scholarly journals Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Foods ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2983
Author(s):  
Yifei Zhang ◽  
Xuhai Yang ◽  
Zhonglei Cai ◽  
Shuxiang Fan ◽  
Haiyun Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Support Vector ◽  
Detection Accuracy ◽  
Success Rates ◽  
Online Detection ◽  
Physiological Disorder ◽  
Full Spectrum ◽  
Band Ratio ◽  
Discrimination Model ◽  
Svm Model

Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

scholarly journals A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Jiang Wu ◽  
Yanju Ji ◽  
Ling Zhao ◽  
Mengying Ji ◽  
Zhuang Ye ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Analysis Method ◽  
Data Set ◽  
Specificity And Sensitivity ◽  
Svm Model ◽  
Tof Ms

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data.Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity.Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively.Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals Classification of Rice Leaf Blast Severity Using Hyperspectral Imaging

2021 ◽  
Author(s):  
Guosheng Zhang ◽  
Tongyu Xu ◽  
Youwen Tian ◽  
Shuai Feng ◽  
Dongxue Zhao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Reflectance ◽  
Support Vector ◽  
Growth Stages ◽  
Leaf Blast ◽  
Full Spectrum ◽  
Rice Leaf ◽  
Svm Model ◽  
Rice Leaf Blast ◽  
Different Growth Stages

Abstract Background: Hyperspectral imaging is an emerging technology applied in plant disease research, including disease detection, multiple disease identification, disease severity assessment, and disease resistance evaluation. Rice leaf blast is prevalent all over the world and is a serious threat to rice yield and quality. In this paper, the standard deviation (STD) of the spectral reflectance of whole leaves was calculated and a support vector machine (SVM) model was built to classify the degree of rice leaf blast at different growth stages.Results: The classification accuracy of the full-spectrum-based SVM model at jointing stage, booting stage and heading stage was 94.44%, 81.58% and 80.48%, respectively. The corresponding macro recall values were 0.9714, 0.715 and 0.79. The average STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also those with the same disease level. Conclusion: The STD of the spectral reflectance of whole leaf could be utilized to classify the rice leaf blast degree at different growth stages. The classification method was derived from physiological phenomena that were visible to the naked eye, making it more intuitive and convincing.

scholarly journals SHIP DETECTION BASED ON MULTIPLE FEATURES IN RANDOM FOREST MODEL FOR HYPERSPECTRAL IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 891-895
Author(s):  
N. Li ◽  
L. Ding ◽  
H. Zhao ◽  
J. Shi ◽  
D. Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Near Infrared ◽  
Texture Features ◽  
Short Wave ◽  
Hyperspectral Images ◽  
Support Vector ◽  
High Signal ◽  
Detection Accuracy ◽  
Multiple Features ◽  
Single Feature

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Nondestructive Determination of Citric Acid Using Successive Projections Algorithm and Spectroscopic Techniques

2011 ◽  
Vol 460-461 ◽  
pp. 9-14
Author(s):  
Fei Liu ◽  
Yong He
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Square ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Optimal Prediction ◽  
Successive Projections Algorithm ◽  
Svm Model ◽  
Successive Projections

Successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.

Variable Selection in Proteomic Profile Classification by Interval Support Vector Machines (iSVM)

2014 ◽  
Vol 556-562 ◽  
pp. 347-350
Author(s):  
Xiao Li Yang ◽  
Huan Yun He
Keyword(s):  
Support Vector Machine ◽  
Variable Selection ◽  
Support Vector ◽  
Proteomic Profile ◽  
Error Level ◽  
Full Spectrum ◽  
Local Modeling ◽  
Svm Model ◽  
Vector Machines ◽  
Selection For

For variable selection in proteomic profile classification, we present a new local modeling procedure called interval support vector machine (iSVM). This procedure builds a series of SVM models in a window that moves over the whole spectral region and then locates useful spectral intervals in terms of the least complexity of SVM models reaching a desired error level. We applied iSVM in variable selection for proteomic profile classification. The results show that the proposed procedure are very promising for classification target-based variable selection and obtain much better classification than full-spectrum SVM model.

scholarly journals Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3222 ◽  
Author(s):  
Di Wang ◽  
Lin Xie ◽  
Simon Yang ◽  
Fengchun Tian
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Near Infrared ◽  
Spectral Response ◽  
Sensor Data ◽  
Support Vector ◽  
True Negative ◽  
Svm Model ◽  
Tobacco Cultivation ◽  
Effective Principal

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.

Spectroscopy quantitative analysis cotton content of blend fabrics

2016 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xudong Sun ◽  
Mingxing Zhou ◽  
Yize Sun
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Svm Model ◽  
Calibration Methods ◽  
Learning Machine ◽  
Mean Square Errors

Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively. Findings – The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics. Originality/value – It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

CWT-Support Vector Regression Model and Its Application

2010 ◽  
Vol 113-116 ◽  
pp. 207-210
Author(s):  
Jie Fang Liu ◽  
Pu Mei Gao ◽  
Bao Lin Ma
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Support Vector ◽  
Traditional Learning ◽  
Risk Minimization ◽  
Support Vector Regression Model ◽  
Analytical Technique ◽  
Learning Machines ◽  
Empirical Risk ◽  
Svm Model

Near-infrared spectroscopy (NIR) analytical technique is simple, fast and low cost, making neither pollution nor damage to the samples, and can determine many components simultaneously. Continuous wavelet transform (CWT), as an application direction of the wavelet analysis, is keener to the signal slight change. Support vector machine (SVM) is based on the principle of structural risk minimization, which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization. In this paper, we use CWT- SVM model to predict meat’s component. Compared with Partial Least Squares (PLS) and SVR, we get more satisfactory result.

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