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Raman spectroscopy has been widely used in discriminant analysis. In order to improve the accuracy of Raman spectroscopy discrimination, a model combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. Firstly, the Raman spectral discriminant data is collected, which is subjected to the fifth-order polynomial smoothing and vector normalization preprocessing to eliminate the influence of noise. Then, the collected unbalanced data is oversampled by the Synthetic Minority Over-sampling Technique algorithm, and the KPCA method is used to extract the features of the balanced data. The SVM discriminant model is established by selecting different kernel functions for the extracted features. In order to evaluate the performance of the KPCA-SVM discriminant model, it is compared with the PCA-SVM discriminant model under the same experimental conditions. The experimental results show that the KPCA-SVM discriminant model achieves a discriminative accuracy rate of 93.75%, which is better than that of the PCA-SVM discriminant model (87.5%). This study provides a new idea for improving the discrimination accuracy of Raman spectroscopy. [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]

This paper proposes for the first time to establish grid search-support vector regression (GS-SVR), random forest (RF) and partial least squares regression (PLSR) models based on the measured Raman spectral data of sheep serum to find a method that can quickly detect glucose (GLU) concentration. The results show that all performance indexes of PLSR model are optimal, root mean square error(R M S E t r a i n) and determination coefficient(R t r a i n 2) of training set are 0.1034 and 0.9726 respectively, and root mean square error(R M S E t e s t) and determination coefficient(R t e s t 2) of test set are 0.0691 and 0.9632 respectively. Studies have shown that the PLSR model has good predictive performance for the detection of glucose in sheep serum. [ABSTRACT FROM AUTHOR]

In this study, we propose a method of tear Raman spectroscopy combined with support vector machine (SVM) to accurately, quickly and economically diagnose cerebral infarction. Tear samples from 15 patients with cerebral infarction and 47 healthy volunteers were assessed, and their Raman spectra were compared. Principal component analysis (PCA) was used for feature extraction. Then, support vector machine (SVM) was used to establish an efficient diagnostic model. To improve the accuracy of the SVM diagnostic model, we used the linear, polynomial, RBF and sigmoid kernel functions of SVM for discrimination and evaluation, and the best results were obtained when the RBF kernel was used, with an accuracy of 96.77 %, a specificity of 100 %, and a sensitivity of 86.67 %. Our results show that the combination of tear Raman spectroscopy and the SVM algorithm has great potential for screening cerebral infarction. This technology is expected to aid in the development of a portable, economic and high accuracy early-assisted screening device for cerebral infarction. [ABSTRACT FROM AUTHOR]

In this study, serum samples from 199 patients with thyroid dysfunction and 183 people with normal thyroid function were collected by Raman spectroscopy, and the data were dimensions-reduced by PCA. The reduced data were input into a multi-feature fusion convolutional neural network (MCNN), the improved AlexNet, VGGNet, GoogLeNet and ResNet, Support Vector Machine (SVM) and Decision Tree (DT) for classification, and the results of the seven classification algorithms were compared. Their classification accuracy are 94.01 %, 91.91 %, 90.34 %, 93.46 %, 92.42 %, 82.78 % and 80.89 %, respectively. The results of this study indicate that the combination of serum Raman spectra and MCNN has a good diagnostic effect for identifying thyroid dysfunction, and it is feasible to improve the classic deep learning models for Raman spectrum classification. [ABSTRACT FROM AUTHOR]

In this study, a rapid, noninvasive and efficient screening method for human papillomavirus (HPV) was proposed by combining cervical secretion Raman spectral data with a pattern recognition algorithm. We measured the cervical secretion Raman spectra of 58 HPV positive and 196 HPV negative samples. The preliminary assignment of the Raman peaks in the measured cervical secretion spectra indicated changes in specific biomolecules between the groups, and we analyzed them according to their differences. The specificity of the airPLS-PLS-GA-SVM model was 100 %, the sensitivity was 94.4 %, and the total accuracy was 98.6 %. The results show that the combination of cervical secretion Raman spectroscopy and multivariate statistical analysis has great potential in the early screening of HPV and the samples of low-level lesions and high-level lesions were preliminarily classified effectively, providing a basis for the design of computer-aided diagnosis systems in the future. [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]

Chronic renal failure (CRF) is a symptom that is caused by kidney damage that deteriorates to the end stage. If not treated in time, CRF will worse into uraemia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine blood and medical image investigations, have poor sensitivity. Therefore, exploring new and efficient diagnostic methods such as urine spectroscopy for CRF is of great significance. In this study, we first explored Raman spectroscopy to classify urine from CRF patients and control subjects with normal renal function. A total of 48 samples from CRF patients and 44 samples from control subjects were accrued. The spectra revealed relatively lower hydroxybutyrate and higher alanine, creatinine and porphyrin in CRF. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP), grid search support vector machine (GS-SVM), genetic algorithms based on support vector machine (GA-SVM), discriminant analysis (DA) and particle swarm optimization support vector machine (PSO-SVM) algorithms were employed to establish discriminant diagnostic models; the diagnostic accuracy of each of the five classifiers was 70.77 %, 84.62 %, 80.77 %, 65.20 % and 74.62 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in rapid screening of CRF urine samples. Based on the limitations of current routine diagnostic methods, urine Raman spectroscopy may be a replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. [ABSTRACT FROM AUTHOR]