三维荧光光谱结合EEMD与SWATLD测定水中多环芳烃

引用本文

张慧, 张立娟, 王玉田, 商凤凯, 张艳, 孙洋洋, 王选瑞, 王书涛. 三维荧光光谱结合EEMD与SWATLD测定水中多环芳烃[J]. 光谱学与光谱分析, 2019,39(8): 2595-2601.
ZHANG Hui, ZHANG Li-juan, WANG Yu-tian, SHANG Feng-kai, ZHANG Yan, SUN Yang-yang, WANG Xuan-rui, WANG Shu-tao. Determination of PAHs in Water by Using EEMD and SWATLD Coupled with Three-Dimensional Fluorescence Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2019,39(8): 2595-2601.
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三维荧光光谱结合EEMD与SWATLD测定水中多环芳烃

张慧¹, 张立娟^1,², 王玉田¹, 商凤凯^1,^*, 张艳¹, 孙洋洋¹, 王选瑞¹, 王书涛¹

1. 燕山大学河北省测试计量技术及仪器重点实验室, 河北秦皇岛 066004

2. 河北环境工程学院, 河北秦皇岛 066102

*通讯联系人 e-mail: shang_f_k@163.com

作者简介：王玉田,1952年生,燕山大学电气工程学院教授 e-mail: y.t.wang@163.com

收稿日期: 2018-07-24

基金: 国家自然科学基金项目（61471312, 61771419）; 河北省自然科学基金项目（F2017203220）资助

摘要

多环芳烃(PAHs)类物质具有致畸、致癌、致突变的性质, 严重污染生态环境, 进而对人类的健康及动植物生长造成威胁。 PAHs通过排污、大气沉降、地表径流等各种循环途径进入水环境中, 由于种类众多且化学性质相似, 常规的检测方法如化学滴定法、电化学法等很难实现快速准确的测定。为实现复杂体系中PAHs的定性与定量, 工作中基于三维荧光光谱分析法, 结合集合经验模态分解(EEMD)去噪与自加权交替三线性分解(SWATLD)二阶校正, 对超纯水以及池塘水环境中的苊(ANA)和萘(NAP)进行分析测定。首先选择合理的浓度配制样本, 用FS920荧光光谱仪测得样品的三维荧光光谱, 利用空白扣除法将光谱数据中的散射消除, 得到真实的光谱数据。然后对去除散射的数据进行EEMD降噪处理, 该方法具有自适应性强、参数设置简便的优点, 能够去除嘈杂信息, 提高数据信噪比, 并将去噪参数与快速傅里叶变换、小波滤波和经验模态分解进行比较。最后用SWATLD算法以“数学分离”代替“化学分离”, 对超纯水和池塘水环境中光谱重叠的ANA和NAP进行定性识别和定量预测, 该算法对组分数的选择不敏感, 能够在未知干扰物共存情况下实现多组分目标分析物的同时检测, 即具有“二阶优势”, 并将预测结果与平行因子分析进行比较。结果表明空白扣除法能够成功将拉曼散射消除。 EEMD降噪方法使ANA和NAP的光谱更加规整平滑, 有效信息更加突出, 该方法去噪后数据信噪比为16.845 2, 均方根误差为11.136 6, 波形相似系数为0.990 9, 三项指标均优于快速傅里叶变换和经验模态分解等其他去噪方法, 能达到小波滤波的去噪效果并且不用设置先验参数。利用SWATLD二阶校正方法得到验证样本中ANA与NAP的分解光谱与实际光谱基本吻合, 平均预测回收率分别为96.4%和104.2%, 预测均方根误差分别为0.105和0.092 μg·L^-1; 在存在未知干扰物的池塘水样本中, 分解出的光谱依然能与实际光谱吻合, ANA与NAP两者的平均预测回收率分别为94.8%和105.5%, 预测均方根误差分别为0.067和0.169 μg·L^-1; 与平行因子分析相比, 两项指标均具有优势。

关键词: 多环芳烃; 三维荧光光谱; 集合经验模态分解; 自加权交替三线性

中图分类号:O657.3 文献标志码:A

Determination of PAHs in Water by Using EEMD and SWATLD Coupled with Three-Dimensional Fluorescence Spectroscopy

ZHANG Hui¹, ZHANG Li-juan^1,², WANG Yu-tian¹, SHANG Feng-kai^1,^*, ZHANG Yan¹, SUN Yang-yang¹, WANG Xuan-rui¹, WANG Shu-tao¹

1. Measurement Technology and Instrument Key Laboratory of Hebei Province,Yanshan University, Qinhuangdao 066004, China

2. Hebei University of Environmental Engineering, Qinhuangdao 066102, China;

*Corresponding author

Abstract

Polycyclic aromatic hydrocarbons (PAHs) have teratogenic, carcinogenic and mutagenic properties, which seriously pollute the ecological environment and threaten the health of human beings and the growth of animals and plants. PAHs enter into the water environment through various ways, such as sewage discharge, atmospheric settlement and surface runoff. Because of their large variety and similar chemical properties, it is difficult to achieve rapid and accurate determination of the conventional methods, such as chemical titration and electrochemical methods. In order to realize the qualitative and quantitative analysis of the PAHs in complex system, this article is based on the three-dimensional fluorescence spectrum analysis, and combined with the ensemble empirical mode decomposition(EEMD) which is used to de-noise, and the self-weighted alternating trilinear decomposition(SWATLD) which is used to do two order correction, the ANA and NAP in water and pond water environment were analyzed and measured. The sample is prepared by a reasonable concentration, and the three-dimensional fluorescence spectrum of the sample is measured by the FS920 fluorescence spectrometer. The real spectral data can be obtained by eliminating the scattering of the spectral data by the blank deduction method. Then the EEMD is carried out to remove the noisy information and improve the signal to noise ratio. This method has the advantages of strong self-adaptive and simple parameter setting. The denoising parameters were compared with fast Fourier transform, wavelet filtering and empirical mode decomposition. Finally, using “mathematical separation” instead of “chemical separation”, the SWATLD algorithm is used to identify and predict the ANA and NAP in ultra pure water and pond water environment. The algorithm is not sensitive to the selection of the group fraction, and can be used to detect the multi component object simultaneously under the coexistence of unknown interferon. It has the “two-order advantage”, and the prediction results are compared with parallel factor analysis. The results show that the EEMD method makes the spectrum of ANA and NAP more regular and smooth, and the effective information is more prominent. The signal to noise ratio of the de-noised data is 16.845 2, the root mean square error is 11.136 6, the waveform similarity coefficient is 0.990 9, and this three indexes are better than the other de-noising methods, such as fast Fourier transform and empirical mode decomposition. It can achieve the denoising effect of wavelet filtering without setting a priori parameter. Using the SWATLD two order correction method, the decomposition spectra of ANA and NAP in the verified samples are basically consistent with the actual spectra, and the average predicted recovery rates are 96.4% and 104.2% respectively. The predicted mean square root errors are 0.105 and 0.092 μg·L^-1 respectively. In the pool water samples with unknown interferon, the decomposition spectrum is still consistent with the actual spectrum. The average prediction recovery of ANA and NAP is 94.8% and 105.5%, respectively, and the root mean square root error is 0.067 and 0.169 μg·L^-1 respectively. Compared with the parallel factor analysis, this two indexes have the advantages.

Keyword: Polycyclic aromatic hydrocarbons; Three-dimensional fluorescence spectroscopy; Ensemble empirical mode decomposition; Self-weighted alternating trilinear decomposition

文章图片

引言

水是人类赖以生存的重要资源之一, 随着煤、石油在社会生产和生活中的广泛应用, 有机污染物多环芳烃(polycyclic aromatic hydrocarbons, PAHs)通过排污、大气沉降、地表径流等途径进入水环境, 引发了严重的水环境污染问题。 PAHs是指分子中两个或两个以上苯环以稠环形式连接在一起的碳氢化合物, 具有“ 三致” 作用, 严重威胁着人类的健康^[1]。 1990年我国提出的水中优先控制的68种污染物中, PAHs有7种^[2], 因此开展对水环境中痕量PAHs的定性和定量分析研究显得尤为重要。

检测水环境中PAHs较为常用的方法主要有气相色谱-质谱联用法(GC-MS)和高效液相色谱法(HPLC)。国外Santos Leila.O等^[3]采用单滴微萃取/气相色谱-质谱法(SDME/GC-MS)测定地表水和地下水中的8种PAHs, 方法检出限为0.010.03 μ g· L^-1, 回收率在36%152%之间; 国内陈静等^[4]采用在线固相萃取-高效液相法(Online SPE-HPLC)测定水体中残留的多环芳烃, 检出限小于0.02 μ g· L^-1, 平均回收率在85%116%之间。但是GC-MS对目标物的纯度要求高, 提取过程复杂, 灵敏度和回收率偏低; HPLC抗干扰能力较差, 且容易出现假阳性问题; 因此近年来灵敏度高、专属性强的荧光分析法在水环境检测领域得到广泛应用。

文章采用的三维荧光光谱分析法, 不仅灵敏度高、选择性强, 而且所包含的目标物信息较为完整。为解决PAHs光谱中的噪声问题, 选用自适应性强、应用简便的集合经验模态分解(ensemble empirical mode decomposition, EEMD)。针对两种目标分析物的光谱重叠严重问题, 采用基于自加权交替三线性分解(self-weighted alternating trilinear decomposition, SWATLD)的二阶校正算法, 简单快速地实现了水中痕量PAHs的检测, 实现了以“ 数学分离” 取代繁琐的“ 化学分离” 。

1 原理部分

1.1 荧光分析原理

不同的物质由于其能级结构不同, 在相同的激励下所产生的荧光特性也不同, 故利用荧光分析法, 能够实现物质的定性鉴别; 另外对于同种物质的稀释溶液, 其荧光强度与物质浓度成正比, 因此荧光分析法也可用于定量检测。

1.2 EEMD去噪原理

EEMD的具体步骤如下:

原始信号x(t)中加入白噪声n_i(t)

$X_{i} (t) = x (t) + n_{i} (t) (1)$

对新的信号X_i(t)进行EMD分解, 对得到s个IMF分量c_i_,_s(t)和残差量r_i_,_s(t)重构

$X_{i} (t) = \overset{s}{\sum_{i = 1}} c_{i, s} (t) + r_{i, s} (t) (2)$

重复加入M次白噪声进行分解, 得到{c_1,_s(t)}, {c_2,_s(t)}, …, {c_M_,_s(t)}; 得到信号的IMF分量c_n(t)。

$c_{n} (t) = \frac{1}{M} \overset{M}{\sum_{i = 1}} c_{i, s} (t) (3)$

在EEMD分解中, 原始信号添加高斯白噪声次数M越多, 重构信号越接近真实信号, 但是M不是无限添加的。

去噪效果用信噪比(signal to noise ratio, SNR)、均方根误差(root mean square error, RMSE)、波形相似系数(correlation coefficient, R)来衡量。

$\begin{array}{l} SNR = 10 \log (\frac{\sum_{n} {\dot{x}}^{2} (n)}{\sum_{n} [x (n) - \dot{x} {(n)]}^{2}}) (4) \\ RMSE = \sqrt[]{\frac{1}{n} \sum_{n} [x (n) - \dot{x} {(n)]}^{2}} (5) \\ R = \frac{\overset{n}{\sum_{i = 1}} (x_{i} - \overset{̅}{x}) ({\dot{x}}_{i} - \bar{\dot{x}})}{\sqrt[]{\overset{n}{\sum_{i = 1}} (x_{i} {- \overset{̅}{x})}^{2}} \sqrt[]{\overset{n}{\sum_{i = 1}} ({\dot{x}}_{i} {- \bar{\dot{x}})}^{2}}} (6) \end{array}$

其中x是原始光谱信号, $\dot{x}$ 是去噪后光谱信号, $\overset{̅}{x}$ = $\frac{1}{n} \sum_{n}$ x_i, $\bar{\dot{x}}$ = $\frac{1}{n} \sum_{n} {\dot{x}}_{i}$ 。

1.3 二阶校正方法原理

1.3.1 三线性模型

三维荧光二阶校正中用到的三线性模型可以表示为

$\begin{array}{l} x_{ijk} = \overset{N}{\sum_{n = 1}} a_{in} b_{jn} c_{kn} + e_{ijk}, \\ i = 1, 2, \dots, I; j = 1, 2, \dots, J; k = 1, 2, \dots, K (7) \end{array}$

其中, K表示实验样本个数, I和J表示在选定的I个激发波长和J个发射波长范围内对样本进行扫描, 组成大小为I× J× K的三维响应矩阵。 a_in对应相对激发阵A中的元素, b_jn对应相对发射阵B中的元素, c_kn对应相对浓度阵C中的元素, e_ijk是相对残差阵E中的元素。

1.3.2 SWATLD算法

SWATLD算法是二阶校正算法, 可以解决数据中的共线性问题, 具有收敛速度快、对过量组分数不敏感的优点。在SWATLD算法中, 损失函数是残差平方和(SSR), 可写成

$SSR = \overset{I}{\sum_{i = 1}} \overset{J}{\sum_{j = 1}} \overset{K}{\sum_{k = 1}} {(x_{ijk} - \overset{N}{\sum_{n = 1}} a_{in} b_{jn} c_{kn})}^{2} (8)$

其迭代公式如下

$\begin{array}{l} {c^{T}}_{(k)} = \frac{1}{2} (diagm (B^{+} X_{.. k} A) . / diagm (A^{T} A) + \\ diagm (A^{+} X_{.. k} B) . / diagm (B^{T} B)) (k = 1, \dots, K) (9) \\ {b^{T}}_{(j)} = \frac{1}{2} (diagm (A^{+} X_{. j .} C) . / diagm (C^{T} C) + \\ diagm (C^{+} X_{. j .} A) . / diagm (A^{T} A)) (j = 1, \dots, J) (10) \\ {a^{T}}_{(i)} = \frac{1}{2} (diagm (C^{+} X_{i ..} B) . / diagm (B^{T} B) + \\ diagm (B^{+} X_{i ..} C) . / diagm (C^{T} C)) (i = 1, \dots, I) (11) \end{array}$

其中diagm(* )表示括号中矩阵对角元素组成的新矢量, 等式左端代表矩阵A, B和C的行矢量, 迭代过程中A和B做逐列归一化处理, 交替迭代求解直至损失函数收敛为止, 收敛准则为SSR< 1× 10^-6。

2 实验部分

2.1 仪器及相关设置

称取实验品所用电子秤为FA1004精密电子秤。实验所用光谱图皆由Edinburgh Instruments公司生产的FS920稳态荧光光谱仪测得, 光谱波长响应范围200900 nm, 液氮制冷范围77320 k, 激发光源为功率450 W的脉冲氙灯, 信噪比6 000: 1, 所有检测均使用1 cm石英比色皿进行激发发射扫描。

取ANA和NAP作为实验对象, 为避免一级瑞利散射的影响, 起始发射波长始终滞后起始激发波长40 nm, 仪器参数设置: 激发波长为200~370 nm, 步长10 nm, 发射波长为240~390 nm, 步长2 nm, 狭缝宽度为1.11 mm, 对应荧光谱分辨率为2 nm。

2.2 试剂及样品配置

实验所用试剂ANA、 NAP、甲醇(均购于上海阿拉丁生化科技公司)均为分析纯, 实验用水均为超纯水, 实验用池塘水取自燕山大学后海池塘(使用前用0.45 μ m滤膜进行过滤)。

储备液和工作液的配制: 称取两种PAHs标准样品各0.01 g, 用甲醇溶解并分别定容于2个10 mL容量瓶中, 得到浓度为1 g· L^-1的一级储备液于4 ℃条件下避光保存。分别取0.1 mL一级储备液用超纯水稀释并定容于2个10 mL容量瓶中, 震荡得到10 mg· L^-1的二级储备液。分别取0.1 mL二级储备液, 用超纯水稀释并定容于2个10 mL的容量瓶中, 震荡得到100 μ g· L^-1的工作液。

校正样的配制: 取10个10 mL容量瓶, 编号为C1— C10, 分别加入两种物质不同体积的工作液, 并用超纯水定容。作为校正样本, 其浓度见表1。

表1 样本配制浓度(μ g· L^-1) Table 1 Sample concentrations(μ g· L^-1)

验证样的配制: 取4个10 mL容量瓶, 编号为T1— T4, 分别加入两种物质不同体积的工作液, 并用超纯水定容。作为验证样本, 其浓度见表1。

实际样的配制: 取2个10 mL容量瓶, 编号为T5— T6, 分别加入两种物质不同体积的工作液, 并用池塘水定容。作为实际样本, 其浓度见表1。

3 结果与讨论

3.1 光谱预处理

采用空白扣除法减去溶剂空白样本, 来达到消除拉曼(Raman)散射的目的。以1 μ g· L^-1的NAP为例, 图1(a, b)为原始光谱图, 可以看出除了物质的荧光峰之外, 周围边缘存在很多由溶剂Raman造成的起伏矮峰, 会对物质的分析与检测造成影响。采用空白扣除法处理后如图2(a, b)所示, 由等高线图可以明显看出Raman散射被消除, 物质原有信息基本保持不变。

	Figure Option View Download New Window
	图2 萘(NAP)消除拉曼后三维光谱图(a)和等高线图(b)Fig.2 Three-dimensional spectrum (a) and contour map of NAP (b) after eliminating Raman

	Figure Option View Download New Window
	图1 萘(NAP)原始三维光谱图(a)和等高线图(b)Fig.1 Original three-dimensional spectrum (a) and contour map of NAP (b)

3.2 EEMD去噪效果分析

以1 μ g· L^-1的ANA为例, 进行EEMD降噪处理, 从降噪效果的衡量指标来看, 与快速傅里叶变换(FFT)、小波滤波(Wavelet)、 EMD的去噪效果对比如表2所示, Wavelet与EEMD的去噪效果较其他两种方法要好, 其中EEMD具有自适应性, 省去了参数的经验选取, 比Wavelet更加简便。

表2 去噪效果对比 Table 2 Comparison of de-noising effect

从去噪效果图来看, 去噪前等高线图和发射光谱图如图3(a, b)所示, 去噪后如图4(a, b)所示, 可以明显看出光谱谱线趋于平滑, 噪声干扰基本消除, 且保留了ANA的两个荧光峰, 物质的光谱信息在保持原有波形特征的基础上更加明显, 有利于进行后续的定性和定量数据分析。

	Figure Option View Download New Window
	图3 ANA去噪前等高线图(a)和发射光谱图(b)Fig.3 Contour map (a) and emission spectrum (b) of NAP before de-noising

	Figure Option View Download New Window
	图4 ANA去噪后等高线图(a)和发射光谱图(b)Fig.4 Contour map (a) and emission spectrum (b) of NAP after EEMD

3.3 二阶校正定性定量分析

近年来随着化学计量学的发展, 三维荧光光谱与二阶校正方法的结合广泛地用于环境污染物检测、食品安全检测、药物含量检测等研究领域^[5]。文章采用SWATLD二阶算法对ANA和NAP的混合物进行分析测定, 结果与讨论如下。

3.3.1 未加干扰的验证样本定性分析

选取样本C1— C10, T1— T4构成18× 76× 14的三维数据矩阵, 根据核一致诊断法(core consistancy diagonosis, CORCONDIA)和实际情况确定组分数为2, 用SWATLD算法进行分析, 如图5(a, b)所示。可以看出算法分解出的ANA与NAP的光谱与实际光谱基本吻合, 波峰位置与强度等关键信息都能够很好的识别出来, 说明SWATLD算法可用于多组分PAHs的种类识别。

	Figure Option View Download New Window
	图5 激发(a)与发射光谱(b)分解图Fig.5 Decomposition of excitation (a) and emission (b) spectrum

3.3.2 加入干扰的实际样本定性分析

选取样本C1— C10, T1— T6构成18× 76× 16的三维数据矩阵, 根据CORCONDIA和实际情况确定组分数为3, 定性结果如图6(a, b)所示。由图可以看出实际光谱与分解光谱基本吻合, 在干扰共存的情况下SWATLD算法依然可以定性识别出两种PAHs。

	Figure Option View Download New Window
	图6 含干扰的激发(a)与发射光谱(b)分解图Fig.6 Decomposition of excitation (a) and emission spectrum (b) with interference

3.3.3 验证样本与实际样本定量分析

对不含干扰的和含干扰的两类样本进行浓度预测, 结果见表3。可以看出平均回收率在94.8%105.5%之间, 均方根误差小于0.169 μ g· L^-1, 在干扰存在的情况下SWATLD算法依然得到了很好的预测效果。与平行因子分析(parallel factor analysis, PARAFAC)相比, 平均预测回收率高出约3.5%, 均方根误差降低约0.05μ g· L^-1, SWATLD算法的总体效果较好。

表3 浓度预测结果 Table 3 Concentration prediction results

4 结论

利用EEMD进行光谱去噪, 去噪效果优于FFT和EMD, 可以达到小波去噪的效果且无需频繁设置参数, 有效解决了光谱中的噪声问题。

实验利用三维荧光光谱与二阶校正方法SWATLD结合, 能够直接测定复杂水环境中光谱重叠严重的多环芳烃苊和萘, 体现了“ 二阶优势” , 其预测回收率和均方根误差优于PARAFAC方法, 实现了“ 数学分离” 代替“ 化学分离” , 符合绿色分析化学理念。

参考文献

文献列表

[1]	DU Fang-fang, YANG Yi, LIU Min, et al(杜芳芳, 杨毅, 刘敏, 等). China Environmental Science(中国环境科学), 2014, 34(4): 989. [本文引用:1]
[2]	MU Qing-lin, FANG Jie, SHAO Jun-bo, et al(母清林, 方杰, 邵君波, 等). Environmental Science(环境科学), 2015, 36(3): 839. [本文引用:1]
[3]	Santos L O, Dos Anjos J P, Ferreira S L C, et al. Microchemical Journal, 2017, 133: 431. [本文引用:1]
[4]	CHEN Jing, DAI Zhen-yu, XU Qun, et al(陈静, 戴振宇, 许群, 等). Chinese Journal of Analytical Chemistry(分析化学), 2014, 42(12): 1785. [本文引用:1]
[5]	SUN Yan-mei, WU Hai-long, WANG Jian-yao, et al(孙艳梅, 吴海龙, 王建瑶, 等). Chemical Journal of Chinese Universities(高等学校化学学报), 2013, 34(5): 1084. [本文引用:1]

2014

0.0

Fang-fang

, YANG

, LIU

Min

, et al(杜芳芳, 杨毅, 刘敏, 等). China Environmental Science(中国环境科学), 2014, 34(4): 989.

The concentrations of sixteen priority polycyclic aromatic hydrocarbons in 80 topsoil samples in Shanghai were determined by GC-MS. The distribution pattern was discussed, and source apportionment of PAHs in surface soil was conducted by the isomer ratio and principal component analysis. Results showed that the concentrations of PAHs ranged between 0.12μg/g and 24.5μg/g, presenting gradient changes of suburb>urban> rural and traffic area>cultural and educational area>park greenbelt>business district>residential in different functional areas in the city. Dominant compounds were 4rings and 5rings PAHs, which accounted for 49.2% and 27.0%, followed by 3and 6rings, which accounted for 14.8% and 6.6% of the total PAH concentrations, respectively. The lowest was the 2rings PAHs, which only accounted for 2.4% of the total PAH concentrations. In addition, fluoranthene, pyrene and benzopyrene were dominant PAH compounds. Source apportionment indicated that the PAHs originated mainly from burning, mainly oil burning.

应用气相色谱-质谱联用仪(GC-MS)对上海市80个表层土壤样品中16种优控多环芳烃的浓度进行了测定,分析了上海市土壤中PAHs的含量分布特征,并利用同分异构体比值、主成分分析方法对表层土壤中的PAHs进行了源解析.结果表明,80个样点PAHs的含量在0.12~24.5μg/g之间,呈现出郊区>市区>农村的梯度变化,市区内不同功能区采样点呈现出交通区>文教区>公园绿地>商业区>住宅区的梯度变化. PAHs组成以4环和5环为主,平均含量分别占∑PAHs的49.2%和27.0%,其次为3环和6环,分别占∑PAHs的14.8%和6.6%,最低为2环PAHs,仅占2.4%,单体PAHs化合物以荧蒽、芘、苯并芘为主.源解析表明,表层土壤中PAHs的主要来源是燃烧源,主要是石油燃烧.

... 作用, 严重威胁着人类的健康^[1] ...

2015

0.0

... 1990年我国提出的水中优先控制的68种污染物中, PAHs有7种^[2], 因此开展对水环境中痕量PAHs的定性和定量分析研究显得尤为重要 ...

2017

0.0

... O等^[3]采用单滴微萃取/气相色谱-质谱法(SDME/GC-MS)测定地表水和地下水中的8种PAHs, 方法检出限为0 ...

2014

0.0

CHEN

Jing

, DAI

Zhen-yu

, XU

Qun

, et al(陈静, 戴振宇, 许群, 等). Chinese Journal of Analytical Chemistry(分析化学), 2014, 42(12): 1785.

[Chen Jing; Zhang Xiang-Min] Fudan Univ, Dept Chem, Shanghai 200433, Peoples R China.;[Dai Zhen-Yu; Xu Qun] ThermoFisher Sci China, Shanghai 201203, Peoples R China.

... 国内陈静等^[4]采用在线固相萃取-高效液相法(Online SPE-HPLC)测定水体中残留的多环芳烃, 检出限小于0 ...

2013

0.0

SUN

Yan-mei

, WU

Hai-long

, WANG

Jian-yao

, et al(孙艳梅, 吴海龙, 王建瑶, 等). Chemical Journal of Chinese Universities(高等学校化学学报), 2013, 34(5): 1084.

Three-dimensional fluorescence spectroscopy combined with second-order calibration method based on alternating trilinear decomposition(ATLD) algorithm was utilized to the determination of melatonin in functional food. When the number of spectral components was set to 2, the obtained content of melatonin in meiyitian TM melatonin and vitamin B6 capsule(MYT) and nature-power melatonin tablets(NSB) were (3.074±0.019) mg/capsule and (1.649±0.059) mg/tablet, and the average spiked recoveries were (97.9±4.0)% and (103.4±2.7)%, respectively, which demonstrated the reliability and accuracy of the established method. Owing to the second-order advantage achieved by the proposed method, satisfactory results can also be obtained even in the presence of interference(s). Additionally, high sensitivity was achieved because of the use of fluorescence spectroscopy. Besides, the method was also validated by high performance liquid chromatography(HPLC) with fluorescent detector, which is the conventional method for the determination of melatonin, and the results obtained by the two methods were compared by t -test and were found to be in good agreement with each other, but this method was environmentally friendly and easy. So the developed method can be an alternative method for the determination of melatonin and can be used to quality monitoring for functional food containing melatonin.

State Key Laboratory of Chemo/Biosensing Chemometrics, Hunan University, Changsha 410082, China

采用三维荧光光谱结合基于交替三线性分解算法(ATLD)的二阶校正方法测定了2种保健品中褪黑素的含量. 当组分数取2时, 测得美益天褪黑素维生素B6胶囊(MYT)和纽斯葆牌褪黑素片(NSB)中褪黑素的含量分别为(3.074±0.019) mg/capsule和(1.649±0.059) mg/tablet, 平均加标回收率分别为(97.9±4.0)%和(103.4±2.7)%, 检出限为1.4 ng/mL. 结果表明, 该方法准确可靠. 该方法具有"二阶优势", 即使样品中存在干扰, 仍可获得较好的结果, 且灵敏度较高. 采用高效液相色谱(HPLC)法对该方法进行了验证, 用 t -test对2种方法获得的结果进行比较发现, 2种方法的结果之间无显著性差异, 具有良好的一致性, 但本文方法更环保、简单. 因此, 三维荧光光谱结合二阶校正方法可作为测定褪黑素含量的新方法, 用于对含褪黑素的保健类产品进行质量监控.

... 3 二阶校正定性定量分析近年来随着化学计量学的发展, 三维荧光光谱与二阶校正方法的结合广泛地用于环境污染物检测、食品安全检测、药物含量检测等研究领域^[5] ...