scholarly journals Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Madiha Bougrini ◽  
Khalid Tahri ◽  
Zouhair Haddi ◽  
Tarik Saidi ◽  
Nezha El Bari ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Electronic Tongue ◽  
Principal Component ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Pattern Recognition Methods ◽  
Argan Oil ◽  
Vector Machines ◽  
Voltammetric Electronic Tongue ◽  
Ensure Product Quality

Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. To the best of our knowledge, this is the first attempt to demonstrate whether the combined e-nose and e-tongue technologies could be successfully applied to the detection of adulteration of argan oil.

scholarly journals Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee

Sensors ◽  
2014 ◽  
Vol 14 (9) ◽  
pp. 17770-17785 ◽  
Author(s):  
Rocio Domínguez ◽  
Laura Moreno-Barón ◽  
Roberto Muñoz ◽  
Juan Gutiérrez
Keyword(s):  
Support Vector Machines ◽  
Electronic Tongue ◽  
Support Vector ◽  
Vector Machines ◽  
Voltammetric Electronic Tongue

scholarly journals Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Haifeng Sun ◽  
Ling Wang ◽  
Shun Wang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Near Infrared ◽  
Scatter Correction ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Gelatin Gels

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines

2009 ◽  
Vol 119 (1-2) ◽  
pp. 32-38 ◽  
Author(s):  
Paula Martiskainen ◽  
Mikko Järvinen ◽  
Jukka-Pekka Skön ◽  
Jarkko Tiirikainen ◽  
Mikko Kolehmainen ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Three Dimensional ◽  
Behaviour Pattern ◽  
Support Vector ◽  
Vector Machines

scholarly journals Evaluation of Feature Selection Techniques in a Multifrequency Large Amplitude Pulse Voltammetric Electronic Tongue

2020 ◽  
Vol 2 (1) ◽  
pp. 62
Author(s):  
Luis F. Villamil-Cubillos ◽  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Diego A. Tibaduiza
Keyword(s):  
Feature Selection ◽  
Large Amplitude ◽  
Electronic Tongue ◽  
Supervised Machine Learning ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Variance Filter ◽  
Supervised Machine Learning Classifiers ◽  
Voltammetric Electronic Tongue ◽  
Feature Selection Techniques

An electronic tongue is a device composed of a sensor array that takes advantage of the cross sensitivity property of several sensors to perform classification and quantification in liquid substances. In practice, electronic tongues generate a large amount of information that needs to be correctly analyzed, to define which interactions and features are more relevant to distinguish one substance from another. This work focuses on implementing and validating feature selection methodologies in the liquid classification process of a multifrequency large amplitude pulse voltammetric (MLAPV) electronic tongue. Multi-layer perceptron neural network (MLP NN) and support vector machine (SVM) were used as supervised machine learning classifiers. Different feature selection techniques were used, such as Variance filter, ANOVA F-value, Recursive Feature Elimination and model-based selection. Both 5-fold Cross validation and GridSearchCV were used in order to evaluate the performance of the feature selection methodology by testing various configurations and determining the best one. The methodology was validated in an imbalanced MLAPV electronic tongue dataset of 13 different liquid substances, reaching a 93.85% of classification accuracy.

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

Pattern recognition of enrichment levels of SELEX-based candidate aptamers for human C-reactive protein

2017 ◽  
Vol 62 (3) ◽  
Author(s):  
Xinliang Yu ◽  
Ruqin Yu ◽  
Xiaohai Yang
Keyword(s):  
Pattern Recognition ◽  
Latent Variables ◽  
Molecular Descriptors ◽  
Principal Component ◽  
Support Vector ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Systematic Evolution ◽  
Model C ◽  
Exponential Enrichment

AbstractSelecting aptamers for human C-reactive protein (CRP) would be of critical importance in predicting the risk for cardiovascular disease. The enrichment level of DNA aptamers is an important parameter for selecting candidate aptamers for further affinity and specificity determination. This paper is the first report on pattern recognition used for CRP aptamer enrichment levels in the systematic evolution of ligands by exponential enrichment (SELEX) process, by applying structure-activity relationship models. After generating 10 rounds of graphene oxide (GO)-SELEX and 1670 molecular descriptors, eight molecular descriptors were selected and five latent variables were then obtained with principal component analysis (PCA), to develop a support vector classification (SVC) model. The SVC model (C=8.1728 and

scholarly journals Cascade Support Vector Machines with Dimensionality Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Oliver Kramer
Keyword(s):  
Support Vector Machines ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Large Data ◽  
Locally Linear Embedding ◽  
Benchmark Problems ◽  
Support Vector ◽  
Training Set ◽  
Support Vectors ◽  
Vector Machines

Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

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