Song S, Li G, Hou X, Zhang S, Yu Y, Lin L. Principal frequency component analysis based on modulate chopper technique used in diffuse reflectance spectroscopy measurement.
APPLIED OPTICS 2018;
57:1043-1049. [PMID:
29469884 DOI:
10.1364/ao.57.001043]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/09/2018] [Indexed: 06/08/2023]
Abstract
Diffuse reflectance spectroscopy (DRS) is significantly affected from the interference of the ambient light and dark current of the instrument. Optical choppers, together with lock-in/synchronous amplification, can overcome these interferences. However, in spectral measurement, the sampling rate of the spectrometer is different from the Δ-pulse sampling, which is not high enough because of the integration time. In addition, the energy distribution is not perfectly concentrated as expected in modulate chopper technology. Therefore, in this study, based on the modulate chopper technique, we proposed a principal frequency component analysis (PFCA) method for DRS. This technique not only effectively eliminated the interference and dark current of the instrument but also improved the measurement precision using the energy of different frequencies. First, experiments were designed to successfully verify the function of optical choppers, eliminating the interference of the ambient light. Second, a set of 64 mixture solutions was designed and measured by DRS using the PFCA method to prove the feasibility of the proposed method. The solution was mixed with intralipid-20% suspension, India ink, and rhodamine B. These samples were analyzed by DRS under different conditions: no-chopper with overlapping and averaging, chopper demodulated by Fourier transform, and chopper demodulated by PFCA. The partial least square regression analysis was implemented to predict the concentration. Compared to the result of three methods, DRS equipped with chopper using the PFCA method showed the best results. The results of this study showed that the PFCA method not only satisfactorily eliminated the interference signals but also extracted useful information as much as possible, improving the analysis accuracy.
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