Grid Search Support Vector Machine Research Articles

A novel technology based on Raman spectroscopy for effective differentiation of alanine chiral isomers

In this study, Raman time-domain spectroscopy (R-TDS) was used to record the spectra of β-alanine, D-alanine, L-alanine, and DL-alanine molecules in the Raman shift range of 250–2000 cm−1. Baseline correction and smoothing of the original spectra yielded considerably different scattering peaks for β-alanine than the other three alanine isomers. The positions and intensities of the peaks in the spectra of D-alanine, L-alanine and DL-alanine were very similar, enabling β-alanine to be easily distinguished from the aforementioned isomers based on the Raman spectra. The Raman spectral data of D-alanine, L-alanine and DL-alanine were subjected to variational mode decomposition (VMD), whereby each spectral line was decomposed into four intrinsic mode functions (IMFs). Finally, a grid search-support vector machine (GS-SVM) classification recognition model was established for Raman spectral analysis and classification analysis using the first four IMFs as datasets. VMD-GS-SVM classification had a 96.67 % discriminative accuracy for predicted samples of the three isomers of D-alanine, L-alanine and DL-alanine. The classification recognition accuracy gradually trended downward as the number of mode components increased. The results show that the fast identification model of alanine isomers based on R-TDS and VMD-GS-SVM is effective and feasible, providing an accurate and fast optical measurement solution for the development of novel drugs and quality monitoring of fine chemicals.

A robust framework for enhancing cardiovascular disease risk prediction using an optimized category boosting model.

Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and it is of utmost importance to accurately assess the risk of cardiovascular disease for prevention and intervention purposes. In recent years, machine learning has shown significant advancements in the field of cardiovascular disease risk prediction. In this context, we propose a novel framework known as CVD-OCSCatBoost, designed for the precise prediction of cardiovascular disease risk and the assessment of various risk factors. The framework utilizes Lasso regression for feature selection and incorporates an optimized category-boosting tree (CatBoost) model. Furthermore, we propose the opposition-based learning cuckoo search (OCS) algorithm. By integrating OCS with the CatBoost model, our objective is to develop OCSCatBoost, an enhanced classifier offering improved accuracy and efficiency in predicting CVD. Extensive comparisons with popular algorithms like the particle swarm optimization (PSO) algorithm, the seagull optimization algorithm (SOA), the cuckoo search algorithm (CS), K-nearest-neighbor classification, decision tree, logistic regression, grid-search support vector machine (SVM), grid-search XGBoost, default CatBoost, and grid-search CatBoost validate the efficacy of the OCSCatBoost algorithm. The experimental results demonstrate that the OCSCatBoost model achieves superior performance compared to other models, with overall accuracy, recall, and AUC values of 73.67%, 72.17%, and 0.8024, respectively. These outcomes highlight the potential of CVD-OCSCatBoost for improving cardiovascular disease risk prediction.

Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed.Furthermore, it was shown that the ratio between α-helix and β-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra.The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

Mudflat surface sediment type mapping by remote sensing considering the effect of the chlorophyll-a content

Mudflats are critical interfaces between the marine and terrestrial environment of muddy coasts. Mapping mudflat surface sediment types and analyzing their dynamic changes are helpful to understand the variations of sedimentary environments and their responses to tidal current movements. To obtain the surface sediment types of the mudflats, this study first took the chlorophyll-a content as an environmental variable and combined it with satellite spectral reflectance data processed by fractional-order derivative (FOD) to establish a machine learning model for retrieving sediment component content (SCC) of the sand, silt, and clay. Then, the three SCCs were linear equilibrium corrected and inputted into Folk's ternary classification model, and the spatial distribution map of the sediment types was accurately obtained. The results showed that the use of the FODs for satellite image enhancement could provide richer spectral information and improve the accuracy of the chlorophyll-a content and SCCs inversed by the grid search-support vector machine (GS-SVM) model. When compared to direct spectral modeling, considering the effect of the chlorophyll-a content as the key input factor, the coefficients of determination (R2) for the sand, silt, and clay contents inversion were increased by 15.1, 9.2, and 38.2%, and the root mean square error (RMSE) were reduced by 9.7, 5.5, and 2.5%, respectively. Surface sediment type maps obtained from the three SCCs showed that the main sediments in the mudflats on the central coast of Jiangsu Province, China were silty sand and sandy silt. Between 2019 and 2021, the sediment types in 84.46% of the total area were unchanged. For the changing area, the sediment transitioned toward finer-grained types near the seawall of the coastal region. Whereas in the offshore area, especially along the edge of the huge tidal channels, the sediments tended to be coarser because of strong hydrodynamic screening. The findings will be helpful for improving the ability of mudflat sedimentation environmental monitoring and spatial resource management via remote sensing.

Estimation of soil salt content in the Bosten Lake watershed, Northwest China based on a support vector machine model and optimal spectral indices.

Low-cost and efficient dynamic monitoring of surface salinization information is critical in arid and semi-arid regions, we conducted a remote sensing inversion exercise for soil salinity in the Bosten Lake watershed in Xinjiang, Northwest China, with a total area of about 43,930 km2, a typical watershed in an arid area. Sentinel MSI and Landsat OLI data were combined with measured soil salinity data in July 2020, and optimal combination bands were selected based on characteristic bands to create a grid search-support vector machine (GS-SVM) inversion model of soil salt content. The maximum value of soil salt content in the Bosten Lake watershed was 11.8 g/kg. The minimum value was 0.41 g/kg, and the average value was 4.77 g/kg, soil salinization is serious. The results of previous studies were applied to the estimation of salt content in Bosten Lake watershed and could not meet the monitoring requirements of the study area, R2 < 0.3. The GS-SVM soil salinity monitoring model was established based on the optimal DI, RI, and NDI remote sensing indexes for the Bosten Lake watershed. After model verification, it was found that the optimal model of image data was the Landsat OLI first-derivative model with R2 of 0.64, RMSE of 3.12, and RPD of 1.64, indicating that the prediction ability of the model was high. We used the first-order derivative model of Landsat OLI data to map the soil salt content in the Bosten Lake watershed in arid area, and found that soil salt content in most of the study area was between 10 and 20 g/kg, indicating severe salinization. This study not only reveals the distribution characteristics of salinization in Bosten Lake watershed, but also provides a scientific basis for soil salinization monitoring in Central Asia to lay a foundation for further soil salinization monitoring in arid areas.

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters.

Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation. The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately. In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve. Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

Identification of multiple raisins by feature fusion combined with NIR spectroscopy.

Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source.

Gas chromatography-ion mobility spectrometric discrimination of trunk borer infested Platycladus orientalis using a novel topographic segmentation strategy

Gas chromatography coupled with ion mobility spectrometry (GC-IMS), a volatile analysis technique, has been widely used in the agricultural field over the past decade. However, complex three-dimensional (3D) fingerprint, including millions of data elements, causes significant challenges for its data process. This study proposed a novel topographic segmentation-based strategy, which could automatically extract the maxima and volume features from individual peaks. Compared with the manual marker selection approach, its efficiency was identified using different classification and prediction models based on trunk borer infested Platycladus orientalis samples. The grid search-support vector machine (GS-SVM) classifier combined with a topographic segmentation strategy could correctly classify at least 93.67% of P. orientalis samples into their corresponding infestation stages. The volume feature performed best, and its cross-validation classification accuracy could reach 97.05%. The PLSR prediction model based on the volume feature got the most satisfactory result, whose Rc2 = 0.9624 and RMSEC = 6.328 in calibration set and Rp2 = 0.9056 and RMSEP = 9.926 in validation set. In a word, our proposed topographic segmentation strategy had enough capability to extract local maxima and volume features of peaks from GC-IMS fingerprints. The GC-IMS-based approach combined with chemometric methods had the potential to discriminate the infestation stages of trunk borer damaged P. orientalis plants.

Behavioral responses of Platycladus orientalis plant volatiles to Phloeosinus aubei by GC-MS and HS-GC-IMS for discrimination of different invasive severity.

In recent years, the invasive cypress bark beetle (Phloeosinus aubei) has caused extensive damage to Platycladus orientalis plants in China, but its infestation is hard to monitor in the early stages. In this study, gas chromatography-mass spectrometry (GC-MS) was initially employed to investigate the volatile organic compound (VOC) emissions of P. aubei-infested P. orientalis saplings. The emissions of total sesquiterpenes were dominating (84-86% of total VOCs) and increased by 3.09-fold in P. aubei-damaged P. orientalis samples compared to undamaged samples, and the monoterpenes, aromatic compounds, and ketone emissions also had varying degrees of increase between 1.39-fold and 5.65-fold. Based on this variation, gas chromatography-ion mobility spectrometry (GC-IMS) was applied, as an untargeted analytical approach, to discriminate P. orientalis samples with different invasive severity. Two different features derived from GC-IMS data were adopted as the input information for classification and prediction models. Results showed that grid search support vector machine (GS-SVM) combined with multilinear principal component analysis (MPCA) based on spectral fingerprint achieved the best classification performances (> 88.98%), and partial least squares discriminant analysis (PLSR) method can accurately predict the pest numbers (R2 > 0.9423 and RMSE < 0.9827). In a word, the VOC profiling-based approach had the potential for evaluating P. aubei invasive severity and pest management.

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study.

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 0.3mm. 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. Graphical Abstract.

Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms.

Breast cancer screening is time consuming, requires expensive equipment, and has demanding requirements for doctors. Hence, a large number of breast cancer patients may miss screening and early treatment, which greatly threatens their health around the world. Infrared spectroscopy may be able to be used as a screening tool for breast cancer detection. Fourier transform infrared (FT-IR) spectroscopy of serum was combined with traditional machine learning algorithms to achieve an auxiliary diagnosis that could quickly and accurately distinguish patients with different stages of breast cancer, including stage 1 disease, from control subjects without breast cancer. FT-IR spectroscopy were performed on the serum of 114 non-cancer control subjects, 35 patients with stage I, 43 patients with stage II, and 29 patients with stage III & IV breast cancer. Due to the experimental sample imbalance, we used the oversampling to process the four classes of sample. The oversampling selected Synthetic Minority Oversampling Technique (SMOTE). Subsequently, we used the random discarding method in undersampling to do experiments as well. The average FT-IR spectroscopy results for the four groups showed differences in phospholipids, nucleic acids, lipids, and proteins between non-cancer control subjects and breast cancer patients at different stages. Based on these differences, four classification models were used to classify stage I, II, III & IV breast cancer patients and non-cancer control subjects. First, standard normal variate transformation (SNV) was used to preprocess the original data, and then partial least squares (PLS) was used for feature extraction. Finally, the five models were established including extreme learning machine (ELM), k-nearest neighbor (KNN), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM) and grid search-support vector machine (GS-SVM). In oversampling experiment, the GS-SVM classifier obtained the highest average classification accuracy of 95.45 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 90 %; breast cancer stage II was 84.62 %; and breast cancer stage III & IV was 100 %. In undersampling experiment, the GA-SVM model obtained the highest average classification accuracy of 100 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 100 %; breast cancer stage II was 100 %; and breast cancer stage III & IV was 100 %. The results show that FT-IR spectroscopy combined with powerful classification algorithms has great potential in distinguishing patients with different stages of breast cancer from non-cancer control subjects. In addition, this research provides a reference for future multiclassification studies of cervical cancer, ovarian cancer and other female high-incidence cancers through serum FT-IR spectroscopy.

Discrimination of alcohol dependence based on the convolutional neural network.

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.

Application of terahertz spectrum and interval partial least squares method in the identification of genetically modified soybeans.

Genetically modified soybeans are the world's most important genetically modified agricultural product. At present, the traditional methods for identifying genetically modified and non-transgenic soybeans are time-consuming, costly, and complicated to operate, which cannot meet the needs of practical applications. Therefore, it is necessary to discover a fast and accurate method for identifying transgenic soybeans. Terahertz (THz) time domain spectra were collected in sequence from 225 transgenic and non-transgenic soybean samples. Fourier transform was used to convert the terahertz time domain spectrum into a THz frequency domain spectrum with a frequency range of 0.1-2.5THz. Firstly, the interval partial least squares (iPLS) method was used to remove interference spectral bands and select appropriate spectral intervals. Secondly, 168 samples were selected as the calibration set. Discriminant partial least squares (DPLS), Grid Search support vector machine (Grid Search-SVM) and principal component analysis back propagation neural network (PCA-BPNN) were used to establish a qualitative identification model. Afterwards, 57 test set samples were predicted. By comparing the experimental results, it was found that iPLS could effectively screen and remove the interference THz band, which was more helpful to improve the efficiency and accuracy of the identification model. After the iPLS and mean center pre-treatment technology, the Grid Search-SVM identification model had the best identification effect, with a total accuracy rate of 98.25% (transgenic identification rate was 96.15%, non-transgenic identification rate was 100%). This study shows that after selecting spectra from iPLS, THz spectroscopy combined with chemometrics can more accurately, quickly, and efficiently identify transgenic and non-transgenic soybeans.

Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure.

Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples.

Steel corrosion prediction based on support vector machines

• The characteristics of sections of corroded bars are quantified to predict sectional corrosion rate of steel. • Seven digital parameters are innovatively proposed to define corroded bar shapes. • The SVM can be used to predict the sectional corrosion rate of steel. • PSO-SVM is more accurate than GS-SVM for prediction sectional corrosion rate of steel. In this paper, the 3D coordinate data of the corrosion condition of rebar are obtained by a 3D scanning method. Seven numerical parameters, such as the roundness, the section roughness, the inscribed circle radius/circumscribed circle radius and the eccentricity, are obtained by the numerical calculation method. These seven parameters are used to characterize the cross-section morphology of rusted steel bars. The particle swarm optimization support vector machine (PSO-SVM) and the grid search support vector machine (GS-SVM) are used to calculate these seven cross-section digitization parameters to predict the sectional corrosion rate of steel. This work concluded that these two optimization support vector machine (SVM) methods can accurately predict the sectional corrosion rate of steel. Compared with the GS-SVM model, the PSO-SVM steel corrosion prediction model is more accurate.

Evaluation of trunk borer infestation duration using MOS E-nose combined with different feature extraction methods and GS-SVM

Trunk borer cause serious damage to plants and it is hard to detect due to larvae mine inside. In this study, E-nose combined with gas chromatography and mass spectrometry (GC–MS) was employed to evaluate Semanotus bifasciatus infestation duration at 0 d, 30 d, 60 d and 90 d. GC–MS result indicated that the most abundant components were 3-carene, α-pinene, β-phellandrene, sabinene and longifolene. The correlation between E-nose sensor responses and VOCs was analyzed by ANNOVA-Partial least square regression (APLSR). Six different features derived from E-nose data were analyzed by principle components analysis (PCA) and grid search-support vector machine (GS-SVM) was used as an optimized classifier to discriminate pest infestation duration based on different feature dataset. The classification results based on wavelet entropy (WE) showed preferable classification performances in both calibration set (100%) and validation set (100%). GS-SVM was also applied to predict S. bifasciatus infestation duration. Result showed that the fitting correlation coefficients (R2) value of calibration set and validation set were 0.9987 and 0.9980, while the root mean square error (RMSE) of which were 0.4506 and 0.4961, respectively. It could be concluded that E-nose is a potential technique for evaluating trunk borer infestation and pest management.

Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid-infrared (FT-MIR) spectrometry combined with multivariate statistical analysis.

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time‐consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid‐infrared (FT‐MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t‐distributed stochastic neighbor embedding based on mid‐level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS‐DA models was mid‐level data fusionq (100%) > mid‐level data fusione (97.06%) = mid‐level data fusionv (97.06%) = stipes (97.06%) > low‐level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid‐search support vector machine models was low‐level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support vector machine was low‐level data fusion (100%) > caps (97.06%) > stipes (88.24%). The mid‐level data fusionq and low‐level data fusion had best discrimination accuracy (100%) allowing each mushroom classed into its real species, which could be used for accurate discrimination of samples. B. edulis mushrooms had highest total polyphenol, with 14.76 mg/g dw and 17.33 in caps and stipes mg/g dw, respectively. The phenols were easier to accumulate in the caps in Leccinum rugosiceps (1.03) and B. tomentipes (1.19), and the opposite phenomenon is observed in B. edulis (0.85) and B. umbriniporus (0.95). The correlation coefficient and residual predictive deviation of best prediction model were 86.76% and 2.40%, respectively, indicating that that there is good relevance between FT‐MIR and total polyphenol content, which could be used to predict roughly polyphenols content in mushrooms.

Machinery Fault Diagnosis Scheme Using Redefined Dimensionless Indicators and mRMR Feature Selection

Machinery fault diagnosis methods based on dimensionless indicators have long been studied. However, traditional dimensionless indicators usually suffer a low diagnostic accuracy for mechanical components. Toward this end, an effective fault diagnosis method based on redefined dimensionless indicators (RDIs) and minimum redundancy maximum relevance (mRMR) is proposed to identify the health conditions of mechanical components. In the proposed method, the vibration signals are first processed by the variational mode decomposition, and multiple RDIs are constructed based on the decomposed signals. Subsequently, the mRMR approach is introduced to select the RDIs and several important RDIs can be obtained. Finally, the obtained RDIs are fed into a grid search support vector machine to perform fault pattern identification. To verify the superiority of the proposed method, two experimental examples for different fault types of mechanical components including rolling bearing and gearbox are conducted. The experimental results demonstrated that the RDIs as new fault features can effectively solve the deficiency of the traditional dimensionless indicator, and has stronger distinguishing ability for machinery faults. Additionally, our proposed method successfully differentiated 12 fault conditions of rolling bearings and nine fault conditions of gears with average accuracies of 97.47% and 97.12% with 11 and 5 RDIs, respectively.

Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms

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.