scholarly journals Klasifikasi Teh Hijau dan Teh Hitam Tambi-Pagilaran dengan Metode Principal Component Analysis (PCA) Menggunakan E-Nose

2018 ◽  
Vol 8 (1) ◽  
pp. 61
Author(s):  
Inca Inca ◽  
Triyogatama Wahyu Widodo ◽  
Danang Lelono
Keyword(s):  
Feature Extraction ◽  
Green Tea ◽  
Principal Component ◽  
Black Tea ◽  
Extraction Process ◽  
Sensor Response ◽  
Tea Quality ◽  
Pca Method ◽  
The Difference

This research aims to classification of samples of green tea and black tea originated from different planting sites,  Tambi and Pagilaran. Samples of green tea and black tea; quality I (BOP), quality II (BP), quality III (Bohea) were each collected from Tambi and Pagilaran to analyze the charasteristic of both sample from both sites. Measurements of tea samples were performed using a dynamic e-nose device based on a MOS gas sensor, with a maximum set point temperature of 40ºC, flushing 300 seconds, collecting 120 seconds, and purging 80 seconds for 10 cycles repeatedly. The resulting sensor response is then processed using the difference method for baseline manipulation. Characteristic of extraction process on the sensor response results is carried out in three methods; relative, fractional change, and integral. Matrix data of the feature extraction results was reduced using the PCA method by mapping the aroma patterns of each sample using 2-PCA components. The PCA reduction results in integral feature extraction showed the largest percentage of cumulative variance in classifying green tea sample data by 97% and black tea by 100%. The large percentage value of cumulative variance indicates PCA can differentiate samples of green tea and black tea from Tambi and Pagilaran well.

scholarly journals Deteksi Formalin Pada Buah Tomat (Lycopersicum esculentum Mill) Dengan Teknologi Hidung Elektronik (Electronic Nose)

2019 ◽  
Vol 4 (2) ◽  
pp. 359-366
Author(s):  
Irfan Maibriadi ◽  
Ratna Ratna ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Lycopersicum Esculentum ◽  
Olfactory Ability ◽  
Pca Method ◽  
The Difference ◽  
Electronic Nose Technology

Abstrak,  Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on  the olfactory ability can distinguish them. 

Detection of XinyangMaojian Green Tea Quality and Age by Electronic Nose

2011 ◽  
Vol 239-242 ◽  
pp. 2096-2100 ◽  
Author(s):  
Hong Mei Zhang ◽  
Ming Xun Chang ◽  
Yong Chang Yu ◽  
Hui Tian ◽  
Yu Qing He ◽  
...  
Keyword(s):  
Neural Network ◽  
Green Tea ◽  
Electronic Nose ◽  
Principal Component ◽  
Correct Identification ◽  
Tea Quality ◽  
Testing Data ◽  
Artificial Neural Network Ann ◽  
Correct Discrimination

In this work, the capacity of an electronic nose (E-nose, PEN2) to classify tea quality grades is investigated. Three tea groups with different quality grades were harvested at different times. Principal component analysis (PCA) and artificial neural network (ANN) were applied to identify the different tea samples. PCA provided perfect classification of tea quality grades. In the analysis of age, six groups of XinyangMaojian green tea were distinguished completely by PCA. The results of ANN analysis gave a high percentage of correct discrimination of green tea samples. The correct identification rates of the training and testing data were 98.6% and 83%, respectively, for three grades of green tea samples harvested in 2009. The correct identification rates of the training and testing data were 100% and 87.8%, respectively, for three grades of green tea samples harvested in 2010. In the analysis of age, the correct discrimination percentages for six groups of XinyangMaojian green tea were 99.4% and 88.9% for training and testing data, respectively. These results indicate that the electronic nose could be successfully used for the detection of teas of different quality grades and ages.

scholarly journals Suhu Pemanas Sampel Optimal Untuk Klasifikasi Teh Hitam Menggunakan Electronic Nose

2019 ◽  
Vol 9 (1) ◽  
pp. 45 ◽  
Author(s):  
Danang Lelono ◽  
Kuwat Triyana
Keyword(s):  
Electronic Nose ◽  
Heating Temperature ◽  
Principal Component ◽  
Black Tea ◽  
Total Sample ◽  
Sensor Response ◽  
Sample Heating ◽  
Total Data ◽  
Temperature Optimization ◽  
Measured Sample

 The optimization of heating temperature of black tea samples for the measurement of aroma with electronic nose (e-nose) has been successfully performed. Sample heating is done because black tea has a low aroma intensity and easily lost. However, the selection of such temperature should be selective because it can result in damage to the aroma of the sample. Therefore, temperature optimization needs to be done so that the resulting sensor response comes from the transformation of the undamaged aroma.The method used to obtain the optimum heating temperature by analyzing the sensor response of the aroma transformation that is captured by e-nose. Consistency and pattern changes formed from the sensor response are used as a comparison of optimal heating temperature selection. The measured sample varied in temperature (30 - 60 °C) so that the resulting sensor response was observed. Change in patterns indicate the aroma has been burning. After optimal temperature is obtained then black tea (50 gr) Broken Orange Pokoe, Broken Pokoe II and Bohea with a total sample of 300 bags were measured with e-nose. For further analysis, the result of classification by method of Principal Component Analysis (PCA) as proof of sample heating temperature optimization successfully done.The experimental results show optimal sample heating for black tea 3 quality 40 - 45 °C. After then with the third PCA the sample can be classified up to 92.5% of the total data variant. This indicates the aroma of tea is relatively constant and there is no pattern change.

scholarly journals Tree crown size as a measure of tree biosocial position in 135-year-old oak (Quercus L.) stand

2016 ◽  
Vol 58 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Katarzyna Kaźmierczak ◽  
Bogna Zawieja
Keyword(s):  
Principal Component ◽  
Tree Height ◽  
Projection Area ◽  
Tree Crown ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Crown Length ◽  
Pca Method ◽  
Crown Projection Area

AbstractThe paper presents an attempt to apply measurable traits of a tree – crown projection area, crown length, diameter at breast height and tree height for classification of 135-year-old oak (QuercusL.) trees into Kraft classes. Statistical multivariate analysis was applied to reach the aim. Empirical material was collected on sample plot area of 0.75 ha, located in 135-year-old oak stand. Analysis of dimensional traits of oaks from 135-year-old stand allows quite certain classification of trees into three groups: pre-dominant, dominant and co-dominant and dominated ones. This seems to be quite promising, providing a tool for the approximation of the biosocial position of tree with no need for assessment in forest. Applied analyses do not allow distinguishing trees belonging to II and III Kraft classes. Unless the eye-estimation-based classification is completed, principal component analysis (PCA) method provided simple, provisional solution for grouping trees from 135-year-old stand into three over-mentioned groups. Discriminant analysis gives more precise results compared with PCA. In the analysed stand, the most important traits for the evaluation of biosocial position were diameter at breast height, crown projection area and height.

Kernel Optimized-Support Vector Machine and MapReduce Framework for Sentiment Classification of Train Reviews

2019 ◽  
Vol 27 (06) ◽  
pp. 1025-1050
Author(s):  
Rashmi K. Thakur ◽  
Manojkumar V. Deshpande
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Mapreduce Framework ◽  
Mapreduce Model

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.

scholarly journals JointL1/2-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Chun-Mei Feng ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Dong-Qin Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Error Function ◽  
Principal Component ◽  
Graph Laplacian ◽  
Specific Gene ◽  
Data Sets ◽  
Pca Method ◽  
Norm Constraint ◽  
Alm Method ◽  
Pca Algorithm

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adoptingL1/2constraint (L1/2gLPCA) on error function for feature (gene) extraction. The error function based onL1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.

scholarly journals A Methodology for Evaluation and Distribution of Patent Applications to INPI-BR Patent Examiners: A Case Study in the Electricity Field

2021 ◽  
Author(s):  
Cesar Vianna Moreira Júnior ◽  
Daniel Marques Golodne ◽  
Ricardo Carvalho Rodrigues
Keyword(s):  
Principal Component ◽  
Patent Classification ◽  
Technological Field ◽  
International Patent ◽  
Relevant Variables ◽  
Pca Method ◽  
Patent Applications ◽  
Examination Process

This paper presents the development of a new methodology for evaluation and distribution of patent applications to the examiners at the Brazilian Patent Office considering a specific technological field, represented by classification of the application according to the International Patent Classification (IPC), and the variables corresponding to the volume of data of the application and its complexity for the examination process. After identifying the most relevant variables, such as the Specific Areas of Expertise (ZAE) of the examiners, a mathematical model was developed, including: (a) application of the principal component analysis (PCA) method; (b) calculation of a General Complexity Ratio (IGC); (c) classification into five classes (very light, light, moderate, heavy and very heavy) according to IGC average ranges and standard deviations; (d) implementation of a logic of distribution, compensating very heavy applications with very light ones, and light applications with heavy ones; and (e) calculation of a Distribution Balancing Ratio (IBD), considering the differences between the samples’ medians. The model was validated using a sample of patent applications including, in addition to the identified variables, the time for substantive examination by the examiner. Then, a correlation analysis of the variables with time and a comparison of the classifications according to the time and the IGC generated by the model were carried out. The results obtained showed a high correlation of the IGC with time, above 80%, as well as correct IGC classes in more than 80% of applications. The model proposed herein suggests that the three main relevant variables are: total number of pages, total number of claims, and total number of claim pages.

scholarly journals Ekstraksi Ciri Orde Pertama dan Metode Principal Component Analysis untuk Mengidentifikasi Jenis Telur Ayam Kampung dan Ayam Arab

2019 ◽  
Vol 9 (2) ◽  
pp. 133
Author(s):  
Oky Dwi Nurhayati ◽  
Dania Eridani ◽  
Ajik Ulinuha
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Color Space ◽  
Principal Component ◽  
Component Analysis ◽  
Domestic Chicken ◽  
Image Size ◽  
First Order ◽  
Pca Method ◽  
Chicken Eggs

Chicken eggs become one of the animal proteins commonly used by people, especially in Indonesia. Eggs have high economic value and have diverse benefits and a high nutritional content. Visually to distinguish between domestic chicken eggs and arabic chicken eggs has many difficulties because physically the shape and color of eggs have similarities. This research was conducted to develop applications that were able to identify the types of domestic chicken eggs and Arab chicken eggs using the Principal Componenet Analysis (PCA) method and first order feature extraction. The application applies digital image processing stages, namely resizing image size, RGB color space conversion to HSV, contrast enhancement, image segmentation using the thresholding method, opening and region filling morphology operations, first order feature extraction and classification using the PCA method. The results of identification of types of native domestic chicken eggs and Arabic chicken eggs using the Principal Component Analysis method showed the results of 95% system accuracy percentage, consisting of 90% accuracy of success in the type of domestic chicken eggs and 100% accuracy of success in the type of Arabic chicken eggs.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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