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As a rapid and non-destructive biological serum detection method, SERS technology was widely used in the screening and medical diagnosis of various diseases by combining the analysis of serum SERS spectrum and multivariate statistical algorithm. Because of the high complexity of serum components and the variability of SERS spectra, which often resulted in the phenomenon that the SERS spectrum of the same biological serum was significantly different due to the different test conditions. In this experiment, through the dilution treatment of the serum and the systematic test of the serum of all concentration gradients with lasers of wavelength of 785, 633 and 532 nm, the most suitable conditions for detecting the serum were investigated. The experimental results showed that only when the serum is diluted to low concentration (10 ppm), the SERS spectrum with high reproducibility and stability could be obtained, furthermore, the low concentration serum had weak tolerance to laser, and 532 nm laser was not suitable for serum detection. In this paper, a set of test scheme for obtaining highly stable serum SERS spectra was established by using high-performance gold nanoparticles (Au NPs) as the active substrate of SERS. Through comparative analysis of SERS spectrum of serum of normal people and cervical cancer, the reliability of the established low-concentration serum test program was verified, as well as its great potential advantages in disease screening and diagnosis. [Display omitted] • The reason of the variability of serum SERS spectrum was verified. • A low-concentration serum SERS detection scheme was established. • High reproducibility serum SERS spectrum can be obtained by using this scheme. • The scheme provides guarantee for the reliability of SERS technology in medical diagnosis. [ABSTRACT FROM AUTHOR]

Cumin and fennel are two types of food raw materials that have similar appearances but great differences in efficacy. Therefore, the efficient identification of these two types of food is very important to the related food processing industry and the development of food detection technology. In this study, a deep learning algorithm was designed to process Fourier NIR spectral data for the classification of cumin and fennel. The method was based on the difference in the signal intensity in NIR spectra between cumin and fennel, and the identification was performed by combining the improved multiscale fusion convolutional neural network (MCNN) and the bidirectional long-short-term memory network (BILSTM) based on NIR spectra. The classification accuracies of the MCNN and BILSTM models were 100% and 98.57%, respectively. The models have high sensitivity and fast analysis speed, which directly promote the development of real-time identification technology of cumin and fennel and verify the feasibility of deep learning algorithms in the field of food detection. [ABSTRACT FROM AUTHOR]

• High-performance Au NPs/PSi was successfully fabricated on a large-scale. • With the advantages of MIL, the MIL-Au NPs /PSi active substrate with strong adsorption property was prepared. • MIL-Au NPs/PSi exhibits excellent sensitivity and stability in the detection for probe molecule. • A novel method for the detection of arsenite was proposed, with the detection limit as low as 0.5 ppb. Developing a novel and simple route for the synthesis ultrasensitive and specific SERS substrates is highly desirable and technically important. Here, a magnetic ionic liquid (MIL) assisted strategy is developed, and the high-quality and ultra-sensitive MIL-gold nanoparticles/porous silicon (MIL-Au NPs/PSi) composite substrate were synthesized by a fast and cost effective strategy with the assistance of MIL (1-methyl-3-hexyl imidazole ferric tetrachloride ([C 6 mim]FeCl 4). Fundamental SERS performance of this composite substrate was evaluated using rhodamine 6G (R6G). The results showed that MIL make the Au NPs/PSi have great advantages in synergetic enhancement and stability, and the hypersensitive detection of specific substances can be realized by selecting or designing IL. Based on the particularity of MIL ([C 6 mim] FeCl 4 ′s function and structure, arsenic was detected with MIL-Au NPs/PSi substrate. The detection limit can reach 0.5 ppb, and the standard curves with regression coefficients of 0.98527 and 0.98965 were established in the linear range of 2–80 ppb and 200–800 ppb. In a word, an advanced method for the preparation of high-performance and designable SERS substrate has been developed, which will certainly show promising applications in detection technology. [ABSTRACT FROM AUTHOR]

In this research, PLSR, GA-SVR and ELM models combined with sheep serum Raman spectroscopy were used for quantitative detection, the results of PLSR determination are better (a). In order to further improve the performance of the model, the SPA is used for band selection (c). The optimized quantitative model result (b) of DOSC-SPA-PLSR-MN performs well. • It is proposed for the first time to use sheep serum Raman spectroscopy combined with a quantitative model to determine the concentration (/enzyme activity) of seven indicators. • It is the first time to deviation standardization of the model evaluation index for many kinds of substances. • The comparison results of SPA band selection show that there is a correlation among the indicators in the serum. • The optimized DOSC-SPA-PLSR-DS quantitative model shows that the average R p 2 of the seven indicators is 0.9834. Hepatic fascioliasis, ketosis of pregnancy, toxemia of pregnancy and other common sheep diseases will directly affect the concentration (/enzymatic activity) of seven indicators, such as cortisol and high-density lipoprotein cholesterol (HDL-C) in sheep serum. Whether the concentrations (/enzymatic activity) of these indicators can be detected quickly will directly affect the prevention of sheep diseases and the targeted adjustment of breeding methods, thereby affecting the economic benefits of sheep breeding. In this research, we established partial least square regression (PLSR), support vector regression based on genetic algorithm optimization (GA-SVR) and extreme learning machine (ELM) models. Due to the large differences in the content of different substances, it is difficult to directly use the RMSE to evaluate the quantitative effect of the model. This study is the first to propose conducting deviation standardization (DS) for the determination results of various substances. To further improve the performance of the model, we use the successive projections algorithm (SPA) to optimize feature extraction and combine it with the better-performing PLSR model for training. The results show that the optimized DOSC-SPA-PLSR-DS quantitative model has better determination results for 101 sheep serum samples. The average RMSE p* of the concentration of the six substances decreased from 0.0408 to 0.0387, the R p 2 increased from 0.9758 to 0.9846, and the running time was reduced from 0.1659 to 0.0008 s. And the determination performance of lipase (LPS) enzymatic activity has also been improved. The results of this research show that sheep serum Raman spectroscopy combined with DOSC-SPA-PLSR-DS optimization can efficiently monitor the concentration (/enzyme activity) of seven indicators in real time and provide a new strategy for future intelligent supervision of animal husbandry. [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]

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]

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 (C5) is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features. [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, a fast and effective classification method of Bachu mushroom is proposed based on Fourier transform infrared (FT-IR) spectroscopy and pattern recognition algorithms. After separately measuring the cap and stem of 35 Bachu mushroom samples, 47 coral fungi samples, and 39 Lentinus edodes samples, we set up two classification groups of Bachu mushroom-coral fungi and Bachu mushroom-Lentinus edodes, and then the obtained FT-IR spectral data of the mushroom powder was combined with PLS-GS-SVM and PLS-ELM modeling to test the performance of the two classifiers. The results showed that the classification accuracy of PLS-GS-SVM was 98.78 % for the cap data of the Bachu mushroom-coral fungi group and 98.78 % for the stem; touching the Bachu mushroom-Lentinus edodes group, the classification accuracy for the cap data is 98.78 % and 100 % for the stem. For PLS-ELM, the classification accuracy for the cap data was 94.87 % of Bachu mushroom-coral fungi group and 77.81 % for the stem; as for Bachu mushroom-Lentinus edodes group, the classification accuracy for the cap data was 99.45 % and 99.18 % for the stem. This study demonstrates FT-IR spectroscopy combined with statistical algorithms can accurately classify the samples of Bachu mushroom mixed with other mushroom. The study provides some basis for consumers and food inspection agencies to identify the authenticity of Bachu mushroom and for scholars to explore more universal sorting methods of mushroom. [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 paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray collection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https://github.com/luhongchun/FCANet. [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]

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]

Persistent high-risk human papillomavirus (HR-HPV) infection is leading cause for the occurrence of cervical cancer, and timely detection and early treatment of HR-HPV infection can effectively reduce incidence of cervical cancer. In this study, a rapid and non-invasive method for detecting HR-HPV was proposed by using Fourier transform infrared (FT-IR) spectra of cervical exfoliated cells combined with multivariate analysis. A total of 100 spectra were recorded from 50 HR-HPV positive patients and 50 normal subjects. The obvious difference in infrared spectrum between the two groups was mainly shown at 1042 cm−1 (mucin), 1246 cm−1 (amide III), 1396 cm-1 (proteins), 1543 cm−1 (amide II), 1651 cm−1 (amide I), 2361 cm−1 (CO 2), 2928 cm−1 (lipids), and 3294 cm−1 (amide A). Then, a principal component analysis-linear discriminant analysis (PCA-LDA) diagnostic model was developed and applied on the FT-IR spectra of normal samples as well as HR-HPV positive patients, and satisfactory classification results were obtained. The diagnostic accuracy, specificity, and sensitivity were 98 %, 98 %, and 98 %, respectively. Furthermore, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.997, which could further demonstrate the feasibility of the PCA-LDA model. Therefore, our exploratory work shows that the combination of FT-IR spectroscopy and PCA-LDA model has great potential for HR-HPV screening. [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]

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]

Uniform, low-cost, surface-enhanced Raman scattering (SERS) active substrates were prepared by sputtering silver onto porous silicon (pSi) fabricated by an electrochemical etching method. The resulting Ag/pSi SERS substrates were characterized by scanning electron microscopy (SEM), and the SERS effect of the substrates was investigated using rhodamine 6G (R6G) as a probe. Furthermore, the signal uniformity of the Ag/pSi SERS substrates was confirmed through mapping measurements. The Ag/pSi SERS substrates exhibited uniform shape and size and stable SERS activity. Then, we demonstrated the capacity of the prepared silicon-based SERS substrates for detecting bovine serum albumin (BSA) and serum samples. Therefore, our Ag/pSi SERS substrates can be easily employed in practical applications to effectively assess healthcare. [ABSTRACT FROM AUTHOR]