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Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future. [ABSTRACT FROM AUTHOR]

Abstract: With optimized parameters and the optimized sequence structure from simulations, proper porous silicon microcavity (PSM) based on silicon-on-insulator (SOI) wafer, constituted of a Fabry–Perot cavity between distributed Bragg reflectors (DBRs), has been achieved. Just as expected, the resonance wavelengths of these two oxidized PSMs are located separately at 1328nm and 1560nm, which are very close to the optical communication wavelengths of 1310nm and 1550nm. The effects of tuning resonance wavelength were shown by composition of Fabry–Perot cavity integrated on each thickness of porous silicon layer. Since the preparation technology of optical devices with these working wavelengths has been well developed, it is expected that such a design could be of great potential in realizing integrated optical devices. Compared with various optical devices on SOI wafer, PSM based on SOI by electrochemical anodization is simple and low-cost. Moreover, a sharp resonance in the reflectance spectrum means a high Q factor which predicts an interesting possibility in sensor application on SOI wafer. [Copyright &y& Elsevier]

[Display omitted] • Rapid diagnosis of systemic lupus erythematosus achieved using Raman spectroscopy. • Achieving accurate discrimination between SLE and SS. • Established three models (ANN, ResNet, SNN) and compared their classification effect. Multiple organs are affected by the autoimmune inflammatory connective tissue disease known as systemic lupus erythematosus (SLE). If not diagnosed and treated in a timely manner, it can lead to nephritis and damage to the blood system in severe cases, resulting in the patient's death. Therefore, correct and timely diagnosis and treatment are essential for patients. In this study, a framework based on neural network algorithm and Raman spectroscopy technique was established to diagnose SLE patients. Firstly, we pre-processed the obtained Raman data by three methods: baseline correction, smoothing processing and normalization methods, before using it as input for the model, and then ANN, ResNet and SNN classification models were established. The respective classification accuracies for SLE patients were 89.61%, 85.71%, and 95.65% for the three models, with corresponding AUC values of 0.8772, 0.8100, and 0.9555. The results of the experimental indicate that SNN possesses a good classification effect, and the number of model parameters is only 525,826, which is 414,221 less than that of ResNet model. Since the network only uses 0 and 1 to transmit information, and only has basic operations such as summation, compared with the second-generation artificial neural network, which simplifies the product operation of floating point numbers into multiple addition operations, the network has low energy consumption and is suitable for embedding portable Raman spectrometer for clinical diagnosis. This research highlights the significant potential for quick and precise SLE patient discrimination offered by Raman spectroscopy in conjunction with spiking neural networks. [ABSTRACT FROM AUTHOR]

[Display omitted] • EWFED achieves risk warning based on the empirical cumulative distribution function. • EWFED designs a food safety risk classification method based on the "3 σ rule". • An explainable unsupervised risk early warning framework applied to food safety. Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the E xplainable unsupervised risk W arning F ramework based on the E mpirical cumulative D istribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3 σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively. [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]

Lung cancer and glioma are common malignancies worldwide and pose a serious threat to human health. There may be a certain correlation between lung cancer patients and glioma patients in serum composition, but to date, no study on the classification and correlation of lung cancer and glioma is available. In this paper, the differences and relationships between lung cancer and glioma were analyzed from serum Raman spectra. The existing detection methods of lung cancer and glioma are time consuming and expensive, so we propose a method based on patient serum Raman spectra combined with deep learning, which can screen lung cancer and glioma accurately with speed and low cost. In this study, features were extracted from the original spectral data of patients with lung cancer, glioma, and control subjects. By adding different decibels of white Gaussian noise to the training set for data enhancement, the enhanced training set data were imported into a multilayer perceptron (MLP), recursive neural network (RNN), convolutional neural network (CNN), and AlexNet using fivefold cross‐validation to build the diagnostic model. The results show that PLS‐AlexNet is the best model. The accuracy of this model in the binary classification experiment of lung cancer and control subjects, lung cancer and glioma, and glioma and control subjects were 99%, 95.2%, and 100%, respectively, and the experimental accuracy of the AlexNet triclassification algorithm is also above 85%. This method has great potential in clinical diagnosis of diseases. [ABSTRACT FROM AUTHOR]

Herein, a novel, convenient, and highly selective electrochemical sensor for determination of nitrite based on a polythiophene-derivative film-modified glassy carbon electrode (GCE) was established. In this work, 2,5-di-thiophen-3-yl-thiazolo[5,4-d]thiazole (DTT), a novel thiophene derivative, was synthesized and used to form an original and excellent polymer film (PolyDTTF) on GCE through one-step electropolymerization for the first time. The modified electrodes were characterized by electron microscopy (SEM), Fourier transform infra-red spectroscopy (FT-IR), UV-visible spectra, Raman spectroscopy, and electrochemical technologies, in which the electrochemical sensor based on PolyDTTF was successfully constructed and demonstrated a significant electrocatalytic effect on nitrite. The influence of pH value, electrodeposition scanning times, scanning speed, and potential on the electrochemical behavior of nitrite were investigated in detail. Furthermore, the nitrite sensor exhibits excellent responses proportional to nitrite concentrations (R2 = 0.9972) over a concentration range of 5.5 × 10−9 ~ 3.5 × 10−5 M with a detection limit (LOD) of 2 nM, and has extremely good anti-interference ability for nitrite detection. This proposed sensor can be used to detect nitrite in actual samples, opening the possibility for applications in the food industry and environmental analysis. [ABSTRACT FROM AUTHOR]

Abstract: Silicon-on-insulator (SOI) wafer is one of the most appealing platforms for optical integrated circuit with the potential to realize high performance Ultra Large Scale Integration (ULSI) and device miniaturization. In this work, based on simulations to obtain appropriate optical properties of a porous silicon microcavity (PSM), we successfully fabricated a highly efficient PSM on SOI wafer by electrochemical etching for DNA detection at optical wavelength 1555.0nm. The narrow resonance peak with a full width at half maximum about 26.0nm in the reflectance spectrum gives a high Q factor which causes high sensitivity for sensing performance. The sensitivity of this sensor is investigated through 19-base pair DNA hybridization in the PSM by surface modification using a standard cross link chemistry method. The red shift of the reflectance spectra shows a good linear relationship with complementary DNA concentration, ranging from 0.625 to 12.500μM, and the detection limit is 43.9nM. This optical PSM on SOI is highly sensitive, fast responsive, easy to fabricate and low-costly, that will broadly benefit to develop a new optical label-free biosensor on SOI wafer and has a great potential for biochips based on integrated optical devices. [Copyright &y& Elsevier]

Abstract: A novel method is demonstrated to detect Antifreeze proteins (AFPs) based on photoluminescence (PL) using porous silicon (PS) coated with silver as a substrate. Ag/PS substrate is obtained through immersion of PS in silver nitrate (AgNO3) solutions and is incubated with Rhodamine B (RB) as PL label. This substrate is easy to be fabricated and the pore size of PS is large enough for biological molecules to infiltrate, which is an ideal platform for biological molecule detection. Through functionalization used glutaraldehyde (GTA) and 4-(N-Maleimidomethyl) cyclohexane-1-carboxylicacid (Sulfo-SMCC) as cross-linkers separately, we test the role of the AFPs antibodies in selective capturing the AFPs antigen and explain the reason of the enhancement of PL intensity. The result shows a significant enhancement of the PL intensity of RB at around 590 nm due to the interaction of antibody–antigen competitive binding with AFPs. Therefore, the PL corresponding to RB was selected to detect the target AFPs and the PL intensity of RB proportional to the AFPs concentration. The detection limit was found to be 1.65 μg/ml for AFPs when GTA was used as cross-linker, and the detection limit was 16.5 ng/ml with Sulfo-SMCC as cross-linker. [Copyright &y& Elsevier]

Abstract: A series of binuclear gold (I) complexes were synthesized. Their structures were characterized by elemental analyses, IR spectrometry, UV–Vis spectroscopy and single crystal X-ray diffraction. Their fluorescent mechanochromic (tribochromic) properties were investigated. The results of the mechanochromic studies suggested that the gold complexes with methylsubstituted phenyl bridges exhibited mechanochromism and a 100 nm red-shift of fluorescent spectrum could be observed after grinding. The complex with a diphenylmethane bridge exhibited mechanochromism with a change in fluorescence from green to blue (25 nm red-shift after grinding). The ground complexes reverted to their original states by treatment with CH2Cl2. No mechanochromism was observed for either the biphenyl or diphenylethane containing complexes. [Copyright &y& Elsevier]

Abstract: Anatase titanium dioxide (TiO2) mesoporous microspheres with core–shell and hollow structure were successfully prepared on a large scale by a one-step template-free chemical vapor deposition method. The effects of various reaction conditions on the morphology, composition and structure of the products were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET) technique and photoluminescence (PL) method. The results indicate that the product near the source was composed of core–shell structure TiO2 microspheres with diameters from 3 to 5μm. With increasing the distance between the source materials and the substrate, the hollow TiO2 spheres with 1–2μm dominant the products. A localized Ostwald ripening can be use to explain the formation of core–shell and hollow structures, and the size of the initial TiO2 solid nanoparticles plays an important role in determining the evacuation manner of the solid in the ripening-induced hollowing process. The surface area of TiO2 hollow microspheres determined by the adsorption isotherms was measured to be 74.67m2/g. X-ray photoelectron spectroscopy (XPS) analysis revealed that the O–H peaks of hollow structures have a chemical shift compared with the core–shell structures. The optical property of the products was also discussed. [Copyright &y& Elsevier]

Abstract: Single crystalline CdTe branched nanowires and well-aligned nanorod arrays were simultaneously synthesized by a simple chemical vapor deposition (CVD) technique. X-ray diffraction (XRD), scanning electronic microscopy (SEM), transmission electronic microscopy (TEM) and selected area electronic diffraction (SAED) were used to study the crystalline structure, composition and morphology of different samples. Vapor–liquid–solid (VLS) and vapor–solid (VS) processes were proposed for the formation of the CdTe branched nanowires and nanorod arrays, respectively. As-grown CdTe nanorod arrays show a strong red emission band centered at about 620nm, which can be well fitted by two Gaussian curves centered at 610nm and 635nm, respectively. [Copyright &y& Elsevier]

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. [ABSTRACT FROM AUTHOR]

• A spirofluorene-based microporous polymer film modified electrochemical sensor was proposed. • The sensor was easily constructed via one-step in-situ electropolymerization. • Simultaneous detection of nitrophenol isomers was achieved by the sensor with high selectivity. • Spirofluorene-based conjugated microporous polymer for sensing of nitro-analytes is achieved. • This proposed method shows simple, controllable, satisfied stability and reproducibility. A novel, convenient, and highly selective electrochemical sensor for simultaneous determination of nitrobenzene isomers based on conjugate microporous polyspirofluorene film modified glassy carbon electrodes (PSF/GCE) was established in this work, which was conducted on GCE through one-step co-electropolymerization for the first time. The proposed strategy exhibited outstanding electrocatalytic activity for the reduction of nitrophenol. Well identifiable redox peaks for each nitrobenzene isomer were obtained and their simultaneous detection can be received due to the high content of π - π orbital of spirofluorene - based conjugated microporous polymer film, which abundant electron significantly enhances the electrochemical response with electron-poor nitro-analytes. Related characterizations have confirmed its excellent electroactive surface area and analytical properties of the as-prepared sensor. Under the optimization conditions, the linear range of the sensor is from 0.1–100 μM for o-nitrophenol, 0.2–100 μM m-nitrophenol, and 0.1–120 μM for p-nitrophenol, while their minimal detection levels were 0.01 μM, 0.03 μM, and 0.01 μM, respectively. The modified electrode also revealed high selectivity, good reproducibility, and promising stability. More importantly, satisfactory recoveries were received for the developed sensor in the detection of target nitrophenol isomers, which exhibited a broad prospect in practical application. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%–40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis. [Display omitted] • A new dual branch attention network (DBAN) is proposed. • The dual branch combines different attention mechanisms to achieve multi-level and multi-scale fusion. • The DBAN model achieves excellent classification performance for diabetic kidney disease diagnosis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nanoparticles are uniformly embedded in PSi chips with 40–60 nm pore size, enhancing BRC serum signal. • The Ag 2 O-Ag core–shell structure effectively prevents silver from being oxidized by sulfur, giving the chip a shelf life of six months. • The PCA-t-SNE rapid screening model combined with the SERS microarray provides the basis for the development of a low-cost, rapid and sensitive technique for early screening of breast cancer. Ag 2 O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag 2 O-Ag nano core shell with a diameter of 40–60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag 2 O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer. [ABSTRACT FROM AUTHOR]

[Display omitted] • Differences in the biochemical composition of five types of cervical tissue were analyzed. • A multi-layered attention mechanism feature fusion architecture (MAFA) is proposed. • MAFA can effectively improve the classification accuracy of deep learning models for tissue samples. • MAFA as a model building method can be easily combined with a variety of deep learning models. Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer. [ABSTRACT FROM AUTHOR]

[Display omitted] • Designed and synthesized two novel pyrene-functionalized Ir-complexes, Pyrene-Ir1 and Pyrene-Ir2 , which demonstrated exceptional coenzyme NADH generation performances and could be easily modified onto carbon nanotube materials by mild π-π conjugated adsorption. • Prepared a novel dual-core drive heterogeneous catalyst Ir@CNT@ADH, and the proposed strategy has significantly improved the mutual inactivation problem between bio-enzymes and iridium complexes. • Novel dual-core drive heterogeneous catalyst Ir@CNT@ADH showed excellent catalytic hydrogeneration performances for various benzaldehyde derivatives, good recyclability. Here, a novel dual-core drive transfer hydrogen heterogeneous catalysts Ir@CNT@ADH were developed by sequentially immobilizing the NADH-dependent alcohol dehydrogenase ADH and high active NADH-regenerated catalyst Pyren-Ir1 on the surface of carbon nanotubes. Two pyrene-functionalized Ir-complex, Pyrene-Ir1 and Pyrene-Ir2 , were synthesized and demonstrated exceptional coenzyme NADH generation performances, ensuring continuous in situ NADH generation in the enzyme-catalyzed process. These complexes could be easily modified onto carbon nanotube materials by mild π-π conjugated adsorption. The catalysts Ir@CNT@ADH showed significantly improved catalytic hydrogenation efficiency in the reduction of benzaldehyde to the corresponding alcohol, compared to the single Pyrene-Ir1 modified carbon nanotube catalyst Ir@CNT and homogeneous catalytic system in the presence of a catalytic amount of NAD+. The yield of benzyl alcohol increased by more than 50 % in all cases. We assume that the designed dual-core drive heterogeneous catalyst strategy could effectively address the mutual inactivation problem of enzyme and Iridium complexes by blocking the contact between enzymes with Iridium complexes and enhancing the in situ regeneration property of coenzyme NADH in the enzyme catalytic system. Furthermore, catalyst Ir@CNT@ADH also demonstrated excellent catalytic hydrogeneration performances for various substituted benzaldehyde with good recycling performance. Therefore, the proposed dual-core drive heterogeneous catalyst strategy holds great potential for the combined industrial application of chemical catalysts and bioenzymes. It is expected to contribute to the development and preparation of higher-value biochemical products. [ABSTRACT FROM AUTHOR]

Food safety is a global issue; risk prevention and control of food physical and chemical testing (PCT) data are critical. However, existing studies have not considered the risk status of qualified products near the detection limit. Therefore, this paper presents a data-driven paradigm to detect the combined risk of multiple indicators across products in real-world food safety datasets for the first time. The following challenges exist to achieve this goal. (1) In real-world scenarios, neighboring samples are somewhat correlated; however, capturing and quantifying the actual testing sample correlation network remains an open problem in food computing. (2) The low-frequency nature of the unqualified samples leads to label imbalance in the data. (3) Labeling dependency limits the application scenarios. This article proposes a novel self-supervised framework (SUCOLA) to address the abovementioned challenges. That jointly learns the optimal graph topology and also inferences the connections between every product node and its well-designed subgraph, helping to resist label imbalance. We applied it to a real-world sterilized PCT dataset from a province in China. Extensive experiments show that SUCOLA performance is outstandingly better than several baselines of the current graph self-supervision field. Meanwhile, we conducted generalizability experiments on five well-known benchmark datasets. The proposed SUCOLA surpasses the current state-of-the-art by a considerable margin (0.39%–8.7% AUC) on public datasets. Thus, our study provides a new idea for early warning research on PCT data in food computing and fills the gap in the study of structural learning under category imbalance. [ABSTRACT FROM AUTHOR]

To further improve the photoelectrocatalytic activity of TiO 2 through metal-free strategy for wastewater treatment, a new type of graphite carbon/TiO 2 heterojunction nanomeshes (GC/TiO 2 HNMs) was successfully prepared by the facile electrochemical-hydrothermal method. By optimizing the precursor (glucose) concentration and reaction current, the GC/TiO 2 HNMs shows excellent photoelectrocatalytic activity towards methylene blue, rhodamine B, methyl orange and methyl violet, which could degrade the dyes by more than 95% within 120 min. The degradation efficiency of the GC/TiO 2 HNMs did not decrease significantly after being reused for 5 times. Besides, modification of graphite carbon on TiO 2 NMs could increase the photocurrent by more than 5 times and dramatically decrease the ionic resistance. The recombination of photogenerated electron-hole pairs was prevented and the electrons were quickly conducted by graphite carbon, which facilitates the production of •O 2 - and enhances the photoelectrocatalytic activity of the GC/TiO 2 HNMs. Free radical quenching experiments and electron paramagnetic resonance spectroscopy (EPR) verified that •O 2 - played the main role in photoelectrocatalytic dye degradation. [Display omitted] [ABSTRACT FROM AUTHOR]

To further improve the photoelectrocatalytic activity of TiO 2 through metal-free strategy for wastewater treatment, a new type of graphite carbon/TiO 2 heterojunction nanomeshes (GC/TiO 2 HNMs) was successfully prepared by the facile electrochemical-hydrothermal method. By optimizing the precursor (glucose) concentration and reaction current, the GC/TiO 2 HNMs shows excellent photoelectrocatalytic activity towards methylene blue, rhodamine B, methyl orange and methyl violet, which could degrade the dyes by more than 95% within 120 min. The degradation efficiency of the GC/TiO 2 HNMs did not decrease significantly after being reused for 5 times. Besides, modification of graphite carbon on TiO 2 NMs could increase the photocurrent by more than 5 times and dramatically decrease the ionic resistance. The recombination of photogenerated electron-hole pairs was prevented and the electrons were quickly conducted by graphite carbon, which facilitates the production of •O 2 - and enhances the photoelectrocatalytic activity of the GC/TiO 2 HNMs. Free radical quenching experiments and electron paramagnetic resonance spectroscopy (EPR) verified that •O 2 - played the main role in photoelectrocatalytic dye degradation. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (GS-SVM), the convolutional neural network (CNN) and the convolutional neural network combined with long and short-term memory (CNN-LSTM) are used to classify and discriminate between alcohol-dependent patients (AD) and the non-alcohol-dependent control group. The results show that 19 SNPs combined with academic qualifications have the best discrimination effect. In the GS-SVM, the area under the receiver operating characteristic (ROC) curve (AUC) is 0.87, the AUC of CNN-LSTM is 0.88, and the performance of the CNN model is the best, with an AUC of 0.92. This study shows that the CNN model can more accurately discriminate AD than the SVM to treat patients in time. [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]

This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B. [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]

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF. [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]

To improve the detection sensitivity of porous silicon microcavity biosensors, CdSe/ZnS quantum dots are used to label complementary DNA molecules for the refractive index amplification and angular spectrum method for detection. In this method, the TE mode laser is used as the detection light and the light source is changed into a parallel beam by collimating and expanding the beam, which illuminates the PSM surface and receives the reflected light from the PSM surface through the detector. The angle corresponding to the weakest reflected light intensity before and after the biological reaction between probe DNA and complementary DNA of different concentrations labeled by quantum dots was measured by the detector, and the relationship between the angle change before and after the biological reaction and the complementary DNA concentration labeled by quantum dots was obtained. The experimental results show that the angle change increases linearly with increasing complementary DNA concentration. The detection limit of the experiment, as determined by fitting, is approximately 36 pM. The detection limit of this method is approximately 1/300 of that without quantum dot labeling. Our method has a low cost because it does not require the use of a reflectance spectrometer, and it also demonstrates high sensitivity. [ABSTRACT FROM AUTHOR]

In order to further improve the performance of the near-infrared (NIR) spectroscopy quantitative model for detecting cyclic adenosine monophosphate (cAMP) content in red jujube, in this paper, support vector regression (SVR) is used for spectral analysis and compared with partial least squares (PLS) model results. The results show that for PLS model, correction coefficient (R 2 c), correction set root mean square error of calibration (RMSEC), prediction coefficient (R 2 p) and prediction set root mean square error of prediction (RMSEP) are 0.9076, 25.2625, 0.8323 and 29.0407, respectively. The performance of the SVR model is much better, and its R 2 c , RMSEC , R 2 p and RMSEP are0.9850, 11.1233, 0.9388 and 13.0739, respectively. The research indicates that the SVR model can greatly improve the predictive performance and stability of the jujube cAMP quantitative model. [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]

Clopidol is one of the most widely used anti-coccidiosis drugs. Its residues in poultry products and the environment pose a serious threat to human health. In this work, microwave-assisted synthesis of magnetic ionic liquid/gold nanoparticles (MIL-Au NPs) as the SERS substrates were first designed for sensitive and reliable determination of clopidol residue in egg samples. The experiment shows that MIL(1-methyl-3-hexyl imidazole ferric tetrachloride ([C6mim]FeCl4)) and microwave play a key role in the dispersion and morphology of Au NPs. Under the optimal conditions, the as-prepared MIL-Au NPs were applied to the SERS detection of clopidol in methanol and egg solution and its detection limits can be as low as to 0.5 μg/kg (equal to 0.5 ppb) in both solutions. The standard curves with regression coefficients of 0.9298 and 0.93496 were constructed in the linear range of 100–1000 ppb and 0.5–50 ppb for clopidol in egg solutions. Moreover, satisfactory recoveries (97.5–103.2%) were obtained for egg samples. The developed SERS method provides a way for quantitation of clopidol and can be applied for the convenient, reliable, and highly sensitive detection of antibiotic residues in food and environment, which has great potential in food safety and biological monitoring. [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]

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]

[Display omitted] • In this paper, a new spectral analysis method based on the spectral seal Minkowski distance is proposed. • This paper also presents an auxiliary alcohol dependence diagnosis technique based on Raman spectral structure distance feature and a convolutional neural network for the first time. • We evaluated the good performance of the proposed technique of the study. • The results demonstrate that this study provides a new scheme for auxiliary diagnosis and medical Raman spectroscopy analysis of alcohol dependence. Alcohol dependence is an addictive disease, which can lead to cancer, depression, and other problems. The current diagnostic methods of alcohol dependence are easily disturbed by patients' subjective consciousness, which could lead to wrong diagnoses. To solve this problem, this paper applies serum Raman spectra and a deep learning algorithm to the diagnosis of alcohol dependence and constructs an innovative diagnostic framework. We collected 228 serum samples for spectral analysis, including 119 alcohol-dependent patients and 109 controls. In this framework, spectra are firstly converted to two-dimensional spectrograms by clipping and stitching. Then, a calculating box extracts the distance features between spectral nonadjacent peaks on the spectrogram. Finally, the eigenvalues of each peak are used as pixels of artificial images and input into the ConvNeXt-T neural network model for classification. The sensitivity and specificity of the method are 81.82% and 100%. The results show that it is feasible to use Raman spectroscopy combined with a deep learning algorithm to diagnose alcohol dependence. Compared with directly using the ConvNeXt-T model, the distance feature performs better. [ABSTRACT FROM AUTHOR]

[Display omitted] • At OLR of 3.6 g/(L·d), butyrate was efficiently degraded with VBP of 1.50 L/(L·d). • Functional microorganisms were Methanosarcina , Syntrophomonas , and Lentimicrobium. • The hydrolysis and acidogenesis stage occurred with butyrate as sole substrate. • Hydrogenotrophic methanogenesis was the main pathway to consume H 2. Investigating the effect of butyric acid concentration on anaerobic digestion systems in complex systems is important for the efficient degradation of butyric acid and improving the efficiency of anaerobic digestion. In this study, different loadings of butyric acid with 2.8, 3.2, and 3.6 g/(L·d) were added to the anaerobic reactor. At a high organic loading rate of 3.6 g/(L·d), methane was efficiently produced with VBP (Volumetric Biogas Production) of 1.50 L/(L·d) and biogas content between 65% and 75%. VFAs concentration remained below 2000 mg/L. Metagenome sequencing revealed changes in the functional flora within different stages. Methanosarcina , Syntrophomonas , and Lentimicrobium were the main and functional microorganisms. That the relative abundance of methanogens exceeded 35% and methanogenic metabolic pathways were increased indicated the methanogenic capacity of the system significantly improved. The presence of a large number of hydrolytic acid-producing bacteria also indicated the importance of the hydrolytic acid-producing stage in the system. [ABSTRACT FROM AUTHOR]

Ag 2 O–Ag-porous silicon Bragg mirror (PSB) composite SERS substrates were successfully synthesized by using a combination of electrochemical and thermochemical methods. Test results showed that the SERS signal increased and decreased as the annealing temperature used for the substrate increased, where the most intense SERS signal was obtained using a substrate annealed at 300 °C. Stability test results showed substantial enhancement of the SERS signal intensity of the Ag 2 O-Ag-PSB composite one month after preparation compared with that of conventional Ag-PSB. We conclude that Ag 2 O nanoshells play an essential role in SERS signal enhancement. Ag 2 O prevents natural oxidation of Ag nanoparticles (AgNPs) and has a solid localized surface plasmon resonance (LSPR). SERS signal enhancement was tested using this substrate for serum from patients with Sjögren's syndrome (SS) and Diabetic nephropathy (DN), as well as from healthy controls (HC). SERS feature extraction was performed using principal component analysis (PCA). The extracted features were analyzed by a support vector machine (SVM) algorithm. Finally, a rapid screening model for SS and HC, as well as DN and HC, was developed and used to perform controlled experiments. The results showed that the diagnostic accuracy, sensitivity and selectivity for SERS technology combined with machine learning algorithms reached 90.7%, 93.4% and 86.7% for SS/HC and 89.3%, 95.6% and 80% for DN/HC, respectively. The results of this study show that the composite substrate has excellent potential to be developed into a commercially available SERS chip for medical testing. [Display omitted] • The optimized morphology of AgNPs and the excellent optical properties of PSB were combined to amplify the serum SERS signal. • The stability of the substrate is twofold: stability to serum samples and stability under different storage cycles. • Analytical modeling of SERS serum spectra using PCA-LDA produced excellent results on both chronic disease datasets. [ABSTRACT FROM AUTHOR]

[Display omitted] • For the first time, Raman spectroscopy combined with deep learning is used for the classification and identification of red wine origin. • Corresponding biomarkers identified by Raman spectroscopy of red wine. • Classification of red wine origin by PCA analysis. • Comparative experiment of four deep learning models. • The combination of MCNN and Raman spectroscopy achieves the best results for the classification of red wine origin. In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field. [ABSTRACT FROM AUTHOR]

[Display omitted] • Differences in the biochemical composition of seven types of tissues are analyzed. • A new one-dimensional convolutional hierarchical network (H-CNN) is proposed. • H-CNN classifies tissue samples in a hierarchy from coarse to fine. • H-CNN outperforms traditional classification models. Cervical cancer is one of the most common cancers with a long latent period and slow onset process. Early and accurate identification of the stage of cervical cancer can significantly improve the cure rate and patient survival time. In this study, we collected 699 Raman spectral data of tissue sections from 233 different patients. We analyzed and compared the characteristics and differences of the mean Raman spectra of the seven tissues and pointed out the main differences in the biochemical composition of the seven tissues. In this study, 1D hierarchical convolutional neural network (H-CNN) is proposed by integrating the prior knowledge of hierarchical classification relations with the research of deep learning in Raman spectroscopy. H-CNN is based on CNN and is added with three network branches. Hierarchical classification is performed from coarse to fine for tissue samples of cervicitis, Low-grade Squamous Cell Carcinoma, High-grade Squamous Cell Carcinoma, Well Differentiated Squamous Cell Carcinoma, Moderately Differentiated Squamous Cell Carcinoma, Poorly Differentiated Squamous Cell Carcinoma and cervical adenocarcinoma. To evaluate the recognition performance of H-CNN, we compared it with traditional methods such as Bayesian classifier (NB), decision tree classifier (DT), support vector machine classifier (SVM) and CNN. The experimental results show that H-CNN can accurately identify different classes of tissue sections and has apparent advantages in several aspects such as recognition accuracy, stability and sensitivity compared with the other four traditional recognition methods. The classification Macro-Accuracy of H-CNN can reach 94.91%, Macro-Recall can reach 95.31%, Macro-F1 can reach 95.23%, and Macro-AUC can reach 97.35%. The hierarchical classification method proposed in this study can diagnose patients more accurately. This could lay the foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer. [ABSTRACT FROM AUTHOR]

Raman spectroscopy combined with artificial intelligence (AI) is widely used in medical diagnostic research and has great application value. However, there are still problems in the research process, such as the low prevalence of some diseases and difficulties in obtaining research samples, which will easily lead to data imbalance in medical Raman spectroscopy research. For AI classification and diagnosis algorithms, when the data imbalance problem is not addressed, majority class samples are selected, and the importance of minority class samples is ignored, reducing the accuracy of disease identification. Based on the above problems, this paper proposes a hybrid sampling technique of Raman-Gaussian distributed oversampling fused with random undersampling (R-GDORUS) to solve the data imbalance problem in medical Raman spectroscopy. The density and distance information carried by the minority samples are used to obtain the selection probability of the minority samples, determine the anchor samples from the minority samples, and generate a new minority sample in the form of a Gaussian distribution. Finally, a random undersampling strategy is used to remove some of the majority class spectral samples. This technique and five other mainstream methods for handling imbalanced data are applied to three major types of imbalanced medical Raman spectroscopy datasets: malignant tumors, class B infectious diseases and autoimmune diseases, and the performance of the technique is evaluated using the AUC and G-mean values. The results demonstrate that the proposed technique can be used to effectively reduce the impact of impaired model performance caused by spectral data imbalance and has good application prospects. • This paper presents a hybrid sampling technique R-GDORUS for the first time. • We evaluated the good performance of the proposed technique of the study. • The results show that the method proposed provides a new solution to the Raman data imbalance in medical studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Triple-negative breast cancer and HER2-positive breast cancer are highly malignant, and early diagnosis is helpful to improve the chances of cure. • Serum Raman spectroscopy combined with deep learning algorithms to diagnose triple-negative breast cancer and HER2-positive breast cancer and achieved great diagnostic results. • The proposed method is helpful for early breast cancer diagnosis and can provide a reference for breast cancer patients to make a personalised diagnosis. Breast cancer is common in women, and its number of patients ranks first among female malignant tumors. Breast cancer is highly heterogeneous, and different types of breast cancer have different biological behaviors and prognoses. Therefore, identifying the different types of breast cancer is of great help in formulating individualized treatment plans. Based on serum Raman spectroscopy and deep learning algorithms, we propose a fast and low-cost diagnosis method for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and healthy controls. We collected 75 serum samples in this study, including 23 triple-negative breast cancers, 22 HER2-positive breast cancers, and 30 healthy controls. Using the preprocessed Raman spectra as the input of deep learning, three deep learning models, neural network language model (NNLM), bidirectional long-short-term memory network (BiLSTM), and convolutional neural network (CNN), were established, and the accuracy rates of the three models were 87.78%, 90.37%, and 91.11%, respectively. The experimental results demonstrate the feasibility of serum Raman spectroscopy combined with deep learning algorithms to diagnose breast cancer, which can be used as an effective auxiliary diagnosis method for breast cancer. [ABSTRACT FROM AUTHOR]

A new way to enhance the photoluminescence of CdSe/ZnS quantum dots embedded in porous silicon (PSi) is reported, which results in the double enhancement of the quantum dot (QD) fluorescence due to Au nanoparticle decoration and the porous silicon having a distributed Bragg reflector (DBR) structure. Through the dropwise addition of a solution of CdSe/ZnS QDs on Au nanoparticle-decorated porous silicon samples, the QDs successfully infiltrated the porous silicon substrate. Among the fluorescence intensities of QDs/single-layer PSi, QDs/multilayer PSi, QDs/Au/single-layer PSi and QDs/Au/multilayer PSi, we find that the fluorescence of the QD-infiltrated multilayer PSi is stronger than that of the single-layer sample, and the multilayer sample decorated with Au nanoparticles shows further improvement of the fluorescence intensity through plasmonic effects. PSi, as a substrate with a spongy structure, offers favorable conditions for enhancing fluorescence intensity. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel spectral fusion diagnosis technology is proposed. • A new and efficient MFCNN network is designed to process spectral data. • This study explored the potential of spectral fusion technology for the diagnosis of multiple cancers in this complex clinical environment. According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used serum samples from 45 healthy controls, 44 non-small cell lung cancer (NSCLC), 38 glioma and 37 esophageal cancer patients, and the Raman spectra and FTIR spectra were collected respectively. Then we performed low-level fusion and feature fusion on the spectral, and used SVM, Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the multi-scale convolutional fusion neural network (MFCNN). The accuracy of low-level fusion and feature fusion models are improved by about 10% compared with single spectral models. [ABSTRACT FROM AUTHOR]

Cancer is one of the most life-threatening diseases to human life, whose accurate diagnosis is the prerequisite for precise treatment. The detection technology with computer-aided vibrational spectroscopy has achieved gratifying results in intelligent cancer diagnosis. However, limited by factors such as the number of cancer instances in clinical practice and the cost of spectral acquisition, it is difficult to obtain a large amount of spectral data, which ultimately puts constraints on the performance optimization and improvement of diagnostic models. Faced with the above challenges, we adopted the different data augmentation strategies in this study to obtain more available training data. In addition to the augmentation methods commonly used in vibrational spectroscopy, such as adding random noise, adding random variations from offset, multiplication and slope, and synthetic minority over-sampling technique (SMOTE), two generative adversarial networks with different architectures were selected for comparison. One is based on artificial neural networks (ANN) and the other on convolutional neural networks (CNN). In the experiments, t-distributed stochastic neighbor embedding (t-SNE) visualization and cosine similarity (CS) measure were opted to evaluate the quality of generated new spectra. New spectra with different manifestations were produced by dissimilar augmentation tactics. Effective merging of heterogeneous data information generated by different augmentation techniques can further enlarge the sample space and increase the diversity of samples. With these factors in mind, we proposed a new spectral data augmentation fusion (SDA-Fusion) method to acquire more available instances. This method is carried out by fusing the new data generated by the five different data augmentation techniques mentioned before. Finally, three groups of experiments, with the original training data, the augmented training data, and the fused training data as input, were designed. Support vector machines (SVM) with different kernel functions, CNN as well as ResNet were used as classification models. Group five-fold (Group5Fold) cross-validation was utilized to assess model performance. We applied the augmentation methods and experimental ideas mentioned above to two real datasets – the Raman spectral dataset of lung cancer and the mid-infrared spectral dataset of glioma, respectively. The results illustrate that the generative adversarial networks working through adversarial learning concepts can produce new data approximate to the original. This technique can be a complementary means for expanding the size of the vibrational spectroscopy data. Moreover, by introducing different augmentation strategies, the classification accuracies of most classifiers were higher than the original training set. In addition, a more extensive and heterogeneous dataset can be yielded using our proposed SDA-Fusion method. We have trained more robust models that provided better predictive performance for both spectral datasets on the foundation of these data. This research aims to address the lack of data volume of vibrational spectra from cancer at the data level. It can provide the solution ideas to be consulted by other researchers in the future when faced with the small-sample learning tasks for vibrational spectra. • A new spectral data augmentation fusion (SDA-Fusion) method was built to capture more available training data. • This study assessed the quality of new spectra generated by different data augmentation strategies. • This study addressed the lack of data volume of vibrational spectra from cancer at the data level. • A more extensive and heterogeneous dataset can provide better predictive performance. [ABSTRACT FROM AUTHOR]

In this work, a new dual signal light detection method based on porous silicon Bragg mirror (PSBM) and biological labelling with quantum dots (QDs) is proposed for the detection of beta-lactoglobulin (β-lg). The first signal light is a probe light emitted by a laser with wavelength of 633 nm, which enters the PSBM and is reflected from the surface. The wavelength of the probe light is located at the edge of the PSBM band gap, where it has the lowest reflectivity. β-lg antibodies is labelled with CdSe/ZnS QDs and reacts with β-lg molecules have been fixed to the inner wall of the porous silicon pores. Due to the specific binding of biomolecules in PSBM, the refractive index of the device increases, resulting in the enhancement of detection reflected light. The QDs play the role of refractive index amplification. The second signal light is the fluorescence of QDs in immune reactants. QDs produce fluorescence at 630 nm when excited by a short-wavelength laser. The fluorescence signal is further enhanced by PSBM. The superimposed images of two kinds of light on the surface of PSBM are obtained by digital microscope at the same time. By calculating the average grey value change of the image before and after biological reaction, β-lg can be detected with high sensitivity. The detection limit of β-lg was 0.12 ng/mL. The experimental results showed that the PSBM-based dual signal light method could be used to detect the content of cow milk adulterated in β-lg free camel milk. • The wavelengths of both probe light and fluorescence are selected at the band gap edge of porous silicon with Bragg structure. • The two changes of fluorescence and refractive index of the sensor before and after biological reaction are used to detect the target biomolecules. • Quantum dots play two roles in detection, one is to generate fluorescence after excitation and another is to achieve refractive index amplification. • Biological detection is implemented by the average grey value change of the images before and after biological reaction. • Proposed method can be applied to detect β-lactoglobulin. [ABSTRACT FROM AUTHOR]

This research innovatively combines Fourier transform infrared spectra, Raman spectra and its first-derivative spectra to develop a rapid diagnosis method for renal cell carcinoma (RCC). The spectral features are extracted by principal component analysis (PCA), and then the normalized fused data are used as input to the improved AlexNet model and MCNN model to train the models. Among them, the AlexNet model performed better, with a classification accuracy rate of 93%. [Display omitted] • It is proposed for the first time to combine FT-IR, Raman spectra and their first-derivative spectra to develop a rapid diagnosis method for renal cell carcinoma. • The accuracy of the multi-spectral fusion method (93%) is greatly improved compared with that of single infrared spectroscopy (74%), signal Raman spectroscopy (75%) and the fusion of infrared spectroscopy and Raman spectroscopy (79%). • It shows that multi-spectral fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC. This research innovatively combines FT-IR, Raman spectroscopy and their first-derivative spectroscopy to develop a rapid diagnosis method for renal cell carcinoma (RCC). After measuring the Raman spectra and FT-IR spectra of 45 cases of control subjects and 28 cases of RCC, the first derivative of the infrared spectra and the Raman spectra were calculated respectively. Principal component analysis (PCA) was used to extract the features of the infrared spectra, first-derivative infrared spectra, Raman spectra and first-derivative Raman spectra. Then the four feature matrices were merged as fused spectral feature matrices. The fused matrices were used as the input of AlexNet and MCNN. The fused spectral feature matrices were used as the input of AlexNet and MCNN. The adjusted AlexNet model performed better, and the classification accuracy of the fused spectral data is 93%. Compared with the classification results of infrared spectra (74%), Raman spectra (75%) and the fusion of infrared and Raman spectra (79%) combined with the adjusted AlexNet model, the classification result of the fusion of infrared spectra, Raman spectra and their first-derivative was significantly improved. The experimental results show that infrared spectroscopy, Raman spectroscopy and their first-derivative fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC. [ABSTRACT FROM AUTHOR]

In this paper, carbon quantum dot-labelled β-lactoglobulin antibodies were used for refractive index magnification, and β-lactoglobulin was detected by angle spectroscopy. In this method, the detection light is provided by a He-Ne laser whose central wavelength is the same as that of the porous silicon microcavity device, and the light source was changed to a parallel beam to illuminate the porous silicon microcavity' surface by collimating beam expansion, and the reflected light was received on the porous silicon microcavity' surface by a detector. The angle corresponding to the smallest luminous intensity before and after the onset of immune response was measured by a detector for different concentrations of β-lactoglobulin antigen and carbon quantum dot-labelled β-lactoglobulin antibodies, and the relationship between the variation in angle before and after the immune response was obtained for different concentrations of the β-lactoglobulin antigen. The results of the experiment present that the angle variations changed linearly with increasing β-lactoglobulin antigen concentration before and after the immune response. The limit of detection of β-lactoglobulin by this method was 0.73 μg/L, indicating that the method can be used to detect β-lactoglobulin quickly and conveniently at low cost. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative use of generative technology established the recognition models of cumin and fennel from three regions. • This study also established the models for cumin and fennel in the same region. • This study provides a new method for classification of NIR spectroscopy. Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future. [ABSTRACT FROM AUTHOR]

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]