Quantitative gas mixture analysis using temperature-modulated micro-hotplate gas sensors: Selection and validation of the optimal modulating frequencies

Abstract

The selectivity of metal oxide gas sensors can be improved by operating the sensors in a temperature-modulated mode. Although the selection of optimal modulating frequencies deserves accurate attention, this aspect has been generally overlooked. In this paper, a systematic method to determine which are the optimal temperature-modulation frequencies to solve a given gas analysis problem has been introduced, discussed in detail and fully validated for the first time. The optimization method is based on the use of multi-level pseudo-random sequences. These sequences share some properties with white noise and allow for the impulse response of the sensor–gas system to be estimated. Using this strategy, it is shown that the best temperature-modulating frequencies to discriminate and quantify gases using an array of four metal oxide gas sensors are identified. The process is illustrated solving a practical application: the quantitative analysis of acetaldehyde, ethylene, ammonia and their binary mixtures (monitoring climacteric fruit during cold storage). By using a multi-sinusoidal temperature-modulating signal, the frequencies of which are a reduced set of the optimal ones, the gases and gas mixtures were discriminated with a 100% success rate. In gas identification, features from the sensors’ dynamic response extracted via the fast Fourier transform (FFT) were used together with a fuzzy ARTMAP neural network. After the identification process, the concentration of the different species was accurately predicted by PLS-based calibration models. These results compare favorably with the ones obtained when the sensor array was operated in a steady-state mode. The optimization method is shown to be consistent and effective, since the process of determining optimal modulation frequencies and the validation process were conducted using different metal oxide gas sensor micro-arrays (of the same type) and different measurement sets.