Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

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Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis

International Journal of Food Engineering ◽

10.1515/ijfe-2019-0386 ◽

2020 ◽

Vol 16 (8) ◽

Author(s):

Haoran Li ◽

Tianhong Pan ◽

Yuqiang Li ◽

Shan Chen ◽

Guoquan Li

Keyword(s):

Principal Component Analysis ◽

Near Infrared ◽

Scatter Correction ◽

Principal Component ◽

Component Analysis ◽

Detection Algorithm ◽

Functional Principal Component Analysis ◽

Support Vector ◽

Edible Fungi ◽

Functional Principal Component

AbstractTricholoma matsutakeis (TM) is the most expensive edible fungi in China. Given its price and exclusivity, some dishonest merchants will sell adulterated TM by combining it with cheaper fungi in an attempt to earn more profits. This fraudulent behavior has broken food laws and violated consumer trust. Therefore, there is an urgent need to develop a rapid, accurate, and nondestructive tool to discriminate TM from other edible fungi. In this work, a novel detection algorithm combined with near-infrared spectroscopy (NIR) and functional principal component analysis (FPCA) is proposed. Firstly, the raw NIR data were pretreated by locally weighted scatterplot smoothing (LOWESS) and multiplication scatter correction (MSC). Then, FPCA was used to extract valuable information from the preprocessed NIR data. Then, a classifier was designed by using the least-squares support-vector machine (LS-SVM) to distinguish categories of edible fungi. Furthermore, the one-versus-one (OVO) strategy was included and the binary LS-SVM was extended to a multi-class classifier. The 166 samples of four varieties of fungi were used to validate the proposed method. The results show that the proposed method has great capability in near infrared spectra classification, and the average accurate of FPCA-LSSVM is 97.3% which is greater than that of PCA-LSSVM (93.5%).

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Black Tea Samples Origin Discrimination Using Analytical Investigations of Secondary Metabolites, Antiradical Scavenging Activity and Chemometric Approach

Molecules ◽

10.3390/molecules23030513 ◽

2018 ◽

Vol 23 (3) ◽

pp. 513 ◽

Cited By ~ 16

Author(s):

Wojciech Koch ◽

Wirginia Kukula-Koch ◽

Łukasz Komsta

Keyword(s):

Antioxidant Properties ◽

Radical Scavenging ◽

Principal Component ◽

Black Tea ◽

Epicatechin Gallate ◽

Tea Infusions ◽

Chemometric Approach ◽

Dpph Scavenging ◽

Dpph Radical Scavenging ◽

Comprehensive Study

A comprehensive study on the composition and antioxidant properties of black tea samples with a chemometric approach was performed via LC-ESI-Q-TOF-MS, DPPH radical scavenging assay, and Folin–Ciocalteu assay (TPC). Marked differences between the teas from seven different countries (China, India, Iran, Japan, Kenya, Nepal, Sri Lanka) were shown. The Indian samples demonstrated the highest total catechin content (184.8 mg/100 mL), the largest TPC and DPPH scavenging potential (58.2 mg/100 mL and 84.5%, respectively). The applied principal component analysis (PCA) and ANOVA revealed several correlations between the level of catechins in tea infusions. EC (epicatechin), ECG (epicatechin gallate), EGC (epigallocatechin), and EGCG (epigallocatechin-3-gallate) content was not correlated with DPPH, gallic acid, and TPC; however, a strong correlation of EC and ECG between themselves and a negative correlation of these two catechins with EGCG and EGC was noted. Interestingly, simple catechins were not found to be responsible for antioxidant properties of the black teas. The samples collected in the higher altitudes were similar.

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Weight-Selected Attribute Bagging for Credit Scoring

Mathematical Problems in Engineering ◽

10.1155/2013/379690 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Jianwu Li ◽

Haizhou Wei ◽

Wangli Hao

Keyword(s):

Credit Risk ◽

Prediction Accuracy ◽

Financial Risk ◽

Evaluation Method ◽

Credit Scoring ◽

Principal Component ◽

Support Vector ◽

Training Samples ◽

Attribute Evaluation ◽

Bagging Method

Assessment of credit risk is of great importance in financial risk management. In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk. Weights of attributes are first computed using attribute evaluation method such as linear support vector machine (LSVM) and principal component analysis (PCA). Subsets of attributes are then constructed according to weights of attributes. For each of attribute subsets, the larger the weights of the attributes the larger the probabilities by which they are selected into the attribute subset. Next, training samples and test samples are projected onto each attribute subset, respectively. A scoring model is then constructed based on each set of newly produced training samples. Finally, all scoring models are used to vote for test instances. An individual model that only uses selected attributes will be more accurate because of elimination of some of redundant and uninformative attributes. Besides, the way of selecting attributes by probability can also guarantee the diversity of scoring models. Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods.

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Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832014000200002 ◽

2014 ◽

Vol 38 (2) ◽

pp. 372-385 ◽

Cited By ~ 12

Author(s):

Rodnei Rizzo ◽

José A. M. Demattê ◽

Fabrício da Silva Terra

Keyword(s):

Spectral Data ◽

Principal Components ◽

Visual Analysis ◽

Numerical Technique ◽

Correlation Coefficients ◽

Principal Component ◽

Soil Classification ◽

Numerical Classification ◽

Similarity Coefficients

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

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Identification of dominant sources of sea level pressure for precipitation forecasting over Wales

Journal of Hydroinformatics ◽

10.2166/hydro.2012.110 ◽

2012 ◽

Vol 15 (3) ◽

pp. 1002-1021 ◽

Cited By ~ 6

Author(s):

Azadeh Ahmadi ◽

Dawei Han

Keyword(s):

General Circulation ◽

Large Scale ◽

Circulation Model ◽

Principal Component ◽

Support Vector ◽

Monthly Precipitation ◽

Climate Variables ◽

Forward Selection ◽

Precipitation Forecasting ◽

Variable Combination

Downscaling methods are utilized to assess the effects of large scale atmospheric circulation on local hydrological variables such as precipitation and runoff. In this paper, a methodology of statistical downscaling using a support vector machine (SVM) approach is presented to simulate and predict the precipitation using general circulation model (GCM) data. Due to the complexity and issues related to finding a relationship between the large scale climatic parameters and local precipitation, the climate variables (predictors) affecting monthly precipitation variations over Wales are identified using a combination of the methods including the principal component analysis (PCA), fuzzy clustering, backward selection, forward selection, and Gamma test (GT). The effectiveness of those tools is illustrated through their implementations in the case study. It has been found that although the GT itself fails to identify the best input variable combination, it provides useful and narrowed-down options for further exploration. The best input variable combination is achieved by the GT and forward selection method. This approach can be a useful way for assessing the impacts of climate variables on precipitation forecasting.

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Multi-Classification Combination Algorithm Based on Logit Model and Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.734-737.2978 ◽

2013 ◽

Vol 734-737 ◽

pp. 2978-2982 ◽

Cited By ~ 1

Author(s):

Xin Lei Zhang ◽

Meng Gang Li ◽

Zuo Quan Zhang

Keyword(s):

Principal Component Analysis ◽

Support Vector Machine ◽

Regression Analysis ◽

Logit Model ◽

Prediction Accuracy ◽

Principal Component ◽

Composite Indicator ◽

Support Vector ◽

Logit Regression ◽

Multi Classification

According to the basic theories of Logit regression analysis and support vector machine, this article involves improved multi-classification combination algorithm. When applying this model, there are some innovations. First, choose optimized composite indicator as a variable through principal component analysis and get more information. Second, introduce Logit parameter model to the quadratic to increase prediction accuracy. Third, put forward a multi-classification combination model of improved Logit model with SVM to increase prediction accuracy.

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Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

Applied Sciences ◽

10.3390/app9194119 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4119 ◽

Cited By ~ 9

Author(s):

Yidan Bao ◽

Chunxiao Mi ◽

Na Wu ◽

Fei Liu ◽

Yong He

Keyword(s):

Hyperspectral Imaging ◽

Large Scale ◽

Scatter Correction ◽

Principal Component ◽

Support Vector ◽

Wheat Grain ◽

Successive Projections Algorithm ◽

Accurate Identification ◽

Wheat Varieties

The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry.

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Prediction of tRNA Based on LS-SVM Algorithm with Principal Component Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.461.753 ◽

2012 ◽

Vol 461 ◽

pp. 753-756

Author(s):

Chong Xing ◽

Yao Wang ◽

You Zhou ◽

Yan Chun Liang

Keyword(s):

Principal Component Analysis ◽

Prediction Accuracy ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Single Nucleotide ◽

Non Coding Rna ◽

Svm Algorithm ◽

The One ◽

Prediction Strategy

Recently, non-coding RNA prediction is the one of the most important researches in bioinformatics. In this paper, on the basis of principal component analysis, we present a tRNA prediction strategy by using least squares support vector machine (LS-SVM). Appearance frequencies of single nucleotide, 2 – nucleotides and (G-C) %, (A-T) % were chosen as characteristics inputs. Results from tests showed that the prediction accuracy was 90.51% on prokaryotic tRNA dataset. Experimental results indicate that the method is effective for prokaryotic ncRNA prediction.

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Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq140418039s ◽

2015 ◽

Vol 21 (3) ◽

pp. 379-390 ◽

Cited By ~ 3

Author(s):

Saeid Shokri ◽

Mohammad Sadeghi ◽

Mahdi Marvast ◽

Shankar Narasimhan

Keyword(s):

Support Vector Regression ◽

Sulfur Content ◽

Vector Quantization ◽

Prediction Accuracy ◽

Principal Component ◽

Component Analysis ◽

Test Time ◽

Support Vector ◽

Predicting Performance ◽

Input Variables

An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS) process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR) was developed and the effects of integrating Vector Quantization (VQ) with Principle Component Analysis (PCA) were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR) was better than (PCA-SVR) in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995) in comparison with investigated models.

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Immovable Cultural Relics Disease Prediction Based on Relevance Vector Machine

Mathematical Problems in Engineering ◽

10.1155/2020/9369781 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Bao Liu ◽

Kun Mu ◽

Fei Ye ◽

Jun Deng ◽

Jingting Wang

Keyword(s):

Prediction Accuracy ◽

Principal Component ◽

Relevance Vector Machine ◽

Likelihood Method ◽

Support Vector ◽

Disease Prediction ◽

Key Factors ◽

Factors Affecting ◽

Prediction Approach ◽

Cultural Relics

The preventive cultural relics protection is one of the most concerned contents in archaeology, which includes environmental monitoring and accurate prediction of cultural relics diseases. In view of the deficiency of the analysis of cultural relics data and the prediction of cultural relics diseases, a prediction model of immovable cultural relics diseases based on relevance vector machine (RVM) is proposed. The key factors affecting the disease of immovable cultural relics are found out by the principal component analysis method, and the dimension reduction of data is realized; then, the RVM model under the framework of Bayesian theory is constructed, and the super parameters are estimated by the maximum edge likelihood method; finally, the prediction accuracy of the model is compared with the traditional diseases prediction methods. The experiment results demonstrate that the proposed RVM-based immovable cultural relics disease prediction approach not only has the advantages of more sparse model but also has better prediction accuracy than the traditional radial basis function neural network-based and support vector machine-based methods.

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