三维荧光光谱结合二阶校正方法同时测定水中两种酚类

引用本文

孙洋洋, 张立娟, 王玉田, 商凤凯, 王选瑞, 张慧. 三维荧光光谱结合二阶校正方法同时测定水中两种酚类[J]. 光谱学与光谱分析, 2020,40(1): 119-124.
SUN Yang-yang, ZHANG Li-juan, WANG Yu-tian, SHANG Feng-kai, WANG Xuan-rui, ZHANG Hui. Determination of Two Phenols in Water by Three Dimensional Fluorescence Spectroscopy Combined with Second-Order Calibration Method[J]. Spectroscopy and Spectral Analysis, 2020,40(1): 119-124.
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三维荧光光谱结合二阶校正方法同时测定水中两种酚类

孙洋洋¹, 张立娟², 王玉田^1,^*, 商凤凯¹, 王选瑞¹, 张慧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: y.t.wang@163.com

作者简介: 孙洋洋, 1991年生, 燕山大学电气工程学院硕士研究生 e-mail: ysu_sunyangyang@163.com

收稿日期: 2018-11-23

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

水是生命之源, 人们日常生产生活离不开水。近年来水体污染日趋严重, 已经危害到人类的健康。酚类化合物(Phenolic Compound)是一种广泛存在且很难降解的有机污染物, 指的是芳香烃中苯环上的氢原子被羟基取代所生成的含羟基衍生物, 毒性很强, 对动植物及人类的生命活动有严重危害。实验研究对象选取间苯二酚(resorcinol, RES)和对苯二酚(hydroquinone, HYD)来配制待测样本, 并且在其中3组预测样本中加入苯酚(phenol, PHE)作为干扰物, 待测样本和空白溶剂分别用FS920稳态荧光光谱仪(edinburgh instruments, EI)扫描得到荧光光谱数据。对所得到的数据通过扣除空白溶剂法来消除拉曼散射的影响, 得到的数据在消除干扰的同时最大程度保留下来原光谱所包含的重要信息。校正后光谱变得更加圆滑, 荧光强度显著增强, 因此, 校正处理后的光谱信息更为准确。利用三维荧光光谱(EEM)结合平行因子分析(PARAFAC)和交替惩罚三线性分解(APTLD)两种二阶校正方法, 分别完成在不含干扰物和含有干扰物、同时激发-发射光谱严重重叠时对间苯二酚、对苯二酚的快速、直接、准确测量, 并给出定性、定量分析结果。 PARAFAC算法对混合体系的组分数(即化学秩)较敏感, 组分数选取过大易使其陷入计算“沼泽”, 迭代次数增多, 计算耗时变长。故本文利用核一致诊断法(CORCONDIA)预估计出准确的组分数, 保证PARAFAC算法更加快速准确。从定性分析结果知, 当不含有干扰物时, PARAFAC能够准确分辨出间苯二酚和对苯二酚, 二者荧光峰位置极为接近, 很难用传统方法分辨, 体现出将三维荧光光谱技术与化学计量学二阶校正方法相结合所具有的“二阶优势”; 定量分析结果给出, 在有干扰物共存时, 分别应用两种二阶校正法解析光谱数据结果显示: PARAFAC的浓度预测回收率为93.4%±0.5%~97.1%±1.0%, 预测均方根误差小于0.190 mg·L^-1; APTLD的浓度预测回收率为95.9%±1.6%~97.2%±0.8%, 预测均方根误差小于0.116 mg·L^-1, 通过比较两种方法性能得: PARAFAC对待测物组分数敏感, 对待分解的光谱数据严格线性要求高; 而APTLD对混合物组分数不敏感, 计算速度快, 抗噪声能力较强, 结果稳定, 具有较明显的优势。

关键词: 交替惩罚三线性分解; 三维荧光光谱; 二阶优势; 精密度; 酚类

中图分类号:O657.3 文献标志码:A

Determination of Two Phenols in Water by Three Dimensional Fluorescence Spectroscopy Combined with Second-Order Calibration Method

SUN Yang-yang¹, ZHANG Li-juan², WANG Yu-tian^1,^*, SHANG Feng-kai¹, WANG Xuan-rui¹, ZHANG Hui¹

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

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

*Corresponding author

Abstract

Water is the source of life, which is indispensable to people’s diurnal production and life. In recent years, water contamination has become increasingly severe, which has endangered humanity health. Phenolic compounds are organic pollutants that are widespread and difficult to degrade, which refers to hydroxyl-containing derivatives produced by hydroxyl substitution of hydrogen atoms in benzene rings of aromatic hydrocarbons. They are highly toxic to animal, plant and human life activities. Resorcinol (RES) and hydroquinone (HYD) were selected as research object of experiment, and phenol (PHE) was added to three groups of predicted samplesas interference. The samples and blank solvent were scanned by the laboratory FS920 steady-state fluorescence spectrometer to obtain fluorescence spectrum data. The influence of Raman scattering would be eliminated through the deduction of blank solvents method. The obtained data contain the important information in the original spectrum with the greatest extent is preserved while eliminating interference. The spectrum becomes smoother and the fluorescence intensity raise significantly, so the spectral information is more accurate after correction. Second-order correction methods: parallel factor analysis (PARAFAC) and alternating penalty trilinear decomposition (APTLD) together with three-dimensional fluorescence spectroscopy (EEM) were used to measure RES and HYD in form of qualitative and quantitative analysis fast, directly and accurately in two cases: under interference and without interfering stuff meanwhile excitation-emission spectra overlap severely. Because PARAFAC algorithm is sensitive to the component number (i. e. chemical rank) of the mixture system, when the component number is too large the algotithem will arise: falling into the “swamp”, iterations number increases more time consuming. In this paper, Core Consistency Diagnosis (CORCONDIA) is used to estimate component number precisely, which ensures algorithm calculating faster and more accurate. As qualitative analysis results showed that PARAFAC can accurately distinguish RES and HYD without interference. The peak position of RES and HYD are very close, thus it is difficult to distinguish them via traditional methods and “second-order advantage” of combining three-dimensional fluorescence spectroscopy with second-order calibration is demonstrated. The results of quantitative analysis give out that the accuracy of this method is slightly reduced and RMSEP value increases slightly in the presence of interference, but these two methods can still complete the determination accurately. The recovery rate of PARAFAC is 93.4%±0.5%~97.1%±1.0% and the predicted root mean square error is less than 0.190 mg·L^-1. Manwhile, the recovery rate of APTLD is 95.9%±1.6%~97.2%±0.8% and the predicted root mean square error is less than 0.116 mg·L^-1. By comparing the performance of the above methods, we know PARAFAC is sensitive to the number of components in the samples and strictly linear to the decomposed spectral data. However APTLD has obvious advantages: being insensitive to the number of components, fast calculation speed, strong anti-noise ability, stable results, all of which highlight its advantages.

Keyword: APTLD; Three-dimensional fluorescence spectroscopy; Second-order advantage; Precision; Phenol

文章图片

引言

酚类是水体污染中的一类主要污染物, 在水中都是痕量级存在^{[1, 2, 3]}。目前, 酚类的测定方法较多, 欧阳等用气相色谱法(GC)测定人血液中的五氯酚^[4]; 朱峰等利用高效液相色谱法(HPLC)测定纺织品中10种酚类^[5]; 顾娟红等用气相色谱— 质谱联用法(GC-MS)测定纺织品中5种苯酚类化合物^[6]。使用这些方法测定时需要对样品进行复杂的预处理, 不但成本高, 还需要大量使用有机溶剂, 实际应用很不方便^[7]。近年来, 三维荧光光谱技术凭借其高灵敏度、不破坏样品、操作简便等优点, 且检测下限通常比分光光度法低2~4个数量级, 成为监测痕量有机物的分析工具。由于国内针对三维荧光光谱法结合化学计量学方法对水中酚类污染监测的报道较少, 因此本研究具有重要意义。

间苯二酚(RES)和对苯二酚(HYD)是苯二酚的2种同分异构体, 官能团性质相似^[8], 理化方法难分离, 不便于定量分析。尽管在酸性或中性介质中有较强的荧光, 但光谱严重重叠, 很难通过常规的荧光法直接测定。化学计量学二阶校正方法能直接对感兴趣的待测物进行快速、准确的测量。将它与三维荧光光谱结合, 可以实现对复杂混合体系中感兴趣多组分的同时测量。

1 实验部分

1.1 仪器与试剂

实验使用英国爱丁堡公司生产的FS920稳态荧光光谱仪, 液氮制冷范围77~320 K, 激发光源使用功率450 W的Xe900氙灯。精密电子秤(实际分度值: 0.1 mg); 超声波清洗机; 调速多用震荡器。所用到的待测样品来自于上海阿拉丁生化科技公司, 均是标准样品, 纯度大于99.5%; 去离子水为实验室自制, 实验室温度20 ℃。测量前参数设置: 激发波长: 230:2:320 nm, 发射波长: 240:2:360 nm, 激发和发射端狭缝宽度均为1.1 nm。

1.2 储备液和工作液的配制

用精密电子秤取两种样品各0.1 g, 用去离子水溶解定容于2个100 mL棕色容量瓶, 得到浓度为1 g· L^-1的一级储备溶液; 分别取上述溶液0.1 mL, 用去离子水定容于2个10 mL的容量瓶中, 得到10 mg· L^-1的二级储备溶液。实验时, 分别取0.1 mL的二级储备液, 用去离子水定容于2个10 mL的容量瓶中, 震荡5 min, 配制成100 mg· L^-1的工作液; 取不同体积的工作液, 用去离子水稀释得到样本溶液。逐个测试后发现三种酚类的线性范围在0~1.0 mg· L^-1, 根据均匀设计表配制各样本浓度如表1所示: C1— C8为校正样, T1— T5为待测样, 另外, 实际水体中的酚类会受到较高浓度的苯酚的影响, 因此L1— L3为加入苯酚作为干扰物的待测样。

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

1.3 光谱数据预处理

测量过程中其他荧光发光体会干扰分析物产生的荧光, 实验得到的光谱数据需要校正, 通过扣除空白溶剂和Delauney法来消除溶剂拉曼散射的影响。

观察图1(a)和(b)得, HYD荧光峰位置稍有变化, 荧光强度显著增大。由此可见, 上述处理后更能够凸显光谱信息。

	Figure Option View Download New Window
	图1 对苯二酚(HYD)消除散射和校正前后对比 (a): 消除散射前三维光谱图; (b): 消除散射后三维光谱图; (c): 消除散射前指纹图; (d): 消除散射后指纹图Fig.1 Eliminates scattering before and after of HYD (a): Before elimination of Romance; (b): Elimination of Romance; (c): Before elimination of Romance; (d): Elimination of Romance

2 方法原理

2.1 三线性模型

设定激发-发射波长个数为: I× J, 对K个混合样进行扫描^[9], 得到一系列光谱矩阵(EEMs), 用这些数据构成一个3D响应数据阵X(I× J× K), 平行因子分析法分解X为相对激发光谱矩阵A、相对发射光谱矩阵B和相对浓度矩阵C, 如图2所示。

	Figure Option View Download New Window
	图2 平行因子分解模型示意图Fig.2 Diagram of PARAFAC decomposition

模型可表示为

$\begin{array}{l} X_{ijk} = \overset{F}{\sum_{i = 1}} a_{if} b_{jf} c_{kf} + e_{ijk} \\ i = 1, 2, \dots, I; j = 1, 2, \dots, J; k = 1, 2, \dots, K \end{array}$ (1)

式中, F表示总组分数(包括分析物, 溶液和干扰物等), X_ijk是X中的元素(i , j , k), a_if是A(I× F)中的元素(i, f), b_if是B(J× F)中的元素(j, f), c_kf是C(K× F)中的元素(k, f), e_ijk是残差矩阵E(I× J× K)中的元素(i, j, k), 表示不能被模型解释的变量。

三线性模型的解具有唯一性, 有明确的意义, 符合著名的Lambert-Beer定律, 即使待测物中含有未知干扰成分, 也能够准确完成对待测物的定性定量分析。

2.2 交替惩罚三线性分解(APTLD)算法

APTLD算法是利用交替最小二乘原理(ALS)和交替惩罚限制^[10], 将三个不同的交替惩罚残差(AP残差)同时进行最小化, 实现三线性分解。其优势是只要选取的组分数不低于实际的组分数时, 就可以提供一个非常准确的结果, 避免确定组分数的繁琐, 提高了计算速度。 APTLD算法的迭代过程简述如下:

确定适当的组分数及惩罚因子r, p, q, 该值越大得到的解越稳定, 取r=p=q=10²⁰;

①初始A和B; ②由式(4)计算C; ③由式(2)计算A; ④由式(3)计算B; ⑤将A逐列单位化; ⑥将B逐列单位化; ⑦由式(4)计算C; ⑧重复③— ⑦直至收敛;

$\begin{array}{l} A = (\overset{K}{\sum_{k = 1}} X_{..k} (B + q (B^{T})^{+} diag (1 . / diag m (C^{T} C))) diag (c_{(k)}) + q \overset{I}{\sum_{i = 1}} {X^{T}}_{.j.} (C^{+})^{T} diag (1 . / diag m (B^{T} B)) diag (b_{(j)})) \cdot \\ (\overset{K}{\sum_{k = 1}} diag (c_{(k)}) (B^{T} B + q diag (1 . / diag m (C^{T} C))) diag (c_{(k)}) + q \overset{J}{\sum_{j = 1}} diag (1 . / diag m (B^{T} B)) diag (b_{(j)})^{2})^{+} \end{array}$ (2)

$\begin{array}{l} B = (\overset{I}{\sum_{i = 1}} X_{i..} (C + r (C^{T})^{+} diag (1 . / diag m (A^{T} A))) diag (a_{(i)}) + r \overset{K}{\sum_{k = 1}} {X^{T}}_{..k} (A^{+})^{T} diag (1 . / diag m (C^{T} C)) diag (c_{(k)})) \cdot \\ (\overset{I}{\sum_{i = 1}} diag (a_{(j)}) (C^{T} C + r diag (1 . / diag m (A^{T} A))) diag (a_{(j)}) + r \overset{K}{\sum_{k = 1}} diag (1 . / diag m (C^{T} C)) diag (c_{(k)})^{2})^{+} \end{array}$ (3)

$\begin{array}{l} C = (\overset{J}{\sum_{j = 1}} X_{j} (A + p (A^{T})^{+} diag (1 . / diagm (B^{T} B))) diag (b_{(j)}) + p \overset{I}{\sum_{i = 1}} {X^{T}}_{i ..} (B^{+})^{T} diag (1 . / diag m (A^{T} a)) diag (a_{(I)})) \cdot \\ (\overset{J}{\sum_{j = 1}} diag (b_{(j)}) (A^{T} A + p diag (1 . / diag m (B^{T} B))) diag (b_{(j)}) + p \overset{I}{\sum_{i = 1}} diag (1 . / diag m (A^{T} A)) diag (a_{(i)})^{2})^{+} \end{array}$ (4)

由A和B可推测感兴趣的组分所在的列数, 再从C矩阵中所感兴趣组分的相对浓度与已知浓度作回归, 预测感兴趣组分在未知样品中的真实浓度。

公式中X_i.., X_.j., X_..k.分别代表三维数据阵X沿着激发、发射及浓度方向的第i, j, k个切片; diag(a₍_i₎), diag(b₍_j₎)和diag(c₍_k₎)分别为a₍_i₎, b₍_j₎和c₍_k₎的对角阵; A⁺, B⁺, C⁺分别为A, B, C的广义逆。

2.3 核一致诊断法

虽然APTLD对组分数不敏感, 但是选取的组分数过大会增大模型的计算误差。在处理得到的光谱数据前有必要确定准确的组分数, 本文采用核一致诊断法(CORCONDIA)^[11]对混合体系进行化学秩估计。

2.4 品质因子

用来评定算法性能的优劣及预测结果的准确度。

预测均方根误差(RMSEP)^[12]公式为

$RMSEP = {[\frac{1}{N - 1} \overset{N}{\sum_{i = 1}} (c_{act} - c_{pred})^{2}]}^{1 / 2}$ (5)

其中, N为预测样本数, c_act和c_pred为待测物的实际浓度和预测浓度。

3 结果与讨论

3.1 未加入干扰的预测样本定性分析

首先用C1— C8(校正样)和T1— T5(预测样)组成三维数据阵X(13× 46× 61), 在进行分辨之前, 采用核一致诊断法对样本数据阵进行因子数估计。如图3, 当所选因子数大于2时, 核一致值明显小于60%, 因此该体系的最佳组分数选2, 分别是由HYD和RES贡献给模型。虽然APTLD对组分数不敏感, 但是提供准确的组分数可以保证分析结果更加稳健。

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	图3 核一致诊断法预测组分数Fig.3 The number of components by the core consistency diagnostic

利用APTLD解析得到的数据, 从图4(a)和(b)中可以看出, 对苯二酚(HYD)和间苯二酚(RES)的荧光峰位置分别在发射/激发波长: 320~340/280~300 nm, 290~320/270~280 nm范围内; 二者光谱严重重叠。通过APTLD算法分辨出混合物各荧光光谱(图中实线)和实际光谱(图中虚线)分别用不同颜色及色线标在图中。从图中知, 用APTLD对两种混合物严重重叠的复杂体系进行解析, 所得的这两种酚类化合物的激发和发射光谱与真实的光谱极为相似, 说明APTLD算法能对两种酚类化合物进行准确分辨。

	Figure Option View Download New Window
	图4 APTLD对混合物定性分析(F=2) (a): 发射光谱图; (b): 激发光谱图Fig.4 Classify the mixture with APTLD (F=2) (a): Classification of emission wavelengths; (b): Classification of excitation wavelengths

3.2 加入苯酚干扰的预测样本分析

用C1— C8及T1— T5(校正样)和L1— L3(预测样)组成加入苯酚干扰的三维数据阵X(16× 46× 61), 并选中组分数为3, 然后用APTLD算法进行解析。

加入一定浓度的苯酚作为干扰物后, 从图5中可以看出苯酚的荧光光谱与对苯二酚的荧光光谱重叠严重。从分辨结果知: 两种酚类的荧光峰位置未发生明显变化, 说明模拟实际水样加入干扰物(苯酚)后, APTLD仍然能实现对两种酚类的准确分辨。

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	图5 APTLD对加入苯酚干扰的混合物定性分析(F=3) (a): 发射光谱图; (b): 激发光谱图Fig.5 Classification of mixtures of phenol by APTLD (F=3) (a): Classification of emission wavelengths; (b): Classification of excitation wavelengths

3.3 对苯二酚(HYD)、间苯二酚(RES)的定量分析

分别用PARAFAC和APTLD两种算法对未加干扰和加入苯酚干扰的混合物进行解析, 加入苯酚干扰后, APTLD能准确的预测出待测样的浓度, 体现出了该方法的二阶优势。浓度预测结果如表2所示, 从表2中发现, APTLD总体的预测结果稍好于PARAFAC, 但二者差异较小。平均回收率和预测均方根误差两种评价指标见表3, 从表3知, 加入干扰物会使两种算法的计算结果误差略有变大, 但差异甚微, 说明两种方法都能在干扰物共存和混合物光谱严重重叠条件下对两种酚类进行定量分析。

表2 预测样两种酚类浓度预测结果 Table 2 Prediction results of two phenols concentration by PARAFAC and APTLD (N=3)

表3 两种方法性能比较 Table 3 Performance comparison between PARAFAC and APTLD

从性能比较结果知, APTLD计算快速、稳定; 从原理角度, PARAFAC对数据线性要求严格而且受组分数影响, 因此, APTLD更适合用于复杂混合体系分析, 二者比较如表4所示。

表4 方法精密度测定结果 Table 4 Results of precision measurement

4 结论

通过扣除空白样本来去除散射, 最大程度保留原光谱信息, 避免光谱分解时出现失真, 提高检测的准确度。

应用PARAFAC与APTLD两种算法解析处理后的数据, 结果表明APTLD凸显以下优势: 准确度高, 不受噪声水平影响, 对混合物的组分数不敏感, 结果较稳定且计算速度快; 两种待测物的平均回收率在92.8%~98.5%之间, 能实现很好的浓度预测, 预测均方根误差(RMSEP)低于0.19 mg· L^-1。以“ 数学分离” 代替了繁琐的“ 理化分离” , 可实时监测, 具有重要意义。

参考文献

文献列表

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[2]	LIU Ping, LI Zheng-yan, LI Jiang-ling(刘萍, 李正炎, 李江玲). Periodical of Ocean University of China(中国海洋大学学报), 2010, 40(11): 134. [本文引用:1]
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[4]	OUYANG Yun-fu, TANG Hong-bing, WU Ying, et al(欧阳运富, 唐宏兵, 吴英, 等). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2011, 28(2): 627. [本文引用:1]
[5]	ZHU Feng, TU Mao-zhen, WANG Li-li, et al(朱峰, 涂貌贞, 王丽莉, 等). Journal of Instrumental Analysis(分析测试学报), 2013, 32(3): 336. [本文引用:1]
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2006

0.0

... 引言酚类是水体污染中的一类主要污染物, 在水中都是痕量级存在^[1,2,3] ...

2010

0.0

... 引言酚类是水体污染中的一类主要污染物, 在水中都是痕量级存在^[1,2,3] ...

2015

0.0

... 引言酚类是水体污染中的一类主要污染物, 在水中都是痕量级存在^[1,2,3] ...

2011

0.0

... 目前, 酚类的测定方法较多, 欧阳等用气相色谱法(GC)测定人血液中的五氯酚^[4] ...

2013

0.0

ZHU

Feng

, TU

Mao-zhen

, WANG

Li-li

, et al(朱峰, 涂貌贞, 王丽莉, 等). Journal of Instrumental Analysis(分析测试学报), 2013, 32(3): 336.

A high performance liquid chromatographic（HPLC） method was developed for the simultaneous extraction and determination of ten phenols,including pentachlorophenol,2,3,4,5-tetrachlorophenol,2,3,4,6-tetrachlorophenol,2,3,5,6-tetrachlorophenol,2,4,5-trichlorophenol,2,4,6-trichlorophenol,2-phenylphenol,3-phenylphenol,2-naphthol and 4-nitrophenol in textiles.The samples were ultrasonically extracted with methanol.The separation and quantification of ten phenols were achieved on an ZORBAX SB-C18 column（4.6 mm×150 mm，5 μm ） with mobile phase of acetonitrile-0.01 mol/L phosphate and the diode array detector（DAD） at 220 nm and 310 nm.Lab-built UV spectroscopy library and external standards were employed for the identification and quantification of target compounds.The result indicated that the calibration curves of ten phenols showed good linear relationships between peak areas and concentrations in the range of 0.3-37 mg/L,with their correlation coefficients of 0.999 1-0.999 5�The limits of detection（LODs,S/N≥3） of ten phenols were in the range of 0.003 0-0.041 1 mg/kg,and the limits of quantitation（LOQs,S/N≥10） were 0.010 0-0.137 0 mg/kg.The average recoveries of ten phenols from 3 typical textile matrices including cotton,wool and ramie, at three spiked concentration levels were in the range of 81%-105% with relative standard deviations（RSDs） of 1.7%-8.5%.The proposed method could be used to identify and detect the 10 phenols simultaneously,which makes it effective in residue surveillance of phenols in textile samples.

建立了高效液相色谱法同时测定纺织品中五氯苯酚、2,3,4,5-四氯苯酚、2,3,4,6-四氯苯酚、2,3,5,6-四氯苯酚、2,4,5-三氯苯酚、2,4,6-三氯苯酚、邻苯基苯酚、间羟基联苯、2-萘酚、对硝基苯酚10种酚类化合物的检测方法。纺织品中的酚类化合物经甲醇超声提取、浓缩后，以ZORBAX SB-C18柱（4.6 mm×150 mm，5 μm ）为分析柱，乙腈和0.01 mol/L磷酸溶液为流动相梯度洗脱，采用二极管阵列检测器，在220 nm和310 nm波长下进行高效液相色谱检测，紫外光谱库确证，外标法定量。10种酚类化合物在0.3~37 mg/L浓度范围内与其峰面积呈良好的线性关系，相关系数为0.999 1~0.999 5；以不低于3倍的信噪比计算10种酚类化合物的检出限（LOD）为0.003 0 ~0.041 1 mg/kg；定量下限（LOQ，S/N≥10）为0.010 0 ~0.1370 mg/kg。棉、麻、毛3种纺织品基质在3个不同加标水平的回收率为81%~105%，相对标准偏差（RSDs）为1.7% ~8.5%。该方法能同时完成10种酚类化合物的确证和分析，可用于纺织品中酚类化合物残留的检测分析。

... 朱峰等利用高效液相色谱法(HPLC)测定纺织品中10种酚类^[5] ...

2014

0.0

... 质谱联用法(GC-MS)测定纺织品中5种苯酚类化合物^[6] ...

2011

0.0

... 使用这些方法测定时需要对样品进行复杂的预处理, 不但成本高, 还需要大量使用有机溶剂, 实际应用很不方便^[7] ...

2011

0.0

... 间苯二酚(RES)和对苯二酚(HYD)是苯二酚的2种同分异构体, 官能团性质相似^[8], 理化方法难分离, 不便于定量分析 ...

2017

0.0

... J, 对K个混合样进行扫描^[9], 得到一系列光谱矩阵(EEMs), 用这些数据构成一个3D响应数据阵X(I#cod#x000D7 ...

2016

0.0

QING

Xiang-dong

, WU

Hai-long

, GU

Hui-wen

, et al(卿湘东, 吴海龙, 谷惠文, 等). Acta Chimica Sinica(化学学报), 2016, 74(3): 277.

Fluorescent and phosphorescent techniques have been widely used in the fields of food, biology, environment, chemistry, medicine, life science and so on. However, the spectral background drift often occurs in the spectral excitation dimension, creating the need for new methods to process this phenomenon. There are many different factors which may lead to this common phenomenon, such as the changes in background noise of instrument or temperature. In the work, a new technique for removal of background drift in three-dimensional spectral arrays is proposed. The basic idea is to perform trilinear decomposition based on the alternating trilinear decomposition (ATLD) algorithm on the instrumental response data. In model building, the background drift is modeled as an additional component or factor as well as the analytes of interest and the interferents. As the optimum number of factors ( N ) is provided by the core consistency diagnostic (CORCONDIA), the ATLD algorithm is applied to decompose the raw data ( X raw ) with the factor number of N , then three profile matrices A , B and C can be obtained. Vectors a n , b n and c n that representing the signal of the background drift can be extracted from these matrices to construct a 3-D background drift data array ( X drift ). After subtracting the X drift from the X raw , the background drift is removed, leaving the new data on a flat baseline. Two simulated data sets were firstly employed to demonstrate the reasonability of the new method. The same and different levels of background drifts along the excitation dimension are added into the two simulated data sets, respectively. Then, it is successfully used to analyze two experimental data sets in which significant background drift are present. These results highlight the fact that this technique yields a good removal of background drift. In addition, the good result is obtained by secondary removal for serious background drift. The proposed method can be viewed as a good spectral pretreatment technique.

a Department of Chemistry and Materials Science, Hunan University of Humanities, Science and Technology, Loudi 417000; b State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082

在光谱测量中, 通常会发生光谱背景漂移现象. 引起光谱背景漂移的因素有很多, 如仪器的背景噪声变化, 测量时环境温度的变化, 光源如氙灯的使用时间等等. 针对三维光谱数阵, 发展了一种基于交替三线性分解(ATLD)算法的化学计量学方法用来处理光谱背景漂移问题. 该方法在进行三线性分解时, 对待光谱背景漂移与感兴趣组分一样, 将其单独当作一个组分或因子来考虑, 并将其从分解得到的相应矩阵中提取出来, 构建一个光谱背景漂移阵, 然后从三维原始响应数阵中将其减掉, 从而达到成功扣除光谱背景漂移的目的. 采用发展的方法处理2组模拟数据和2组实验数据, 都获得了满意的结果. 另外, 对于非线性较严重的背景漂移, 通过再进行一次扣除, 即“二次扣除”, 也达到了理想的效果. 该方法有望发展成为一种很有潜力的光谱预处理技术.

... 2 交替惩罚三线性分解(APTLD)算法APTLD算法是利用交替最小二乘原理(ALS)和交替惩罚限制^[10], 将三个不同的交替惩罚残差(AP残差)同时进行最小化, 实现三线性分解 ...

2003

0.0

... 在处理得到的光谱数据前有必要确定准确的组分数, 本文采用核一致诊断法(CORCONDIA)^[11]对混合体系进行化学秩估计 ...

2013

0.0

... 预测均方根误差(RMSEP)^[12]公式为 ...