Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization

Abstract

Inherent variability of chemical sensors makes it necessary to calibrate chemical detection systems individually. This shortcoming has traditionally limited usability of systems based on Metal Oxide gas sensor arrays and prevented mass-production for some applications. Here, aiming at exploring calibration transfer between chemical sensor arrays, we exposed five twin 8-sensor detection units to different concentration levels of Ethanol, Ethylene, CO, or Methane. First, we built calibration models using data acquired with a master unit. Second, to explore the transferability of the calibration models, we used Direct Standardization to map the signals of a slave unit to the space of the master unit in calibration. In particular, we evaluated the transferability of the calibration models to other detection units, and within the same unit measuring days apart. Our results show that signals acquired with one unit can be successfully mapped to the space of a reference unit. Hence, calibration models trained with a master unit can be extended to slave units using a reduced number of transfer samples, diminishing thereby calibration costs. Similarly, signals of a sensing unit can be transformed to match sensor behavior in the past to mitigate drift effects. Therefore, the proposed methodology can reduce calibration costs in mass-production and delay recalibrations due to sensor aging. Acquired dataset is made publicly available.Only recently calibration transfer techniques have been used in regression tasks. In con-54 trast to classification tasks, regression is a more challenging problem, but also offers a more 55 sensitive measure of the quality of the calibration transfer. Lei Zhang et al. presented a 56 methodology for on-line calibration transfer [22]. They built six twin units: a master unit 57 and five slave units. Each unit was composed of four MOX gas sensors along with tempera-58 ture and humidity sensors. They fit univariate linear regression curves between each of the 59 slave units and the master unit to transform the signals acquired with slave units to the space 60of the master unit. Although the units were exposed to formaldehyde, benzene and toluene, 61 3 only the former was used as reference for calibration transfer. Their results show that a sim-62 ple homogeneous linear transformation provides good signal mapping between sensing units. 63In another study by Deshmukh et al., the authors proposed calibration transfer between two 64 chemical sensor arrays by means of box-behnken design and robust regression [23]. Two 65 twin systems with six MOX gas sensors each were built and tested simultaneously. Artificial 66 neural network models were built with the master unit to predict the concentration of four 67 compounds relevant for the paper industry: hydrogen sulfide, methyl mercaptan, dimethyl 68 disulphide, and dimethyl sulphide. The authors showed that the calibration model developed 69 for the master system, built upon 100 calibration samples, can be transferred to the slave unit 70 using a smaller s...

Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach

“…Orthogonal signal correction (OSC) is another multivariate method similar to CC-PCA, which aims to find the undesired component through searching for subspace that is orthogonal to the target variable [40]. Both generalized least squares weighting (GLSW) [41] and direct standardization (DS) [42] are calibration transfer methods. DRCA is a recent subspace method proposed by Zhang et al [21].…”

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

“…It is worth mentioning that some of the techniques, although targeting different problems, can also help transfer the model from one dataset to another using transfer learning [22,29]. Moreover, instead of detecting and learning the drift problem directly, some of the researchers have contributed by detecting sensors with degrading performance so as to replace them [10,30,31].…”

“…In the past few decades, some researchers chose to gather different classification models to build a robust one that could resist the drift to some extent [10,19,20], while others attempt to map the unknown response to a proper tuned model [21,22]. Although the two types of models can somehow alleviate the effect of sensor drift, ensemble based method may require proper choosing of the subclassifier, and techniques like transfer learning-based methods require manual labeling of some samples in unknown domain.…”

Electronic Noses and Tongues in Wine Industry

“…Many groups are working on the development of new sensors with improved selectivity, and the performance of multisensor systems has improved greatly, particularly with the use of biosensors. Validation is also a problem in e-noses and e-tongues (Fonollosa et al, 2016). In the case of wines, the problem of weak validations is particularly important, because wines change with time, and quality depends on many odd conditions (i.e., weather).…”