基于卡尔曼滤波算法的葡萄糖酶生物传感器高精度检测方法

doi:10.16085/j.issn.1000-6613.2022-1506

摘要/Abstract

摘要：

发酵过程关键底物葡萄糖的原位在线检测对提高发酵效率，实时评估发酵状态有着关键作用，目前传统离线检测存在操作复杂、误差大、滞后时间长等问题，难以满足发酵过程浓度反馈控制需求。针对发酵过程葡萄糖在线精准、宽范围检测问题，本文基于自制葡萄糖酶生物传感器提出一种自适应卡尔曼滤波高精度检测方法。首先搭建检测模块，建立浓度响应特征方程进行定标，提出自动调整进样量策略实现宽范围浓度下的高精度检测。分析10^-6级电流采集过程中噪声干扰特性，高浓度检测下结合移动平均滤波算法，分区段进一步提取噪声下的有效信号。基于自制在线检测仪器进行乙醇发酵实验，与商用检测仪器SENSEP进行对比，实验结果表明在宽范围浓度检测下（1~180g/L）误差均小于2%，实现了发酵过程中葡萄糖浓度的高精度检测。

关键词: 原位在线检测, 发酵, 酶生物传感器, 自适应卡尔曼滤波, 宽范围, 高精度检测

Abstract:

The online detection of glucose, a key substrate in the fermentation process, plays a key role in improving the fermentation efficiency and assessing the fermentation status in real-time. At present, traditional offline detection has problems such as complicated operation, large errors, and long lag time, which make it difficult to meet the requirements of concentration feedback control in the fermentation process. To address the problem of online accurate and wide range detection of glucose in the fermentation process, an adaptive Kalman filter high-precision detection method was proposed based on a homemade glucose enzyme biosensor. Firstly, a detection module was built, a concentration-response characteristic equation was established for calibration, and an automatic adjustment of the feed volume strategy was proposed to achieve high accuracy detection under a wide range of concentrations. The noise interference characteristics during the 10^-6 level current acquisition process were analyzed, and the moving average filtering algorithm was combined with the high concentration detection to further extract the effective signal under the noise by partitioning the segments. The experimental results showed that the error was less than 2% for a wide range of concentrations (1—180g/L), achieving high accuracy detection of glucose concentration in the fermentation process.

Key words: accurate online inspection, fermentation, enzyme biosensors, adaptive Kalman filtering algorithm, wide range, high precision detection

中图分类号:

TQ281

秦凯, 杨仕林, 李俊, 储震宇, 薄翠梅. 基于卡尔曼滤波算法的葡萄糖酶生物传感器高精度检测方法[J]. 化工进展, 2023, 42(6): 3177-3186.

QIN Kai, YANG Shilin, LI Jun, CHU Zhenyu, BO Cuimei. A Kalman filter algorithm-based high precision detection method for glucoamylase biosensors[J]. Chemical Industry and Engineering Progress, 2023, 42(6): 3177-3186.

图/表 9

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常规方法				自动调整进样量
葡萄糖浓度/g·L^-1	测量浓度/g·L^-1	进样量/μL	检测误差/%	葡萄糖浓度/g·L^-1	测量浓度/g·L^-1	进样量/μL	检测误差/%
3	3.24	45	8	3	2.99	90	0.33
4	3.86	45	3.5	4	4.02	90	0.5
5	4.96	45	0.8	5	5.10	90	2
8	8.10	45	1.25	8	8.10	45	1.25
10	10.12	45	1.2	10	10.04	45	0.4
15	15.12	45	0.8	15	15.12	45	0.8
20	20.25	45	1.25	20	20.37	45	1.85
25	24.70	45	1.2	25	25.20	5	0.8
28	27.50	45	1.79	28	28.14	5	0.5
30	28.90	45	3.67	30	29.96	5	0.13
32	30.51	45	4.69	32	31.44	5	1.75
35	33.11	45	5.43	35	35.06	5	0.17

常规方法				自动调整进样量
葡萄糖浓度/g·L^-1	测量浓度/g·L^-1	进样量/μL	检测误差/%	葡萄糖浓度/g·L^-1	测量浓度/g·L^-1	进样量/μL	检测误差/%
3	3.24	45	8	3	2.99	90	0.33
4	3.86	45	3.5	4	4.02	90	0.5
5	4.96	45	0.8	5	5.10	90	2
8	8.10	45	1.25	8	8.10	45	1.25
10	10.12	45	1.2	10	10.04	45	0.4
15	15.12	45	0.8	15	15.12	45	0.8
20	20.25	45	1.25	20	20.37	45	1.85
25	24.70	45	1.2	25	25.20	5	0.8
28	27.50	45	1.79	28	28.14	5	0.5
30	28.90	45	3.67	30	29.96	5	0.13
32	30.51	45	4.69	32	31.44	5	1.75
35	33.11	45	5.43	35	35.06	5	0.17

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