Raman spectroscopy is an efficient and non-destructive analytical method for obtaining chemical information. The characteristic peaks in a Raman spectrum contain chemical information about the substance. The symmetric zero-area conversion is a commonly employed peak-seeking method. However, before peak seeking, various parameters related to the spectral line should be input, such as window width, Lorentz function half-width, and Gaussian function half-width. For different Raman spectra, these parameters to be input may be different, and if the input parameters do not match the current Raman spectrum, the obtained peak positions may be inaccurate. Currently, some open Raman databases only contain raw Raman spectral data without corresponding peak information. Preprocessing the raw spectral data and obtaining the corresponding peak positions and intensities by peak-seeking algorithms lead to better and more convenient utilization. Although the symmetric zero-area conversion method has advantages in automatic peak seeking and can obtain the intensity information corresponding to the spectral peaks, this peak-seeking algorithm requires various parameters related to the spectral data, such as window width, Lorentz function half-width, and Gaussian function half-width. Therefore, the universality of the symmetric zero-area conversion method is relatively limited during processing different Raman spectra in the database. We propose an improved symmetric zero-area method to reduce the input of parameters related to spectral data and adapt it to data with different spectral resolutions. We hope that this algorithm can automatically search peaks in batches for many raw Raman spectral data in the Raman database to generate a more concise and convenient database.

This algorithm improves the peak-seeking algorithm of symmetric zero-area conversion by combining noise reduction and baseline removal algorithms. First, the Whittaker Smoother algorithm is employed to remove noise from the raw Raman spectrum, which can quickly and easily remove noise without producing peak position shifts. Then, the asymmetrically weighted penalized least squares (arPLS) algorithm is utilized to remove the spectrum baseline. Next, we improve the symmetric zero-area method by normalizing the half-width of the Raman spectrum peaks, thus reducing the number of required input parameters and suppressing peak-seeking offsets. After peak seeking, the found peak positions are further corrected to reduce offsets and accurately locate peaks. Finally, the WALPSZ peak-seeking algorithm is formed by combining the Whittaker Smoother and arPLS. Additionally, the algorithm is leveraged to automatically search for peaks in ROD's raw Raman spectral data and adopted for experimental Raman spectral analysis of Anhydrite, Pyrite, and Moissanite. The obtained peak positions are compared with the literature's data to verify their reliability and universality for different Raman spectral data.

First, the traditional symmetric zero-area conversion method and the WALPSZ algorithm are applied to analyze the peak seeking of ROD's Calcite, Analcime, Bindheimite, and Brookite original spectral data. When utilizing the traditional symmetric zero-area peak-seeking algorithm with fixed parameters, it has the best peak-seeking effect on Calcite [Fig. 3(a)] and a better peak-seeking effect on Analcime, but there is a situation where a peak is searched twice at 1000-1500 cm^-1 [Fig. 3(b)]. The peak seeking of Bindheimite shows an obvious peak-seeking offset and a situation where one peak is searched twice [Fig. 3(c)]. The peak seeking of Brookite exhibits a clear missing peak case [Fig. 3(d)]. By employing the WALPSZ peak-seeking algorithm, it maintains a sound peak-seeking effect on Calcite and solves the above inaccurate peak-seeking problems when facing other Raman spectra, which indicates that the WALPSZ peak-seeking algorithm has better universality. To further verify the universality and accuracy of the WALPSZ peak-seeking algorithm and explore whether the algorithm can still be applied in actual measured Raman spectra, Anhydrite, Pyrite, and Moissanite are prepared for Raman spectral measurement, and the WALPSZ peak-seeking algorithm is adopted for peak-seeking analysis (Fig. 12). The found peaks are compared with those found by the WALPSZ peak-seeking algorithm in the original spectral data of these three samples in ROD and RRUFF and literature data, and we find that these peaks can correspond to each other (Table 2).

The symmetric zero-area conversion method is improved by reducing the input parameters and then is combined with the Whittaker Smoother and arPLS baseline removal algorithm to form the WAPLSZ peak-seeking algorithm, which enhances its universality. The WAPLSZ peak-seeking algorithm is compared with the traditional symmetric zero-area conversion method and the peak-seeking results of other original Raman spectra of ROD by the WAPLSZ peak-seeking algorithm. The results show that reducing the input parameters makes this algorithm capable of automatically batch searching for spectral data in open Raman databases. Meanwhile, we employ the WALPSZ peak-seeking algorithm to obtain the peak positions of Anhydrite, Pyrite, and Moissanite in ROD and RRUFF's Raman spectra, obtain the peak positions of the measured Raman spectra of these samples by this algorithm, and compare them with the peak positions in literature. The results reveal that the WALPSZ peak-seeking algorithm is effective for automatically searching for peaks in measured Raman spectral data and original data in ROD and that the obtained peak positions can correspond to each other and are consistent with the data recorded in the literature. Then, the reliability and accuracy of the WALPSZ peak-seeking algorithm are verified for automatically searching for peaks in Raman original data. Finally, this algorithm can help establish a database of automatically searched peak positions in ROD and correspond to data recorded in literature to analyze chemical information from measured Raman spectra.