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. 

scholarly journals Deteksi Bubuk Kopi Luwak Murni dan Bubuk Kopi Luwak Campuran Dengan Teknologi Hidung Elektronik

2019 ◽  
Vol 4 (3) ◽  
pp. 105-114
Author(s):  
Mubarak Hulda ◽  
Fachruddin Fachruddin ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Powder Spectrum ◽  
Pca Method ◽  
Coffee Powder ◽  
Electronic Nose Technology ◽  
Spectrum Data

Abstrak. Kopi luwak merupakan kopi yang berasal dari hasil konsumsi hewan luwak (musang) yang  telah mengalami fermentasi di dalam pencernaan luwak selam 12 jam. Kopi luwak merupakan komoditi yang sangat diminati dan bernilai jual tinggi. Tujuan dari penelitian ini untuk membedakan bubuk kopi luwak murni dan bubuk kopi luwak campuran dengan memanfaatkan instrumen berbasis teknologi hidung elektronik (electronic nose). Penelitian ini menggunakan bubuk kopi luwak murni dan bubuk kopi arabika yang dicampurkan dengan perbandingan (50:50, 60:40. 70:30, 80:20 dan 90:10). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 10 sampel. Pengukuran intensitas sinyal aroma bubuk kopi menggunakan sensor piezoelectric tranducers. Klasifikasi data spektrum bubuk kopi menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma bubuk kopi pada detik ke-5.64, dan dapat mengklasifikasikan bubuk kopi pada detik ke 11.09. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksi bubuk kopi luwak murni dan bubuk kopi luwak campuran dengan tingkat keberhasilan sebesar 100% (PC-1 sebesar 100% dan PC-2 sebesar 0%).Deteksi Murni Powder Kopi Luwak dan Campuran Kopi Luwak Bubuk Menggunakan Teknologi Hidung ElektronikAbstract. Civet coffee is coffee that comes from the consumption of civet animals (ferrets) that have undergone fermentation in the digestion of mongoose for 12 hours. Civet coffee is a commodity that is very popular and has a high selling value. The purpose of this study is to distinguish pure civet coffee powder and mixed civet coffee powder by using an instrument based on electronic nose technology. This study used pure civet coffee powder and arabica coffee powder mixed with comparisons (50:50, 60:40. 70:30, 80:20 and 90:10). The number of samples used in this study were 10 samples. The measurement of the intensity of coffee powder’s smell signals using piezoelectric tranducers. The classification of coffee powder spectrum data using the Principal Component Analysis (PCA) method with its pretreatment is Gap Reduction. The results of this study were obtained: The electronic nose starts responding to the smell of coffee powder at 5.85 seconds, and can classify coffee powder in 11.09 seconds. The electronic nose combined with the principal component analysis (PCA) method has succeeded in detecting pure civet coffee powder and mixed Civet coffee powder with a success rate of 100 % (PC-1 of 100% and PC-2 of 0%).     

scholarly journals Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose

2019 ◽  
Vol 1 (1) ◽  
pp. 5-8 ◽  
Author(s):  
Imam Tazi ◽  
Nur Laila Isnaini ◽  
Mutmainnah Mutmainnah ◽  
Avin Ainur
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Meat Products ◽  
Principal Component ◽  
Component Analysis ◽  
Cumulative Number ◽  
Organoleptic Evaluation ◽  
Pca Method ◽  
Meat Samples

There are several testing processes for consuming meat products. Organoleptic evaluation is an evaluation based on color, texture, smell, and taste. This research aims to find out the response pattern of 10 gas sensor array contained in the electronic nose against the odor pattern of beef and pork base on a smell. The classification method used is using the Principal Component Analysis (PCA) method. This method is expected to simplify the test of differences in beef and pork based on the aroma. The meat used is standard beef and pork consumption that has been sold in supermarkets. Samples of beef and pork are then ground until smooth. After that, it is weighed until it reaches 1 ounce. The meat samples were tested using an electronic nose consisting of 10 gas sensors. The multivariate analysis method was used to classify the aroma of beef and pork. The results of the data processing showed that the aroma classification of beef and pork which was indexed by the electronic nose was perfect. Based on the PCA method, the proportion of PC1 is 93.4%, and PC2 is 4.9%. From the second cumulative number, the value of the first PC was obtained 98.3%. This value indicates that only with 2-dimensional data, can represent ten dimensions of data. The loading plot shows that the MQ-138 and MQ-3 sensors are the most powerful sensors in testing samples of beef and pork.

scholarly journals Diagnosis of Esophagitis Based on Face Recognition Techniques

2010 ◽  
Vol 4 (1) ◽  
pp. 58-62
Author(s):  
Santosh S Saraf ◽  
Gururaj R Udupi ◽  
Santosh D Hajare
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Techniques ◽  
The Face ◽  
Pca Method ◽  
Recognition Efficiency ◽  
Motivating Factor

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.

scholarly journals The Classification of Primitive-Shaped Patterns by Using Principal Component Analysis Method

2019 ◽  
Vol 20 (2) ◽  
pp. 12
Author(s):  
IGA Widagda ◽  
Hery Suyanto
Keyword(s):  
Principal Component Analysis ◽  
Predictive Accuracy ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Moment Invariant ◽  
Pca Method ◽  
The Moment

Abstrak – The recognition or classification of patterns is a major problem in computer vision. Many methods have been applied such as: moment invariant, Artificial Neural Networks (ANN), K-mean, Support Vector Machine (SVM) and others. These methods have a few limitations. The moment invariant fashion is highly vulnerable to noise. ANN methods require a long computing time (especially multi-layer ANN) during the training process. On the other hand, the dimensions of the features generated from the methods are relatively high, which requires large storage space (memory). In addition, this leads to the long computing time when the testing process is carried out. Based on these facts, this research makes use of methods that being able to reduce the feature dimensions, namely the Principal Component Analysis (PCA). In the PCA method the dimensions of the sample image are converted to principal components (face space), whose dimensions are much smaller than the dimensions of the sample image itself. Our works exhibit that the PCA method is highly effective in carrying out the pattern classification process. This can be indicated by the relatively high values of Predictive Accuracy, Precision and Recall (close to 1) while the FP Rate is low (close to 0). Moreover, the location of the point coordinates (FP Rate, TP Rate) in ROC graphs is fallen in the upper left region (approaching the perfect classifier region).

Application of Electronic Nose in Tea Quality Recognition

2011 ◽  
Vol 422 ◽  
pp. 43-46
Author(s):  
Hong Mei Zhang ◽  
Fen Ling Chang ◽  
Yong Chang Yu ◽  
Yu Jing He ◽  
He Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Electronic Nose ◽  
Rapid Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Tea Quality ◽  
Electronic Nose Technology

The current study uses the electronic nose FOX 4000 to inspect Xinyang Maojian tea in three quality levels. Principal component analysis (PCA) and statistical quality control (SQC) are adopted to analyze and recognize the data. PCA shows that there is a certain difference in the odor of the tea samples in the three quality levels. PCA can evidently distinguish three kinds of samples. SQC analysis shows that X800 and X600 are located outside the controllable range, indicating that they differ from X1200 in odor. This result is consistent with the PCA result. The study shows that electronic nose technology is expected to be applied widely in the rapid detection of tea.

scholarly journals Assessment of the stability of bat ectoparasite communities using a Principal Component Analysis (PCA) method

2019 ◽  
Vol 8 (1) ◽  
pp. 119-124
Author(s):  
Galina Anatolievna Fadeeva ◽  
Elena Evgenievna Boryakova
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Parasite Communities ◽  
Parasitic System ◽  
Pca Method ◽  
The Difference ◽  
The Stability ◽  
The One

The paper deals with a research of epiparasite communities in native karst caves in the South of Nizhny Novgorod Region. Six species of bats such as Daubentons water bat, Brandts bat, whiskered bat, pond bat, northern bat and long-eared bat were examined. A Principal Component Analysis was used to identify factors influencing the composition of ectoparasites as well as the number and distribution of mites in mixed colonies of bats. As the cave and its inhabitants can be considered as a microbiotope, it is obvious that there are specific relations between inhabitants in caves. Special habitat conditions indirectly influence the parasitic systems developing there which are characterized by certain stability. Mann-Whitney U-test was used to estimate the difference between samples of animals from different habitats. Methods of nonparametric statistics didnt find significant distinctions by the hosts, years and biotopes, the bat colony and their ectoparasites can be estimated as a single complexly organized system, existing long in space and time. From all possible factorial space four factors have significant effect on systems. The contribution of the first and second factors is equal to 65% of variance (specificity of parasites to hosts and a factor of dominant species presence). In parasite communities of bats interrelations which cause successful existence of all types without the expressed competition are observed. Our results indicate a complex relationship between the parasites in the community on the one hand, and long-term existence of the community on the other hand. Each member of parasitic system has a position in factorial space. In parasite communities of bats we met only one factor-dependent species ( Spinturnix acuminatus, Sp. plecotinus, Leptotrombidium russicum ). Species that show moderate and positive, moderate and negative correlation dependence with several factors are found. For example, Spinturnix myoti , Sp. kolenatii , Macronyssus heteromorphus , etc. Heterogeneity of environmental impact on the parasitic systems which are formed in natural caves provides stability of bat parasite communities in general.

scholarly journals The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

2014 ◽  
Vol 39 (1) ◽  
pp. 7-23 ◽  
Author(s):  
Anna Justyna Milewska ◽  
Dorota Jankowska ◽  
Dorota Citko ◽  
Teresa Więsak ◽  
Brian Acacio ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Data Preparation ◽  
Vitro Fertilization ◽  
Multidimensional Datasets ◽  
Different Types ◽  
Pca Method

Abstract Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.

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.

Sign in / Sign up

Export Citation Format

Share Document