Fiber-optic Raman Spectroscopy Research Articles

Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy.

Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.

O21 Raman spectroscopy for detection of squamous cell carcinoma in recessive dystrophic epidermolysis bullosa skin

Abstract Introduction and aims Recessive dystrophic epidermolysis bullosa (RDEB) is a genetic skin disorder caused by biallelic pathogenic variants in COL7A1, which encodes type VII collagen (C7). Lack of C7 leads to blistering, chronic wounds, scarring and early onset of life-shortening squamous cell carcinomas (SCCs). Currently, the suspicion of SCC is mostly determined by clinical observation of visible changes in chronic wounds, and often requires multiple biopsies for histological diagnosis. This project aims to improve clinical care by developing a new noninvasive means of detecting SCC. To achieve this, we used Raman spectroscopy, a nondestructive analytical technique to study the changes occurring in RDEB skin when SCC is present, with the goal of creating a bedside test that can aid clinicians in the early diagnosis of SCC. Methods Raman spectroscopic imaging was performed on 7-µm frozen skin sections from RDEB (n = 3), RDEB SCC (n = 3) and normal skin (n = 3) to identify signalling changes between nonlesional and lesional states. An in-depth molecular composition analysis of the samples was performed using multivariate analysis with the goal of highlighting changes in abundance of specific molecules when SCC is present. Results For both nonmalignant and RDEB SCC skin sites we found specific Raman peaks associated with protein (1004 cm−1, 1650 cm−1) or lipids (1450 cm−1). RDEB SCC indicated a relative increase in DNA and protein. High-quality Raman spectroscopic images could be acquired that showed clear correlation with histological appearances. Current work is under way to determine which specific biological compounds contribute to the Raman spectra observed. Conclusions Raman spectroscopic imaging can provide comprehensive and noninvasive molecular tissue mapping with resolution comparable to histological sections, enabling molecular-based detection of SCC. Future work will focus on characterization of the SCC molecular profile, in addition to determining whether fibre optic Raman spectroscopy can be used to guide the biopsy/resection of RDEB lesions.

The accuracy of Fiber-Optic Raman Spectroscopy in the detection and diagnosis of head and neck neoplasm in vivo: a systematic review and meta-analysis

Purpose The aim of this article was to review and collectively assess the published studies of fiber-optic Raman spectroscopy (RS) of the in vivo detection and diagnosis of head and neck carcinomas, and to derive a consensus average of the accuracy, sensitivity and specificity. Methods The authors searched four databases, including Ovid-Medline, Ovid-Embase, Cochrane Library, and the China National Knowledge Infrastructure (CNKI), up to February 2023 for all published studies that assessed the diagnostic accuracy of fiber-optic RS in the in vivo detection of head and neck carcinomas. Nonqualifying studies were screened out in accordance with the specified exclusion criteria, and relevant information about the diagnostic performance of fiber-optic RS was excluded. Publication bias was estimated by Deeks’ funnel plot asymmetry test. A random effects model was adopted to calculate the pooled sensitivity, specificity and diagnostic odds ratio (DOR). Additionally, the authors conducted a summary receiver operating characteristic (SROC) curve analysis and threshold analysis, reporting the area under the curve (AUC) to evaluate the overall performance of fiber-optic RS in vivo. Results Ten studies (including 16 groups of data) were included in this article, and a total of 5365 in vivo Raman spectra (cancer = 1,746; normal = 3,619) were acquired from 877 patients. The pooled sensitivity and specificity of fiber-optic RS of head and neck carcinomas were 0.88 and 0.94, respectively. SROC curves were generated to estimate the overall diagnostic accuracy, and the AUC was 0.96 (95% CI [0.94–0.97]). No significant publication bias was found in this meta-analysis by Deeks’ funnel plot asymmetry test. The heterogeneity of these studies was significant; the Q test values of the sensitivity and specificity were 106.23 (P = 0.00) and 64.21 (P = 0.00), respectively, and the I2 index of the sensitivity and specificity were 85.88 (95% CI [79.99–91.77]) and 76.64 (95% CI [65.45–87.83]), respectively. Conclusion Fiber-optic RS was demonstrated to be a reliable technique for the in vivo detection of head and neck carcinoma with high accuracy. However, considering the high heterogeneity of these studies, more clinical studies are needed to reduce the heterogeneity, and further confirm the utility of fiber-optic Raman spectroscopy in vivo.

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms.

Oral cancer, a predominant malignancy in developing nations, represents a global health challenge with a five-year survival rate below 50%. Nonetheless, substantial reductions in both its incidence and mortality rates can be achieved through early detection and appropriate treatment. Crucial to these treatment plans and prognosis predictions is the identification of the pathological type of oral cancer. Toward this end, fiber-optic Raman spectroscopy emerges as an effective tool. This study combines Raman spectroscopy technology with deep learning algorithms to develop a portable intelligent prototype for oral case analysis. We propose, for the first time, a multi-task network (MTN) Raman spectroscopy classification model that utilizes a shared backbone network to simultaneously achieve different clinical staging and histological grading diagnoses. The developed model demonstrated accuracy rates of 94.88%, 94.57%, and 94.34% for tumor staging, lymph node staging, and histological grading, respectively. Its sensitivity, specificity, and accuracy compare closely with the gold standard: routine histopathological examination. Thus, this prototype proposed in this study has great potential for rapid, non-invasive, and label-free pathological diagnosis of oral cancer.

Deep multi-feature fusion residual network for oral squamous cell carcinoma classification and its intelligent system using Raman spectroscopy

Using fiber optic Raman spectroscopy and deep neural networks, we develop an intelligent system which will be used to assist surgeons accurately and efficiently to identify oral squamous cell carcinomas (OSCC). This system is able to classify 6 types of oral tissues. To achieve this goal, a novel classification framework called deep multi-feature fusion residual network (DMFF-ResNet) is proposed. This model is based on 16,200 Raman spectral data, obtained from the normal oral tissues and the OSCC of 90 patients through the surgical resection. Firstly, the 1-dimensional RestNet50 is taken as its backbone network. Then, the output spectral features of last three blocks are extracted from backbone network for feature fusion, which is expected to learn more spatial representations and have more discriminative power. Lastly, the derived spectral features are sent into a fully-connected neural network for performing the multiclassification task. Experimental results show that the proposed model achieves a competitive classification performance compared with state-of-the-art classifiers, and its accuracy, precision, and sensitivity reach 93.28%, 93.53%, and 93.13%, respectively. Further, the proposed framework is deployed on an edge computing device to form a prototype intelligent system for OSCC detection. To validate this system, we perform an offline test experiment in another 20 patients which demonstrates the developed intelligent system can successfully discriminate OSCC and normal oral tissues, with accuracy, precision, and recall of 92.78%, 92.33%, and 92.57%, respectively. The code was available at https://github.com/ISCLab-Bistu/retinanet-OSCC.

Development of a multi-needle fiberoptic Raman spectroscopy technique for simultaneous multi-site deep tissue Raman measurements in the brain.

We report on the development of a multi-needle fiberoptic Raman spectroscopy (MNF-RS) technique for simultaneous multi-site deep Raman measurements in brain tissue. The multi-needle fiberoptic Raman probe is designed and fabricated using a number of 100 µm core diameter, aluminum-coated fibers under a coaxial laser excitation and Raman collection scheme, enabling simultaneous collection of deep tissue Raman spectra from a number of tissue sites. We have also developed a Raman retrieval algorithm based on the transformation matrix of each individual needle fiber probe projected to different pixels of a charge-coupled device (CCD) for recovering the tissue Raman spectra collected by each needle fiber probe, allowing simultaneous multi-channel detection by a single Raman spectrometer. High-quality tissue Raman spectra of different tissue types (e.g., muscle, fat, gray matter, and white matter in porcine brain) can be acquired in both the fingerprint (900-1800 cm-1) and high-wavenumber (2800-3300 cm-1) regions within sub-second times using the MNF-RS technique. We also demonstrate that by advancing the multi-needle fiberoptic Raman probe into deep porcine brain, tissue Raman spectra can be acquired simultaneously from different brain regions (e.g., cortex, thalamus, midbrain, and cerebellum). The significant biochemical differences across different brain tissues can also be distinguished, suggesting the promising potential of the MNF-RS technique for label-free neuroscience study at the molecular level.

In situ monitoring of the hydration of calcium silicate minerals in cement with a remote fiber-optic Raman probe

This study utilized a novel in situ fiber-optic Raman probe to continuously monitor the hydration progress of tricalcium silicate (C3S) and dicalcium silicate (C2S) without the need for sampling, from early hydration stage to later stages, and from fresh to hardened states of paste samples. By virtue of the remarkable ability of this technique in characterizing either dry or wet and crystalline or amorphous samples, the hydration processes of C3S and C2S pastes with different water-to-solid (w/s) ratios could be monitored from the start of the hydration reaction. The main hydration products, calcium silicate hydrate (C–S–H) and portlandite/calcium hydroxide (CH), have been successfully identified and continuously monitored for variations in their respective amounts in situ. The effect of w/s ratio on the hydration processes of C3S and C2S pastes was also considered. Meanwhile, the x-ray diffraction (XRD) and thermogravimetric analysis (TGA) results showed a great correlation with the in situ Raman test results about hydration products, which demonstrated the reliability of this technology. Moreover, the signal-to-noise ratio (SNR) of this Raman probe is significantly superior to existing technologies for in situ fiber-optic Raman spectroscopy. This remote fiber-optic Raman probe enables the use of Raman spectroscopy in future construction projects for on-site monitoring and evaluation of health conditions and performance of concrete structures.

In Situ and Real-Time Mold Flux Analysis Using a High-Temperature Fiber-Optic Raman Sensor for Steel Manufacturing Applications

Continuous casting in steel production uses specially developed oxyfluoride glasses (mold fluxes) to lubricate a mold and control the solidification of the steel in the mold. The composition of the flux impacts properties, including basicity, viscosity, and crystallization rate, all of which affect the stability of the casting process and the quality of the solidified steel. However, mold fluxes interact with steel during the casting process, resulting in flux chemistry changes that must be considered in the flux design. Currently, the chemical composition of mold flux must be determined by extracting flux samples from the mold during casting and then processing these samples offline to estimate the working chemical composition and, therefore, the expected properties of the flux. Raman spectroscopy offers an alternative method for performing flux analysis with the potential to perform measurements online during the casting process. Raman spectroscopy uniquely identifies specific chemical bonds and symmetries in the glassy flux by revealing peaks that are a fingerprint of the vibration modes of molecules in the flux. The intensities of specific peaks in Raman spectra can be correlated with the chemical composition of the melt and associated properties such as basicity and viscosity. This paper reports on the first use of a portable fiber-optic Raman sensor for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> Raman spectroscopic measurements of molten flux at 1400°C. The work demonstrates the advantages of fiber-optic Raman spectroscopy to document the structure and chemical composition of flux samples at temperatures typically encountered in the mold during continuous caster operation. Experimental results demonstrate that the composition-dependent Raman signal shifts can be detected at caster operating temperatures, and the use of high-temperature Raman analysis for in-line flux monitoring shows significant promise for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> detection of changes in flux composition and physical properties during casting.

New insights on Teotihuacan color technology: a proposal of a technological style for mural painting

Teotihuacan, the main city of the Classic period in Central Mexico (ca. 150/200–650 CE), was among many things, a colorful city. Through its application on mural painting, ceramics, lapidary, bone, textiles, and the human body itself, coloring materials gave meaning to the Teotihuacan reality. This research presents color as a socio-technological phenomenon from the archaeology of color, the anthropology of technology, and the application of the concept of technological style.From this scope, pictorial palettes of mural painting fragments from three Teotihuacan architectural compounds were studied by digital microscopy, colorimetry, fiber optic reflectance spectroscopy (FORS), X-ray fluorescence (XRF), Raman spectroscopy, infrared spectroscopy (FTIR), and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) in order to identify the color technology of pigments. This approach led us to identify materials shared by the three studied compounds, red and yellow earths, cinnabar, malachite, pseudomalachite, azurite, and the Teotihuacan grayish blue, found to contain a mix of bone black with calcite. Our results contribute to the discussion on the use of raw materials and their mixtures. We observed a standardization of pigment technology in Teotihuacan mural painting through the use of shared and standard formulations with four indicators that we propose as markers of a technological style for pigments, along with variations in the color materials that evidence the use of different strategies for the procurement and distribution of raw materials among the diverse architectural assemblages of the city.

Fiber optic Raman spectroscopy for the evaluation of disease state in Duchenne muscular dystrophy: An assessment using the mdx model and human muscle.

Introduction/AimsRaman spectroscopy is an emerging technique for the evaluation of muscle disease. In this study we evaluate the ability of in vivo intramuscular Raman spectroscopy to detect the effects of voluntary running in the mdx model of Duchenne muscular dystrophy (DMD). We also compare mdx data with muscle spectra from human DMD patients.MethodsThirty 90‐day‐old mdx mice were randomly allocated to an exercised group (48‐hour access to a running wheel) and an unexercised group (n = 15 per group). In vivo Raman spectra were collected from both gastrocnemius muscles and histopathological assessment subsequently performed. Raman data were analyzed using principal component analysis–fed linear discriminant analysis (PCA‐LDA). Exercised and unexercised mdx muscle spectra were compared with human DMD samples using cosine similarity.ResultsExercised mice ran an average of 6.5 km over 48 hours, which induced a significant increase in muscle necrosis (P = .03). PCA‐LDA scores were significantly different between the exercised and unexercised groups (P < .0001) and correlated significantly with distance run (P = .01). Raman spectra from exercised mice more closely resembled human spectra than those from unexercised mice.DiscussionRaman spectroscopy provides a readout of the biochemical alterations in muscle in both the mdx mouse and human DMD muscle.

Rapid identification of flaxseed oil based on portable fiber optic Raman spectroscopy combined with an oil microscopy method.

To explore a fast, simple, and accurate method to identify adulteration in flaxseed oil, the Raman spectral data of 130 samples containing flaxseed, canola, cottonseed, and adulterated oils were obtained using a portable fiber optic Raman spectrometer. The Raman spectral results showed that the Raman spectra of the flaxseed and canola oils had noticeable peak shifts, whereas the peak positions of the flaxseed and cottonseed oils were relatively similar. Clear peak intensity differences were observed in the flaxseed, cottonseed, and canola oils, mainly at 868cm-1 , 1022cm-1 , 1265cm-1 , and 1655cm-1 , with Raman shift intensities in the following order: Iflaxseed oil >Icottonseed oil >Icanola oil . Similarly, the peak intensity of the flaxseed and adulterated oils also exhibited certain differences (at 868cm-1 , 1022cm-1 , 1265cm-1 , and 1655cm-1 ), and the Raman shift intensity tended to decrease gradually with the increasing content of canola and cottonseed oils in the flaxseed oil. Additionally, the results of Raman spectroscopy combined with the "oil microscopy" method exhibited large variations in the radar patterns of the flaxseed, canola, and cottonseed oils, whereas the radar patterns of the flaxseed and adulterated oils closely resembled each other. The results indicated that Raman spectroscopy in combination with oil microscopy more effectively revealed the subtle differences in the Raman shift intensity, serving as a more visual and comprehensive approach for differentiating the quality variations between pure flaxseed oil and other oil species and adulterated oil. PRACTICAL APPLICATION: This study analyzed the Raman spectra of flaxseed, canola, cottonseed, and adulterated oils using fiber optic Raman spectroscopy. Combined with the oil microscopy method for comprehensive evaluation and analysis, it is feasible to effectively identify the quality differences among flaxseed, canola, cottonseed, and adulterated oils.

Assessment of Easily Accessible Spectroscopic Techniques Coupled with Multivariate Analysis for the Qualitative Characterization and Differentiation of Earth Pigments of Various Provenance

Natural minerals and earths with coloring properties have been widely used as artistic pigments since prehistoric times. Despite being extensively studied, the complex chemistry of earth pigments is still unsatisfactory described with respect to their mineralogical and structural variability and origin. In this study, a large group of earth pigments from various geographical locations was investigated using easily accessible spectroscopic techniques and multivariate analysis with the aim to identify distinctive mineralogical and chemical characteristics of natural pigment sources. Portable X-ray fluorescence (p-XRF), Fourier transform infrared spectroscopy (FTIR) and fiberoptic Raman spectroscopy were used for the elemental, molecular and structural characterization of the investigated pigments. Diagnostic spectral features and chemical patterns (fingerprints) were identified and discussed with respect to their geological sources. Due to the occurrence of similar accompanying minerals, it was observed that the differentiation of red and yellow ochers is more challenging compared to green, brown and black pigments. However, for some of the investigated pigments, the presence of certain accessory minerals and/or of certain chemical patterns can have diagnostic value. Principal component analysis (PCA) of the FTIR and XRF data matrices showed promising results in terms of geological attribution, highlighting a promising tool for provenance research. The results of the study demonstrate the potential benefits of this rapid and nondestructive approach for the characterization and differentiation of earth pigments with similar hues coming from different geological sources.

Study of spurious optical signals in a fiber-optic Raman spectroscopy system

We have studied spurious signal effects in a typical configuration of a fiber-optic setup used for registering Raman scattering. In such a system, we have localized the most likely sources of interference that can affect the fluorescent, Raman, and laser signals. These parasitic artifacts are superimposed on the informative Raman signal from the sample, leading to false Raman bands in the spectrum recorded by the camera and a reduction in the signal-to-noise ratio. We measured the emission spectra of the probing diode laser. We also examined the Raman luminescence signals generated by the optical fibers with low-content hydroxyl groups and optical substrates of lenses and filters, such as BK7, CaF2, SiO2, MgF2, Al2O3. By successively analyzing the data obtained for each system component, we found that the lateral-longitudinal modes of the lasers with a volume Bragg grating cause one of the most substantial spurious signals. The presence of parasitic peaks in the recorded Raman spectrum has been experimentally confirmed in the example of crystalline silicon.

Rapid identification of human muscle disease with fibre optic Raman spectroscopy.

The diagnosis of muscle disorders (“myopathies”) can be challenging and new biomarkers of disease are required to enhance clinical practice and research. Despite advances in areas such as imaging and genomic medicine, muscle biopsy remains an important but time-consuming investigation. Raman spectroscopy is a vibrational spectroscopy application that could provide a rapid analysis of muscle tissue, as it requires no sample preparation and is simple to perform. Here, we investigated the feasibility of using a miniaturised, portable fibre optic Raman system for the rapid identification of muscle disease. Samples were assessed from 27 patients with a final clinico-pathological diagnosis of a myopathy and 17 patients in whom investigations and clinical follow-up excluded myopathy. Multivariate classification techniques achieved accuracies ranging between 71–77%. To explore the potential of Raman spectroscopy to identify different myopathies, patients were subdivided into mitochondrial and non-mitochondrial myopathy groups. Classification accuracies were between 74–89%. Observed spectral changes were related to changes in protein structure. These data indicate fibre optic Raman spectroscopy is a promising technique for the rapid identification of muscle disease that could provide real time diagnostic information. The application of fibre optic Raman technology raises the prospect of in vivo bedside testing for muscle diseases which would significantly streamline the diagnostic pathway of these disorders.

Label-free fibre optic Raman spectroscopy with bounded simplex-structured matrix factorization for the serial study of serum in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease in urgent need of disease biomarkers for the assessment of promising therapeutic candidates in clinical trials. Raman spectroscopy is an attractive technique for identifying disease related molecular changes due to its simplicity. Here, we describe a fibre optic fluid cell for undertaking spontaneous Raman spectroscopy studies of human biofluids that is suitable for use away from a standard laboratory setting. Using this system, we examined serum obtained from patients with ALS at their first presentation to our centre (n = 66) and 4 months later (n = 27). We analysed Raman spectra using bounded simplex-structured matrix factorization (BSSMF), a generalisation of non-negative matrix factorisation which uses the distribution of the original data to limit the factorisation modes (spectral patterns). Biomarkers associated with ALS disease such as measures of symptom severity, respiratory function and inflammatory/immune pathways (C3/C-reactive protein) correlated with baseline Raman modes. Between visit spectral changes were highly significant (p = 0.0002) and were related to protein structure. Comparison of Raman data with established ALS biomarkers as a trial outcome measure demonstrated a reduction in required sample size with BSSMF Raman. Our portable, simple to use fibre optic system allied to BSSMF shows promise in the quantification of disease-related changes in ALS over short timescales.

Deep Learning-Guided Fiberoptic Raman Spectroscopy Enables Real-Time In Vivo Diagnosis and Assessment of Nasopharyngeal Carcinoma and Post-treatment Efficacy during Endoscopy

In this work, we develop a deep learning-guided fiberoptic Raman diagnostic platform to assess its ability of real-time in vivo nasopharyngeal carcinoma (NPC) diagnosis and post-treatment follow-up of NPC patients. The robust Raman diagnostic platform is established using innovative multi-layer Raman-specified convolutional neural networks (RS-CNN) together with simultaneous fingerprint and high-wavenumber spectra acquired within sub-seconds using a fiberoptic Raman endoscopy system. We have acquired a total of 15,354 FP/HW in vivo Raman spectra (control: 1761; NPC: 4147; and post-treatment (PT): 9446) from 888 tissue sites of 418 subjects (healthy control: 85; NPC: 82; and PT: 251) during endoscopic examination. The optimized RS-CNN model provides an overall diagnostic accuracy of 82.09% (sensitivity of 92.18% and specificity of 73.99%) for identifying NPC from control and post-treatment patients, which is superior to the best diagnosis performance (accuracy of 73.57%; sensitivity of 89.74%; and specificity of 58.10%) using partial-least-squares linear-discriminate-analysis, proving the robustness and high spectral information sensitiveness of the RS-CNN model developed. We further investigate the saliency map of the best RS-CNN models using the correctly predicted Raman spectra. The specific Raman signatures that are related to the cancer-associated biomolecular variations (e.g., collagens, lipids, and nucleic acids) are uncovered in the map, validating the diagnostic capability of RS-CNN models to correlate with biomolecular signatures. Deep learning-based Raman spectroscopy is a powerful diagnostic tool for rapid screening and surveillance of NPC patients and can also be deployed for longitudinal follow-up monitoring of post-treatment NPC patients to detect early cancer recurrences in the head and neck.

First applications of non-invasive techniques on Algerian heritage manuscripts: the LMUHUB ULAHBIB ancient manuscript collection from Kabylia region (Afniq n Ccix Lmuhub)

This paper reports a comparative study on inks and pigments of nine old manuscripts belonging to the Algerian heritage. The manuscripts are held by Afniq n Ccix Lmuhib, one of the richest and best known private libraries from Kabylia region – Algeria. They date back from the 14th to the 19th century and treat disciplines ranging from astronomy to poetry. To identify the inks and pigments used by the scribes we applied several non-invasive techniques such as X-ray fluorescence, fiber optic reflectance and Raman spectroscopy. While XRF gives information about the elemental composition of pigments and inks, FORS and Raman analyses give the direct identification of the pigment. This synergy gives us a strong confidence in our identification even and allows to individuate critical cases on which deeper investigations should be done, as some results seems to contradict what is generally believed by art historians. In particular, we identified iron gall ink and ivory/bone black, while the black ink used in the Kabylia region is always reported to be made from burned sheep wool. Our work is the first case study that allows positive identification of inks and pigments used in Algerian manuscripts; interesting aspect, we could compare our findings are with manufacturing of pigments reported in a manuscript from the 17th century, held in the same library.

A new approach to study human perivascular adipose tissue of the internal mammary artery by fiber-optic Raman spectroscopy supported by spectral modelling.

Perivascular adipose tissue (PVAT) regulates vascular function and represents a novel therapeutic target in vascular diseases. In this work, a new approach based on fiber-optic Raman spectroscopy and spectral modelling was used to characterize the chemical content of the PVAT of the internal mammary artery (IMA) of patients with advanced coronary atherosclerosis (n = 10) undergoing coronary bypass surgery. Our results showed a high degree of lipid unsaturation and low carotenoid content in the PVAT of the IMA of patients with more advanced coronary artery disease. Moreover, the spectral modelling of the IMA's PVAT composition indicated that glyceryl trioleate was a major PVAT lipid and for patients with relatively low levels of β-carotene, it was accompanied by arachidonic acid and glyceryl trilinolenate. In summary, our proof-of-concept study suggests that carotenoid content and lipid unsaturation degree may reflect the PVAT functional status and a Raman-based assessment of the PVAT of the IMA could prove useful as a novel diagnostic tool to rapidly define the PVAT phenotype in a grafted artery in patients undergoing coronary bypass.

Integrating liquid chromatography mass spectrometry into an analytical protocol for the identification of organic colorants in Japanese woodblock prints

Three Japanese woodblock prints from the Edo period (1603–1868) underwent a scientific investigation with the aim of understanding the changes in the colorants used in Japanese printing techniques. A multi-analytical approach was adopted, combining non-invasive techniques, such as fiber optic reflectance spectroscopy (FORS), Raman spectroscopy, multispectral imaging (MSI), and macro X-ray fluorescence (MA-XRF) with minimally invasive surface-enhanced Raman spectroscopy (SERS). The results enabled many of the pigments to be identified and their distribution to be studied, apart from two shades of purple of organic composition. Consequently, the potential of high-pressure liquid chromatography tandem mass spectrometry (HPLC–MS/MS) was explored for the first time with application to Japanese woodblock prints. The intrinsic sensitivity of the instrument and an effective extraction protocol allowed us to identify a mixture of dayflower (Commelina communis) blue and safflower (Carthamus tinctorius) red in purple samples constituted of 2–3 single fibers. In addition to the innovative integration of MA-XRF and HPLC–MS/MS to investigate these delicate artworks, the study concluded on the use of traditional sources of colors alongside newly introduced pigments in late Edo-period Japan. This information is extremely important for understanding the printing practices, as well as for making decisions about display, conservation, and preservation of such artworks.

Diverse spectral band-based deep residual network for tongue squamous cell carcinoma classification using fiber optic Raman spectroscopy.

The research is to propose a new classification framework, called diverse spectral band-based deep residual network (DSB-ResNet), which can distinguish tongue squamous cell carcinoma (TSCC) from non-cancerous tissue. A fiber optic Raman spectroscopy system is used to collect Raman spectral data of TSCC and normal tissues. DSB-ResNet takes advantage of diverse spectral band-based spectra without processing to derive spectral representations from different spectral bands of Raman spectra, which improves the ability to identify TSCC. To show the superiority of the proposed method, the existing methods are used as the competitive methods to compare with the DSB-RestNet, the results demonstrate our method has the highest performance with 97.38 %, 98.75 %, and 98.25 % for sensitivity, specificity, and accuracy, respectively. The experimental results show that the DSB-ResNet is able to distinguish TSCC from non-cancerous tissue successfully. The proposed method is expected to provide a theoretical and methodological base for accurate detection of TSCC.