;0.05, 0.22%) was more robust than FOP mode (i.e., MDL_0.05;0.05, 0.48%). In conclusion, this research proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS–NIR technology and successful to compare two sampling accessories.

选取a, b, c和d四种类型近红外光谱仪, 分别采用透射模式测定清开灵注射液近红外光谱, 以高效液相色谱法测定值作为参考值, 建立不同仪器类型清开灵注射液中黄芩苷偏最小二乘(PLS)和间隔偏最小二乘(iPLS)定量模型, 并计算模型的多变量检测限(MDL)。 四种仪器的PLS模型决定系数(R2)和预测均方差(SEP)分别为0.976 2和230.4 μg·mL-1(a), 0.956 1和246.4 μg·mL-1(b), 0.966 2和264.4 μg·mL-1(c), 0.998 5和71.5 μg·mL-1(d), 其中d型仪器较其他三种类型能获得更好的模型性能。 经iPLS变量筛选后, a和b两种类型仪器得到的iPLS模型R2pre和SEP分别为0.977 1和218.4 μg·mL-1, 0.975 4和219.4 μg·mL-1, 相较其PLS模型预测性能未见明显提高; c和d未筛选出变量。 不同仪器的MDL(Δ0.05, 0.05)均低于250 μg·mL-1, 其中c和d型MDL分别低至58和2.9 μg·mL-1。 表明不同类型仪器定量预测性能和MDL不同。 创新性采用多变量检测限理论探讨了不同类型近红外仪器的检测性能, 这一方法具有可行性。 在实际应用中应根据研究载体的特征选择合适的仪器类型, 以确保定量准确性。

水汽吸收线分布于整个中红外波段,它的存在干扰红外光谱的定量分析。分析了水汽谱吸收对测 量精度的影响。利用傅里叶变换红外光谱法分别测量了经过干燥处理的待测气体和未经干燥处理的待 测气体,并分别对两次测量中CO、CO2 、N2 O和CH4 浓度的测量精度及检测限进行了比较。实验表明: 对样气进行干燥预处理可以提高测量精度和检测限。

差分吸收光谱法(DOAS)是一种高灵敏测量大气痕量气体成分含量的有效的光学遥感方法，该方法基于最小二乘拟合模型，利用获得的痕量气体的差分吸收光学密度与标准的吸收截面进行拟合，反演待测气体的浓度。建立了基于径向基(RBF)神经网络的痕量气体浓度反演的新模型，对网络的隐层参数采用改进最近邻聚类学习算法训练，对输出层权值的训练采用梯度下降算法，使得网络收敛快，能更好地实时、在线反演测量光谱。并针对DOAS技术的特点，把拟合残差输入网络集中训练，使得RBF网络在反演真实痕量气体吸收时，效果更佳。实验结果表明该新型反演方法提高了DOAS系统的反演精度，降低了DOAS系统的探测限。