scholarly journals Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

2020 ◽  
Author(s):  
K. Przybył ◽  
J. Gawałek ◽  
K. Koszela
Keyword(s):  
Principal Component ◽  
Neural Model ◽  
Significance Level ◽  
Image Descriptors ◽  
Graphic Data ◽  
Color Features ◽  
Input Variables ◽  
Hidden Layer ◽  
Spray Dried

Abstract The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high (“H”) and low (“L”) level of saccharification a chosen carrier (potato maltodextrin).

scholarly journals A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 361
Author(s):  
Georgy Minaev ◽  
Philipp Müller ◽  
Katri Salminen ◽  
Jussi Rantala ◽  
Veikko Surakka ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Support Vector ◽  
Quadratic Discriminant Analysis ◽  
Raw Data ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Storage Times

The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than 3% when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least 95% or 99% of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit.

scholarly journals Exploring the Extreme Learning Machine for Classification of Brain MRIs

2019 ◽  
Vol 9 (2) ◽  
pp. 3654-3657
Keyword(s):  
Extreme Learning Machine ◽  
Principal Component ◽  
Classification Performance ◽  
Feature Reduction ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
Learning Machine ◽  
Hidden Layer ◽  
Method Accuracy

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Chemometric Analysis for the Classification of some Groups of Drugs with Divergent Pharmacological Activity on the Basis of some Chromatographic and Molecular Modeling Parameters

2018 ◽  
Vol 21 (2) ◽  
pp. 125-137
Author(s):  
Jolanta Stasiak ◽  
Marcin Koba ◽  
Marcin Gackowski ◽  
Tomasz Baczek
Keyword(s):  
Correlation Analysis ◽  
Pharmacological Activity ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Cardiovascular Drugs ◽  
New Drugs ◽  
Analgesic Drugs ◽  
Starting Point ◽  
Chromatographic Parameters

Aim and Objective: In this study, chemometric methods as correlation analysis, cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D) structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11 cardiovascular drugs and 36 “other” compounds and especially to choose the most important data of them. Material and Methods: All chemometric analyses have been carried out, graphically presented and also discussed for each group of drugs. At first, compounds’ structural and chromatographic parameters were correlated. The best results of correlation analysis were as follows: correlation coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36 “other” compounds, respectively. Next, part of molecular and HPLC experimental data from each group of drugs were submitted to FA/PCA and CA techniques. Results: Almost all results obtained by FA or PCA, and total data variance, from all analyzed parameters (experimental and calculated) were explained by first two/three factors: 84.28%, 76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 “other” compounds, respectively. Compounds clustering by CA method had similar characteristic as those obtained by FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely characterized and discussed in case of their molecular structure and pharmacological activity. Conclusion: Proposed QSAR strategy of reduced number of parameters could be useful starting point for further statistical analysis as well as support for designing new drugs and predicting their possible activity.

Fingerprint of PGI Mantova Cucumis melo by ICP-MS and Chemometric Analysis

2020 ◽  
Vol 17 (1) ◽  
pp. 94-104
Author(s):  
Antonio F. Mottese ◽  
Maria R. Fede ◽  
Francesco Caridi ◽  
Giuseppe Sabatino ◽  
Giuseppe Marcianò ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Principal Component ◽  
Inorganic Elements ◽  
Discrimination Power ◽  
Icp Ms ◽  
High Discrimination ◽  
Strong Relation ◽  
Trace Element Contents ◽  
Safe Food

Background and Objectives: In this work, yellow and green varieties of Cucumis melo fruits belonging to different cultivars were studied. In detail, three Sicilian cultivars of winter melons tutelated by TAP (Traditional agro-alimentary products) labels were considered, whereas asun protected the Calabrian winter melon was studied too. With the aim to compare the selective uptakes of inorganic elements among winter and summer fruits, the “PGI Melone Mantovano” was investigated. The purpose of this work was to apply the obtained results i) to guarantee the quality and healthiness of fruits, ii) to producers defend, iii) to help the customers in safe food purchase. Method: All samples were analyzed by ICP-MS and the obtained results, subsequently, were subjected to Cluster analysis (CA), Principal component analysis (PCA) and Canonical discriminant analysis (CDA). Results: CA results were generally in agreement with samples origin, whereas the PCA elaboration has confirmed the presence of a strong relation between fruit origins and trace element contents. In particular, two principal components justified the 57.32% of the total variance (PC1= 40.95%, PC2= 16.37%). Finally, the CDA approach has provided several functions with high discrimination power, confirmed by the correct classification of all samples (100%). Conclusions: CA, PCA and CDA could represent an integrated to label to discriminate the origin of agri-food products and, thus, protect and guarantee their healthiness.

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

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.

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