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Journal articles on the topic 'Deep Raman Spectroscopy'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

Luo, Ruihao, Juergen Popp, and Thomas Bocklitz. "Deep Learning for Raman Spectroscopy: A Review." Analytica 3, no. 3 (2022): 287–301. http://dx.doi.org/10.3390/analytica3030020.

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Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman si

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Zhou, Qian, Zhiyong Zou, and Lin Han. "Deep Learning-Based Spectrum Reconstruction Method for Raman Spectroscopy." Coatings 12, no. 8 (2022): 1229. http://dx.doi.org/10.3390/coatings12081229.

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Raman spectroscopy, measured by a Raman spectrometer, is usually disturbed by the instrument response function and noise, which leads to certain measurement error and further affects the accuracy of substance identification. In this paper, we propose a spectral reconstruction method which combines the existing maximum a posteriori (MAP) method and deep learning (DL) to recover the degraded Raman spectrum. The proposed method first employs the MAP method to reconstruct the measured Raman spectra, so as to obtain preliminary estimated Raman spectra. Then, a convolutional neural network (CNN) is

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Mosca, Sara, Priyanka Dey, Marzieh Salimi, et al. "Spatially Offset Raman Spectroscopy—How Deep?" Analytical Chemistry 93, no. 17 (2021): 6755–62. http://dx.doi.org/10.1021/acs.analchem.1c00490.

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Arnold, Bradley R., Christopher E. Cooper, Michael R. Matrona, Darren K. Emge, and Jeffrey B. Oleske. "Stand-off deep-UV Raman spectroscopy." Canadian Journal of Chemistry 96, no. 7 (2018): 614–20. http://dx.doi.org/10.1139/cjc-2017-0678.

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UV Raman spectra were measured using a novel experimental configuration. This configuration allows many of the difficulties associated with UV excitation and high-power pulsed laser sources to be mitigated. Large sample areas are imaged into the detection system allowing high power excitation sources to be used while simultaneously avoiding sample degradation and multi-photon absorption effects. Such large detection areas allow large numbers of molecular scatters to be probed even with minimal penetration depth. Alignment issues between sample and collection optics are also simplified. Several

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5

Matousek, P. "Raman Signal Enhancement in Deep Spectroscopy of Turbid Media." Applied Spectroscopy 61, no. 8 (2007): 845–54. http://dx.doi.org/10.1366/000370207781540178.

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A new, passive method for enhancing spontaneous Raman signals for the spectroscopic investigation of turbid media is presented. The main areas to benefit are transmission Raman and spatially offset Raman spectroscopy approaches for deep probing of turbid media. The enhancement, which is typically several fold, is achieved using a multilayer dielectric optical element, such as a bandpass filter, placed within the laser beam over the sample. This element prevents loss of the photons that re-emerge from the medium at the critical point where the laser beam enters the sample, the point where major

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Macleod, Neil A., and Pavel Matousek. "Deep Noninvasive Raman Spectroscopy of Turbid Media." Applied Spectroscopy 62, no. 11 (2008): 291A—304A. http://dx.doi.org/10.1366/000370208786401527.

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7

Liu, Yuping, Junchi Wu, Yuqing Wang, and Sicen Dong. "Direct recognition of Raman spectra without baseline correction based on deep learning." AIP Advances 12, no. 8 (2022): 085212. http://dx.doi.org/10.1063/5.0100937.

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Raman spectroscopy, widely used for material analysis, has formed an extensive spectral library. In practical applications, it is usually necessary to preprocess Raman spectroscopy of the target material and then identify the material through spectral-library comparisons. Baseline correction is an important step during pre-processing and it usually requires a special algorithm. However, it demands time and high-level professional skill, confining Raman spectroscopy to laboratories rather than large-scale applications. Therefore, to improve its efficiency and take advantage of the big data in t

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Domes, Robert, Christian Domes, Christian R. Albert, Gerhard Bringmann, Jürgen Popp, and Torsten Frosch. "Vibrational spectroscopic characterization of arylisoquinolines by means of Raman spectroscopy and density functional theory calculations." Physical Chemistry Chemical Physics 19, no. 44 (2017): 29918–26. http://dx.doi.org/10.1039/c7cp05415g.

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9

Cao, Zheng, Xiang Pan, Hongyun Yu, et al. "A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra." BME Frontiers 2022 (May 2, 2022): 1–10. http://dx.doi.org/10.34133/2022/9872028.

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Objective and Impact Statement. Distinguishing tumors from normal tissues is vital in the intraoperative diagnosis and pathological examination. In this work, we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues. Introduction. Raman spectra can reflect the substance components of the target tissues. However, the feature peak is slight and hard to detect due to environmental noise. Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches. Methods. First, we

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Zhang, Yilong, Tianke Wang, Kang Du, Peng Chen, Haixia Wang, and Haohao Sun. "General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning." Applied Sciences 14, no. 22 (2024): 10245. http://dx.doi.org/10.3390/app142210245.

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Raman spectroscopy is a powerful tool for identifying substances, yet accurately analyzing mixtures remains challenging due to overlapping spectra. This study aimed to develop a deep learning-based framework to improve the identification of components in mixtures using Raman spectroscopy. We propose a three-branch feature fusion network that leverages spectral pairwise comparison and a multi-head self-attention mechanism to capture both local and global spectral features. To address limited data availability, traditional data augmentation techniques were combined with deep convolutional genera

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11

Eliasson, C., M. Claybourn, and P. Matousek. "Deep Subsurface Raman Spectroscopy of Turbid Media by a Defocused Collection System." Applied Spectroscopy 61, no. 10 (2007): 1123–27. http://dx.doi.org/10.1366/000370207782217770.

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A simple procedure for the recovery of deep subsurface Raman spectra in stratified turbid samples by defocusing a conventional Raman instrument is presented. The method is based on effects present with spatially offset Raman spectroscopy (SORS) and, although not as efficient as the standard SORS approach, it permits a simple way of recovering subsurface Raman spectra from less challenging samples. Demonstration of the effect is performed using a standard SORS device and a commercial Raman instrument on the noninvasive measurement of paracetamol tablets held within a nontransparent white plasti

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Nakashima, Shinichi, and Takeshi Mitani. "Characterization of SiC Crystals by Using Deep UV Excitation Raman Spectroscopy." Materials Science Forum 527-529 (October 2006): 333–38. http://dx.doi.org/10.4028/www.scientific.net/msf.527-529.333.

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Raman spectroscopy using deep UV (DUV) light excitation has been applied to characterizing process-induced defects in surface layers in SiC. Raman spectra of P+-ion implanted and post annealed SiC have been measured as a function of dose level and annealing temperature. The recovery of the crystallinity and electrical activity have been evaluated. Precipitation of excess phosphorus was found in heavily doped specimens. High dose implanted and post annealed samples show uneven distribution of residual defects, which is demonstrated by mapping of Raman bandwidth. Damage in 4H-SiC surfaces, which

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13

Orlando, Andrea, Filippo Franceschini, Cristian Muscas, et al. "A Comprehensive Review on Raman Spectroscopy Applications." Chemosensors 9, no. 9 (2021): 262. http://dx.doi.org/10.3390/chemosensors9090262.

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Raman spectroscopy is a very powerful tool for material analysis, allowing for exploring the properties of a wide range of different materials. Since its discovery, Raman spectroscopy has been used to investigate several features of materials such carbonaceous and inorganic properties, providing useful information on their phases, functions, and defects. Furthermore, techniques such as surface and tip enhanced Raman spectroscopy have extended the field of application of Raman analysis to biological and analytical fields. Additionally, the robustness and versatility of Raman instrumentations re

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Ghita, Adrian, Pavel Matousek, and Nicholas Stone. "Exploring the effect of laser excitation wavelength on signal recovery with deep tissue transmission Raman spectroscopy." Analyst 141, no. 20 (2016): 5738–46. http://dx.doi.org/10.1039/c6an00490c.

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15

Hufziger, Kyle T., Sergei V. Bykov, and Sanford A. Asher. "Ultraviolet Raman Wide-Field Hyperspectral Imaging Spectrometer for Standoff Trace Explosive Detection." Applied Spectroscopy 71, no. 2 (2016): 173–85. http://dx.doi.org/10.1177/0003702816680002.

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We constructed the first deep ultraviolet (UV) Raman standoff wide-field imaging spectrometer. Our novel deep UV imaging spectrometer utilizes a photonic crystal to select Raman spectral regions for detection. The photonic crystal is composed of highly charged, monodisperse 35.5 ± 2.9 nm silica nanoparticles that self-assemble in solution to produce a face centered cubic crystalline colloidal array that Bragg diffracts a narrow ∼1.0 nm full width at half-maximum (FWHM) UV spectral region. We utilize this photonic crystal to select and image two different spectral regions containing resonance R

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He, Jiongheng, Rigui Zhou, Pengju Ren, Yaochong Li, and Shengjun Xiong. "RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis." Chemosensors 12, no. 2 (2024): 29. http://dx.doi.org/10.3390/chemosensors12020029.

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The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the escalating demand for high-efficiency analytical methods, deep learning models have progressively been introduced into the realm of Raman spectroscopy. However, the application of these models to portable Raman spectrometers has posed a series of challenges due to the computational intensity inherent to deep learning approaches. This paper propose

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17

Walther, Anders Runge, Morten Østergaard Andersen, Christine Kamstrup Dam, Frederikke Karlsson, and Martin Aage Barsøe Hedegaard. "Simple Defocused Fiber Optic Volume Probe for Subsurface Raman Spectroscopy in Turbid Media." Applied Spectroscopy 74, no. 1 (2019): 88–96. http://dx.doi.org/10.1177/0003702819873933.

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We investigated the ability to perform deep subsurface Raman spectroscopy in turbid media using a simple fiber optic volume probe. Being able to collect Raman signals from regions deep within a biological sample provides the ability to noninvasively study underlying living tissue and tissue engineered constructs with high chemical specificity. Spatially offset Raman spectroscopy has shown great potential for obtaining subsurface Raman signals in biological samples. The applicability of the method for in vivo studies depends on the system complexity and small size probes are often desirable. Mo

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18

Kozu, Tomomi, Makoto Yamaguchi, Masayuki Fujitsuka, Olga Milikofu, and Ken Nishida. "Residual Stress Analysis of Indentation on 4H-SiC by Deep-Ultraviolet Excited Raman Spectroscopy." Materials Science Forum 821-823 (June 2015): 233–36. http://dx.doi.org/10.4028/www.scientific.net/msf.821-823.233.

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In this work, we analyze residual stress on 4H-SiC with Raman spectroscopy that excitation wavelength is deep ultraviolet (DUV) laser 266nm. The residual stress area is created by Vickers Hardness test technique and the area is measured by 2D DUV Raman map. The result is different from visible light excited Raman, because DUV light penetration is shallower than visible light. DUV Raman signal has exactly brings only the sample surface information. We present the advantage of DUV excited Raman to analyze sample surface.

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19

Vardaki, Martha Z., Konstantinos Seretis, Georgios Gaitanis, Ioannis D. Bassukas, and Nikolaos Kourkoumelis. "Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy." Applied Sciences 11, no. 20 (2021): 9498. http://dx.doi.org/10.3390/app11209498.

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Skin cancer is currently the most common type of cancer with millions of cases diagnosed worldwide yearly. The current gold standard for clinical diagnosis of skin cancer is an invasive and relatively time-consuming procedure, consisting of visual examination followed by biopsy collection and histopathological analysis. Raman spectroscopy has been shown to efficiently aid the non-invasive diagnosis of skin cancer when probing the surface of the skin. In this study, we employ a recent development of Raman spectroscopy (Spatially Offset Raman Spectroscopy, SORS) which is able to look deeper in t

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20

Melnik N. N., Tregulov V. V., Litvinov V. G., et al. "Current Transfer in a Semiconductor Structure with a Porous Silicon Film formed by Metal-Stimulated Etching." Semiconductors 56, no. 4 (2022): 283. http://dx.doi.org/10.21883/sc.2022.04.53235.9782.

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It is shown that during a porous Si film formation by metal-stimulated etching a barrier layer is formed on a monocrystal p-Si substrate. The rectifying properties of the semiconductor structure can be explained by the fixation of the Fermi level in the near-surface layer of porous Si due to a high concentration of electrically active defects (deep centers or traps). It causes to energy bands bending and the appearance of a potential barrier. The study of Raman scattering showed the absence of size effects and a change in the band gap in the porous Si film. Activation energies of deep centers

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21

Li, Lianfu, Zengfeng Du, Xin Zhang, et al. "In Situ Raman Spectral Characteristics of Carbon Dioxide in a Deep-Sea Simulator of Extreme Environments Reaching 300 ℃ and 30 MPa." Applied Spectroscopy 72, no. 1 (2017): 48–59. http://dx.doi.org/10.1177/0003702817722820.

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Deep-sea carbon dioxide (CO2) plays a significant role in the global carbon cycle and directly affects the living environment of marine organisms. In situ Raman detection technology is an effective approach to study the behavior of deep-sea CO2. However, the Raman spectral characteristics of CO2 can be affected by the environment, thus restricting the phase identification and quantitative analysis of CO2. In order to study the Raman spectral characteristics of CO2 in extreme environments (up to 300 ℃ and 30 MPa), which cover most regions of hydrothermal vents and cold seeps around the world, a

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22

Gardner, Benjamin, Pavel Matousek, and Nick Stone. "Direct monitoring of light mediated hyperthermia induced within mammalian tissues using surface enhanced spatially offset Raman spectroscopy (T-SESORS)." Analyst 144, no. 11 (2019): 3552–55. http://dx.doi.org/10.1039/c8an02466a.

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Here we demonstrate light mediated heating of nanoparticles confined deep inside mammalian tissue, whilst directly monitoring their temperature non-invasively using a form of deep Raman spectroscopy, T-SESORS.

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23

Kouri, Maria Anthi, Maria Karnachoriti, Ellas Spyratou, et al. "Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis." International Journal of Molecular Sciences 24, no. 23 (2023): 16582. http://dx.doi.org/10.3390/ijms242316582.

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Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal componen

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24

Blake, Nathan, Riana Gaifulina, Lewis D. Griffin, Ian M. Bell, and Geraint M. H. Thomas. "Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature." Diagnostics 12, no. 6 (2022): 1491. http://dx.doi.org/10.3390/diagnostics12061491.

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Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers usin

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Balakrishnan, Gurusamy, Ying Hu, Steen Brøndsted Nielsen, and Thomas G. Spiro. "Tunable kHz Deep Ultraviolet (193–210 nm) Laser for Raman Applications." Applied Spectroscopy 59, no. 6 (2005): 776–81. http://dx.doi.org/10.1366/0003702054280702.

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The performance characteristics of a kilohertz solid-state laser source for ultraviolet Raman spectroscopy are described. Deep ultraviolet (UV) excitation in the 193–210 nm region is provided by mixing of the fundamental and third harmonics of a Ti–sapphire laser, which is pumped by the second harmonic of a Q-Switched Nd–YLF laser. The combination of tunability, narrow linewidth, high average power, good stability, and kilohertz repetition rate makes this laser suitable for deep UV resonance Raman applications. The short pulse duration (∼20 ns) permits nanosecond time resolution in pump–probe

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Yan, Ping, Qian Liu, Hui Zhang, Luchun Qiu, Hao Bin Wu, and Xin-Yao Yu. "Deeply reconstructed hierarchical and defective NiOOH/FeOOH nanoboxes with accelerated kinetics for the oxygen evolution reaction." Journal of Materials Chemistry A 9, no. 28 (2021): 15586–94. http://dx.doi.org/10.1039/d1ta03362j.

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Hollow nanostructuring can facilitate the deep reconstruction of NiFeP into low-crystalline and defective heterostructured NiOOH/FeOOH with superior OER performance. In situ Raman spectroscopy shows evidence of the deep reconstruction process.

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27

Hara, Ellie. "Detecting organics with Deep UV Raman and fluorescence spectroscopy." Nature Reviews Earth & Environment 3, no. 3 (2022): 164. http://dx.doi.org/10.1038/s43017-022-00275-y.

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28

ZHang, Xin, William J. Kirkwood, Peter M. Walz, Edward T. Peltzer, and Peter G. Brewer. "A Review of Advances in Deep-Ocean Raman Spectroscopy." Applied Spectroscopy 66, no. 3 (2012): 237–49. http://dx.doi.org/10.1366/11-06539.

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29

Matousek, Pavel, and Nicholas Stone. "Emerging concepts in deep Raman spectroscopy of biological tissue." Analyst 134, no. 6 (2009): 1058. http://dx.doi.org/10.1039/b821100k.

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30

Li, J. H., W. T. Li, and G. H. Zhang. "Detection of nasopharyngeal carcinoma using deep NIR Raman spectroscopy." Laser Physics 24, no. 12 (2014): 125601. http://dx.doi.org/10.1088/1054-660x/24/12/125601.

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31

Pasteris, Jill Dill, Brigitte Wopenka, John J. Freeman, et al. "Raman Spectroscopy in the Deep Ocean: Successes and Challenges." Applied Spectroscopy 58, no. 7 (2004): 195A—208A. http://dx.doi.org/10.1366/0003702041389319.

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32

Huang, Cheng-Yen, Gurusamy Balakrishnan, and Thomas G. Spiro. "Protein secondary structure from deep-UV resonance Raman spectroscopy." Journal of Raman Spectroscopy 37, no. 1-3 (2006): 277–82. http://dx.doi.org/10.1002/jrs.1440.

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33

Kuramochi, Hikaru, Tomotsumi Fujisawa, Satoshi Takeuchi, and Tahei Tahara. "Broadband stimulated Raman spectroscopy in the deep ultraviolet region." Chemical Physics Letters 683 (September 2017): 543–46. http://dx.doi.org/10.1016/j.cplett.2017.02.015.

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34

Troyanova-Wood, M. A., G. I. Petrov, and V. V. Yakovlev. "Simple and inexpensive instrument for deep-UV Raman spectroscopy." Journal of Raman Spectroscopy 44, no. 12 (2013): 1789–91. http://dx.doi.org/10.1002/jrs.4394.

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35

Taguchi, Atsushi, Norihiko Hayazawa, Kentaro Furusawa, Hidekazu Ishitobi, and Satoshi Kawata. "Deep-UV tip-enhanced Raman scattering." Journal of Raman Spectroscopy 40, no. 9 (2009): 1324–30. http://dx.doi.org/10.1002/jrs.2287.

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36

Ariunbold, Gombojav O., Bryan Semon, Supriya Nagpal, and Prakash Adhikari. "Coherent Anti-Stokes–Stokes Raman Cross-Correlation Spectroscopy: Asymmetric Frequency Shifts in Hydrogen-Bonded Pyridine-Water Complexes." Applied Spectroscopy 73, no. 9 (2019): 1099–106. http://dx.doi.org/10.1177/0003702819857771.

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Hydrogen bonding is a vital molecular interaction for bio-molecular systems, yet deep understanding of its ways of creating various complexes requires extensive empirical testing. A hybrid femtosecond/picosecond coherent Raman spectroscopic technique is applied to study pyridine-water complexes. Both the coherent Stokes and anti-Stokes Raman spectra are recorded simultaneously as the concentration of water in pyridine varied. A 3 ps and 10 cm−1 narrowband probe pulse enables us to observe well-resolved Raman spectra. The hydrogen bonding between pyridine and water forms the complexes that have

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Hsieh, Yu-Li, Wen-Shao Chen, Liann-Be Chang, et al. "Deep Etched Gallium Nitride Waveguide for Raman Spectroscopic Applications." Crystals 9, no. 3 (2019): 176. http://dx.doi.org/10.3390/cryst9030176.

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Gallium nitride (GaN) materials with a high chemical stability and biocompatibility are well suited for bio-sensing applications and evanescent wave spectroscopy. However, GaN poses challenges for processing, especially for deep etching using conventional etching techniques. Here, we present a dry-etching technique using tetraethyl orthosilicate (TEOS) oxide as an etching barrier. We demonstrate that a sharp, vertically-etched waveguide pattern can be obtained with low surface roughness. The fabricated GaN waveguide structure is further characterized using field-emission scanning electron micr

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Elderderi, Suha, Laura Wils, Charlotte Leman-Loubière, et al. "In Situ Water Quantification in Natural Deep Eutectic Solvents Using Portable Raman Spectroscopy." Molecules 26, no. 18 (2021): 5488. http://dx.doi.org/10.3390/molecules26185488.

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Raman spectroscopy is a label-free, non-destructive, non-invasive analytical tool that provides insight into the molecular composition of samples with minimum or no sample preparation. The increased availability of commercial portable Raman devices presents a potentially easy and convenient analytical solution for day-to-day analysis in laboratories and production lines. However, their performance for highly specific and sensitive analysis applications has not been extensively evaluated. This study performs a direct comparison of such a commercially available, portable Raman system, with a res

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Bodnar, Robert J., and Maria Luce Frezzotti. "Microscale Chemistry: Raman Analysis of Fluid and Melt Inclusions." Elements 16, no. 2 (2020): 93–98. http://dx.doi.org/10.2138/gselements.16.2.93.

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Raman spectroscopy is a commonly applied nondestructive analytical technique for characterizing fluid and melt inclusions. The exceptional spatial resolution (~1 µm) and excellent spectral resolution (≤1 cm−1) permits the characterization of micrometer-scale phases and allows quantitative analyses based on Raman spectral features. Data provided by Raman analysis of fluid and melt inclusions has significantly advanced our understanding of complex geologic processes, including preeruptive volatile contents of magmas, the nature of fluids in the deep crust and upper mantle, the generation and evo

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Xue, Yingchao, and Hui Jiang. "Monitoring of Chlorpyrifos Residues in Corn Oil Based on Raman Spectral Deep-Learning Model." Foods 12, no. 12 (2023): 2402. http://dx.doi.org/10.3390/foods12122402.

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This study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through Raman spectroscopy using a combined long short-term memory network (LSTM) and convolutional neural network (CNN) architecture. The QE Pro Raman+ spectrometer was employed to collect Raman spectra of corn oil samples with varying concentrations of chlorpyrifos residues. A deep-learning model based on LSTM combined with a CNN structure was designed to realize feature self-learning and model training of Raman spectra of corn oil samples. In the study, it was discovered that the LSTM-CNN

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Liu, Yiheng, Changqing Liu, Yanqing Xin, Ping Liu, Ayang Xiao, and Zongcheng Ling. "A Signal-Based Auto-Focusing Method Available for Raman Spectroscopy Acquisitions in Deep Space Exploration." Remote Sensing 16, no. 5 (2024): 820. http://dx.doi.org/10.3390/rs16050820.

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With the development of technology and methodologies, Raman spectrometers are becoming efficient candidate payloads for planetary materials characterizations in deep space exploration missions. The National Aeronautics and Space Administration (NASA) already deployed two Raman instruments, Super Cam and SHERLOC, onboard the Perseverance Rover in the Mars 2020 mission. In the ground test, the SHERLOC team found an axial offset (~720 μm) between the ACI (Autofocus Context Imager) and the spectrometer focus, which would obviously affect the acquired Raman intensity if not corrected. To eliminate

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Kajendirarajah, Usant, María Olivia Avilés, and François Lagugné-Labarthet. "Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks." Physical Chemistry Chemical Physics 22, no. 32 (2020): 17857–66. http://dx.doi.org/10.1039/d0cp02950e.

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Rahman, Md Hasan-Ur, Rabbi Sikder, Manoj Tripathi, et al. "Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications." Chemosensors 12, no. 7 (2024): 140. http://dx.doi.org/10.3390/chemosensors12070140.

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Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on a

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Fan, Xiaqiong, Wen Ming, Huitao Zeng, Zhimin Zhang, and Hongmei Lu. "Deep learning-based component identification for the Raman spectra of mixtures." Analyst 144, no. 5 (2019): 1789–98. http://dx.doi.org/10.1039/c8an02212g.

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Tuschel, David D., Aleksandr V. Mikhonin, Brian E. Lemoff, and Sanford A. Asher. "Deep Ultraviolet Resonance Raman Excitation Enables Explosives Detection." Applied Spectroscopy 64, no. 4 (2010): 425–32. http://dx.doi.org/10.1366/000370210791114194.

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Lun, Zhichen, Xiaohong Wu, Jiajun Dong, and Bin Wu. "Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers." Foods 14, no. 13 (2025): 2350. https://doi.org/10.3390/foods14132350.

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Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices an

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Vardaki, Martha Z., Benjamin Gardner, Nicholas Stone, and Pavel Matousek. "Studying the distribution of deep Raman spectroscopy signals using liquid tissue phantoms with varying optical properties." Analyst 140, no. 15 (2015): 5112–19. http://dx.doi.org/10.1039/c5an01118c.

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We studied experimentally the magnitude and origin of Raman signals in a transmission Raman geometry as a function of optical properties of the medium and the location of Raman scatterer within the phantom.

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Jalil, Muhammad Arif Bin. "A Review on the Helium Silver Laser." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 1344–48. https://doi.org/10.22214/ijraset.2024.65904.

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One type of metal-vapour laser is the helium silver laser. Light with a wavelength of 224.5 nm, which is in the deep ultraviolet range, can be produced by this laser. It is appropriate for use in fluorescence suppressed Raman Spectroscopy due to its output wavelength. The helium-silver laser generates extremely narrow spectral line widths while operating at low pressure. The primary applications of this laser are in fluorescence suppressed Raman spectroscopy and scientific research.[22]

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Liao, Chien-Sheng, Pu Wang, Ping Wang, et al. "Spectrometer-free vibrational imaging by retrieving stimulated Raman signal from highly scattered photons." Science Advances 1, no. 9 (2015): e1500738. http://dx.doi.org/10.1126/sciadv.1500738.

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In vivo vibrational spectroscopic imaging is inhibited by relatively slow spectral acquisition on the second scale and low photon collection efficiency for a highly scattering system. Recently developed multiplex coherent anti-Stokes Raman scattering and stimulated Raman scattering techniques have improved the spectral acquisition time down to microsecond scale. These methods using a spectrometer setting are not suitable for turbid systems in which nearly all photons are scattered. We demonstrate vibrational imaging by spatial frequency multiplexing of incident photons and single photodiode de

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Matousek, Pavel, and Nicholas Stone. "Development of deep subsurface Raman spectroscopy for medical diagnosis and disease monitoring." Chemical Society Reviews 45, no. 7 (2016): 1794–802. http://dx.doi.org/10.1039/c5cs00466g.

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