Calibration of Dual-Channel Raman Spectrometer via Optical Frequency Comb.

The portable Raman spectrometer boasts portability, rapid analysis, and high flexibility. It stands as a crucial and powerful technical tool for analyzing the chemical composition of samples, whether biological or non-biological, across diverse fields. To improve the resolution of grating spectrometers and ensure a wide spectral range, many spectrometer systems have been designed with double-grating structures. However, the impact of external forces, such as installation deviations and inevitable collisions, may cause differences between the actual state of the internal spectrometer components and their theoretical values. Therefore, spectrometers must be calibrated to establish the relationship between the wavelength and the pixel positions. The characteristic peaks of commonly used calibration substances are primarily distributed in the 200-2000 cm-1 range. The distribution of characteristic peaks in other wavenumber ranges is sparse, especially for spectrometers with double-channel spectral structures and wide spectral ranges. This uneven distribution of spectral peaks generates significant errors in the polynomial fitting results used to calibrate spectrometers. Therefore, to satisfy the calibration requirements of a dual-channel portable Raman spectrometer with a wide spectral range, this study designed a calibration method based on an optical frequency comb, which generates dense and uniform comb-like spectral signals at equal intervals. The method was verified experimentally and compared to the traditional calibration method of using a mercury-argon lamp. The results showed that the error bandwidth of the calibration results of the proposed method was significantly smaller than that of the mercury-argon lamp method, thus demonstrating a substantial improvement in the calibration accuracy.

Recognition of big data mixed Raman spectra based on deep learning with smartphone as Raman analyzer.

Raman spectral detection has emerged as a powerful analytical technique due to the advantages of fast acquisition, non-invasion, and low cost. The on-site application is highly dependent on Raman automatic analysis algorithm. However, current Raman algorithm research mainly focuses on small sample Raman spectroscopy (RS) identification with defects of low accuracy and detection rate. It is also difficult to realize rapid RS measurement under big data. In this paper, rapid recognition of mixtures in complex environments was realized by establishing a fast Raman analysis model based on deep learning through data training, self-learning, and parameter optimization. The cloud network architecture was proposed to apply deep learning to real-time detection using Smartphone-based Raman devices. This research solves the technical problems about mixture recognition under big data and thus could be used as a new method for fast and field RS detection in complex environments.

Paper-Based Versatile Surface-Enhanced Raman Spectroscopy Chip with Smartphone-Based Raman Analyzer for Point-of-Care Application.

With the advanced development of miniaturized Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS) has extended its applications into the field of point-of-care testing (POCT) and demonstrated its great significance by virtue of its noninvasive property and capability of fingerprint identification. In the SERS-based analysis and/or sensing system, the preparation of a low-cost, high-performance SERS substrate is critically important. In this manuscript, vacuum filtration is utilized to fabricate the silver nanoparticles (AgNPs)-embedded nylon filter membrane (ANFM) as flexible paper-based SERS chips. By characterizing the typical analytes with a miniaturized smartphone-based Raman analyzer, the proposed SERS chips have successfully demonstrated good sensitivity, repeatability, and stability. The lowest concentration as detected can approach 1 pmol for rhodamine 6G (RH6G) and 10 pmol for both crystal violet (CV) and malachite green (MG), respectively. With the help of the microporous structure of the membrane, the ANFM-based SERS chips can implement the separation of small molecules from a complex mixture and can achieve "purified" SERS signals of targeted molecules. Besides, with the function of antifriction resistance and flexibility, the ANFM can serve as SERS papers to preconcentrate the contaminates by multiple swapping and further enhance the SERS signals for point-of-care analysis. Therefore, we demonstrate multifunctions of the flexible ANFM-based SERS chips, which provide a promising solution for the POCT analysis with the SERS technique on account of their flexibility and low fabrication cost.

The Improved Suppression and Analysis of Stray Light in the Miniature Raman Spectrometer.

Stray light analysis has great benefits in designing Raman spectrometry system, which is the sensitive detection system of weak optical signal. In this paper, optical design software and Solidworks are utilized to optimize the optical and mechanical structure. The system resolution is 0.7 nm, and the volume is 110 mm×95 mm, which belongs to portable and miniature Raman spectrometer. Based on the stray light simulation model, we made an analysis of ray tracing simulation for this system. First, the stray light come with the incident ray were suppressed by the aperture stop. Then the receiver of stray light was introduced and improved in the design progress to suppress internal stray light, especially for zero-order diffraction light of plane grating. The improved receiver of stray light is more effectively using the internal space of the spectrometer and analysis results show that a 50% reduction in the number of stray light and stray light normalized irradiance intensity from 10-5 down to 10-7. The analysis shows that the improved receiver of stray light can effectively suppress stray light, which is beneficial to weak signal detection, and provide reference for design and adjustment of the miniature Raman spectrometer.

Determining the orientation of transition moments and depolarization by fluorescence polarizing angle spectrum.

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.

[Rapid recognition of common machine oils based on laser induced fluorescence].

A rapid recognition method of common engine oils, based on the principle of laser induced fluorescence (LIF), is proposed in the present paper. A 355 nm ultraviolet laser is used to induce fluorescence emission of 9 kinds of common machine oil samples. In total 450 groups of fluorescence spectral data are collected, of which 360 groups of data are used for classification training and 90 sets of data for identification. It was found that the fluorescence spectra of engine oils are distinct from each other visibly. The rapid identification of 90 groups of data is realized by using clustering analysis combined with principal component analysis. The recognition rate could reach up to 97.8%. Experiment demonstrated that the fast identification of diverse engine oils could be realized by using LIF combined with multivariate analysis method.

Motor oil classification based on time-resolved fluorescence.

A time-resolved fluorescence (TRF) technique is presented for classifying motor oils. The system is constructed with a third harmonic Nd:YAG laser, a spectrometer, and an intensified charge coupled device (ICCD) camera. Steady-state and time-resolved fluorescence (TRF) measurements are reported for several motor oils. It is found that steady-state fluorescence is insufficient to distinguish the motor oil samples. Then contour diagrams of TRF intensities (CDTRFIs) are acquired to serve as unique fingerprints to identify motor oils by using the distinct TRF of motor oils. CDTRFIs are preferable to steady-state fluorescence spectra for classifying different motor oils, making CDTRFIs a particularly choice for the development of fluorescence-based methods for the discrimination and characterization of motor oils. The two-dimensional fluorescence contour diagrams contain more information, not only the changing shapes of the LIF spectra but also the relative intensity. The results indicate that motor oils can be differentiated based on the new proposed method, which provides reliable methods for analyzing and classifying motor oils.

[Laser induced fluorescence spectrum characteristics of common edible oil and fried cooking oil].

In order to detect the trench oil the authors built a trench oil rapid detection system based on laser induced fluorescence detection technology. This system used 355 nm laser as excitation light source. The authors collected the fluorescence spectrum of a variety of edible oil and fried cooking oil (a kind of trench oil) and then set up a fluorescence spectrum database by taking advantage of the trench oil detection system It was found that the fluorescence characteristics of fried cooking oil and common edible oil were obviously different. Then it could easily realize the oil recognition and trench oil rapid detection by using principal component analysis and BP neural network, and the overall recognition rate could reach as high as 97.5%. Experiments showed that laser induced fluorescence spectrum technology was fast, non-contact, and highly sensitive. Combined with BP neural network, it would become a new technique to detect the trench oil.