Honeycomb-Inspired Surface-Enhanced Raman Scattering Microarray for Large-Area Automated Testing of Urease in Saliva Samples.

Surface-enhanced Raman scattering (SERS) technology, as an important analytical tool, has been widely applied in the field of chemical and biomedical sensing. Automated testing is often combined with biochemical analysis technologies to shorten the detection time and minimize human error. The present SERS substrates for sample detection are time-consuming and subject to high human error, which are not conducive to the combination of SERS and automated testing. Here, a novel honeycomb-inspired SERS microarray is designed for large-area automated testing of urease in saliva samples to shorten the detection time and minimize human error. The honeycomb-inspired SERS microarray is decorated with hexagonal microwells and a homogeneous distribution of silver nanostars. Compared with the other four common SERS substrates, the optimal honeycomb-inspired SERS microarray exhibits the best SERS performance. The RSD of 100 SERS spectra continuously collected from saliva samples is 6.56%, and the time of one detection is reduced from 5 min to 10 s. There is a noteworthy linear relationship with a R2 of 0.982 between SERS intensity and urease concentration, indicating the quantitative detection capability of the urease activity in saliva samples. The honeycomb-inspired SERS microarray, combined with automated testing, provides a new way in which SERS technology can be widely used in biomedical applications.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Identification and quantification of pigments in plant leaves using thin layer chromatography-Raman spectroscopy (TLC-Raman).

Carotenoids are yellow, orange, and red pigments commonly found in plants. In leaves, these molecules are essential for photosynthesis, but they also play a major role in plant growth and development. Efficiently monitoring concentrations of specific carotenoids in plant tissues could help to explain plant responses to environmental stressors, infection and disease, fertilization, and other conditions. Previously, Raman methods have been used to demonstrate a correlation between plant fitness and the carotenoid content of leaves. Due to solvatochromatic effects and structural similarities within the carotenoid family, current Raman spectroscopy techniques struggle to assign signals to specific carotenoids with certainty, complicating the determination of amounts of individual carotenoids present in a sample. In this work, we use thin layer chromatography-Raman spectroscopy, or TLC-Raman, to identify and quantify carotenoids extracted from tomato leaves. These quick and accurate methods could be applied to study the relationship between pigment content and a number of factors affecting plant health.

Light-Initiated Imprinted Membrane-Based Biomimetic SERS Sensor toward Selective Detection of Trace MC-LR.

Microcystin-LR (MC-LR) is a severe threat to human and animal health; thus, monitoring it in the environment is essential, especially in water quality protections. Herein, in this work, we synthesize PVDF/CNT/Ag molecular imprinted membranes (PCA-MIMs) via an innovative combination of surface-enhanced Raman spectroscopy (SERS) detection, membrane separation, and molecular-imprinted technique toward the analysis of MC-LR in water. In particular, a light-initiated imprint is employed to protect the chemical structure of the MC-LR molecules. Furthermore, in order to ensure the detection sensitivity, the SERS substrates are combined with the membrane via the assistance of magnetism. The effect of synthesis conditions on the SERS sensitivity was investigated in detail. It is demonstrated from the characteristic results that the PCA-MIMs present high sensitivity to the MC-LR molecules with excellent selectivity against the interfere molecules. Results clearly show that the as-prepared PCA-MIMs hold great potential applications to detect trace MC-LR for the protection of water quality.

Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification.

Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.

Real-Time Quantitative Evaluation of a Drug during Liposome Preparation Using a Probe-Type Raman Spectrometer.

During the manufacturing process of liposome formulations, it is considered difficult to evaluate their physicochemical properties and biological profiles due to the complexity of their structure and manufacturing process. Conventional quality evaluation is labor-intensive and time-consuming; therefore, there was a need to introduce a method that could perform in-line, real-time evaluation during the manufacturing process. In this study, Raman spectroscopy was used to monitor in real time the encapsulation of drugs into liposomes and the drug release, which are particularly important quality evaluation items. Furthermore, Raman spectroscopy combined with partial least-squares (PLS) analysis was used for quantitative drug evaluation to assess consistency with results from UV-visible spectrophotometry (UV), a common quantification method. The prepared various ciprofloxacin (CPFX) liposomes were placed in cellulose tubes, and a probe-type Raman spectrophotometer was used to monitor drug encapsulation, the removal of unencapsulated drug, and drug release characteristics in real time using a dialysis method. In the Raman spectra of the liposomes prepared by remote loading, the intensities of the CPFX-derived peaks increased upon drug encapsulation and showed a slight decrease upon removal of the unencapsulated drug. Furthermore, the peak intensity decreased more gradually during the drug release. In all Raman monitoring experiments, the discrepancy between quantified values of CPFX concentration in liposomes, as measured by Raman spectroscopy combined with partial least-squares (PLS) analysis, and those obtained through ultraviolet (UV) spectrophotometry was within 6.7%. The results revealed that the quantitative evaluation of drugs using a combination of Raman spectroscopy and PLS analysis was as accurate as the evaluation using UV spectrophotometry, which was used for comparison. These results indicate the promising potential of Raman spectroscopy as an innovative method for the quality evaluation of liposomal formulations.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Simple Matrices.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for highly sensitive qualitative and quantitative analyses of trace targets. However, sensitive SERS detection can only be facilitated with a suitable sample pretreatment in fields related to trace amounts for food safety and clinical diagnosis. Currently, the sample pretreatment for SERS detection is normally borrowed and improved from the ones in the lab, which yields a high recovery but is tedious and time-consuming. Rapid detection of trace targets in a complex environment is still a considerable issue for SERS detection. Herein, we proposed a liquid-liquid extraction method coupled with a back-extraction method for sample pretreatment based on the pH-sensitive reversible phase transition of the weak organic acids and bases, where the lowest detectable concentrations were identical before and after the pretreatment process. The sensitive (µg L-1 level) and rapid (within 5 min) SERS detection of either koumine, a weak base, or celastrol, a weak acid, was demonstrated in different drinking water samples and beverages. Furthermore, target generality was demonstrated for a variety of weak acids and bases (2 < pKa < 12), and the hydrophilicity/hydrophobicity of the target determines the pretreatment efficiency. Therefore, the LLE-BE coupled SERS was developed as an easy, rapid, and low-cost tool for the trace detection of the two types of targets in simple matrices, which paved the way toward trace targets in complex matrices.

Sandwich-like CuNPs@AgNPs@PSB SERS substrates for sensitive detection of R6G and Forchlorfenuron.

The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10-15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 µg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 µg/L -100 µg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology.

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.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Colorimetric and SERS dual-mode detection of GSH in human serum based on AuNPs@Cu-porphyrin MOF nanozyme.

BACKGROUND: Rapid and accurate detection of glutathione content in human blood plays an important role in real-time tracking of related diseases. Currently, surface-enhanced Raman scattering/spectroscopy (SERS) combined with nanozyme material has been proven to have excellent properties in the detection applications compared to many other methods because of it combines the advantages of trace detection capability of SERS and efficient catalytic activity of nanozymes. However, there are still existing problems in real sample detection, and to achieve quantitative detection is still challenging. RESULTS: In this study, gold nanoparticles (AuNPs) were synthesized in situ on the surface of two-dimensional Cu-porphyrin metal-organic framework (MOF) nanosheets to produce the AuNPs@Cu-porphyrin MOF nanozyme, which exhibited both oxidase-like activity and SERS detection ability. On one hand, the intrinsic oxidase-like activity of the nanozyme could be inhibited due to the chelation of glutathione (GSH) and Cu, which thus led to the visual color change of the solution. On the other hand, the abundant Raman "hot spots" at the nanogap generated by Au NPs and the internal standard (IS) signal provided by Cu-meso-tetra (4-carboxyphenyl) porphine (Cu-TCPP) MOF improved the sensitivity and quantitative accuracy of detection. SIGNIFICANCE AND NOVELTY: A dual-mode signal output sensor based on the nanozyme was thus established, which could be used in the trace detection of GSH. Such a dual-mode sensor possesses excellent detection performance, with the advantage of both wide detection range from 1 to 300 µM in the colorimetric detection mode and high sensitivity with LOD of 5 nM in the SERS detection mode, and can be applied to GSH detection in actual serum samples with reliable results.

Discrimination of ivory from extant and extinct elephant species using Raman spectroscopy: A potential non-destructive technique for combating illegal wildlife trade.

The use of elephant ivory as a commodity is a factor in declining elephant populations. Despite recent worldwide elephant ivory trade bans, mammoth ivory trade remains unregulated. This complicates law enforcement efforts, as distinguishing between ivory from extant and extinct species requires costly, destructive and time consuming methods. Elephant and mammoth ivory mainly consists of dentine, a mineralized connective tissue that contains an organic collagenous component and an inorganic component of calcium phosphate minerals, similar in structure to hydroxyapatite crystals. Raman spectroscopy is a non-invasive laser-based technique that has previously been used for the study of bone and mineral chemistry. Ivory and bone have similar biochemical properties, making Raman spectroscopy a promising method for species identification based on ivory. This study aimed to test the hypothesis that it is possible to identify differences in the chemistry of mammoth and elephant ivory using Raman spectroscopy. Mammoth and elephant tusks were obtained from the Natural History Museum in London, UK. Included in this study were eight samples of ivory from Mammuthus primigenius, two samples of carved ivory bangles from Africa (Loxodonta species), and one cross section of a tusk from Elephas maximus. The ivory was scanned using an inVia Raman micro spectrometer equipped with a x50 objective lens and a 785nm laser. Spectra were acquired using line maps and individual spectral points were acquired randomly or at points of interest on all samples. The data was then analysed using principal component analysis (PCA) with use of an in-house MATLAB script. Univariate analysis of peak intensity ratios of phosphate to amide I and III peaks, and carbonate to phosphate peaks showed statistical differences (p<0.0001) in the average peak intensity ratios between Mammuthus primigenius, Loxodonta spp. and Elephas maximus. Full width at half maximum hight (FWHM)analysis of the phosphate peak demonstrated higher crystal maturity of Mammuthus primigenius compared to living elephant species. The results of the study have established that spectra acquired by Raman spectroscopy can be separated into distinct classes through PCA. In conclusion, this study has shown that well-preserved mammoth and elephant ivory has the potential to be characterized using Raman spectroscopy, providing a promising method for species identification. The results of this study will be valuable in developing quick and non-destructive methods for the identification of ivory, which will have direct applications in archaeology and the regulation of international trade.

Rapid detection of melamine by DNA Walker mediated SERS sensing technique based on signal amplification function.

A new method is proposed for detecting typical melamine dopants in food using surface-enhanced Raman scattering (SERS) biosensing technology. Melamine specific aptamer was used as the identification probe, and gold magnets (AuNPs@MNPs) and small gold nanoparticles (AuNPs@MBA) were used as the basis for Raman detection. The Raman signal of the detection system can directly detect melamine quantitatively. Under optimized conditions, the detection of melamine was carried out in the low concentration range of 0.001-500 mg/kg, the enhancement factor (EF) was 2.3 × 107, and the detection limit was 0.001 mg/kg. The method is sensitive and rapid, and can be used for the rapid detection of melamine in the field environment.

A breakthrough in phytochemical profiling: ultra-sensitive surface-enhanced Raman spectroscopy platform for detecting bioactive components in medicinal and edible plants.

A perennial challenge in harnessing the rich biological activity of medicinal and edible plants is the accurate identification and sensitive detection of their active compounds. In this study, an innovative, ultra-sensitive detection platform for plant chemical profiling is created using surface-enhanced Raman spectroscopy (SERS) technology. The platform uses silver nanoparticles as the enhancing substrate, excess sodium borohydride prevents substrate oxidation, and methanol enables the tested molecules to be better adsorbed onto the silver nanoparticles. Subsequently, nanoparticle aggregation to form stable "hot spots" is induced by Ca2+, and the Raman signal of the target molecule is strongly enhanced. At the same time, deuterated methanol was used as the internal standard for quantitative determination. The method has excellent reproducibility, RSD ≤ 1.79%, and the enhancement factor of this method for the detection of active ingredients in the medicinal plant Coptis chinensis was 1.24 × 109, with detection limits as low as 3 fM. The platform successfully compared the alkaloid distribution in different parts of Coptis chinensis: root > leaf > stem, and the difference in content between different batches of Coptis chinensis decoction was successfully evaluated. The analytical technology adopted by the platform can speed up the determination of Coptis chinensis and reduce the cost of analysis, not only making better use of these valuable resources but also promoting development and innovation in the food and pharmaceutical industries. This study provides a new method for the development, evaluation, and comprehensive utilization of both medicinal and edible plants. It is expected that this method will be extended to the modern rapid detection of other medicinal and edible plants and will provide technical support for the vigorous development of the medicinal and edible plants industry.

Alkyne-tagged SERS nanoprobe for understanding Cu+ and Cu2+ conversion in cuproptosis processes.

Simultaneously quantifying mitochondrial Cu+ and Cu2+ levels is crucial for evaluating the molecular mechanisms of copper accumulation-involved pathological processes. Here, a series of molecules containing various diacetylene derivatives as Raman reporters are designed and synthesized, and the alkyne-tagged SERS probe is created for determination Cu+ and Cu2+ with high selectivity and sensitivity. The developed SERS probe generates well-separated distinguishable Raman fingerprint peaks with built-in corrections in the cellular silent region, resulting in accurate quantification of Cu+ and Cu2+. The present probe demonstrates high tempo-spatial resolution for real-time imaging and simultaneously quantifying mitochondrial Cu+ and Cu2+ with long-term stability benefiting from the probe assembly with designed Au-C≡C groups. Using this powerful tool, it is found that mitochondrial Cu+ and Cu2+ increase during ischemia are associated with breakdown of proteins containing copper as well as conversion of Cu+ and Cu2+. Meanwhile, we observe that parts of Cu+ and Cu2+ are transported out of neurons by ATPase. More importantly, cuproptosis in neurons is found including the oxidative stress process caused by the conversion of Cu+ to Cu2+, which dominates at the early stage (<9 h), and subsequent proteotoxic stress. Both oxidative and proteotoxic stresses contribute to neuronal death.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach.

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02 nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R2 score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.

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.