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Gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) were used as substrates in combination with a low-concentration serum surface-enhanced Raman spectroscopy (SERS) detection scheme to obtain spectral signals from healthy individuals and cervical cancer patients. The best principal component scores were selected by principal component analysis (PCA) combined with linear discriminant analysis (LDA) and support vector machine (SVM) to analyze the spectral differences distinguishing healthy and cervical cancer patients. The accuracy of the two models was 97.9% and 96.9%, respectively. SERS technique based on Au NPs/785 PSi PhCs has great potential to improve the screening of cervical cancer. [ABSTRACT FROM AUTHOR]

The characteristics of the spectral data are essential for the qualitative analysis of substances. Traditional classification models often need to preprocess the data. However, misuse of preprocessing may change the characteristic information carried by the original data which result in poor model performance. This paper proposes an end‐to‐end deep learning method that combines residual modules to learn features from raw data to improve model performance, which called ResidualSpectra. ResidualSpectra model is compared with three convolutional neural network (CNN) models on the original data. The 15 preprocessing approaches are used to evaluate the preprocessing impact by testing five open‐access mid‐infrared, near‐infrared, and Raman spectra datasets (fruits, meats, olive_oils, Tablets_Nir, Tablets_Raman). In most cases, the ResidualSpectra model performs better than the other three CNN models on five datasets and obtains better results in original data than in preprocessed data. The model is compared with linear discriminant analysis (LDA), nonlinear artificial neural network (ANN), support vector machines (SVM) for original and preprocessed data. The results show that the ResidualSpectra method provides improved results over traditional classification methods in most scenarios. Residual Spectra model is developed to learn features from the original spectra without pre‐processing. This study detailed a deep learning method for qualitative analysis of vibrational spectra. The model includes convolutional layers combined with residual structure to learn features from spectra data. [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]