scholarly journals Klasifikasi Kemurnian Daging Sapi Berbasis Electronic Nose dengan Metode Principal Component Analysis

2016 ◽  
Vol 6 (1) ◽  
pp. 47 ◽  
Author(s):  
Fachri Rosyad ◽  
Danang Lenono
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Total Sample ◽  
Component Analysis ◽  
Test Method ◽  
Total Variance ◽  
Sample Mass ◽  
Metal Oxide Gas Sensor ◽  
Classification Test

AbstrakDaging merupakan bahan makanan yang dikonsumsi secara luas, sehingga dibutuhkan standar kualitas tertentu agar dapat aman dikonsumsi dan tidak merugikan konsumen. Standar tersebut diantaranya adalah kesegaran dan kemurnian. Dalam praktek jual beli daging ditemukan adanya kasus pencampuran daging sapi dengan daging babi sehingga dapat merugikan konsumen. Untuk mengetahui kemurnian daging sapi tersebut dibutuhkan pengujian dengan menggunakan tes aroma berbasis electronic nose.Sampel daging sapi campuran dibuat dengan variasi kandungan daging babi sebesar 20%, 40%, 60%, dan 80% dari total massa sampel, dengan massa sampel adalah 20 gram. Pengambilan data selama 10 hari dilakukan dengan proses sensing dan flushing masing-masing selama 180 detik dengan pengulangan sebanyak 6 kali per hari. Pengolahan data dilakukan dalam beberapa tahap yang meliputi prapemrosesan sinyal dengan manipulasi baseline, ekstraksi ciri dengan menghitung luas kurva sinyal menggunakan pendekatan integral aturan trapesium, dan analisis multivariat menggunakan Principal Component Analysis (PCA).Hasil persentase variansi kumulatif dua komponen utama pada pengujian klasifikasi antara daging sapi dengan daging babi adalah sebesar 99,9%, sedangkan pada pengujian klasifikasi antara daging sapi murni dengan daging sapi campuran adalah sebesar 99,6%. Dengan demikian, electronic nose dapat membedakan antara daging sapi murni dengan daging sapi campuran. Kata kunci— Electronic nose, sensor gas metal oksida, klasifikasi, kemurnian daging, Principal Component Analysis. AbstractMeat is a widely consumed food, therefore it requires certain quality standards to be safe to consumed and does not harm the consumers. Several of those standards including meat freshness and meat purity. Recently it has been found some cases of pork adulteration in beef which consequently could harm the consumers. In order to examine the purity of beef, it required test method based on odor characteristics by using electronic nose.Adulterated beef samples were prepared with pork content within samples varied by 20%, 40%, 60%, and 80% of total sample mass where the sample mass is 20 grams. The 10 days data collecting consists of sensing and flushing cycles for 180 seconds each cycles, with 6 times process repeating over 1 day. Data processing was carried out in several stages which including signal preprocessing based on baseline manipulation, feature extraction by calculating the area of the response signal curve by using trapezoidal rule of integral approximation, and multivariate analysis using PCA.Cumulative percentage of variance of two principal components of beef and pork classification test yields at 99.9% of total variance, and classification test between pure beef and adulterated beef resulting in 99.6% of total variance. Therefore, it can be concluded that electronic nose can classify between pure beef and adulterated beef. Keywords— Electronic nose, metal-oxide gas sensor, classification, meat purity, Principal Component Analysis.

scholarly journals Rice Odours’ Readings Investigation Using Principal Component Analysis

2018 ◽  
Vol 7 (2.29) ◽  
pp. 488
Author(s):  
Nurul Aini Abdul Wahab ◽  
Shamshuritawati Sharif
Keyword(s):  
Principal Component Analysis ◽  
High Dimension ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Total Variance ◽  
Scree Plot ◽  
Cumulative Proportion ◽  
Principle Component

The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important variables contributing to the rice odour readings. The principal component analysis (PCA) is implemented to determine the component that best represent the all 10 variables in order to eliminate the interdependency problem, and (2) to identify which variable is considered as important and influential to the newly-formed principle component (PC). The results from PCA suggested that the first two principle components is chosen. It is based on three assessments which are Kaiser’s criterion larger than 1, cumulative proportion of total variance, and scree plot. These two principle components explained 89% of total variance. Results showed that sensor 1 (0.931) and sensor 2 (0.966) are the two important variables that highly contribute to PC1. On the other hand, for PC2, the highest contribution is from sensor 8 (0.828). This study demonstrate that PCA is effective for investigating rice odour readings.  

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

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 Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

scholarly journals Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 38 ◽  
Author(s):  
Xiaohong Wu ◽  
Jin Zhu ◽  
Bin Wu ◽  
Chao Zhao ◽  
Jun Sun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Extraction Algorithm ◽  
Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

Sign in / Sign up

Export Citation Format

Share Document