三维荧光结合二阶校正快速测定水中酚类

引用本文

王选瑞, 张立娟, 王玉田, 商凤凯, 孙洋洋, 张慧, 张艳, 王书涛. 三维荧光结合二阶校正快速测定水中酚类[J]. 光谱学与光谱分析, 2020,40(1): 113-118.
WANG Xuan-rui, ZHANG Li-juan, WANG Yu-tian, SHANG Feng-kai, SUN Yang-yang, ZHANG Hui, ZHANG Yan, WANG Shu-tao. Rapid Determination of Phenol in Water by Three-Dimensional Fluorescence Combined with Second-Order Calibration[J]. Spectroscopy and Spectral Analysis, 2020,40(1): 113-118.
Doi:10.3964/j.issn.1000-0593(2020)01-0113-06 复制到剪切板

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《光谱学与光谱分析》期刊社所有

三维荧光结合二阶校正快速测定水中酚类

王选瑞¹, 张立娟^1,², 王玉田¹, 商凤凯^1,^*, 孙洋洋¹, 张慧¹, 张艳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: 王选瑞, 1993年生, 燕山大学电气工程学院硕士研究生 e-mail: xuanruiw@163.com

收稿日期: 2018-11-03

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

摘要

酚类化合物在冶金、炼油、机械制造、医药、农药和油漆等工业有广泛的应用, 但酚类化合物具有毒性, 若不加以处理将会对环境造成污染。水是生命之源, 水环境中酚类化合物检测显得尤为重要。三维荧光光谱分析法具有灵敏度高、检测速度快、预处理方便和痕量检测等特点, 二阶校正分析法可以在混合物中分辨出感兴趣的成分。采用三维荧光光谱结合二阶校正方法对水环境中酚类化合物进行测定。实验选用间甲酚和间苯二酚作为被测物质, 配置添加干扰物和不添加干扰物两类样本, 通过FLS920稳态荧光光谱仪测得8个校正样本和8个预测样本的三维荧光光谱数据, 并对其进行数据预处理, 扣除原始光谱中所包含的散射干扰, 并对原始光谱数据进行激发/发射校正, 然后采用db3小波函数生成的小波包对光谱数据进行数据压缩, 去除光谱数据中的冗余信息, 其中压缩分数达到91.67%, 恢复分数达到96.62%。然后分别采用平行因子分析(PARAFAC)和自加权交替三线性分解(SWATLD)两种二阶校正方法对预处理后的光谱数据进行定性和定量分析。根据核一致分析法结合残差判别分析法的分析结果, 设定未添加干扰物样品组分数为2, 添加干扰物样品组分数为3。定性分析结果显示, 无论有无添加干扰物, 两种二阶校正法都能准确的分辨出样本中的间甲酚和间苯二酚, 其中间甲酚的荧光峰位置为 λ_em=298 nm/ λ_ex=274 nm; 间苯二酚的荧光峰位置为 λ_em=304 nm/ λ_ex=275 nm。定量分析结果显示, 用PARAFAC算法测定不添加干扰物的样本时, 对间甲酚和间苯二酚浓度的平均回收率分别达到了93.37%±4.92%和95.19%±5.25%; 测定添加干扰物样本时, 对间甲酚和间苯二酚浓度的平均回收率达到92.09%±2.64%和97.08%±5.26%。用SWATLD算法测定不添加干扰物样本时, 对间甲酚和间苯二酚浓度的平均回收率分别达到了93.11%±4.73%和96.80%±5.04%; 测定添加干扰物样本时, 对间甲酚和间苯二酚浓度的平均回收率达到97.30%±4.52%和96.92%±5.61%, 且两种二阶校正方法得出的预测样本均方差(RMSEP)均小于0.03 mg·L^-1。实验结果表明, 在荧光光谱峰位置相近、光谱严重重叠且有干扰物的情况下, PARAFAC和SWATLD两种二阶校正算法都能对水溶液中的酚类化合物进行快速、准确地测定。

关键词: 三维荧光; 二阶校正; 酚类化合物; 小波包; 平均回收率

中图分类号:O657.3 文献标志码:A

Rapid Determination of Phenol in Water by Three-Dimensional Fluorescence Combined with Second-Order Calibration

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

1. Measurement Technology and Instrument Key Lab of Hebei Provice, Yanshan University, Qinhuangdao 066004, China

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

*Corresponding author

Abstract

Phenolic compounds are widely used in metallurgy, oil refining, machinery manufacturing, medicine, pesticide and paint industries, but they are toxic, if not treated properly they will pollute environment. Water is the source of life, and the detection of phenols in the water environment is particularly important. Three- dimensional fluorescence spectrometry has the characteristics of highly sensitivity, fast detection speed, convenient pretreatment and tracing detection. The second-order correction method can identify the interesting components in compounds. In this paper, three-dimensional fluorescence spectroscopy will be combined with second-order correction method to test phenols in the water. M-cresol and resorcinol were selected as the tested substances in this experiment, and they were divided into two kinds of samples: adding interference and without interference. The data of three-dimensional fluorescence spectra of eight corrected samples and eight predicted samples were measured by FLS920 steady-state fluorescence spectrometer, and the data above were preprocessed: scattering interference contained in the original spectrum was removed; corrected by the excitation/emission correction. Then, the spectral data were compressed by the wavelet packet generated by db3 wavelet function, and the redundant information in the spectral data was removed through this approach. The compression score achieved 91.67%, and the recovery score achieved 96.62%. Then two second-order calibration methods: parallel factor analysis (PARAFAC) and self-weighted alternating trilinear decomposition (SWATLD) were used to analyse the preprocessed data qualitatively and quantitatively separately. According to the results of consistency analysis combined with residual discriminant analysis, the component number of samples without interference was chose as 2, and the samples with interference was chosen as 3. Qualitative analysis showed that regardless of the existing or inexisting interference, these two second-order calibration methods all could identify m-cresol and resorcinol in samples accurately. The fluorescence peak position of m-cresol and resorcinol were located at λ_em=298 nm/ λ_ex=274 nm and λ_em=304 nm/ λ_ex=275 nm separately. The quantitative analysis results show that the average recovery rate of m-cresol and resorcinol reached 93.37%±4.92% and 95.19%±5.25% respectively by PARAFAC without adding interference and under interference, meanwhile the average recovery rate of m-cresol and resorcinol were 92.09%±2.64% and 97.08%±5.28%. Under the same conditions, when we chose SWATLD , the average recovery rate of m-cresol and resorcinol reached 93.11%±4.73% and 96.80%±5.04% respectively, meanwhile the average recovery rate of m-cresol and resorcinol were 97.30%±4.52% and 96.92%±5.61% respectively. The root mean square error of prediction (RMSEP) of the two methods are all less than 0.03 mg·L^-1. The experimental results show that two second-order calibration algorithms: PARAFAC and SWATLD can all quickly and accurately test phenols in water when the fluorescence peaks are contiguous, and the spectra overlap seriously meanwhile there are interference in the compounds.

Keyword: Three-dimensional fluorescence; Second-order correction; Phenolic compounds; Wavelet packet; Average recovery rate

文章图片

引言

酚类化合物在工业生产中应用广泛, 但是工业废水若处理不当将会污染环境, 且酚类化合物具有毒性, 自然水环境的污染, 将会对生命健康造成危害^[1]。酚类化合物能溶于水, 且在水环境中呈现浓度低, 因此需要一种灵敏度高、选择性好的测量方法。

关于酚类的测定方法较多, 吴宏伟^[2]等用离子色谱-化学发光法检测水中的间苯二酚和间苯三酚, 取得满意的结果; 龙珍^[3]等通过高效液相色谱-电化学检测(HPLC-ECD)方法测定丹参中5种酚类, 回收率达到95%以上; 沈丹红^[4]等用高效液相色谱-紫外/质谱检测法联合检测烟叶中的25种酚类, 取得了很好的效果; 徐小民^[5]等采用气质联用法检测水产品中酚类物质, 结果较好。这些方法虽然检测结果较好, 但是需要将被检查的物质与背景干扰物预先分离^[6], 分离步骤繁琐, 且有时难以实现。

三维荧光光谱数据中常常含有环境噪声等冗余信息^[7], 通过小波包变换能对实验数据进行压缩去除冗余信息。三维荧光光谱法不需要繁琐的样本预处理, 且具有灵敏度高、快捷、痕量检测的特点, 可用于多组分混合物的测定。二阶校正法具有“ 二阶优势” , 能在多组分混合样本中对感兴趣的组分直接测定。二阶校正法与三维荧光法结合以提高选择性。

本文采用三维荧光光谱分别与PARAFAC和SWATLD两种二阶校正算法结合, 来测定间甲酚和间苯二酚混合液样本, 取得较满意的结果。

1 实验部分

1.1 仪器

实验使用的光谱仪器是由Edinburgh Instruments公司生产的FLS920稳态荧光光谱仪, 温度设定为20 ℃, 激发波长设定为240:2:320 nm, 发射波长为260:2:360 nm。测量得到的荧光数据在MATLAB的环境下进行数据处理。

1.2 材料与样品配制

实验使用的间甲酚、间苯二酚和对苯二酚(干扰物)都是上海阿拉丁生化科技公司的标准样品, 纯度大于99.5%。

样本配制: (1)称取间甲酚、间苯二酚和对苯二酚各1 g, 分别用去离子水溶解并定容于100 mL的容量瓶中, 得到三种浓度为10 g· L^-1的一级储备液。 (2)分别取三种一级储备液各0.1 mL, 用去离子水定容于3个100 mL的容量瓶中, 震荡5 min, 得到三种10 mg· L^-1的标准溶液。 (3)取不同体积的三种标准溶液进行混合, 用去离子水定容, 形成不同浓度比例混合溶液。如表1所示。

表1 预测样品配制浓度(mg· L^-1) Table 1 Predict samples preparation concentration(mg· L^-1)

2 原理

2.1 三线性模型

用I个激发波长、 J个发射波长对K个样品进行测定^[8], 得到的激发-发射光谱矩阵构成三维荧光光谱矩阵X, 这个矩阵满足下面的三线性成分模型

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

式(1)中, x_ijk是三维光谱矩阵X的一个元素, N为实际对荧光有贡献的组分数, a_in是相对激发光谱阵A(I× N)中的元素, b_jn是相对发射光谱阵B(J× N)中的元素, c_kn是相对浓度阵C(K× N)的元素^[8], e_ijk是X的三维残差数据阵, i代表第i个激发波长, j代表第j个激发波长, k代表第k个样本。

2.2 平行因子分解

平行因子分析(PARAFAC)算法是基于三线性分解理论, 采用交替最小二乘原理的迭代类型三维数据分解算法^[9], 将一个三维阵列X分解为三个载荷矩阵A, B, C。

$σ_{1} (A) = \overset{I}{\sum_{i = 1}} ‖ X_{i..} - B diag (a_{i}) (C^{T})^{+} ‖_{F^{2}}$ (2)

$σ_{2} (B) = \overset{J}{\sum_{j = 1}} ‖ X_{.j.} - C diag (b_{j}) (A^{T})^{+} ‖_{F^{2}}$ (3)

$σ_{3} (C) = \overset{K}{\sum_{k = 1}} ‖ X_{..k} - A diag (c_{k}) (B^{T})^{+} ‖_{F^{2}}$ (4)

式(2)— 式(4)为取得载荷矩阵A, B, C时计算出的残差。

分解过程中的迭代公式如下

${c^{T}}_{k} = diag [A^{+} X_{..k} (B^{T})^{+}]$ (5)

${b^{T}}_{j} = diag [C^{+} X_{.j.} (A^{T})^{+}]$ (6)

${a^{T}}_{i} = diag [B^{+} X_{i..} (C^{T})^{+}]$ (7)

2.3 自加权交替三线性分解

自加权交替三线性分解(SWATLD)^[10]是交替三线性分解(ATLD)的基础上发展起来的, 在内在关系上交替地最小化三个目标函数。 SWATLD利用式(8)— 式(10)对A, B, C进行迭代

$\begin{array}{l} {c^{T}}_{k} = 0.5 [diagm (B^{+} {X^{T}}_{..k} A) / diagm (A^{T} A) + \\ diagm (A^{+} {X^{T}}_{..k} B) \cdot / diagm (B^{T} B)] \end{array}$ (8)

$\begin{array}{l} {b^{T}}_{j} = 0.5 [diagm (A^{+} {X^{T}}_{.j.} C) / diagm (C^{T} C) + \\ diagm (C^{+} {X^{T}}_{.j.} A) \cdot / diagm (A^{T} A)] \end{array}$ (9)

$\begin{array}{l} {a^{T}}_{i} = 0.5 [diagm (C^{+} {X^{T}}_{i..} B) / diagm (B^{T} B) + \\ diagm (B^{+} {X^{T}}_{i..} C) \cdot / diagm (C^{T} C)] \end{array}$ (10)

2.4 小波包变换

小波包^[11]能在压缩信号的同时保留信号中高频有用成分, 是一种很好的数据压缩方法。因此, 选用小波包变换对光谱数据进行压缩。

$\{\begin{array}{l} d_{k}^{j + 1, 2 n} = \sum_{l \in z} h_{l - 2 k} d_{l}^{j, n} \\ d_{k}^{j + 1, 2 n + 1} = \sum_{l \in z} g_{l - 2 k} d_{l}^{j, n} \end{array}$ (11)

$d_{k}^{j, n} = \sum_{l \in z} (h_{k - 2 l} d_{k}^{j + 1, 2 n} + g_{k - 2 l} d_{k}^{j + 1, 2 n + 1})$ (12)

式(11)为小波包分解算法, 式(12)为小波包重构函数。式中 $d_{k}^{j, n}$ 为小波包系数, h_k和g_k分别为正交小波基的滤波器系数, n为小波包函数振荡次数, k为位移时间。

3 数据预处理

3.1 去散射和进行校正

荧光光谱仪扫描得到的三维荧光光谱包含着散射光谱以及光谱仪器对光谱的干扰, 需要进行光谱数据预处理后才能进行分析。

实验采用扣除空白样本法去除光谱的拉曼散射; 对光谱的瑞利散射带作置零处理, 会对混合物的荧光光谱成分分析造成不利影响, 故用Delaunay插值法对瑞利散射带的数据点进行插值处理, 从而去除光谱的瑞利散射。由于光谱受到光谱仪器本身的影响^[7], 所以需要对光谱进行激发和发射校正, 从而去除仪器影响, 得到真实的三维荧光光谱。以表1中C1样本为例, 预处理前后对比如图1所示。

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图1 间甲酚消除散射和校正前后对比
(a): 去除散射和校正前的三维荧光光谱图; (b): 去除散射和校正后的三维荧光光谱图; (c): 去除散射和校正前的指纹图; (d): 去除散射和校正后的指纹图Fig.1 M-cresol eliminates scattering and correction before and after
(a): The three-dimensional fluorescence spectra before correction and decrease scattering; (b): The three-dimensional fluorescence spectra after correction and decrease scattering; (c): The fluoresceoce fingerprint before correction and decrease scattering; (d): The fluorescence fingerprint after correction and decrease scattering

从图1中看出, 原始光谱数据经过预处理后, 光谱的波峰强度明显增强, 瑞利散射明显去除, 得到真实光谱。

3.2 小波包压缩

预处理后的三维荧光光谱数据还有一定的冗余信息, 选用小波函数db3生成的小波包对数据进行压缩。以表1中C1样本为例, 压缩结果如图2所示。

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图2 间甲酚压缩前后对比
(a): 压缩前发射光谱图; (b): 压缩后发射光谱图; (c): 压缩前荧光指纹图; (d): 压缩后荧光指纹图Fig.2 Before and after compression of m-cresol
(a): The emission spectrum before compression; (b): The emission spectrum after compression; (c): The fluorescence fingerprint before compression; (d): The fluorescence fingerprint after compression

从图2中可看出, 间甲酚光谱压缩前后的主要光谱信息没有明显变化。压缩分数达到91.67%, 恢复分数达到96.62%, 说明在对间甲酚光谱的分解量化和重构过程中, 删掉了一些冗余信息, 保留了有用的信息。

4 结果与讨论

4.1 定性分析

用C1— C8(校正样本)和T1— T5(测试样本)组成三维数据阵X(13× 51× 41), 然后分别用PARAFAC和SWATLD算法进行分析。本次实验组分数估计采用核一致和残差分析结合。如图3所示。

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	图3 核一致和残差判别组分数Fig.3 Discriminant the number of components by the core consistency diagnostic and residual analysis

从图3中可以看出当组分数超过2时, 核一致值急剧下降, 一般认为核一致值大于或等于60%时所对应的组分数为正确组分数^[7]; 组分数超过2时残差值基本不变, 因此选择组分数为2。分辨结果如图4所示。

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	图4 分辨的发射波长(a)和分辨的激发波长(b)Fig.4 Resolved emission wavelengths (a) and resolved excitation wavelengths (b)

用C1— C8(校正样本)和L1— L3(测试样本)组成加入干扰时的三维数据阵X(11× 51× 41), 组分数为3。分辨结果如图5所示。

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	图5 分辨的发射波长(a)和分辨的激发波长(b)Fig.5 Resolved emission wavelengths (a) and resolved excitation wavelengths (b)

图4和图5中的(a)为荧光发射光谱图, (b)为荧光激发光谱图, 从(a)和(b)中可以看出, 间甲酚的荧光峰波长位置为λ _em=298 nm/λ _ex=274 nm; 间苯二酚的荧光峰波长位置为λ _em=304 nm/λ _ex=275 nm。

从图4和图5中可以看出两种算法都能对严重重叠的复杂光谱体系进行正确分解, 在荧光峰值附近两种算法分辨结果都接近真实光谱, 说明两种算法都能有效准确的分辨出混合样本中的间甲酚和间苯二酚。

4.2 定量分析

分别用PARAFAC和SWATLD算法得到的归一化相对荧光强度和组分浓度的线性回归方程见表2, 浓度预测结果见表3。

表2 归一化相对荧光强度和组分浓度的线性回归方程 Table 2 Linear regression equation of normalized relative fluorescence intensity and component concentration

表3 间甲酚和间苯二酚的浓度预测和回收率 Table 3 Concentration prediction and recovery of m-cresol and resorcinol

从表3中可以看出, PARAFAC算法对间甲酚的回收率达到87.50%~99.41%, 对间苯二酚的回收率达到88.65%~103.81%; SWATLD算法对间甲酚的回收率达到87.88%~103.33%, 对间苯二酚的回收率达到89.02%~101.82%; SWATLD的平均回收率比PARAFAC的更好, 但是两种算法预测结果差异较小。说明两种二阶校正方法都能准确的定量分析混合物中的间甲酚和间苯二酚。

$RMSEP = {[\frac{1}{K} \overset{K}{\sum_{k = 1}} (c_{act} - c_{pred})^{2}]}^{1 / 2}, K : 样本数; c_{act} : 实际浓度; c_{pred} : 预测浓度。$

5 结论

实验采用db3小波函数生成的小波包对光谱数据进行压缩变换, 压缩分数达到91%, 恢复分数达到96%, 表明小波包压缩变换能有效去除光谱数据中冗余信息。

实验采用三维荧光光谱法分别与SWATLD和PARAFAC两种二阶校正算法相结合来测定间甲酚和间苯二酚的混合样本, SWATLD和PARAFAC算法均能准确分辨出混合物样本中被测物质, 且对预测样本中被测物质浓度预测取得较好结果, 预测均方差(RMSEP)小于0.03 mg· L^-1。实验表明三维荧光光谱结合二阶校正算法能快速、准确测定水环境中的酚类化合物。

参考文献

文献列表

[1]	CHEN Jie-xia, WEI En-ze, XIAN Qi-ming(陈洁霞, 韦恩泽, 鲜啟鸣). Chinese Journal of Chromatography(色谱), 2014, 32(8): 843. [本文引用:1]
[2]	WU Hong-wei, CHEN Mei-lan, SHOU Dan, et al(吴宏伟, 陈梅兰, 寿旦, 等). Chinese Journal of Analytical Chemistry(分析化学), 2012, 40(11): 1747. [本文引用:1]
[3]	LONG Zhen, GAMACHE Paul, GUO Zhi-mou, et al(龙珍, GAMACHE Paul, 郭志谋, 等). Chinese Journal of Chromatography(色谱), 2017, 35(8): 897. [本文引用:1]
[4]	SHEN Dan-hong, LU Xin, CHANG Yu-wei, et al(沈丹红, 路鑫, 常玉玮, 等). Chinese Journal of Chromatography(色谱), 2014, 32(1): 40. [本文引用:1]
[5]	XU Xiao-min, ZHU Yan, LI Rui, et al(徐小民, 朱岩, 李蕊, 等). Chinese Journal of Analytical Chemistry(分析化学), 2011, 39(2): 253. [本文引用:1]
[6]	CAI Qing, WU Hai-long, LI Yuan-na, et al(蔡晴, 吴海龙, 李元娜, 等). Chemistry(化学通报), 2011, 74(1): 47. [本文引用:1]
[7]	WANG Yu-tian, LIU Ting-ting, LIU Ling-fei, et al(王玉田, 刘婷婷, 刘凌妃, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(4): 1171. [本文引用:3]
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[10]	YU Li-li, WU Hai-long, FU Hai-yan, et al(于丽丽, 吴海龙, 付海燕, 等). Chinese Journal of Analytical Chemistry(分析化学), 2011, 39(1): 27. [本文引用:1]
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2014

0.0

CHEN

Jie-xia

, WEI

En-ze

, XIAN

Qi-ming

(陈洁霞, 韦恩泽, 鲜啟鸣). Chinese Journal of Chromatography(色谱), 2014, 32(8): 843.

A determination method of 12 phenolic compounds in soil and sediment samples by gas chromatography-mass spectrometry (GC-MS) analysis coupled with accelerated solvent extraction (ASE) and gel permeation chromatography (GPC) for clean-up was developed. The method detection limits (MDLs) varied from 0.410 μg/kg to 13.1 μg/kg (dry weight), and the average recoveries ranged from 70.7% to 122% with the relative standard deviations (RSDs) of 1.2% to 16%. Based on this method, the levels of 12 phenolic compounds were investigated in 17 soil surrounding a chemical industrial park along the Yangtze River and seven sediment samples collected in the river. It was found that 11 of the 12 phenolic compounds were detected in all of the 24 samples, and only hydroquinone was below the MDL. The contents of the total 12 phenolic compounds were 10.16-30.66 mg/kg in the soil and 18.00-29.83 mg/kg in the sediment, with the average contents of 18.26 and 22.51 mg/kg respectively. It showed that 4-nitrophenol, 4-chloro-3-methylphenol, 2-chlorohydroquinone, 2-methyl-4,6-dinitrophenol and 2,4,6-trichlorophenol were five major phenolic contaminants in the soil and sediment in this study. The pollution levels of the 12 phenolic compounds were low in the soil of the chemical industrial park as well as in the sediment of the Yangtze River, which implied a comparatively low risk for the environment.

State Key Laboratory of Pollution Control & Resource Reuse, Department of Environmental Science, Nanjing University, Nanjing 210023, China

建立了同时测定土壤（底泥）样品中12种酚类化合物的检测方法。采用加速溶剂萃取（ASE）与凝胶渗透色谱（GPC）协同净化进行前处理，气相色谱和质谱联用技术（GC-MS）进行定性定量分析。方法检出限为0.410~13.1 μg/kg（干重），回收率在70.7%~122%之间，相对标准偏差（RSD）为1.2%~16%。基于上述分析方法研究了长江沿岸某化工园区土壤及长江底泥中12种酚类化合物的污染水平。17个土壤样品和7个底泥样品中除对苯二酚外的11种酚类化合物均有检出。土壤和底泥中酚类污染物总含量范围分别为10.16~30.66 mg/kg和18.00~29.83 mg/kg，平均含量分别为18.26 mg/kg和22.51 mg/kg。土壤和底泥中最主要的酚类污染物为4-硝基苯酚和4-氯-3-甲酚，其次为邻氯对苯二酚、4，6-二硝基邻甲基苯酚和2，4，6-三氯酚。该化工园区周边土壤及长江底泥中12种酚类污染物污染水平较低、环境风险较小。

... 引言酚类化合物在工业生产中应用广泛, 但是工业废水若处理不当将会污染环境, 且酚类化合物具有毒性, 自然水环境的污染, 将会对生命健康造成危害^[1] ...

2012

0.0

Hong-wei

, CHEN

Mei-lan

, SHOU

Dan

, et al(吴宏伟, 陈梅兰, 寿旦, 等). Chinese Journal of Analytical Chemistry(分析化学), 2012, 40(11): 1747.

A novel method was established for the simultaneous determination of resorcinol and phloroglucinol in environmental water samples by ion chromatography with chemiluminescence detection. Using 50 mM NaOH as eluent, resorcinol and phloroglucinol can be separated well with IonPac AS19 column and determined on the basis of chemiluminescence reaction of luminol and K4Fe(CN)(6) in a alkaline medium. The optimal CL conditions were 6.0 x 10(-4) M luminol, 0.1 M K4Fe(CN)(6) and 5.5 x 10(-5) M K3Fe(CN)(6). The absence of methanol and acetonitrile in mobile phase made the proposed method more sensitive. Under the optimal conditions, the detection limit (LOD) of resorcinol and phloroglucinol were 4.0 and 4.3 mu g L-1, respectively. The method showed good linearity within the range of 0.05-1.0 mg L-1. The relative standard deviations (RSD) for 0.1 mg L-1 resorcinol and phloroglucinol standard solutions were 0.9% and 1.1% (n = 11). The method was successfully applied to the determination of resorcinol and phloroglucinol in environmental water samples.

[Wu Hong-Wei; Zhu Yan] Hangzhou Univ, Dept Chem, Hangzhou 310028, Zhejiang, Peoples R China.;[Wu Hong-Wei] Xinxiang Med Univ, Dept Chem, Xinxiang 453003, Peoples R China.;[Chen Mei-Lan] Zhejiang Shuren Univ, Coll Biol & Environm Engn, Hangzhou 310015, Zhejiang, Peoples R China.;[Shou Dan] Zhejiang Acad Tradit Chinese Med, Hangzhou 310000, Zhejiang, Peoples R China.

... 关于酚类的测定方法较多, 吴宏伟^[2]等用离子色谱-化学发光法检测水中的间苯二酚和间苯三酚, 取得满意的结果 ...

2017

0.0

... 龙珍^[3]等通过高效液相色谱-电化学检测(HPLC-ECD)方法测定丹参中5种酚类, 回收率达到95%以上 ...

2014

0.0

SHEN

Dan-hong

, LU

Xin

, CHANG

Yu-wei

, et al(沈丹红, 路鑫, 常玉玮, 等). Chinese Journal of Chromatography(色谱), 2014, 32(1): 40.

25 phenolic compounds in tobacco were analyzed using high performance liquid chromatography-ultraviolet/mass spectrometry (HPLC-UV/MS), including absolute quantification of 11 main phenols by HPLC-UV and relative quantification of other 14 phenols by HPLC-MS. The validation results of this method were satisfactory. 11 phenol standards showed good linearity with correlation coefficients (r2) of 0.9993-0.9999 over the concentration range from 0.90 μg/mL to 99.00 μg/mL (the range of chlorogenic acid and rutin was from 0.95 μg/mL to 380.00 μg/mL). Recoveries of the 11 standards were 91.0%-112.4% with the RSD of 0.33%-8.11% at the levels of 22.5-24.8, 45.0-49.5 and 67.5-74.3 μg/mL. The reproducibility of the method was good with the RSD of 1.48%-13.40%. In addition, the intra-day and inter-day precision were also satisfactory (RSD was 0.35%-15.54%). Mature fresh tobacco leaves from Yunnan, Henan and Guizhou were analyzed using this method. The results showed that the total amount of main phenols was Guizhou＞Yunnan＞Henan and there was significant difference between Guizhou and Henan. The amount of rutin was Yunnan＞Guizhou＞Henan, and there were significant differences between each other. The method is reproducible with a simple pretreatment covering a wide range of phenols, and can be applied in batch sample analysis of tobacco leaves.

建立了烟叶中25种酚类物质的高效液相色谱-紫外/质谱（HPLC-UV/MS）测定方法，其中11种主要酚类物质采用HPLC-UV法进行绝对定量分析，其余采用HPLC-MS法进行半定量分析。方法验证结果表明：11种酚类标准物质在0.90~99.00 μg/mL的范围内（绿原酸及芸香苷为0.95~380.00 μg/mL）呈现很好的线性关系，相关系数在0.9993~0.9999之间。在22.5~24.8、45.0~49.5、67.5~74.3 μg/mL三个水平的加标回收率为91.0%~112.4%。方法重复性的相对标准偏差为1.48%~13.40%，日内日间精密度的相对标准偏差为0.35%~15.54%。采用本方法对云南、河南、贵州的成熟期新鲜烟叶样品进行了分析，主要酚类物质总量为贵州＞云南＞河南，贵州与河南两地间存在显著性差异；芸香苷含量为云南＞贵州＞河南，且相互间有显著性差异。本方法预处理简单，酚类物质的分析覆盖面广，可用于批量烟叶样品检测。

... 沈丹红^[4]等用高效液相色谱-紫外/质谱检测法联合检测烟叶中的25种酚类, 取得了很好的效果 ...

2011

0.0

Xiao-min

, ZHU

Yan

, LI

Rui

, et al(徐小民, 朱岩, 李蕊, 等). Chinese Journal of Analytical Chemistry(分析化学), 2011, 39(2): 253.

A method was developed for the determination of phenols in soak water from drinking water related products by gas chromatography-mass spectrometry (GC-MS) with on-column large volume injection (OCI-LVI) and coupled column separation. The sample was firstly soaked according to related standard method. And then, 0.2 g/L of Na(2)S(2)O(3) was used as reductant to remove excessive chlorine. After spiked 2,4-dibromo-phenol as internal standard, 10 mL of the soak water was adjusted to pH 6. 8, saturated by NaCl and extracted by 1 mL of organic solvent. Approximately 30 mu L. of up-layer extract was used as injecting sample. The advantage of the short coupled polar column in increasing retention capacity of volatile phenols in OCI-GC-MS system was discussed. The LODs were 0.1-1 mu g/L. The recoveries were 76. 7%-112% with 5. 94%-12. 4% of RSD.

[Xu Xiao-Min; Song Guo-Liang; Han Jian-Long; Zhao Yong-Xin; Ying Ying; Feng Liang; Zhang Nian-Hua; Ren Yi-Ping] Zhejiang Prov Ctr Dis Control & Prevent, Hangzhou 310051, Zhejiang, Peoples R China.;[Xu Xiao-Min; Zhu Yan] Zhejiang Univ, Dept Chem, Hangzhou 310028, Peoples R China.;[Li Rui] Wenzhou Med Coll, Wenzhou 325035, Peoples R China.

... 徐小民^[5]等采用气质联用法检测水产品中酚类物质, 结果较好 ...

2011

0.0

... 这些方法虽然检测结果较好, 但是需要将被检查的物质与背景干扰物预先分离^[6], 分离步骤繁琐, 且有时难以实现 ...

2018

0.0

... 三维荧光光谱数据中常常含有环境噪声等冗余信息^[7], 通过小波包变换能对实验数据进行压缩去除冗余信息 ...

... 由于光谱受到光谱仪器本身的影响^[7], 所以需要对光谱进行激发和发射校正, 从而去除仪器影响, 得到真实的三维荧光光谱 ...

... 从图3中可以看出当组分数超过2时, 核一致值急剧下降, 一般认为核一致值大于或等于60%时所对应的组分数为正确组分数^[7] ...

2011

0.0

... 1 三线性模型用I个激发波长、 J个发射波长对K个样品进行测定^[8], 得到的激发-发射光谱矩阵构成三维荧光光谱矩阵X, 这个矩阵满足下面的三线性成分模型 ...

... N)的元素^[8], e_ijk是X的三维残差数据阵, i代表第i个激发波长, j代表第j个激发波长, k代表第k个样本 ...

2017

0.0

... 2 平行因子分解平行因子分析(PARAFAC)算法是基于三线性分解理论, 采用交替最小二乘原理的迭代类型三维数据分解算法^[9], 将一个三维阵列X分解为三个载荷矩阵A, B, C ...

2011

0.0

Li-li

, WU

Hai-long

, FU

Hai-yan

, et al(于丽丽, 吴海龙, 付海燕, 等). Chinese Journal of Analytical Chemistry(分析化学), 2011, 39(1): 27.

A new and effective approach was developed for the quantitative analysis of oxaprozin in human urine samples by the incorporation of excitation-emission matrix fluorescence (EEM) and second-order calibration methodologies based on self-weighted alternating trilinear decomposition (SWATLD) and alternating normalization-weighted error (ANWE) algorithms, respectively. With the application of a second-order advantage, excitation spectral profiles and emission spectral profiles of oxaprozin were effectively resoluted, even in the presence of urine and other homogeneous drugs such as nabumetone or naproxen. The average recoveries of oxaprozin in urine samples and the precision of test results are satisfactory. In addition, the accuracy of these two algorithms was also evaluated through figures of merit, such as sensitivity (SEN), selectivity (SEL), limit of detection (LOD) and limit of quanti-ication (LOQ). Both methods were verified by t-test and the elliptical joint confidence region. The results show that both algorithms were accurate and reliable. The method is simple, without previous separation procedure.

[Yu Li-Li; Wu Hai-Long; Fu Hai-Yan; Yu Yong-Jie; Liu Ya-Juan; Qing Xiang-Dong; Yu Ru-Qin] Hunan Univ, State Key Lab Chemo Biosensing & Chemometr, Changsha 410082, Hunan, Peoples R China.

... 3 自加权交替三线性分解自加权交替三线性分解(SWATLD)^[10]是交替三线性分解(ATLD)的基础上发展起来的, 在内在关系上交替地最小化三个目标函数 ...

2013

0.0

... 4 小波包变换小波包^[11]能在压缩信号的同时保留信号中高频有用成分, 是一种很好的数据压缩方法 ...