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Accurate measurement of oxygen concentration for pharmaceutical glass bottles is of great significance to ensure the asepsis of medicine and stability of ingredients. With merits of high sensitivity, low cost, non-contact, and real time response, the wavelength-modulation-based tunable diode laser absorption spectroscopy (TDLAS/WMS) technology shows great potential to achieve in-site oxygen concentration detection by the single-line spectrum measurement. This paper focuses on headspace oxygen concentration measurement in open-path optical environment, which is extremely challenging owing to the short light path length and random ambient noises. First, a signal reconstruction method is established based on discrete-time wavelet packet transform (DWPT), where random noise suppression is implicitly achieved. Then, oxygen concentration is inversed among the data between two adjacent valley values of the demodulated 2 nd harmonic signal by multiple linear regression (MLR), and the linear discriminant analysis (LDA) is imported to enhance the information sparsity of 2 nd harmonic signal matrix and to address the multi-collinearity problem. Simulation results prove our proposed method achieved considerable detection accuracy with average absolute error of 0.05%. This paper also designed a TDLAS/WMS prototype, the experimental results aiming at glass bottles with different oxygen concentration of 0%, 5%, 10% and 21% in open path optical environment indicate our method has achieved an encouraging average absolute error of 0.54%, and can survive well when the normalized SNR is within 0.85 to 1. These results promise that the proposed methodology can be widely applied in in-site AOI instrumentation of headspace oxygen concentration measurement for glass vials.

Inspired by the empirical mode decomposition (EMD)-enhanced gas detection work, this paper develops a further improved signal reconstruction method (namely EWT-ASG) for the demodulated harmonics of tunable diode laser absorption spectroscopy (TDLAS), which is mainly based on empirical wavelet transform (EWT) and Savitzky-Golay (S-G) filtering. First, the imported EWT performs better on the decomposition precision as it successfully bypasses the mode aliasing problem of EMD resulting by the lack of mathematical basis. Second, the improved S-G filter effectively suppresses the noisy components of the wavelet coefficients by updating its one key parameter (i.e., window size w) dynamically according to the correlation coefficients between the raw signals and the decomposed wavelet coefficients. The EWT-ASG scheme was first applied on the oxygen concentration detection for pharmaceutical glass vials. The preliminary experimental results indicate that the EWT-ASG method performs better than recent state-of-the-arts, with an average correct discrimination rate of 98.14% when the normalized SNR is 1. Even when the normalized SNR is degenerated from 1 to 0.85, our detection system still survived well, with a highest average correct discrimination rate of 90.45%. The detection system precision is also improved to a large extent, with a minimal Allan deviation of 0.856@(117s).

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