Search

[Display omitted] • Nanoparticles are uniformly embedded in PSi chips with 40–60 nm pore size, enhancing BRC serum signal. • The Ag 2 O-Ag core–shell structure effectively prevents silver from being oxidized by sulfur, giving the chip a shelf life of six months. • The PCA-t-SNE rapid screening model combined with the SERS microarray provides the basis for the development of a low-cost, rapid and sensitive technique for early screening of breast cancer. Ag 2 O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag 2 O-Ag nano core shell with a diameter of 40–60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag 2 O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer. [ABSTRACT FROM AUTHOR]

Abstract This study presents a rapid and non-invasive method to screen high renin hypertension using serum Raman spectroscopy combined with different classification algorithms. The serum samples taken from 24 high renin hypertension patients and 22 non-high renin hypertension samples were measured in this experiment. Tentative assignments of the Raman peaks in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was first used for feature extraction and reduced the dimension of high-dimension spectral data. Then, support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (KNN) algorithms were employed to establish the discriminant diagnostic models. The accuracies of 93.5%, 93.5% and 89.1% were obtained from PCA-SVM, PCA-LDA and PCA-KNN models, respectively. The results from our study demonstrate that the serum Raman spectroscopy technique combined with multivariate statistical methods have great potential for the screening of high renin hypertension. This technique could be used to develop a portable, rapid, and non-invasive device for screening high renin hypertension. Graphical abstract Raman spectroscopy technique combined with multivariate statistical methods for the screening of high renin hypertension. Unlabelled Image Highlights • Raman spectroscopy has the potential to screen high renin hypertension patients. • The high accuracy of 93.5% is obtained from multivariate statistical method. • Our method is expected to develop a clinical diagnostic tool. [ABSTRACT FROM AUTHOR]

[Display omitted] • For the first time, Raman spectroscopy combined with deep learning is used for the classification and identification of red wine origin. • Corresponding biomarkers identified by Raman spectroscopy of red wine. • Classification of red wine origin by PCA analysis. • Comparative experiment of four deep learning models. • The combination of MCNN and Raman spectroscopy achieves the best results for the classification of red wine origin. In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field. [ABSTRACT FROM AUTHOR]

This research innovatively combines Fourier transform infrared spectra, Raman spectra and its first-derivative spectra to develop a rapid diagnosis method for renal cell carcinoma (RCC). The spectral features are extracted by principal component analysis (PCA), and then the normalized fused data are used as input to the improved AlexNet model and MCNN model to train the models. Among them, the AlexNet model performed better, with a classification accuracy rate of 93%. [Display omitted] • It is proposed for the first time to combine FT-IR, Raman spectra and their first-derivative spectra to develop a rapid diagnosis method for renal cell carcinoma. • The accuracy of the multi-spectral fusion method (93%) is greatly improved compared with that of single infrared spectroscopy (74%), signal Raman spectroscopy (75%) and the fusion of infrared spectroscopy and Raman spectroscopy (79%). • It shows that multi-spectral fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC. This research innovatively combines FT-IR, Raman spectroscopy and their first-derivative spectroscopy to develop a rapid diagnosis method for renal cell carcinoma (RCC). After measuring the Raman spectra and FT-IR spectra of 45 cases of control subjects and 28 cases of RCC, the first derivative of the infrared spectra and the Raman spectra were calculated respectively. Principal component analysis (PCA) was used to extract the features of the infrared spectra, first-derivative infrared spectra, Raman spectra and first-derivative Raman spectra. Then the four feature matrices were merged as fused spectral feature matrices. The fused matrices were used as the input of AlexNet and MCNN. The fused spectral feature matrices were used as the input of AlexNet and MCNN. The adjusted AlexNet model performed better, and the classification accuracy of the fused spectral data is 93%. Compared with the classification results of infrared spectra (74%), Raman spectra (75%) and the fusion of infrared and Raman spectra (79%) combined with the adjusted AlexNet model, the classification result of the fusion of infrared spectra, Raman spectra and their first-derivative was significantly improved. The experimental results show that infrared spectroscopy, Raman spectroscopy and their first-derivative fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC. [ABSTRACT FROM AUTHOR]

Echinococcosis is a zoonotic parasitic disease transmitted by animals and distributed all over the world. There is no standardized and widely accepted treatment method, and early and accurate diagnosis is crucial for the prevention and cure of echinococcosis. Here, we explored the feasibility of using derivative Raman in combination with autofluorescence (AF) to improve the diagnosis performance of echinococcosis. The spectra of serum samples from patients with echinococcosis, as well as healthy volunteers, were recorded at 633 nm excitation. The normalized mean Raman spectra showed that there is a decrease in the relative amounts of β carotene and phenylalanine and an increase in the percentage of tryptophan, tyrosine, and glutamic acid contents in the serum of echinococcosis patients as compared to that of healthy subjects. Then, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were adopted to distinguish echinococcosis patients from healthy volunteers. Based on the area under the ROC curve (AUC) value, the derivative Raman + AF spectral data set achieved the optimal results. The AUC value was improved by 0.08 for derivative Raman + AF (AUC = 0.98), compared to Raman alone. The results demonstrated that the fusion of derivative Raman and AF could effectively improve the performance of the diagnostic model, and this technique has great application potential in the clinical screening of echinococcosis. Combining derivative Raman with autofluorescence can improve the performance of echinococcosis diagnosis model. Unlabelled Image • Early and accurate diagnosis is the key to prevent and treat echinococcosis. • The autofluorescence background contains useful information for diagnosis. • The optimal result was obtained by combining derivative Raman with autofluorescence. • The proposed methods could possibly be used to diagnose other diseases. [ABSTRACT FROM AUTHOR]