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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]

In recent years, Raman spectroscopy combined with deep learning techniques has been widely used in various fields such as medical, chemical, and geological. However, there is still room for optimization of deep learning techniques and model compression algorithms for processing Raman spectral data. To further optimize deep learning models applied to Raman spectroscopy, in this study time, accuracy, sensitivity, specificity and floating point operations numbers(FLOPs) are used as evaluation metrics to optimize the model, which is named RamanCompact(RamanCMP). The experimental data used in this research are selected from the RRUFF public dataset, which consists of 723 Raman spectroscopy data samples from 10 different mineral categories. In this paper, 1D-EfficientNet adapted to the spectral data as well as 1D-DRSN are proposed to improve the model classification accuracy. To achieve better classification accuracy while optimizing the time parameters, three model compression methods are designed: knowledge distillation using 1D-EfficientNet model as a teacher model to train convolutional neural networks(CNN), proposing a channel conversion method to optimize 1D-DRSN model, and using 1D-DRSN model as a feature extractor in combination with linear discriminant analysis(LDA) model for classification. Compared with the traditional LDA and CNN models, the accuracy of 1D-EfficientNet and 1D-DRSN is improved by more than 20%. The time of the distilled model is reduced by 9680.9s compared with the teacher model 1D-EfficientNet under the condition of losing 2.07% accuracy. The accuracy of the distilled model is improved by 20% compared to the CNN student model while keeping inference efficiency constant. The 1D-DRSN optimized with channel conversion method saves 60% inference time of the original 1D-DRSN model. Feature extraction reduces the inference time of 1D-DRSN model by 93% with 94.48% accuracy. This study innovatively combines lightweight models and model compression algorithms to improve the classification speed of deep learning models in the field of Raman spectroscopy, forming a complete set of analysis methods and laying the foundation for future research. [Display omitted] • Proposed RamanCMP, a Raman spectral classification acceleration method. • Proposed 1D-EfficientNet and 1D-DRSN for spectral data. • Proposed channel conversion method and 1D-DRSN feature extractor. • Testing RamanCMP using the RRUFF public dataset and the external validation dataset for Sjögren's syndrome. [ABSTRACT FROM AUTHOR]

In this study, the Raman spectra of serum samples from patients with echinococcosis and healthy controls were recorded by a portable spectrometer under 532 nm excitation. Due to the resonance Raman effect, the two Raman peaks (at 1154 and 1515 cm-1) belonging to β carotene were significantly enhanced, resulting in relatively weak signal values for other peaks. In order to amplify the weak Raman peaks and improve the capability of Raman spectroscopy for the differentiation of healthy and echinococcosis infected serum samples, a logarithmic transformation preprocessing method was used. Furthermore, principal component analysis (PCA), combined with linear discriminant analysis (LDA), were used to distinguish echinococcosis patients from healthy volunteers. The results show that the diagnostic models formed by the log-transformed spectral data sets perform better in the balance of sensitivity and specificity. The accuracy, sensitivity, and specificity of the diagnostic model based on the spectral data set of optimal scale transformation were 98.00%, 98.10%, and 98.00%, respectively, which were slightly superior to the untransformed spectral data set. Our findings suggest that serum log-transformed Raman spectroscopy combined with the PCA-LDA method has great potential for improving the detection of echinococcosis. [ABSTRACT FROM AUTHOR]

In this study, the Raman spectra of serum samples from patients with echinococcosis and healthy controls were recorded by a portable spectrometer under 532 nm excitation. Due to the resonance Raman effect, the two Raman peaks (at 1154 and 1515 cm-1) belonging to β carotene were significantly enhanced, resulting in relatively weak signal values for other peaks. In order to amplify the weak Raman peaks and improve the capability of Raman spectroscopy for the differentiation of healthy and echinococcosis infected serum samples, a logarithmic transformation preprocessing method was used. Furthermore, principal component analysis (PCA), combined with linear discriminant analysis (LDA), were used to distinguish echinococcosis patients from healthy volunteers. The results show that the diagnostic models formed by the log-transformed spectral data sets perform better in the balance of sensitivity and specificity. The accuracy, sensitivity, and specificity of the diagnostic model based on the spectral data set of optimal scale transformation were 98.00%, 98.10%, and 98.00%, respectively, which were slightly superior to the untransformed spectral data set. Our findings suggest that serum log-transformed Raman spectroscopy combined with the PCA-LDA method has great potential for improving the detection of echinococcosis. [ABSTRACT FROM AUTHOR]

The survival time of patients with pancreatic tumors is closely related to the type of tumor. To diagnose patients with malignant pancreatic tumors as early as possible in order to improve patient survival, we proposed a new method for the assisted diagnosis of pancreatic tumors. First, we used partial least squares (PLS) to extract the Raman spectrum information of patients with pancreatic tumors. The study extracted ten eigenvalues experimentally. The cumulative variance of the first six PLS components reached 97.045 %, and then the feature information extracted by PLS was classified. This experiment used three classification algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). Among them, the cubic kernel of SVM achieved the best classification effect, and its classification accuracy reached 96.4 %. This is the first time that serum Raman spectroscopy has been used to distinguish patients with benign and malignant tumors of the pancreas. The experimental results show that serum Raman spectroscopy may become a new auxiliary method for the clinical diagnosis of pancreatic cancer. [ABSTRACT FROM AUTHOR]

This study presents a method combining serum Raman spectroscopy with a multiscale convolution independent circulation neural network (MSCIR) for hepatitis B virus (HBV) diagnosis. It simplifies the steps of serum Raman spectroscopy data preprocessing and improves the accuracy of hepatitis B diagnosis. Serum samples were obtained from 499 healthy people and 435 HBV patients. First, feature extraction of serum Raman spectroscopy data through principal component analysis (PCA) was performed to reduce the dimensionality of spectral data. Then, the linear discriminant analysis (LDA), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), artificial neural network (ANN) and MSCIR algorithms were employed to establish the discriminant diagnostic models. Their accuracies are 80.77%, 77.69%, 89.23%, 86.92%, 91.53% and 96.15%, respectively. The results show that the MSCIR prediction accuracy is higher than that of the five traditional algorithms, and the fitness is stable. Therefore, the MSCIR algorithm can effectively diagnose hepatitis B patients. [ABSTRACT FROM AUTHOR]