Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform.
ABSTRACT: In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages-all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R² = 0.9942) worked much better than PLSR.
Project description:The accurate and noninvasive preoperative prediction of the state of the axillary lymph nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this study, we analyzed the possibility of axillary lymph node metastasis directly based on Magnetic Resonance Imaging (MRI) of the breast in cancer patients. After mass segmentation and feature analysis, the SVM, KNN, and LDA three classifiers were used to distinguish the axillary lymph node state in 5-fold cross-validation. The results showed that the effect of the SVM classifier in predicting breast axillary lymph node metastasis was significantly higher than that of the KNN classifier and LDA classifier. The SVM classifier performed best, with the highest accuracy of 89.54%, and obtained an AUC of 0.8615 for identifying the lymph node status. Each feature was analyzed separately and the results showed that the effect of feature combination was obviously better than that of any individual feature on its own.
Project description:In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH of paddy soils in Zhejiang province, China. Firstly, the linear partial least squares regression (PLSR) was performed on the in situ spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the non-linear least-square support vector machine (LS-SVM) algorithm was carried out aiming to extract more useful information from the in situ spectra and improve predictions. Results show that in terms of OC, OM, TN, AN and pH, (i) the predictions were worse using in situ spectra compared to laboratory-based spectra with PLSR algorithm (ii) the prediction accuracy using LS-SVM (R2>0.75, RPD>1.90) was obviously improved with in situ vis-NIR spectra compared to PLSR algorithm, and comparable or even better than results generated using laboratory-based spectra with PLSR; (iii) in terms of AP and AK, poor predictions were obtained with in situ spectra (R2<0.5, RPD<1.50) either using PLSR or LS-SVM. The results highlight the use of LS-SVM for in situ vis-NIR spectroscopic estimation of soil properties of paddy soils.
Project description:Objective: The purpose of the current study is to investigate whether texture analysis-based machine learning algorithms could help devise a non-invasive imaging biomarker for accurate classification of meningiomas using machine learning algorithms.Method: The study cohort was established from the hospital database by reviewing the medical records. Patients were selected if they underwent meningioma resection in the neurosurgery department between January 2015 and December 2018. A total number of 40 texture parameters were extracted from pretreatment postcontrast T1-weighted (T1C) images based on six matrixes. Three feature selection methods were adopted, namely, distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Multiclass classification methods of linear discriminant analysis (LDA) and support vector machine (SVM) algorithms were employed to establish the classification models. The diagnostic performances of models were evaluated with confusion matrix based on which the areas under the curve, accuracy, and Kappa value of models were calculated.Result: Confusion matrix showed that the LDA-based models represented better diagnostic performances than SVM-based models. The highest accuracy among LDA-based models was 75.6%, shown in the combination of Lasso + LDA. The optimal models for SVM-based models was Lasso+SVM, with accuracy of 59.0% in the testing group. One of the SVM-based models, GBDT+SVM, was overfitting, suggesting that this model was not suitable for application.Conclusion: Machine learning algorithms with texture features extracted from T1C images could potentially serve as the assistant imaging biomarkers for presurgically grading meningiomas.
Project description:The aim of this study is to determine the performance of a lab-made electronic nose (e-nose) composed of an array of metal oxide semiconductor (MOS) gas sensors in the detection and differentiation of Listeria monocytogenes (L. monocytogenes) and Bacillus cereus (B. cereus) incubated in trypticsoy broth (TSB) media. Conventionally, the detection of L. monocytogenes and B. cereus is often performed by enzyme link immunosorbent assay (ELISA) and polymerase chain reaction (PCR). These techniques require trained operators and expert, expensive reagents and specific containment. In this study, three types of samples, namely, TSB media, L. monocytogenes (serotype 4b American Type Culture Collection (ATCC) 13792), and B. cereus (ATCC) 10876, were used for this experiment. Prior to measurement using the e-nose, each bacterium was inoculated in TSB at 1 × 103-104 CFU/mL, followed by incubation for 48 h. To evaluate the performance of the e-nose, the measured data were then analyzed with chemometric models, namely linear and quadratic discriminant analysis (LDA and QDA), and support vector machine (SVM). As a result, the e-nose coupled with SVM showeda high accuracy of 98% in discriminating between TSB media and L. monocytogenes, and between TSB media and B. cereus. It could be concluded that the lab-made e-nose is able to detect rapidly the presence of bacteria L. monocytogenes and B. cereus on TSB media. For the future, it could be used to identify the presence of L. monocytogenes or B. cereus contamination in the routine and fast assessment of food products in animal quarantine.
Project description:One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.
Project description:Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient (R) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx.The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx. Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx. Abbreviations used: 1st der: First-order derivative; 2nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection.
Project description:Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, whereas a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion. We found that there was a significant difference between ERP trials with positive and negative emotions of around 170 ms at the occipitotemporal electrodes (i.e., N170). Then, we further proposed the application of the single-trial N170 as a feature for the classification of facial emotion, which could avoid the fact that ERPs were obtained by averaging most of the time while ignoring the trial-to-trial variation. In order to find an optimal classifier for emotional classification with single-trial N170 as a feature, three types of classifiers, namely, linear discriminant analysis (LDA), L1-regularized logistic regression (L1LR), and support vector machine with radial basis function (RBF-SVM), were comparatively investigated. The results showed that the single-trial N170 could be used as a classification feature to successfully distinguish positive emotion from negative emotion. L1-regularized logistic regression classifiers showed a good generalization, whereas LDA showed a relatively poor generalization. Moreover, when compared with L1LR, the RBF-SVM required more time to optimize the parameters during the classification, which became an obstacle while applying it to the online operating system of brain-computer interfaces (BCIs). The findings suggested that face-related N170 could be affected by facial expression and that the single-trial N170 could be a biomarker used to monitor the emotional states of subjects for the BCI domain.
Project description:It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by <i>Penicillium expansum</i>. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: R<sub>c</sub> = 0.953, root mean square error of calibration (RMSEC) = 1.28, R<sub>p</sub> = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.
Project description:Purpose: The aim of this study was to test whether radiomics-based machine learning can enable the better differentiation between glioblastoma (GBM) and anaplastic oligodendroglioma (AO). Methods: This retrospective study involved 126 patients histologically diagnosed as GBM (n = 76) or AO (n = 50) in our institution from January 2015 to December 2018. A total number of 40 three-dimensional texture features were extracted from contrast-enhanced T1-weighted images using LIFEx package. Six diagnostic models were established with selection methods and classifiers. The optimal radiomics features were separately selected into three datasets with three feature selection methods [distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT)]. Then datasets were separately adopted into linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Specificity, sensitivity, accuracy, and area under curve (AUC) of each model were calculated to evaluate their diagnostic performances. Results: The diagnostic performance of machine learning models was superior to human readers. Both classifiers showed promising ability in discrimination with AUC more than 0.900 when combined with suitable feature selection method. For LDA-based models, the AUC of models were 0.986, 0.994, and 0.970 in the testing group, respectively. For the SVM-based models, the AUC of models were 0.923, 0.817, and 0.500 in the testing group, respectively. The over-fitting model was GBDT + SVM, suggesting that this model was too volatile that unsuitable for classification. Conclusion: This study indicates radiomics-based machine learning has the potential to be utilized in clinically discriminating GBM from AO.
Project description:In recent years, electronic nose (e-nose) systems have become a focus method for diagnosing pulmonary diseases such as lung cancer. However, principles and patterns of sensor responses in traditional e-nose systems are relatively homogeneous. Less study has been focused on type-different sensor arrays. In this paper, we designed a miniature e-nose system using 14 gas sensors of four types and its subsequent analysis of 52 breath samples. To investigate the performance of this system in identifying and distinguishing lung cancer from other respiratory diseases and healthy controls, five feature extraction algorithms and two classifiers were adopted. Lastly, the influence of type-different sensors on the identification ability of e-nose systems was analyzed. Results indicate that when using the LDA fuzzy 5-NN classification method, the sensitivity, specificity and accuracy of discriminating lung cancer patients from healthy controls with e-nose systems are 91.58%, 91.72% and 91.59%, respectively. Our findings also suggest that type-different sensors could significantly increase the diagnostic accuracy of e-nose systems. These results showed e-nose system proposed in this study was potentially practicable in lung cancer screening with a favorable performance. In addition, it is important for type-different sensors to be considered when developing e-nose systems.