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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]

Purpose: Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. Methods: In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. Results: Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). Conclusion: In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value. [ABSTRACT FROM AUTHOR]

With the development of commodity economy, the emergence of fake and shoddy raisin has seriously harmed the interests of consumers and enterprises. To deal with this problem, a classification method combining near-infrared spectroscopy and pattern recognition algorithms were proposed for adulterated raisins. In this study, the experiment was performed by three kinds of raisins in Xinjiang (Hongxiangfei, Manaiti, Munage). After collecting and normalizing the spectral data, we compared the spectra of three kinds of raisins. Next the principal component analysis (PCA) was preformed to compress the dimension of the spectral data, and then classification models including support vector machine (SVM), multiscale fusion convolutional neural network (MCNN) and improved AlexNet were established to identify raisins. The accuracy of SVM, MCNN, and improved AlexNet is 100%, 92.83%, and 97.78% respectively. This study proves that near-infrared spectroscopy combined with pattern recognition is feasible for the raisin inspection. [ABSTRACT FROM AUTHOR]

Zhejiang Suichang native honey, which is included in the list of China's National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey. [ABSTRACT FROM AUTHOR]

In this experiment, we collected 45 samples of cervicitis, 29 samples of low‐grade squamous intraepithelial lesion (LSIL), 44 samples of high‐grade squamous intraepithelial lesion (HSIL), 39 samples of cervical squamous cell carcinoma, and 38 cases of cervical adenocarcinoma. After preprocessing of the Raman spectral data, partial least squares (PLS) was used to reduce the dimensionality, and then extreme gradient boosting (XGBoost) was used for feature selection to obtain the first 30‐dimensional features. The preprocessed Raman spectral data also used a fast Fourier transform (FFT) to obtain amplitude information, and then PLS and XGBoost were used to obtain the first 30‐dimensional features. Finally, K nearest neighbor (KNN), extreme learning machine (ELM), artificial bee colony support vector machine (ABC‐SVM), support vector machine optimized by the cuckoo search algorithm (CS‐SVM), particle swarm optimization coupled with support vector machine (PSO‐SVM), and the convolutional neural network combined with long‐ and short‐term memory (CNN‐LSTM) classification models were established. In the raw Raman spectral features experiments, the classification accuracies of KNN, ELM, ABC‐SVM, CS‐SVM, PSO‐SVM, and CNN‐LTSM were 60.76%, 65.81%, 76.21%, 77.66%, 73.50%, and 69.19%, respectively. In the feature fusion experiments, the classification accuracies were 60.91%, 67.84%, 77.64%, 78.49%, 75.54%, and 70.72%, respectively. The experimental results show that feature fusion can further improve model performance regardless of whether using linear classification models or nonlinear classification models. Therefore, it provides a new strategy for extracting features and screening multiple cervical pathological tissues in the future. [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]

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect. [ABSTRACT FROM AUTHOR]

This study proposes a rapid, noninvasive method for screening invasive ductal carcinoma (IDC) and noninvasive ductal carcinoma (non-IDC) using serum Fourier transform infrared (FT-IR) spectroscopy combined with multivariate statistical methods. Serum samples from 114 healthy patients, 74 IDC patients, and 41 non-IDC patients were examined in this experiment. Tentative assignments of the FT-IR peaks in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis was used for feature extraction to reduce spectral dimension and improve the diagnostic model rate. Linear, polynomial, and radial basis function kernels were used to build support vector machine models for the extracted features. Polynomial kernel achieves the best results with an accuracy of 95.7 %, and sensitivity and specificity of 91.7 % and 100 %, respectively. The results of the study indicate that serum FT-IR spectroscopy combined with multivariate statistical analysis has considerable potential for screening IDC in breast cancer. This technology can be used to develop a portable, rapid screening device for discriminating healthy patients and those with IDC and non-IDC. [ABSTRACT FROM AUTHOR]

This study aimed to screen for thyroid dysfunction using Raman spectroscopy combined with an improved support vector machine (SVM). In spectral analysis, in order to further improve the classification accuracy of the SVM algorithm model, a genetic particle swarm optimization algorithm based on partial least squares is proposed to optimize support vector machine (PLS-GAPSO-SVM). In order to evaluate the performance of the algorithm, five optimization algorithms are used: grid search-based SVM (Grid-SVM), particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), artificial fish coupled uniform design algorithm-based SVM (AFUD-SVM), and simulated annealing particle swarm optimization algorithm-based SVM (SAPSO-SVM). In this experiment, serum samples from 95 patients with confirmed thyroid dysfunction and 90 serum samples from normal thyroid function were used for Raman spectroscopy. The experimental results show that the GAPSO-SVM algorithm has a high average diagnostic accuracy of 95.08% and has high sensitivity and specificity (91.67%, 97.96%). Compared with the traditional optimization algorithm, the algorithm has high diagnostic accuracy, short execution time, and good reliability. It can be seen that Raman spectroscopy combined with GAPSO-SVM diagnostic algorithm has enormous potential in noninvasive screening of thyroid dysfunction. [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]

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, 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]

For the Sagnac fiber perimeter security system, the recognition rate of intrusion signal is not high. This paper proposes a new endpoint detection algorithm and recognition algorithm to effectively improve the recognition rate of intrusion signals. The short-term logarithmic energy and spectral entropy characteristics are combined to form a new endpoint detection algorithm to improve the accuracy of endpoint detection. The recognition algorithm uses variational mode decomposition to extract the spectral entropy, energy ratio and kurtosis of the eigenmode function. Including the multi-dimensional features of time domain and frequency domain, using the uncertainty to reduce the feature dimension. The support vector machine is selected to realize the intrusion signal recognition. The experimental results show that the proposed recognition algorithm can effectively identify the tapping, walking and stone throwing signals. The correct recognition rate reached 98.0 %. [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]