Abstract
Photoacoustic spectroscopy technology is an important method to detect the concentration of trace gases, so it is of great significance to improve the detection accuracy of the photoacoustic spectroscopy system (PAS). In this paper, a multiple linear regression algorithm was proposed to correct the accuracy of the PAS based on the high-precision cavity ring-down spectroscopy measurement system. The results showed that the correlation coefficient R2 between the corrected values of the multiple linear regression model and the reference true values was 0.903. It can be seen that the algorithm can effectively improve the detection accuracy of the PAS. A comparative experiment was carried out with the long optical path differential absorption spectroscopy system (LP-DOAS) for measuring the NO2 concentration in an ambient atmosphere. The experimental results showed that the corrected PAS and the LP-DOAS had a good correlation in measuring the NO2 concentration, the slope of linear fitting was 1.012 ± 0.040, and the correlation coefficient was 0.948.
Highlights
Photoacoustic spectroscopy technology is an indirect detection technology based on the photoacoustic effect
The analysis model of the multiple linear regression algorithm is established to effectively analyze the measured data of photoacoustic spectroscopy under the influence of temperature, humidity, and photoacoustic response value so as to improve the measurement accuracy of the photoacoustic spectroscopy system
This paper mainly studies the influence of temperature, humidity, and photoacoustic response value on the measurement accuracy of the photoacoustic spectroscopy system, so Eq (1) can be rewritten as Eq (5)
Summary
Photoacoustic spectroscopy technology is an indirect detection technology based on the photoacoustic effect It has advantages such as high selectivity, high sensitivity, continuous reliability, low cost, and real-time detection.. The hardware design correction mainly improves the detection accuracy of the photoacoustic spectroscopy system by reducing the system noise and increasing the signal-to-noise ratio. Waclawek et al. used an independent moisture sensor to compensate for the influence of water vapor on quartz enhanced photoacoustic signals so as to improve the detection accuracy of the system. Xu et al. applied the weighted penalized least-squares and wavelet transform algorithm to the prediction of soil organic matter content, which effectively removed the baseline shift existing in the spectral data and improved the prediction accuracy of the model for soil organic matter content. It should be pointed out that the correction effects of different models and algorithms need to be analyzed according to the characteristics of specific data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
