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This paper presents the optical design of a high-resolution double-grating spectrometer for extracting the multiple lines in the Stokes or anti-Stokes branch of the pure rotational Raman spectra of nitrogen. The spectrometer is composed of collimating and focusing mirrors, two reflective gratings, and a linear detector. The structural parameters were calculated using geometric configuration, dispersion, and aberrational theory, and conditions for first-order correction of keystone distortion with divergent grating illumination were derived. Based on this method, we simulated a spectrometer with a 16-channel linear array photomultiplier tube, resulting in uniformly distributed single-branch lines on each detector channel. The resolution reached 0.225 nm per channel, and the keystone distortion was less than 0.7%. The spectrometer avoids the interference of elastic signals by not detecting them, enabling the extraction of atmospheric temperature profiles via separated single-branch lines with high precision. Our design provides a promising solution to extract atmospheric temperature profiles for pure rotational Raman lidar.

A 64-channel detection system for laser-induced fluorescence (LIF) detection at the cell level is established and applied to single event counting. Generally, fluorescence detection at the cellular level requires a dyeing label to enhance the scattered light intensity for the photodetector. However, the dyeing labels, such as fluorophores, probes, and dyes, complicate sample preparation and increase cytotoxicity in the process. Therefore, label-free detection becomes essential for in vivo research. The presented 64-channel detection system is designed for label-free detection with the ability to record feeble emissions. Two linear photodetector devices are included in the system, extending the wavelength detection range to 366-680 nm with an improved spectral resolution at an average of 4.9 nm. The performance of the system was validated by detecting unlabeled human hepatocytes (L-02) and other cell-level biologic samples. In addition, the 64-channel detection system was also used for particle counting with a quartz microfluidic chip. The counting method is based on fluorescence spectra differs from those of other devices (i.e., flow cytometry and cell-sorting equipment), which use isolated irradiance intensities.

This publisher's note serves to correct an error in Appl. Opt.61, 2881 (2022)APOPAI0003-693510.1364/AO.453852.

The gain ratio is a critical parameter in a polarization Mie lidar. Calibrating the gain ratio is essential in aerosol classification studies. We developed a ray-tracing-based simulation method to investigate the impact of mounting errors on the gain ratio. In this method, a computational model for each element of the lidar was built, and Zemax was used to simulate the lidar receiver to obtain the optical gain ratio by theoretical calculations. This method can analyze the influence of each element's mounting errors and offer a theoretical foundation for the machining and mounting accuracy of the lidar design. The correctness of the model was verified by applying it to a single-wavelength polarization Mie Raman lidar.

Wind vector estimation method with high accuracy in the low signal-to-noise ratio region improves the performance of pulsed coherent Doppler lidar. The key to improving accuracy is to process the incorrect radial wind estimates or the distorted power spectra better. The smoothed accumulated spectra based weighted sine wave fitting method proposed here minimizes the effects of bad radial wind estimates by considering both signal intensity and wind spatial continuity. Leveraging spatial continuity from smoothed accumulated spectra, the weight coefficients and real-time wind vector profiles can be quickly determined with non-looped operations. Simulations and field experiments showed that the proposed method provides comparable or even slightly better quality and more available wind vector estimates than the filtered sine wave fitting method.

The laser-induced fluorescence (LIF) technique, which has been widely used for food testing, can be combined with various algorithms to classify and recognize different kinds of honey. This paper proposes the Kolmogorov-Smirnov test-Gaussian mixture model (KS-GMM) algorithm, which is coupled with the LIF technique to realize accurate classification and recognition of different types of pure honey. The experiments are designed and carried out to obtain a set of LIF spectrum data from various honey and syrup samples. The proposed KS-GMM algorithm is applied for classification and recognition, with GMM, k-nearest neighbor (kNN), and decision tree algorithms as cross-validation methods. By comparing recognition results of training sets containing different amounts of data, it is found that the KS-GMM algorithm exhibits a maximum recognition accuracy of 96.52%. The research results prove that the KS-GMM algorithm outperforms, to the best of our knowledge, the other three algorithms in classifying and recognizing the honey types.

Accurate and rapid spectrum fitting is very important for quantitatively analyzing laser-induced breakdown spectroscopy (LIBS). The Voigt function is often used to fit LIBS spectral lines. We propose a new approximate Voigt function formula. Based on the classic Lorentz-Gauss linear combination formula, a summation term was added that contained a specific convolution operation to improve the Voigt function's calculation and fitting accuracy. This formula can be used for the approximate calculation of the Voigt function with an overall accuracy of 0.31% and a full width at half-maximum internal accuracy of 0.25% when the ratio of Lorentzian linewidth to Gaussian linewidth is 1:1. The formula was then applied to LIBS data processing to fit four element spectral lines of calcium (Ca-393.37, 396.85, and 422.67 nm) and potassium (K-766.49 nm). The fitting results showed that this new approximate formula could fit at least seven data points, and compared with the complex plane partition method and the classic linear combination formula, the new formula had better fitting speed and accuracy.

We present a dual-channel mobile lidar system based on laser-induced fluorescence (LIF) for real-time standoff detection and concentration distribution analysis of tryptophan. The system employs an ultraviolet laser excitation source and signal detectors for receiving fluorescence signals within two different wavelength bands. The performed experiments measured tryptophan aerosols at two different standoff distances. Moreover, distilled water and ethanol solutions were also detected for comparison. The results show that the system can detect LIF signals of tryptophan, give early warnings, locate the diffusion sources, and monitor the variation of the aerosol concentration distribution in real time.

Conventional Czerny-Turner spectrometers exhibit keystone distortion due to astigmatism, which affects their performance when a linear array photomultiplier tube (LaPMT) is used as the detector. We propose a novel optical design of a crossed Czerny-Turner spectrometer with a 32-channel LaPMT detector. We use convergent illumination of the grating to modify the astigmatism of the off-axis spherical mirrors, and the image spot radius at the central wavelength in the sagittal plane is matched with the width of the LaPMT. A first-order equation is derived for the elimination of the broadband keystone distortion. After optimization, the image size over the entire bandwidth matches well with the LaPMT size, which is consistent with the theory, and the spectral resolution is ∼4  nm per channel.

Wind vector estimation method that provides more available data in low signal-to-noise ratio (SNR) regime improves the performance of pulsed coherent Doppler lidar. The adaptive iteratively reweighted sine wave fitting (airSWF) method proposed here reweights the contribution of each radial wind velocity adaptively and iteratively when estimating the wind vector. Based on the processing results of both the simulated and real-captured signal, the airSWF method provides more available wind vector estimates with little computational time increment, compared with the direct sine wave fitting (DSWF) and weighted DSWF methods. Specifically, the proportion of available wind vector estimates determined using the airSWF method increases by >20% when the detection height exceeds 1 km. Another significant advantage of the airSWF method over the filtered sine wave fitting (FSWF) method is that no prior knowledge is required. Moreover, the computational complexity of the airSWF method is lower than that of the FSWF and maximum of the function of accumulated spectra methods.

The performance of the laser-induced fluorescence (LIF) technique is greatly affected by the excitation wavelength (EW). This study aims to find an appropriate EW that can be used for analyzing extra virgin olive oil (EVOO) adulteration quantification by comparing the effect of different EWs. The EWs of 405 nm, 450 nm, and 532 nm were selected to perform the comparative experiments. By using the three EWs in the experiments, the LIF spectra of EVOO samples adulterated with peanut oil (PO) or soybean oil (SO) in different proportions, as well as the prediction models established through different multivariate analysis algorithms were analyzed. The linear discriminant analysis (LDA) was applied for qualitative analysis, while the partial least squares regression (PLSR), backpropagation neural network, and k-nearest neighbor were employed for quantitative analysis. The results show that the performance of 450 nm EW is always superior to that of 405 and 532 nm EWs in any model, with a smaller root mean square error (RMSE). Using the LDA-PLSR model, the RMSE is 1.35% for SO adulterants and 1.36% for PO adulterants, respectively.

An optimized dimensionality reduction technique is proposed as the improved continuous locality preserving projection (ICLPP), which was developed by modifying and optimizing the weighting functions and weighting factors of the continuous locality preserving projection (CLPP) algorithm. With only one adjustable parameter, this optimized technique not only enhances CLPP's capability of maintaining the continuity of the massive data, but also results in better simplicity and adaptability of the algorithm. In this paper, the performance of ICLPP is validated through quantification analysis of the adulteration of extra virgin olive oil (EVOO) with low-cost oils based on laser-induced fluorescence spectroscopy. Through cross validation and comparative studies, ICLPP, combined with the regression algorithm, is employed to predict and screen adulteration in EVOO, and is found to generally outperform other state-of-the-art dimensionality reduction algorithms, especially for prediction of adulterants at low level (<10%). It is evidenced that the ICLPP-based framework is superior in detecting adulteration by using spectral data.

Since the periodogram maximum (PM) algorithm fails to provide consistent estimates, more robust techniques are developed, especially in a low signal-to-noise ratio (SNR) regime. The methods are formulated in a subspace fitting-based framework, such as the eigenvector (EV) method and the proposed weighted subspace fitting (WSF) method by introducing an optimal weighting matrix, which exploits the low-rank properties of the covariance matrix of the coherent Doppler lidar echo data. Simulation results reveal that the number of the reliable estimates by the WSF method is more than the other two methods, and the standard deviation is the smallest. Furthermore, the predicted best-fit Gaussian model for the probability density function of the estimates has a narrower spectral width than that of PM and EV methods. Experimental results also validate the simulation results, which show that the WSF approach outperforms the PM and EV algorithms in the furthest detectable range. The proposed method improves the detection range approximately up to 14.2% and 26.6% when compared to the EV method and the PM method, respectively. In conclusion, the proposed method can reduce the statistical uncertainties and enhance the accuracy in wind estimation specifically for a low SNR regime.

Auto-adaptive background subtraction (AABS) is proposed as a denoising method for data processing of the coherent Doppler lidar (CDL). The method is proposed specifically for a low-signal-to-noise-ratio regime, in which the drifting power spectral density of CDL data occurs. Unlike the periodogram maximum (PM) and adaptive iteratively reweighted penalized least squares (airPLS), the proposed method presents reliable peaks and is thus advantageous in identifying peak locations. According to the analysis results of simulated and actually measured data, the proposed method outperforms the airPLS method and the PM algorithm in the furthest detectable range. The proposed method improves the detection range approximately up to 16.7% and 40% when compared to the airPLS method and the PM method, respectively. It also has smaller mean wind velocity and standard error values than the airPLS and PM methods. The AABS approach improves the quality of Doppler shift estimates and can be applied to obtain the whole wind profiling by the CDL.

An advanced optical design for a low-cost and astigmatism-corrected spectrometer with a high resolution is presented. The theory and method of astigmatism correction are determined with the use of a concave toroidal mirror. The performances of a modified spectrometer and a traditional spectrometer are compared, and the analysis is verified. Experimentally, the limiting resolution of our spectrometer is 0.1 nm full width at half-maximum, as measured for 579.1 nm.

A new calibration method for pure rotational Raman lidar temperature measurement is described in this work. The method forms a temperature-dependent term in the intensity ratio, which is calculable with the radiosonde data, and then derives a calibration factor, with which the temperature is retrievable from the lidar return. The method is demonstrated and compared with existing methods through simulations and experiments. Results of the comparison show that the proposed method could provide more accurate calibrations under low signal-to-noise ratio conditions and could thus reduce the lidar performance requirement for temperature retrieval.

In this paper, fluorescence polarizing angle spectrum, combined with degree of polarization(DOP), is proposed to determine the spatial orientation of transition dipole moments (TDMs) and depolarization of chlorophyll in solution. It is found that, due to the oriented TDMs under polarized laser excitation, the projections of angle of polarization(AOP) and DOP on the three orthogonal planes are different from each other. Experiments demonstrate that we can acquire the spatial orientation by detecting the projections of AOP on two orthogonal planes (xOz and yOz). Meanwhile, The depolarization can also be determined by the DOP spectrums. The validity of this method has been verified by another projection on the xOy plane.

Apodization is a mathematical tool used in Fourier transform spectrometry to reduce the spurious oscillation in the output spectrum. We investigate Norton-Beer apodizing functions and take advantage of the main series to introduce a family of new functions. They weigh the interferogram to zero continuously within the same optical path difference and, accordingly, improve the convergence property of the instrumental line shape (ILS) for the apodized spectrum. In the meantime, the range of spectral smearing is well reduced. Compared with the Norton-Beer functions, the new functions are more flexible in practice and are more applicable with regard to distinguishing the spectral lines, especially the weak lines, which are not too close to the center of a strong line but are still susceptible to the ringing of ILS.