Determination of Raman spectrum under different culture conditions: preliminary research on bacterial fish pathogens

The intensive labour and time required for conventional methods to identify bacterial fish pathogens have revealed the need to develop alternative methods. Raman spectroscopy has been used in the rapid optical identification of bacterial pathogens in recent years as an alternative method in microbiology. Strains of bacterial fish pathogens (Vibrio anguillarum, Lactococcus garvieae and Yersinia ruckeri) that often cause infectious diseases in fish were here identified and analyzed in terms of their biochemical structures in different media and at different incubation times, and the data were specified by using Raman spectroscopy. The results demonstrated that Raman spectroscopy presents species-specific Raman spectra of each disease-causing bacteria and that it would be more appropriate to choose general microbiological media over selective media for routine studies. Additionally, it was found that species-specific band regions did not differ in 24- and 48-hour cultures, but there could be a difference in peak intensity which may lead to difficult characterization of spectrum. The current study, conducted for the first time with bacterial fish pathogens under different incubation conditions, is believed to provide a basis for the routine use of Raman spectroscopy for quick pathogen identification and the precise determination of the methodology for further research.

Quantitative analysis of the quality constituents of Lonicera japonica Thunberg based on Raman spectroscopy

Quantitative analysis of the quality constituents of Lonicera japonica (Jinyinhua [JYH]) using a feasible method provides important information on its evaluation and applications. Limitations of sample pretreatment, experimental site, and analysis time should be considered when identifying new methods. In response to these considerations, Raman spectroscopy combined with deep learning was used to establish a quantitative analysis model to determine the quality of JYH. Chlorogenic acid and total flavonoids were identified as analysis targets via network pharmacology. High performance liquid chromatograph and ultraviolet spectroscopy were used to construct standard curves for quantitative analysis. Raman spectra of JYH extracts (1200) were collected. Subsequently, models were built using partial least squares regression, Support Vector Machine, Back Propagation Neural Network, and One-dimensional Convolutional Neural Network (1D-CNN). Among these, the 1D-CNN model showed superior prediction capability and had higher accuracy (R2 = 0.971), and lower root mean square error, indicating its suitability for rapid quantitative analysis.

Analysis of intra-specific variations in the venom of individual snakes based on Raman spectroscopy

The knowledge of variations in the composition of venoms from different snakes is important from both theoretical and practical points of view, in particular, at developing and selecting an antivenom. Many studies on this topic are conducted with pooled venoms, while the existence and significance of variations in the composition of venoms between individual snakes of the same species are emphasized by many authors. It is important to study both inter- and intra-specific, including intra-population, venom variations, because intra-specific variations in the venom composition may affect the effectiveness of antivenoms as strongly as inter-specific. In this work, based on venom Raman spectroscopy with principal component analysis, we assessed the variations in venoms of individual snakes of the Vipera nikolskii species from two populations and compared these intra-specific variations with inter-specific variations (with regard to the other related species). We demonstrated intra-specific (inter- and intra-population) differences in venom compositions which are smaller than inter-specific variations. We also assessed the compositions of V. nikolskii venoms from two populations to explain inter-population differences. The method used is rapid and requires virtually no preparation of samples, used in extremely small quantities, allowing the venoms of individual snakes to be analyzed. In addition, the method is informative and capable of detecting fairly subtle differences in the composition of venoms.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Resonance Raman study of oxoiron(IV) porphyrin π-cation radical complex: Porphyrin ligand effect on ν(Fe=O) frequency

Resonance Raman (rR) spectroscopy has been applied to study the nature of the iron-oxo (Fe=O) moiety of oxoiron(IV) porphyrin π-cation radical complex (CompI). While the axial ligand effect on the nature of the Fe=O moiety has been studied with rR spectroscopy, the porphyrin ligand effect has not been studied well. Here, we investigated the porphyrin ligand effect on the Fe=O moiety with rR spectroscopy. The porphyrin ligand effect was modulated by the electron-withdrawing effect of the porphyrin substituent at the meso-position. This study shows that the frequency of the Fe=O stretching band, ν(Fe=O), hardly change even when the electron-withdrawing effect of the porphyrin substituent changes. This result is further supported by theoretical calculation of CompI. The natural atomic charge analysis reveals that the oxo and axial ligands work to buffer the electron-withdrawing effect of the porphyrin substituent. The electron-withdrawing porphyrin substituent shifts an electron population from the ferryl iron to the porphyrin, but the decreased electron population on the ferryl iron is compensated by the shift of the electron population from the oxo ligand and the axial ligand. The shift of the electron population makes the Fe-axial ligand bond length short, but the Fe=O bond length unchanged, resulting in the invariable ν(Fe=O) frequency.

Swept-source Raman spectroscopy of chemical and biological materials

Significance

Raman spectroscopy has been used as a powerful tool for chemical analysis, enabling the noninvasive acquisition of molecular fingerprints from various samples. Raman spectroscopy has proven to be valuable in numerous fields, including pharmaceutical, materials science, and biomedicine. Active research and development efforts are currently underway to bring this analytical instrument into the field, enabling in situ Raman measurements for a wider range of applications. Dispersive Raman spectroscopy using a fixed, narrowband source is a common method for acquiring Raman spectra. However, dispersive Raman spectroscopy requires a bulky spectrometer, which limits its field applicability. Therefore, there has been a tremendous need to develop a portable and sensitive Raman system.

Aim

We developed a compact swept-source Raman (SS-Raman) spectroscopy system and proposed a signal processing method to mitigate hardware limitations. We demonstrated the capabilities of the SS-Raman spectroscopy by acquiring Raman spectra from both chemical and biological samples. These spectra were then compared with Raman spectra obtained using a conventional dispersive Raman spectroscopy system.

Approach

The SS-Raman spectroscopy system used a wavelength-swept source laser (822 to 842 nm), a bandpass filter with a bandwidth of 1.5 nm, and a low-noise silicon photoreceiver. Raman spectra were acquired from various chemical samples, including phenylalanine, hydroxyapatite, glucose, and acetaminophen. A comparative analysis with the conventional dispersive Raman spectroscopy was conducted by calculating the correlation coefficients between the spectra from the SS-Raman spectroscopy and those from the conventional system. Furthermore, Raman mapping was obtained from cross-sections of swine tissue, demonstrating the applicability of the SS-Raman spectroscopy in biological samples.

Results

We developed a compact SS-Raman system and validated its performance by acquiring Raman spectra from both chemical and biological materials. Our straightforward signal processing method enhanced the quality of the Raman spectra without incurring high costs. Raman spectra in the range of 900 to 1200    cm - 1 were observed for phenylalanine, hydroxyapatite, glucose, and acetaminophen. The results were validated with correlation coefficients of 0.88, 0.84, 0.87, and 0.73, respectively, compared with those obtained from dispersive Raman spectroscopy. Furthermore, we performed scans across the cross-section of swine tissue to generate a biological tissue mapping plot, providing information about the composition of swine tissue.

Conclusions

We demonstrate the capabilities of the proposed compact SS-Raman spectroscopy system by obtaining Raman spectra of chemical and biological materials, utilizing straightforward signal processing. We anticipate that the SS-Raman spectroscopy will be utilized in various fields, including biomedical and chemical applications.

Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network

With the increasing interest in microplastics (MPs) pollutants, quantitative analysis of MPs in water environment is an important issue. Vibrational spectroscopy, represented by Raman spectroscopy, is widely used in MP detection because they can provide unique fingerprint characteristics of chemical components of MPs, but it is difficult to provide quantitative information. In this paper, an ingenious method for quantitative analysis of MPs in water environment by combining Raman spectroscopy and convolutional neural network (CNN) is proposed. It is innovatively proposed to collect the average mapping spectra (AMS) of the samples to improve the uniformity of Raman spectroscopy detection, and to increase the effective detection range of concentration by filtering different volumes of the same MP solutions. In order to verify the universality and effectiveness of the proposed method, 6 different sizes of Polyethylene (PE) MPs were used as detection objects and mixed into 5 different actual water environments. The R2 and RMSE of CNN for identifying the concentration of PE solutions could reach 0.9972 and 0.033, respectively. Meanwhile, by comparing machine learning models such as Random Forest (RF) and Support Vector Machine (SVM) were compared, and CNN combined with Raman spectroscopy has significant advantages in identifying the concentration of MPs.

Investigating the Middle Iron Age ceramics of Van Fortress through multi-analytical techniques

This work presents the characterization results of Middle Iron Age pottery fragments excavated in Van Fortress, the historical capital of the Urartu Kingdom, located on the eastern coast of Lake Van in Turkey. A multi-analytical approach combining optical microscopy, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDS), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FT-IR) has been employed to investigate the mineralogical composition of ceramics. Micro-Raman spectrometer was also used for the characterization of the pigments used for decoration. The data collected from the analyses offered information on the minerals that were discovered in the ceramics, as well as the temperature at which the ceramics were fired and the atmosphere that they were exposed to. The existence of hematite suggests that they were subjected to firing in an oxidizing environment, with the exception of one sample, which has a sandwich shape characterized by a red-edge and a black center, indicating exposure to both reducing and oxidative atmospheres during the fire process. The ceramics utilized in this investigation are hypothesized to have been crafted from elemental substances procured from two to three distinct clay origins.

Comprehensive investigation of pressure-induced gelatinization of starches using in situ and ex-situ technical analyses

The pressure-induce gelatinization of pea starch, potato starch and corn starch was investigated by a combination of in situ and ex-situ technical analyses. According to in-situ observation of gelatinization process and the analysis of granular morphology by scanning electronic microscopy (SEM), the pressure that caused potato starch gelatinization was the highest at 600 MPa. This was followed by pea starch, and the pressure that caused the gelatinization of corn starch was the lowest at 400 MPa. In situ Raman spectral analysis revealed the molecular mechanism of starch gelatinization. This indicated that high pressure treatment resulted in the modification of the structure of the double helical polymers and the degree of a double helix of the starch crystalline varied as well. This study dynamically monitors the starch gelatinization process, aiming to better understand the gelatinization mechanism and provide a theoretical basis for the application of pressure in the starch field.

Activation of epidermal growth factor receptors in triple-negative breast cancer cells by morphine; analysis through Raman spectroscopy and machine learning

Triple negative breast cancer (TNBC) is a very aggressive form of breast cancer, and the analgesic drug morphine has been shown to promote the proliferation of TNBC cells. This article investigates whether morphine causes activation of epidermal growth factor receptors (EGFR), the roles of μ-opioid and EGFR receptors on TNBC cell proliferation and migration. While examining the changes with molecular techniques, we also aimed to investigate the analysis ability of Raman spectroscopy and machine learning-based approach. Effects of morphine on the proliferation and migration of MDA.MB.231 cells were evaluated by MTT and scratch wound-healing tests, respectively. Morphine-induced phosphorylation of the EGFR was analyzed by western blotting in the presence and absence of μ-receptor antagonist naltrexone and the EGFR-tyrosine kinase inhibitor gefitinib. Morphine-induced EGFR phosphorylation and cell migration were significantly inhibited by pretreatments with both naltrexone and gefitinib; however, morphine-increased cell proliferation was inhibited only by naltrexone. While morphine-induced changes were observed in the Raman scatterings of the cells, the inhibitory effect of naltrexone was analyzed with similarity to the control group. Principal component analysis (PCA) of the Raman confirmed the epidermal growth factor (EGF)-like effect of morphine and was inhibited by naltrexone and partly by gefitinib pretreatments. Our in vitro results suggest that combining morphine with an EGFR inhibitor or a peripherally acting opioidergic receptor antagonist may be a good strategy for pain relief without triggering cancer proliferation and migration in TNBC patients. In addition, our results demonstrated the feasibility of the Raman spectroscopy and machine learning-based approach as an effective method to investigate the effects of agents in cancer cells without the need for complex and time-consuming sample preparation. The support vector machine (SVM) with linear kernel automatically classified the effects of drugs on cancer cells with ∼95% accuracy.

A novel method for quantifying the short-range order in non-crystalline starch by Raman spectroscopy

A quantitative method was developed to characterize the short-range order in non-crystalline starch by Raman spectroscopy. The Raman spectra of three forms of non-crystalline starches (just-gelatinized starch, which was heated to the point of having just lost its long-range order but still retaining essentially all of its short-range order, gelatinized starch and amorphous starch) were resolved into subspectra to calculate the short-range ordered phases. By deducting the spectra of amorphous starch using a subtraction technique, the areas of subspectra for short-range ordered phases in just-gelatinized and gelatinized starches were obtained. The ratio of the area for short-range ordered phases in gelatinized starch relative to that in just-gelatinized starch was negatively correlated with water content for gelatinization. Based on this, we propose that this ratio of areas provides a quantitative measure for assessing the short-range order in non-crystalline starch. This study provides an alternative and simpler method to an X-ray diffraction protocol proposed previously.

Insight into the effect of osmosis agents on macro- and micro- texture, water distribution, and thermal stability of instant controlled pressure drop drying peach chips

To investigate the influence of sugar structure on the quality of peach chips produced using osmotic dehydration (OD) in combination with instant controlled pressure drop (DIC) drying, erythritol, glucose, maltose, and trehalose were selected as osmotic agents. The properties of the osmotic solutions, as well as the macro- and micro-texture, water distribution, and thermal stability of peach chips were investigated. Results showed that OD pretreatments inhibited the formation of large cavity structures. The highest hardness (101.34 N) and the lowest hydrophobicity (0°) were obtained in erythritol-OD samples. Trehalose-OD samples with the most homogeneous pore structure exhibited the highest crispness (1.05 mm) and the highest glass transition temperature (52.06 °C). Various absorption peaks of peach chips pretreated with different OD methods, characterized by Raman spectroscopy, suggested changes in composition and functional groups due to the diffusion of sugars into the cells of peach tissues, which also contributed to the higher Tg.

Rapid analysis of Radix Astragali using a portable Raman spectrometer with 1064-nm laser excitation and data fusion with PLS-DA

Radix Astragali is a medicinal herb with various physiological activities. There were high similarities among Radix Astragali samples from different regions owing to similarities in their major chemical compositions. Raman spectroscopy is a non-invasive and non-des- tructive technique that can be used in in-situ analysis of herbal samples. Dispersive Raman scattering, excited at 1064 nm, produced minimal fluorescence background and facilitated easy detection of the weak Raman signal. By moving the portable Raman probe point-by- point from the centre of the Radix Astragali sample to the margin, the spectral fingerprints, composed of dozens of Raman spectra representing the entire Radix Astragali samples, were obtained. Principal component analysis and partial least squares discriminant analysis (PLS-DA) were applied to the Radix Astragali spectral data to compare classification results, leading to efficient discrimination between genuine and counterfeit products. Furthermore, based on the PLS-DA model using data fusion combined with different pre- processing methods, the samples from Shanxi Province were separated from those belonging to other habitats. The as-proposed combination method can effectively improve the recognition rate and accuracy of identification of herbal samples, which can be a valuable tool for the identification of genuine medicinal herbs with uneven qualities and various origins.

Raman spectroscopy study of 7,8-dihydrofolate inhibition on the Wuhan strain SARS-CoV-2 binding to human ACE2 receptor

Emerging evidence suggests that elevated levels of folic acid in the bloodstream may confer protection against Wuhan-SARS-CoV-2 infection and mitigate its associated symptoms. Notably, two comprehensive studies of COVID-19 patients in Israel and UK uncovered a remarkable trend, wherein individuals with heightened folic acid levels exhibited only mild symptoms and necessitated no ventilatory support. In parallel, research has underscored the potential connection between decreased folic acid levels and the severity of Covid-19 among hospitalized patients. Yet, the underlying mechanisms governing this intriguing inhibition remain elusive. In a quest to elucidate these mechanisms, we conducted a molecular dynamics simulation approach followed by a Raman spectroscopy study to delve into the intricate interplay between the folic acid metabolite, 7,8-dihydrofolate (DHF), and the angiotensin-converting enzyme ACE2 receptor, coupled with its interaction with the receptor-binding domain (RBD) of the Wuhan strain of SARS-CoV-2. Through a meticulous exploration, we scrutinized the transformation of the ACE2 + RBD complex, allowing these reactants to form bonds. This was juxtaposed with a similar investigation where ACE2 was initially permitted to react with DHF, followed by the exposure of the ACE2 + DHF complex to RBD. We find that DHF, when bonded to ACE2, functions as a physical barrier, effectively inhibiting the binding of the Wuhan strain RBD. This physicochemical process offers a cogent explanation for the observed inhibition of host cell infection in subjects receiving supplementary folic acid doses, as epidemiologically substantiated in multiple studies. This study not only sheds light on a potential avenue for mitigating SARS-CoV-2 infection but also underscores the crucial role of folic acid metabolites in host-virus interactions. This research paves the way for novel therapeutic strategies in the battle against COVID-19 and reinforces the significance of investigating the molecular mechanisms underlying the protective effects of folic acid in the context of viral infections.

Molecular alterations in metaphase chromosomes induced by bleomycin

Chromosomes are intranuclear structures, their main function is to store and transmit genetic information during cell division. They are composed of tightly packed DNA in the form of chromatin, which is constantly exposed to various damaging factors. The resulting changes in DNA can have serious consequences (e.g. mutations) if they are not repaired or repaired incorrectly. In this article, we studied chromosomes isolated from human cervical cancer cells (HeLa) exposed to a genotoxic drug causing both single- and double-strand breaks. Specifically, we used bleomycin to induce DNA damage. We followed morphological and chemical changes in chromosomes upon damage induction. Atomic force microscopy was used to visualize the morphology of chromosomes, while Raman microspectroscopy enabled the detection of changes in the chemical structure of chromatin with the resolution close to the diffraction limit. Additionally, we extracted spectra corresponding to chromosome I or chromatin from hyperspectral Raman maps with convolutional neural networks (CNN), which were further analysed with the principal component analysis (PCA) algorithm to reveal molecular markers of DNA damage in chromosomes. The applied multimodal approach revealed simultaneous morphological and molecular changes, including chromosomal aberrations, alterations in DNA conformation, methylation pattern, and increased protein expression upon the bleomycin treatment at the level of the single chromosome.

TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy

Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R2) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery.

Robust platform for inline Raman monitoring and control of perfusion cell culture

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Detection of carbon nanotubes in bovine raw milk through Fourier transform Raman spectroscopy

The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

Simultaneous determination of multiple quality indices of dried shrimp (Parapenaeopsis hardwickii) during storage using Raman spectroscopy

BACKGROUND

Dried shrimp is a high-value fishery product worldwide, but rapid and accurate assessment of its quality remains challenging. In the present study, a new method based on Raman spectroscopy was developed for assessing the quality changes in dried shrimp (Parapenaeopsis hardwickii) during storage.

RESULTS

A high-quality Raman spectrum of astaxanthin (AST) was obtained from the third abdominal segment of dried shrimp. The intensity ratio (I1520/I1446) of the band from 1520 cm-1 to that at 1446 cm-1, which was ascribed to AST and protein/lipid, respectively, was calculated. I1520/I1446 can probe AST degradation in dried shrimp during storage at both 37 and 4 °C and further reflect quality changes of dried shrimp, as indicated by indices including total volatile basic nitrogen, pH and thiobarbituric acid reactive substances.

CONCLUSION

Compared to conventional methods, the proposed method avoids complex and time-consuming preprocessing and provides significant advantages including cost-effectiveness and rapid detection. © 2024 Society of Chemical Industry.

Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models

Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.

Modern optical approaches in redox biology: Genetically encoded sensors and Raman spectroscopy

The objective of the current review is to summarize the current state of optical methods in redox biology. It consists of two parts, the first is dedicated to genetically encoded fluorescent indicators and the second to Raman spectroscopy. In the first part, we provide a detailed classification of the currently available redox biosensors based on their target analytes. We thoroughly discuss the main architecture types of these proteins, the underlying engineering strategies for their development, the biochemical properties of existing tools and their advantages and disadvantages from a practical point of view. Particular attention is paid to fluorescence lifetime imaging microscopy as a possible readout technique, since it is less prone to certain artifacts than traditional intensiometric measurements. In the second part, the characteristic Raman peaks of the most important redox intermediates are listed, and examples of how this knowledge can be implemented in biological studies are given. This part covers such fields as estimation of the redox states and concentrations of Fe-S clusters, cytochromes, other heme-containing proteins, oxidative derivatives of thiols, lipids, and nucleotides. Finally, we touch on the issue of multiparameter imaging, in which biosensors are combined with other visualization methods for simultaneous assessment of several cellular parameters.

Immature porcine cortical bone mechanical properties and composition change with maturation and displacement rate

Computational models of mature bone have been used to predict fracture; however, analogous study of immature diaphyseal fracture has not been conducted due to sparse experimental mechanical data. A model of immature bone fracture may be used to aid in the differentiation of accidental and non-accidental trauma fractures in young, newly ambulatory children (0-3 years). The objective of this study was to characterize the evolution of tissue-level mechanical behavior, composition, and microstructure of maturing cortical porcine bone with uniaxial tension, Raman spectroscopy, and light microscopy as a function of maturation. We asked: 1) How do the monotonic uniaxial tensile properties change with maturation and displacement rate; 2) How does the composition and microstructure change with maturation; and 3) Is there a correlation between composition and tensile properties with maturation? Elastic modulus (p < 0.001), fracture stress (p < 0.001), and energy absorption (p < 0.014) increased as a function of maturation at the quasistatic rate by 110%, 86%, and 96%, respectively. Fracture stress also increased by 90% with maturation at the faster rate (p = 0.001). Fracture stress increased as a function of increasing displacement rate by 28% (newborn p = 0.048; 1-month p = 0.004; 3-month p= < 0.001), and fracture strain decreased by 68% with increasing displacement rate (newborn p = 0.002; 1-month p = 0.036; 3-month p < 0.001). Carbonate-to-phosphate ratio was positively linearly related to elastic modulus, and fracture stress was positively related to carbonate-to-phosphate ratio and matrix maturation ratio. The results of this study support that immature bone is strain-rate dependent and becomes more brittle at faster rates, contributing to the foundation upon which a computational model can be built to evaluate immature bone fracture.

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

Characterization of individual spores of two biological insecticides, Bacillus thuringiensis and Lysinibacillus sphaericus, in response to glutaraldehyde using single-cell optical approaches

Bacillus thuringiensis (Bt) and Lysinibacillus sphaericus (Ls) are the most widely used microbial insecticides. Both encounter unfavorable environmental factors and pesticides in the field. Here, the responses of Bt and Ls spores to glutaraldehyde were characterized using Raman spectroscopy and differential interference contrast imaging at the single-cell level. Bt spores were more sensitive to glutaraldehyde than Ls spores under prolonged exposure: <1.0% of Bt spores were viable after 10 min of 0.5% (v/v) glutaraldehyde treatment, compared to ~ 20% of Ls spores. The Raman spectra of glutaraldehyde-treated Bt and Ls spores were almost identical to those of untreated spores; however, the germination process of individual spores was significantly altered. The time to onset of germination, the period of rapid Ca2+-2,6-pyridinedicarboxylic acid (CaDPA) release, and the period of cortex hydrolysis of treated Bt spores were significantly longer than those of untreated spores, with dodecylamine germination being particularly affected. Similarly, the germination of treated Ls spores was significantly prolonged, although the prolongation was less than that of Bt spores. Although the interiors of Bt and Ls spores were undamaged and CaDPA did not leak, proteins and structures involved in spore germination could be severely damaged, resulting in slower and significantly prolonged germination. This study provides insights into the impact of glutaraldehyde on bacterial spores at the single cell level and the variability in spore response to glutaraldehyde across species and populations.

Development of generic metabolic Raman calibration models using solution titration in aqueous phase and data augmentation for in-line cell culture analysis

This study presents a novel approach for developing generic metabolic Raman calibration models for in-line cell culture analysis using glucose and lactate stock solution titration in an aqueous phase and data augmentation techniques. First, a successful set-up of the titration method was achieved by adding glucose or lactate solution at several different constant rates into the aqueous phase of a bench-top bioreactor. Subsequently, the in-line glucose and lactate concentration were calculated and interpolated based on the rate of glucose and lactate addition, enabling data augmentation and enhancing the robustness of the metabolic calibration model. Nine different combinations of spectra pretreatment, wavenumber range selection, and number of latent variables were evaluated and optimized using aqueous titration data as training set and a historical cell culture data set as validation and prediction set. Finally, Raman spectroscopy data collected from 11 historical cell culture batches (spanning four culture modes and scales ranging from 3 to 200 L) were utilized to predict the corresponding glucose and lactate values. The results demonstrated a high prediction accuracy, with an average root mean square errors of prediction of 0.65 g/L for glucose, and 0.48 g/L for lactate. This innovative method establishes a generic metabolic calibration model, and its applicability can be extended to other metabolites, reducing the cost of deploying real-time cell culture monitoring using Raman spectroscopy in bioprocesses.

Solid-State Synthesis of B←N Adducts by the Amine-Facilitated Trimerization of the Phenylboronic Acid

Stable boroxine-amine adducts comprising dative B←N bond(s) were prepared by mechanochemically-induced reactions of phenylboronic acid (PBA) and amines (pyridine, DMAP, 1H-pyrazole, piperidine, DABCO, hexamethylenetetramine, or 4,4'-bipyridine). In-situ Raman monitoring, ex-situ PXRD and DFT calculations were used for product identification. Stoichiometry of the product (3 : 1, 3 : 2 or 6 : 1 adduct) was controlled by the amine structure and the molar ratio of the reactants. The 1 : 2 H-bonded assembly of PBA and 4,4'-bipyridine (bpy) was confirmed as an intermediate in the adduct formation for bpy. Competitive binding experiments indicated that the exchange of the amines in the 3 : 1 adducts follows the computed adduct stabilities that increase with the amine basicity. Following the DFT prediction, the first adduct with two different amines, DMAP and pip, bound to one boroxine moiety was isolated and structurally characterized. Results show that calculations can be used to predict possible and preferred product(s) and their spectral characteristics.

Interactions of deprotonated phenylalanine with gold Clusters: Theoretical study with prospects for amino acid detection

Nanomaterials are widely used nowadays in industry and medicine. The specific properties of gold nanoclusters (Au NCs) are chemical stability, low cytotoxicity, low photobleaching, high sensitivity to the molecular environment. This set of properties allows to use Au NCs as nanosensors in bioimaging and diagnostics. We have investigated gold cluster complexes with proteinogenic amino acid phenylalanine (Phe). Detection of phenylalanine is essential for diagnostics of phenylketonuria, vitiligo, sclerosis, cancer, tuberculosis, etc. We have studied the complexes of Phe with Aunq clusters with atomic number equal 1-6, 8, 20 and a charge equal 0-2. We have established that the clusters Au40, Au21+ and Au32+ form the most stable complexes with Phe among NCs with charge 0, +1 and + 2, respectively. Intracomplex interactions have been studied using Atoms-In-Molecules (AIM) theory and Natural Bond Orbital (NBO) analysis. It has been shown that metal-ligand intracomplex interactions are partially covalent and partially electrostatic. Also, we have simulated the UV-vis absorption and Raman spectra of the Phe-Au NCs. We have established that the clusters possess prospective features if being used for colorimetric and Raman detection of Phe. Au20 cluster is remarkable for its six-times enhancement of the Raman signal. Moreover, our study provides insights into metal-ligand interactions for clusters synthesized inside a polypeptide globula. Hence, to the best of our knowledge this is a first attempt to perform a detailed analysis of Phe interactions with gold using quantum chemical calculations.

High pressure Raman study of isobutyramide

Isobutyramide (IBA) has attracted considerable attention due to its expansive prospects for practical applications in the synthesis of drugs, dyes and other organic compounds. Herein we perform the high-pressure studies of IBA crystal by Raman spectral measurements at room temperature from ambient pressure to 30 GPa by using diamond anvil cells (DACs) to gain comprehensive insights into its structure and stability. Raman vibrational modes of IBA crystal at ambient pressure are resolved based on the experimental results and the first-principles theoretical calculations. High-pressure Raman scattering results show the Raman bands splitting, emergence/disappearance of the Raman bands and discontinuous wavenumber shifts at 1, 2 and 10 GPa, which indicate that IBA crystal undergoes three structural phase transitions at corresponding pressure. In addition, softening of the C = O and N-H stretching vibrational modes of IBA with increasing pressure can be interpreted by the reorganization of the hydrogen bond network of IBA molecules due to phase transition.

Distinct spectral signatures unfold ECM stiffness-triggered biochemical changes in breast cancer cells

Cancer progression often accompanies the stiffening of extracellular matrix (ECM) in and around the tumor, owing to extra deposition and cross-linking of collagen. Stiff ECM has been linked with poor prognosis and is known to fuel invasion and metastasis, notably in breast cancer. However, the underlying biochemical or metabolic changes and the cognate molecular signatures remain elusive. Here, we explored Raman spectroscopy to unveil the spectral fingerprints of breast cancer cells in response to extracellular mechanical cues. Using stiffness-tuneable hydrogels, we showed that cells grown on stiff ECM displayed morphological changes with high proliferation. We further demonstrated that Raman Spectroscopy, a label-free and non-invasive technique, could provide comprehensive information about the biochemical environment of breast cancer cells in response to varying ECM stiffness. Raman spectroscopic analysis classified the cells into distinct clusters based on principal component-based linear discriminant analysis (PC-LDA). Multivariate curve resolution-alternating least squares (MCR-ALS) analysis indicated that cells cultured on stiff ECM exhibited elevated nucleic acid content and lesser lipids. Interestingly, increased intensity of Raman bands corresponding to cytochrome-c was also observed in stiff ECM conditions, suggesting mitochondrial modulation. The key findings harboured by spectral profiles were also corroborated by transmission electron microscopy, confirming altered metabolic status as reflected by increased mitochondria number and decreased lipid droplets in response to ECM stiffening. Collectively, these findings not only give the spectral signatures for mechanoresponse but also provide the landscape of biochemical changes in response to ECM stiffening.

Multi-sensor characterization for an improved identification of polymers in WEEE recycling

Polymers represent around 25% of total waste from electronic and electric equipment. Any successful recycling process must ensure that polymer-specific functionalities are preserved, to avoid downcycling. This requires a precise characterization of particle compounds moving at high speeds on conveyor belts in processing plants. We present an investigation using imaging and point measurement spectral sensors on 23 polymers including ABS, PS, PC, PE-types, PP, PVC, PET-types, PMMA, and PTFE to assess their potential to perform under the operational conditions found in recycling facilities. The techniques applied include hyperspectral imaging sensors (HSI) to map reflectance in the visible to near infrared (VNIR), short-wave (SWIR) and mid-wave infrared (MWIR) as well as point Raman, FTIR and spectroradiometer instruments. We show that none of the sensors alone can identify all the compounds while meeting the industry operational requirements. HSI sensors successfully acquired simultaneous spatial and spectral information for certain polymer types. HSI, particularly the range between (1600-1900) nm, is suitable for specific identification of transparent and light-coloured (non-black) PC, PE-types, PP, PVC and PET-types plastics; HSI in the MWIR is able to resolve specific spectral features for certain PE-types, including black HDPE, and light-coloured ABS. Fast-acquisition Raman spectroscopy (down to 500 ms) enabled the identification of all polymers regardless their composition and presence of black pigments, however, it exhibited limited capacities in mapping applications. We therefore suggest a combination of both imaging and point measurements in a sequential design for enhanced robustness on industrial polymer identification.

A review on advancement of cocrystallization approach and a brief on screening, formulation and characterization of the same

The objective of this review is, to discuss recent advancements in screening methods for co-formers, evaluation cum confirmation methods and co-crystallization with examples. Co-crystals are considered as a new form of an old drug entity. Co-crystals improve the stability, hygroscopicity, solubility, dissolution, and physicochemical properties of pure drugs without altering chemical and pharmacological properties. Advancement in co-crystal formulation methods like electrospray and laser-irradiation methods are showing potential for solvent-free co-crystallization and tends to give better yield and lesser loss of materials. Screening methods are also transformed from trial and error to in-silico methods, which facilitate the selection process by reducing the time of screening and increasing the number of co-formers to be screened. Advanced evaluation methods like Raman and solid-state NMR spectroscopy provide a better understanding of crystal lattice by pinpointing the interaction between drug/co-former molecules. The same evaluation methods can also differentiate between the formation of salt and co-crystals. Co-crystals are helping open a new door in pharmaceutical industries in the field of formulation for the improvement of physicochemical properties in existing old molecules and several new molecules. With a motto of "making a good drug better", co-crystals show scope for vast research and give researchers an ocean of opportunities to make the impossible, possible.

SLE diagnosis research based on SERS combined with a multi-modal fusion method

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques

Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of Mycobacterium tuberculosis (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, RocheTM solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.

Application of one-dimensional hierarchical network assisted screening for cervical cancer based on Raman spectroscopy combined with attention mechanism

Cervical cancer is one of the most common malignant tumors among women, and its pathological change is a relatively slow process. If it can be detected in time and treated properly, it can effectively reduce the incidence rate and mortality rate of cervical cancer, so the early screening of cervical cancer is particularly critical and significant. In this paper, we used Raman spectroscopy technology to collect the tissue sample data of patients with cervicitis, Low-grade Squamous Intraepithelial Lesion, High-grade Squamous Intraepithelial Lesion, Well differentiated squamous cell carcinoma, Moderately differentiated squamous cell carcinoma, Poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. A one-dimensional hierarchical convolutional neural network based on attention mechanism was constructed to classify and identify seven types of tissue samples. The attention mechanism Efficient Channel Attention Networks module and Squeeze-and-Excitation Networks module were combined with the established one-dimensional convolutional hierarchical network model, and the results showed that the combined model had better diagnostic performance. The average accuracy, F1, and AUC of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model after 5-fold cross validations could reach 96.49%±2.12%, 0.9663±0.0253, and 0.9815±0.0224, respectively, which were 1.58%, 0.0140, and 0.008 higher than those of hierarchical network. The recall rate of the Principal Component Analysis-Efficient Channel Attention-hierarchical network model was as high as 96.78%±2.85%, which is 1.47% higher than hierarchical network. Compared with the classification results of traditional CNN and ResNet for seven types of cervical cancer staging, the accuracy of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model is 3.33% and 11.05% higher, respectively. The experimental results indicate that the model established in this study is easy to operate and has high accuracy. It has good reference value for rapid screening of cervical cancer, laying a foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths annually and 10 million new cases reported each year1. The causative organism, Mycobacterium tuberculosis (Mtb) can take nearly 40 days to culture2,3, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the MtB complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and in patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all 5 BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Evaluating the Efficiency of Boron Nitride Coating in Single-Walled Carbon-Nanotube-Based 1D Heterostructure Films by Optical Spectroscopy

Single-walled carbon nanotube (SWCNT) films exhibit exceptional optical and electrical properties, making them highly promising for scalable integrated devices. Previously, we employed SWCNT films as templates for the chemical vapor deposition (CVD) synthesis of one-dimensional heterostructure films where boron nitride nanotubes (BNNTs) and molybdenum disulfide nanotubes (MoS2NTs) were coaxially nested over the SWCNT networks. In this work, we have further refined the synthesis method to achieve precise control over the BNNT coating in SWCNT@BNNT heterostructure films. The resulting structure of the SWCNT@BNNT films was thoroughly characterized using a combination of electron microscopy, UV-vis-NIR spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and Raman spectroscopy. Specifically, we investigated the pressure effect induced by BNNT wrapping on the SWCNTs in the SWCNT@BNNT heterostructure film and demonstrated that the shifts of the SWCNT's G and 2D (G') modes in Raman spectra can be used as a probe of the efficiency of BNNT coating. In addition, we studied the impact of vacuum annealing on the removal of the initial doping in SWCNTs, arising from exposure to ambient atmosphere, and examined the effect of MoO3 doping in SWCNT films by using UV-vis-NIR spectroscopy and Raman spectroscopy. We show that through correlation analysis of the G and 2D (G') modes in Raman spectra, it is possible to discern distinct types of doping effects as well as the influence of applied pressure on the SWCNTs within SWCNT@BNNT heterostructure films. This work contributes to a deeper understanding of the strain and doping effect in both SWCNTs and SWCNT@BNNTs, thereby providing valuable insights for future applications of carbon-nanotube-based one-dimensional heterostructures.

Structure and complexation mechanism of aqueous Zn(II)-acetate complex studied by XAFS and Raman spectroscopies

In this work, the structure of Zn acetate has been determined by a combination of X-ray absorption fine structure and Raman spectroscopy. We have analyzed the local atomic environment and the main vibrational bands of the acetate and Zn acetate at different pH. The results suggest that Zn acetate complex acquires a bidentate structure that modifies its first coordination shell. Meanwhile, the coordination shell of the hydrated Zn cation is formed by 6 hydroxides at a mean distance of 2.06 Å, the coordination shell of the Zn cation in the complex is formed by 2 hydroxides and 2 oxygens from the carboxyl group of the acetate, at a mean Zn-O distance of 1.96 Å. The structure of the Zn acetate complex is compared to those of Zn malonate and Zn citrate, none of which present a reduction in the coordination shell neither a shrinkage of the Zn-O shell distance.

Early diagnosis of thyroid-associated ophthalmopathy using label-free Raman spectroscopy and multivariate analysis

Thyroid-associated ophthalmopathy (TAO) is the most common orbital disease in adults, with complex clinical manifestations and significant impacts on the life quality of patients. The current diagnosis of TAO lacks reliable biomarkers for early and non-invasive screening and detection, easily leading to poor prognosis. Therefore, it is essential to explore new methods for accurately predicting TAO development in its early stage. In this study, Raman spectroscopy, with non-destructive, label-free, and high-sensitivity characteristics, was used to analyze the differences in biochemical components of orbital adipocyte and tear samples between TAO and control groups. Furthermore, a multivariate analysis method (i.e., Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA)) was applied for data processing and analysis. Compared with controls, PCA-LDA yielded TAO diagnostic accuracies of 72.7% and 75.0% using orbital adipocytes and tears, respectively. Our proof-of-concept results suggest that Raman spectroscopy holds potential for exploring the underlying pathogenesis of TAO, and its potential application in early screening of other thyroid-associated diseases can be further expanded.

A Multi-Analytical Approach to Identify Colorants in the Qajar Painted Wooden Decorative False Ceilings, From Northwest Iran

The purpose of this study was to analyze the pigments used in the wooden paintings of Zarir Mosque, dating back to the Safavid era in Maragheh, Iran. These paintings, known as "Pardu," were commonly used for decorative false ceilings in various regions of Iran, particularly the northwest, during the Safavid and Qajar periods. Raman spectroscopy, scanning electron microscopy-energy-dispersive X-ray spectroscopy, multiband imaging, and optical microscopy were employed to examine the pigments. Fourier-transform infrared spectroscopy was also used to investigate the binder. The results indicated the use of orpiment, red lead, artificial ultramarine, carbon black, and gypsum for yellow, red, blue, black, and white pigments, respectively. Light blue colour was achieved by mixing gypsum and indigo, and gypsum was used as the primer layer in the painting. The presence of a protein-based binder, likely egg tempera, was confirmed through FTIR spectroscopy. The presence of artificial ultramarine suggests that these paintings were created after 1828, when it was first synthesized, placing them in the Qajar period. The presence of an inscription from 1280 AH suggests that these panels may have been produced during the extensive renovations of the mosque in 1280 AH (1864 AD), during the Qajar era.

13CH4/12CH4 sensing using Raman spectroscopy

The paper presents a technique for measuring the concentration of 13CH4 in natural methane using Raman spectroscopy. The peak positions and the relative scattering cross-sections of the Q-branches for the most intense vibrational bands of 13CH4 are determined. Features of the 13CH4/12CH4 ratio measurement methods using Q-branches of the ν1 and ν3 bands were considered. It was shown that the 13CH4/12CH4 ratio can be determined by simulation of the ν3 bands of these molecules without the use of experimental spectra. In our experiments the measurement error of δ13C value was 10 ‰ using the 100-s exposure spectrum at a gas pressure close to 1 atm recorded on the developed Raman spectrometer. In addition, the Raman spectra of alkanes (up to n-hexane) in the range of 2850-3050 cm-1 at a resolution of 0.4 cm-1 are presented, and their integrated intensities in the ranges of the characteristic bands of 13CH4 and 12CH4 are provided. The data obtained make it possible to expand the capabilities of Raman gas analyzers in the mud gas logging industry.

Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room

PURPOSE

Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence.

METHODS

A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth.

RESULTS

The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively.

CONCLUSION

Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.

A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.

Monitoring plastic pellet pollution in coastal environments through handheld Raman spectroscopy: Data from the Mediterranean coasts (Southern Italy)

This paper examines the distribution and chemical properties of beached plastic pellets along the Ionian and Tyrrhenian coasts of Southern Italy. Three locations have been sampled: Agnone Bagni (SR) and Paradiso (ME) on the Ionian coast of Sicily, Baia del Tono in Milazzo (ME) on the Sicilian Tyrrhenian coast, and Pizzo Calabro (VV) in Calabria on the Tyrrhenian coast. Variations in shape, size, compactness, color, and other physical features, correlated with residence times and transport, has been highlighted. Raman spectroscopy, used in a portable configuration, enabled rapid identification of polymer types, demonstrating its utility for on-site plastic pollutant monitoring. Polyethylene and polypropylene were the predominant polymers. Principal component analysis of the spectra determined the optimal chemometric classification of pellets by composition, avoiding interference or distortion. In conclusion, the study provided preliminary insights into pellet abundance, composition, weathering extent, and distribution across these shorelines, underscoring the importance of regular beach monitoring.

A Systematic Approach toward Enabling Maximal Targeting Efficiency of Cell Surface Proteins with Actively Targeted SERS Nanoparticles

With their intricate design, nanoparticles (NPs) have become indispensable tools in the quest for precise cellular targeting. Among various NPs, gold NPs stand out with unique features such as chemical stability, biocompatibility, adjustable shape, and size-dependent optical properties, making them particularly promising for molecular detection by leveraging the surface-enhanced Raman scattering (SERS) effect. Their multiplexing abilities for the simultaneous identification of multiple biomarkers are important in the rapidly evolving landscape of diverse cellular phenotypes and biomolecular profiling. However, the challenge is ensuring that SERS NPs can effectively target specific cells and biomarkers among intricate cell types and biomolecules with high specificity. In this study, we improve the functionalization of SERS NPs, optimizing their targeting efficiency in cellular applications for ca. 160 nm NP-based probes. Spherical SERS NPs, conjugated with antibodies targeting epidermal growth factor receptor and human epidermal growth factor receptor 2, were incubated with cells overexpressing these proteins, and their specific binding potential was quantified at each stage by using flow cytometry to achieve optimal targeting efficiency. We determined that maintaining an average of 3.5 × 105 thiols per NP, 300 antibodies per NP, 18,000 NPs per cell, conducting a 15 min staining incubation at 4 °C in a shaker, and using SM(PEG)12 as a cross-linker for the NP conjugation were crucial to achieve the highest targeting efficiency. Fluorescence and Raman imaging were used with these parameters to observe the maximum ability of these NPs to efficiently target suspended cells. These highly sensitive contrast agents demonstrate their pivotal role in effective active targeting, making them invaluable for multiplexing applications across diverse biological environments.

Combinatorial Effect of Tricomponent Dual-Rim Nanoring Building Blocks: Label-Free SERS Detection of Biomolecules

Detecting weakly adsorbing molecules via label-free surface-enhanced Raman scattering (SERS) has presented a significant challenge. To address this issue, we propose a novel approach for creating tricomponent SERS substrates using dual-rim nanorings (DRNs) made of Au, Ag, and CuO, each possessing distinct functionalities. Our method involves depositing different metals on Pt nanoring skeletons to obtain each nanoring with varying surface compositions while maintaining a similar size and shape. Next, the mixture of these nanorings is transferred into a monolayer assembly with homogeneous intermixing on a solid substrate. The surface of the CuO DRNs has dangling bonds (Cu2+) that facilitate the strong adsorption of carboxylates through the formation of chelating bonds, while the combination of Au and Ag DRNs significantly enhances the SERS signal intensity through a strong coupling effect. Notably, the tricomponent assemblies enable the successful SERS-based analysis of biomolecules such as amino acids, proteins, nucleobases, and nucleotides.

Possible charge ordering and anomalous transport in graphene/graphene quantum dot heterostructure

Observations of superconductivity and charge density waves (CDW) in graphene have been elusive thus far due to weak electron-phonon coupling (EPC) interactions. Here, we report a unique observation of anomalous transport and multiple charge ordering phases at high temperatures (T1∼213K,T2∼325K) in a 0D-2D van der Waals (vdW) heterostructure comprising of single layer graphene (SLG) and functionalized (amine) graphene quantum dots (GQD). The presence of functionalized GQD contributed to charge transfer with shifting of the Dirac point ∼ 0.05 eV above the Fermi level (ab initio   simulations) and carrier densityn∼-0.3×1012 cm-2confirming p-doping in SLG and two-fold increase in EPC interaction was achieved. Moreover, we elucidate the interplay between electron-electron and electron-phonon interactions to substantiate high temperature EPC driven charge ordering in the heterostructure through analyses of magnetotransport and weak anti-localization (WAL) framework. Our results provide impetus to investigate strongly correlated phenomena such as CDW and superconducting phase transitions in novel graphene based heterostructures.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

Serum Raman spectroscopy: Evaluation of tumour load variations in experimental carcinogenesis

Several serum Raman spectroscopy (RS) studies have demonstrated its potential as an oral cancer screening tool. This study investigates influence of low tumour load (LTL) and high tumour load (HTL) on serum RS using hamster buccal pouch model of experimental oral carcinogenesis. Sera of untreated control, LTL, and HTL groups at week intervals during malignant transformation were employed. Serum Raman spectra were subjected to multivariate analyses-principal component analysis, principal component-based linear discriminant analysis (for stratification of study groups), and multivariate curve resolution-alternating least squares (MCR-ALS) (to comprehend biomolecular differences). Multivariate analysis revealed misclassifications between LTL and HTL at all week intervals. MCR-ALS components showed statistically significant abundances between control versus LTL and control versus HTL, but could not discern LTL and HTL. MCR-ALS components exhibited spectral mixtures of proteins, lipids, heme and nucleic acids. Thus, these findings support use of serum RS as a screening tool as varying tumour load is not a confounding factor influencing the technique.

Analysis of the molecular alterations in cancer cells following nanotechnology-assisted targeted radiotherapy using Raman spectroscopy

The study unveiled an innovative strategy for precise radiation targeting in cancer treatment, along with the monitoring of molecular changes induced by this therapeutic approach. In this research, we explored the impact of administering anti-HER2-AgNPs nanoconjugates either individually or in conjunction with gamma irradiation on the viability of SKBR3 breast cancer cells. The utilization of nanoconjugates resulted in an enhancement of cellular sensitivity toward radiation. The viability of the cells exhibited a decline as the dose of irradiation increased, and this decrease was further exacerbated by the passage of time following irradiation. The analysis of RS revealed distinct cellular responses in varying conditions. The observed increase in SERS intensity, resulting from the increment in dose from 0 to 2 Gy, can be attributed to the probable upregulation of HER2 expression induced by irradiation. The observed decrease in SERS intensity at doses of 4 and 6 Gy can be attributed to the likely reduction in HER2 expression. It was illustrated that the analysis of Raman spectroscopy data can aid in the identification of radiation-induced biochemical alterations in cancer cells during the application of nanoconjugates-based radiotherapy. The findings revealed that nanoconjugates have the potential to enhance cellular sensitivity to radiation along with facilitating the detection of radiation-induced biochemical alterations within cancer cells.