基于空载数据的鉴别食醋电子鼻信号漂移校正方法

由于传感器老化,环境温度等因素,电子鼻信号的漂移是不可避免的,且严重降低电子鼻的长期稳健检测能力。为了实现电子鼻对6种食醋样品的长期稳健检测,该文提出了一种基于空载数据的小波包分解系数的漂移递归校正方法。通过小波包对电子鼻空载数据的分解,给出空载阈值函数(no-load threshold function,NLTF),然后将NLTF转换为适合样本数据的样本阈值函数(samplethresholdfunction,STF)。在获得的STF基础上,构建样本检测数据小波包分解系数的校正函数。借助于所构建的样本测试数据的校正函数,对6种食醋样品的电子鼻数据进行漂移校正。同时,运用"样本测量时间窗口(sample measurement time window,SMTW)"的概念,实现电子鼻数据的递归校正,进而建立了可实现长期稳健检测的递归鉴别模型。针对6种食醋样品,进行了为期16个月的间歇式测试。当SMTW选为4个月的测试样本及每次递推前移1个月样本数据时,建立的基于递归校正的Fisher判别分析(Fisher discriminant analysis,FDA)模型可完全实现6种食醋样品的长期稳健鉴别,正确鉴别率达到100%,使紧随SMTW后1个月内的测试样本能得到准确鉴别。该校正方法能够有效的去除漂移并且实现了电子鼻的长期稳健检测。

E-nose drift correction method based on no-load data and its application of robust identification for identifying vinegar

Abstract: Electronic nose (E-nose) signal drift is inevitable due to sensor aging and fluctuation of ambient temperature and humidity, which could compromise its ability of robust long-term detection. To ensure long-term robust service of the E-nose for detecting vinegar samples, a drift recursive correction method is proposed in this paper using the wavelet packet decomposition coefficients based on no-load data. The method does not require special correction processing and the sensor drift can be corrected based only on the no-load response data and the sample response data of the E-nose. In the model, the Symlet wavelet function was first used to decompose the no-load data of the E-nose using a no-load threshold function (NLTF) given in the paper. The NLTF was then converted to sample threshold function (STF) suiting the samples data by a constructed adjustment coefficient. Using the STF, a correction function based on the wavelet packet decomposition coefficient of the samples of E-nose data was constructed. The E-nose data of six vinegars were subject to a drift correction by means of the correction function. We also introduced the concept of "sample measurement time window" (SMTW), and used the correction function to process the sample data within the SMTW. As the SMTW progresses recursively, the drift in all sample data at different times (or SMTW) could be used to recursively correct the samples of the vinegars. To validate the drift correction method and test the applicability of the SMTW, the sample data in the SMTW were used as a training set and the sample data between one month and two months after the SMTW were used as test set. A recursive Fisher discriminant analysis (FDA) model was built, which was proven capable of long-term robust detection of the vinegars. The samples of the vinegars were tested intermittently for 16 months, and the SMTW in which was 6, 5, 4 and 3 months, respectively. With the change in SMTW, the correct discrimination rate for the training set and the test set also changes. When the SMTW was more than 4 months or less than 4 months, the correction identification rate of FDA was less than 100%, and the correction identification rate was only 92.22% under certain circumstance. Therefore, when the SMTW was 6, 5 or 3 months, the samples of the vinegars cannot be identified robustly in long term. When SMTW was 4 months, the test samples in SMTW and the samples within one month after the SMTW were effectively identified by the established recursive FDA model, and the vinegar samples can be identified robustly in long term, with a correction identification rate of 100%. That is, the test samples within one month after the SMTW could be accurately identified using the FDA model built from the sample E-nose data when the SMTW was within 4 months. We believe that our results has implications as the proposed method is applicable to other E-nose data.