Principal Component Linear Discriminant Analysis Research Articles

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

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

BackgroundSurface-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. ResultsSerum 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. SignificanceThe 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.

Vis/NIR reflectance spectroscopy for non-destructive diagnosis of Fusarium spp. infection in postharvest potato tubers (Solanum tuberosum)

Conventional methods of detecting Fusarium spp. infection, which causes significant economic losses in potato production, are time-consuming and expensive. This study explored rapid and non-destructive detection techniques using visible/near-infrared (Vis/NIR) spectroscopy. Potato seeds of the Granola L variety were intentionally infected with Fusarium spp. by fungal inoculation, then stored at 12°C, 25°C, and a combination of both. Healthy potatoes were stored under the same conditions in containers for 30 days and monitored every five days. Principal component analysis-linear discriminate analysis (PCA-LDA) was used to classify potato tubers based on their infection status. PCA-LDA analysis revealed significant spectral differences between healthy and infected potato seeds across all storage temperatures. Calibration reliability values were 95.87% (for samples stored at 12°C), 97.52% (stored at 25°C), and 98.35% (for the combination of 12°C and 25°C). Similar trends were observed for accuracy: 91.96% (12°C), 98.29% (25°C), and the highest accuracy of 98.65% for the combined temperature. These techniques facilitate rapid identification of infections, aiding farmers and producers in implementing more efficient preventive actions, resulting in decreased crop losses and waste products and enhanced productivity in the agricultural sector.

Real-Time Authentication of Camellia Oil by Rapid Evaporative Ionization Mass Spectrometry

Camellia oil is a high-value product with rich nutrients. Recently, the adulteration of camellia oil has become an increasingly concerning issue related to human health. In this study, electric soldering iron coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the identification and analysis of camellia oil without any sample preparation. REIMS technology coupled with chemometrics was applied to develop an analysis model for the authentication of camellia oil adulterated with soybean oil, peanut oil, rapeseed oil, sunflower oil, and corn oil (5–40%, v/v). The results showed that different types of vegetable oils could be classified using principal component analysis-linear discriminant analysis (PCA-LDA) with a correct classification of 93.8% in leave-20%-out cross-validation and 100% correctly identified in real-time recognition. The established prediction models were found to be particularly sensitive when the camellia oil samples were adulterated with 5–40% of other oils, indicating that REIMS could be a powerful tool for the authentication and adulteration analysis of camellia oil, particularly for cases where the adulteration levels are relatively high. In conclusion, the results provide valuable insights into the potential of REIMS for the rapid, accurate, and real-time authentication and adulteration analysis of camellia oil.

Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as “fingerprint”, in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms.

Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

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.

Serum-based ATR-FTIR spectroscopy combined with multivariate analysis for the diagnosis of pre-diabetes and diabetes.

Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.

Machine learning-driven SERS fingerprinting of disintegrated viral components for rapid detection of SARS-CoV-2 in environmental dust

Surveillance of airborne viruses in crowded indoor spaces is crucial for managing outbreaks, as highlighted by the SARS-CoV-2 pandemic. However, the rapid and on-site detection of fast-mutating viruses, such as SARS-CoV-2, in complex environmental backgrounds remains challenging. Our study introduces a machine learning (ML)-driven surface-enhanced Raman spectroscopy (SERS) approach for detecting viruses within environmental dust matrices. By decomposing intact virions into individual structural components via a Raman-background-free lysis protocol and concentrating them into nanogap SERS hotspots, we significantly enhance the SERS signal intensity and fingerprint information density from viral structural components. Utilizing Principal Component Analysis (PCA), we establish a robust connection between the SERS data of these structural components and their biological sequences, laying a solid foundation for virus detection through SERS. Furthermore, we demonstrate reliable quantitative detection of SARS-CoV-2 using identified SARS-CoV-2 peaks at concentrations down to 102 pfu/ml through Gaussian Process Regression (GPR) and a digital SERS methodology. Finally, applying a Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm, we identify SARS-CoV-2, influenza A virus, and Zika virus within an environmental dust background with over 86% accuracy. Therefore, our ML-driven SERS approach holds promise for rapid environmental virus monitoring to manage future outbreaks.

Lipid profiling of electrosurgical vapors for real-time assistance of soft tissue sarcoma resection.

Soft tissue sarcomas (STS) constitute a heterogeneous group of rare tumor entities. Treatment relies on challenging patient-tailored surgical resection. Real-time intraoperative lipid profiling of electrosurgical vapors by rapid evaporative ionization mass spectrometry (REIMS) may aid in achieving successful surgical R0 resection (i.e., microscopically negative-tumor margin resection). Here, we evaluate the ex vivo accuracy of REIMS to discriminate and identify various STS from normal surrounding tissue. Twenty-seven patients undergoing surgery for STS at Maastricht University Medical Center+ were included in the study. Samples of resected STS specimens were collected and analyzed ex vivo using REIMS. Electrosurgical cauterization of tumor and surrounding was generated successively in both cut and coagulation modes. Resected specimens were subsequently processed for gold standard histopathological review. Multivariate statistical analysis (principal component analysis-linear discriminant analysis) and leave-one patient-out cross-validation were employed to compare the classifications predicted by REIMS lipid profiles to the pathology classifications. Electrosurgical vapors produced during sarcoma resection were analyzed in vivo using REIMS. In total, 1200 histopathologically-validated ex vivo REIMS lipid profiles were generated from 27 patients. Ex vivo REIMS lipid profiles classified STS and normal tissues with 95.5% accuracy. STS, adipose and muscle tissues were classified with 98.3% accuracy. Well-differentiated liposarcomas and adipose tissues could not be discriminated based on their respective lipid profiles. Distinction of leiomyosarcomas from other STS could be achieved with 96.6% accuracy. In vivo REIMS analyses generated intense mass spectrometric signals. Lipid profiling by REIMS is able to discriminate and identify STS with high accuracy and therefore constitutes a potential asset to improve surgical resection of STS in the future.

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis.

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.

Blood molecular profile to predict genotoxicity from exposure to antineoplastic drugs.

Genotoxicity is an important information that should be included in human biomonitoring programmes. However, the usually applied cytogenetic assays are laborious and time-consuming, reason why it is critical to develop rapid and economic new methods. The aim of this study was to evaluate if the molecular profile of frozen whole blood, acquired by Fourier Transform Infrared (FTIR) spectroscopy, allows to assess genotoxicity in occupational exposure to antineoplastic drugs, as obtained by the cytokinesis-block micronucleus assay. For that purpose, 92 samples of peripheral blood were studied: 46 samples from hospital professionals occupationally exposed to antineoplastic drugs and 46 samples from workers in academia without exposure (controls). It was first evaluated the metabolome from frozen whole blood by methanol precipitation of macromolecules as haemoglobin, followed by centrifugation. The metabolome molecular profile resulted in 3 ratios of spectral bands, significantly different between the exposed and non-exposed group (p<0.01) and a spectral principal component-linear discriminant analysis (PCA-LDA) model enabling to predict genotoxicity from exposure with 73 % accuracy. After optimization of the dilution degree and solution used, it was possible to obtain a higher number of significant ratios of spectral bands, i.e., 10 ratios significantly different (p<0.001), highlighting the high sensitivity and specificity of the method. Indeed, the PCA-LDA model, based on the molecular profile of whole blood, enabled to predict genotoxicity from the exposure with an accuracy, sensitivity, and specificity of 92 %, 93 % and 91 %, respectively. All these parameters were achieved based on 1μL of frozen whole blood, in a high-throughput mode, i.e., based on the simultaneous analysis of 92 samples, in a simple and economic mode. In summary, it can be conclude that this method presents a very promising potential for high-dimension screening of exposure to genotoxic substances.

Rapid and non-destructive approach for characterization and differentiation of sealing wax using ATR-FTIR spectroscopy.

Sealing wax is used for maintaining the integrity and authenticity of a document or physical evidence. Any tampering with the seal calls into question the overall integrity and authenticity of the tangible evidence or document. In these circumstances, determining the authenticity of the sealing material (physical and chemical) becomes imperative. In this study, ATR-FTIR spectroscopy supported by chemometrics has been used to differentiate sealing wax samples belonging to 12 different brands available across India. All the samples were first melted, cooled, and then analyzed using ATR-FTIR spectroscopy in the mid-infrared region (4000-600 cm-1 ). The obtained spectra were first examined visually for the presence of different functional groups. Principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) were employed to analyze the sample clustering patterns and to categorize them into their respective groups, respectively. For classification, a PCA-LDA training model was applied, and it demonstrated 95.83% accuracy. The validation test resulted in an accuracy of 83.33%. PCA-LDA model offered 100% accurate prediction for samples on various substrates, including cloth, cardboard, and paper. A blind study was also performed using five unknown samples, which were accurately classified into their respective groups. PCA-LDA model will be helpful in providing investigative leads by linking a questioned sealing wax sample with its respective group.

Rapidly identifying the geographical origin of Lilium bulbs by nano-effect excitation-emission matrix fluorescence combined with chemometrics

Lilium bulbs (LB) are a highly nutritious food, and their quality and price are influenced by origin. The present study proposed a nano-effect excitation-emission matrix fluorescence (EEMF) combined with chemometrics strategy for rapidly identifying the geographical origin of LB. Nano-effect EEMF spectra of 280 LB samples from different origins were collected after reaction with bovine serum albumin-modified gold and silver nanoclusters (BSA-AuAgNCs). Further, partial least squares-discrimination analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) were used to establish classification models for identifying the geographical origin of LB based on the obtained nano-effect EEMF spectra. The result showed that PCA-LDA model gained the optimal performance, and the classification accuracy of the training set and the prediction set was 95.9% and 90.5%, respectively. The nano-effect EEMF spectra was based on the reaction of BSA-AuAgNCs with the components, such as phenolic acids, in LB through hydrogen bonding, which amplified the spectral difference. This study demonstrated that the proposed strategy is effective for identifying the geographical origins of LB, which provides a new idea for the geographic traceability of other foods.

Fluorophores-Assisted Excitation Emission Matrix Fluorescence Method for the Origin Traceability of Lily

In this work, a fluorophores-assisted excitation/emission matrix (EEM) fluorescence method was proposed to trace the origin of lily in the Chinese market. There are few active components in lilies that have fluorescent signals, and too few characteristic variables may lead to unsatisfactory accuracy in the subsequent classification. Therefore, three fluorophores, 2-Aminoethyl diphenylborinate (DPBA), o-Phthalaldehyde (OPA) and Rhodamine B (RB), were used to enrich the information of the fluorescent fingerprint of lily, which can improve the classification accuracy. The lily samples were characterized by using EEM fluorescence coupled with the alternating trilinear decomposition (ATLD) algorithm, which was able to extract information of various fluorophores in lily samples. Two chemical pattern recognition methods, principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares-discrimination analysis (PLS-DA), were used to model and trace the origin of different lilies. When the fluorophores were added, the accuracy of the test set and prediction set obtained by the classification model increased from 71.4% to 92.9% and 66.7% to 100%, respectively. The proposed method combined fluorophores-assisted EEM fluorescence with multi-way chemometric methods to extract comprehensive information on the samples, which provided a potential method for the origin traceability of traditional Chinese medicine.

Diagnostic strategy for malignant and benign thyroid nodules smaller than 10 mm based on surface-enhanced Raman spectroscopy and machine learning

The diagnostic rate of thyroid nodules has increased considerably in recent years. Malignant nodules can reduce the quality of and even endanger the lives of patients. Fine needle aspiration (FNA) with cytological diagnosis is the most common modality for diagnosing malignant and benign nodules. However, a certain percentage of nodules still cannot be diagnosed definitively, especially when they are less than 10 mm in diameter. Surface-enhanced Raman spectroscopy (SERS) is a molecule-specific analysis method suitable for tumor diagnosis. In this study, the SERS technique was used to analyze FNA samples of thyroid nodules smaller than 10 mm and combined with multiple machine learning algorithms (principal component analysis-linear discriminant analysis, partial least squares discriminant analysis (PLS-DA), and support vector machines) to develop a novel and precise diagnosis model for malignant and benign thyroid nodules. The results showed that the hyphenated method of SERS and the PLS-DA algorithm efficiently diagnosed malignant and benign thyroid nodules, with an area under the curve (AUC) of 97.479% and accuracies of 95.59% and 85.58% in the training and cross-validation sets, respectively. Moreover, the classification model outperformed cytology in diagnosing FNA samples from microscopic thyroid cancers. This study demonstrates that a novel diagnostic strategy based on SERS and machine learning can constitute a complementary diagnosis method for nodules with unknown FNA cytological findings. Furthermore, this strategy can improve the diagnostic accuracy of thyroid nodules smaller than 10 mm, achieve an early and exact diagnosis of microscopic thyroid cancer, and prevent misdiagnoses, underdiagnoses, and overtreatment of thyroid nodules.

Component identification for the SERS spectra of microplastics mixture with convolutional neural network

With the increasing interest in microplastics (MPs) pollutants, relevant detection technologies are also developing. In MPs analysis, vibrational spectroscopy represented by surface-enhanced Raman spectroscopy (SERS) is widely used because they can provide unique fingerprint characteristics of chemical components. However, it is still a challenge to separate various chemical components from the SERS spectra of MPs mixture. In this study, it is innovatively proposed to combine the convolutional neural networks (CNN) model to simultaneously identify and analyze each component in the SERS spectra of six common MPs mixture. Different from the traditional method, which requires a series of spectral preprocessing such as baseline correction, smoothing and filtering, the average identification accuracy of MP components is as high as 99.54 % after the unpreprocessed spectral data is trained by CNN, which is better than other classical algorithms such as support vector machine (SVM), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), Random Forest (RF), and K Near Neighbor (KNN), with or without spectral preprocessing. The high accuracy shows that CNN can be used to quickly identify MPs mixture with unpreprocessed SERS spectra data.

Label-Free Detection and Classification of Glaucoma Based on Drop-Coating Deposition Raman Spectroscopy

Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are prevailing eye diseases that can lead to blindness. In order to provide a non-invasive diagnostic method for glaucoma, we investigated the feasibility of using drop-coating deposition Raman spectroscopy (DCDRS) to discriminate glaucoma patients from healthy individuals based on tear samples. Tears from 27, 19 and 27 POAG patients, PACG patients and normal individuals, respectively, were collected for Raman measurement. For high-dimension data analysis, principal component analysis–linear discriminant analysis (PCA-LDA) was used to discriminate the features of the Raman spectra, followed by a support vector machine (SVM) used to classify samples into three categories, which is called a PCA-LDA-based SVM. The differences in the characteristic peaks of Raman spectra between glaucoma patients and normal people were related to the different contents of various proteins and lipids. For the PCA-LDA-based SVM, the total accuracy reached 93.2%. With the evaluation of 30% test dataset validation, the classification accuracy of the model was 90.9%. The results of this work reveal that tears can be used for Raman detection and discrimination by combining the process with the PCA-LDA-based SVM, supporting DCDRS being a potential method for the diagnosis of glaucoma in the future.

Automated high-throughput characterization of microbial metabolites and species using array ballpoint electrospray ionization technique.

Microbial metabolites are widely used in agriculture, food, environment, and medicine. However, there is a lack of high-throughput, nonclogging, and simple methods for the identification of microbial metabolites and their subspecies using ambient mass spectrometry (MS). Here, we proposed a method for analyzing the microbial metabolites and identifying their species using the array ballpoint electrospray ionization (aBPESI) technique. The previously developed BPESI was combined with the array analysis technique to form a high-throughput analysis technique called aBPESI. The bacteria cultured on the plate medium were directly analyzed using MS with aBPESI. Principal component analysis-linear discriminant analysis (PCA-LDA)algorithm was used to analyze the different subspecies groups. The results showed that aBPESI was capable of completing a sample analysis within 30 s, and the level of metabolite detection was comparable to existing techniques. The accuracy of bacterial subspecies identification was 90% (Pseudomonas aeruginosa) and 100% (Serratia marcescens). A new high-throughput and robust MS technique aBPESI was proposed. It does not require sample pretreatment and greatly reduces the sample analysis time. aBPESI shows a strong ability in microbial analysis and is expected to be further applied in other research fields.

A New Detection Method of Oral and Oropharyngeal Squamous Cell Carcinoma Based on Multivariate Analysis of Surface Enhanced Raman Spectra of Salivary Exosomes.

Raman spectroscopy recently proved a tremendous capacity to identify disease-specific markers in various (bio)samples being a non-invasive, rapid, and reliable method for cancer detection. In this study, we first aimed to record vibrational spectra of salivary exosomes isolated from oral and oropharyngeal squamous cell carcinoma patients and healthy controls using surface enhancement Raman spectroscopy (SERS). Then, we assessed this method's capacity to discriminate between malignant and non-malignant samples by means of principal component-linear discriminant analysis (PC-LDA) and we used area under the receiver operating characteristics with illustration as the area under the curve to measure the power of salivary exosomes SERS spectra analysis to identify cancer presence. The vibrational spectra were collected on a solid plasmonic substrate developed in our group, synthesized using tangential flow filtered and concentrated silver nanoparticles, capable of generating very reproducible spectra for a whole range of bioanalytes. SERS examination identified interesting variations in the vibrational bands assigned to thiocyanate, proteins, and nucleic acids between the saliva of cancer and control groups. Chemometric analysis indicated discrimination sensitivity between the two groups up to 79.3%. The sensitivity is influenced by the spectral interval used for the multivariate analysis, being lower (75.9%) when the full-range spectra were used.