组成图示
示意图生成中
传感器类型
电化学生物传感器
检测对象
儿茶酚（catechol）、4-氯苯酚（4-chlorophenol, 4-CP）；样品基质为 20 mM 磷酸盐缓冲液含 0.1 M KCl（pH 7）的标准溶液/流动注射样品
检测原理
酪氨酸酶（TYR）识别并催化儿茶酚和4-氯苯酚等酚类氧化为邻醌，同时消耗氧气；在-50 mV下，生成的邻醌在石墨电极表面被还原回邻二酚，形成酶促氧化-电催化还原循环，使电流信号随酚类浓度增加而放大。不同酚类的扩散速率和反应动力学不同，导致流动注射电流峰形状存在差异。系统采集每个峰的24个时间电流值，经基线校正、时间对齐和参考样品乘性漂移校正后，用PCA/PLS-R建立峰形与浓度的多元模型，从而同时定量二元混合物中的各组分。
检测灵敏度
线性范围: catechol 0, 5, 10, 15, 20 and 25 µM; 4-chlorophenol 0, 15, 30, 45, 60 and 75 µM；R^2: 0.96 (catechol), 0.98 (4-chlorophenol)（PLS-R full cross-validation, catechol/4-chlorophenol reference correction）
效应效果
传感器稳定性较差，连续测量10 h后儿茶酚灵敏度下降50%。未校正时PLS-R全交叉验证相对误差为儿茶酚11%、4-氯苯酚9.1%；采用儿茶酚/4-氯苯酚加和参考校正后，36个混合物的相对误差降至7.4%和5.5%，RMSECV分别为1.85 µM和4.13 µM，r2为0.96和0.98。用不同批次酪氨酸酶制备的新传感器测定18个验证混合物，并以第一传感器模型预测，儿茶酚RMSEP为1.75 µM、相对误差7.0%，4-氯苯酚相对误差16.0%且存在高估。结果表明参考漂移校正可补偿老化和酶批次差异，使不稳定传感器用于连续在线测量而无需重新完整校准。
传感器的构成
- 工作电极/基底：固体石墨电极（solid graphite electrode, SGL Carbon RW001），作为换能器电极并承载酶层
- 修饰/固定层：Eastman 55 AQ 聚合物（poly(ester-sulfonic acid) polymer, Eastman 55 AQ），与酶混合涂覆以固定酪氨酸酶
- 识别元件：蘑菇酪氨酸酶（mushroom tyrosinase, TYR），催化酚类氧化为邻醌
- 反应介质：20 mM 磷酸盐缓冲液含 0.1 M KCl（pH 7），作为流动注射载液和酶反应介质
- 三电极体系：Ag/AgCl（0.1 M KCl）参比电极和铂丝对电极，用于恒电位安培测量
- 流动注射检测池：PTFE 电极夹持器与 wall-jet 安培池，50 µl 进样环，流速 0.3 ml/min
- 信号读出：三电极恒电位仪（Zäta Elektronik）在 -50 mV vs Ag/AgCl 记录电流，并由 Gilson Unipoint 数字化
中文摘要
本文证明，基于流动注射峰动态响应的多元数据分析工具，可让单受体生物传感器定量测定二元混合物中各分析物。以酪氨酸酶修饰的石墨电极为工作电极，在相对于Ag/AgCl -50 mV下安培测定不同浓度的儿茶酚（catechol）和4-氯苯酚（4-chlorophenol）二元混合物。通过样品间参考测量建立校正算法，补偿生物传感器老化及不同制备批次造成的差异。校正后，使用同一传感器分析序列的偏最小二乘回归（PLS-R）对儿茶酚和4-氯苯酚的相对预测误差分别为7.4%和5.5%。另用相同程序但不同批次酪氨酸酶制备的新传感器测定18个验证混合物，并以第一传感器的36个混合物响应作为PLS-R校准集；校正后验证集相对预测误差为儿茶酚7.0%、4-氯苯酚16.0%。初步结果表明，结合校正算法与多元数据分析，可在使用稳定性较差的生物传感器进行连续在线测量时避免耗时的重新校准。
英文摘要
In this paper, it is demonstrated that a single-receptor biosensor can be used to quantitatively determine each analyte in binary mixtures using multivariate data analysis tools based on the dynamic responses received from flow injection peaks. Mixtures with different concentrations of two phenolic compounds, catechol and 4-chlorophenol, were measured with a graphite electrode modified with tyrosinase enzyme at an applied potential of -50mV versus Ag/AgCl. A correction algorithm based on measurements of references in-between samples was applied to compensate for biosensor ageing as well as differences caused by deviations between biosensor preparations. After correction, the relative prediction errors with partial least squares regression (PLS-R) for catechol and 4-chlorophenol were 7.4 and 5.5%, respectively, using an analysis sequence measured on one biosensor. Additional validation mixtures of the two phenols were measured with a new biosensor, prepared with the same procedure but with a different batch of tyrosinase enzyme. Using the mixture responses for the first sensor as a calibration set in PLS-R, the relative prediction errors of the validation mixtures, after applying correction procedures, were 7.0% for catechol and 16.0% for 4-chlorophenol. These preliminary results indicate that by applying correction algorithms it could be possible to use less stable biosensors in continuous on-line measurements together with multivariate data analysis without time-consuming calibration procedures.