Data reconstruction using iteratively reweighted L1-principal component analysis for an electronic nose system

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.

Download Full-text

Tensor Principal Component Analysis

Advances in Information and Communication Technology Education - Advanced Pattern Recognition Technologies with Applications to Biometrics ◽

10.4018/978-1-60566-200-8.ch008 ◽

2011 ◽

pp. 150-171

Author(s):

David Zhang ◽

Fengxi Song ◽

Yong Xu ◽

Zhizhen Liang

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Data Reconstruction ◽

Potential Applications

Tensor principal component analysis (PCA) is an effective method for data reconstruction and recognition. In this chapter, some variants of classical PCA are introduced and the properties of tensor PCA are analyzed. Section 8.1 gives the background and development of tensor PCA. Section 8.2 introduces tensor PCA. Section 8.3 discusses some potential applications of tensor PCA in biometrics. Finally, we summarize this chapter in Section 8.4.

Download Full-text

Data Compression and Reconstruction for EV Charging Stations Based on Principal Component Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.4317 ◽

2014 ◽

Vol 556-562 ◽

pp. 4317-4320

Author(s):

Qiang Zhang ◽

Li Ping Liu ◽

Chao Liu

Keyword(s):

Principal Component Analysis ◽

Data Compression ◽

Simulated Data ◽

Principal Component ◽

Component Analysis ◽

Reconstruction Error ◽

Data Reconstruction ◽

Reconstruction Method ◽

Charging Stations ◽

Ev Charging

As a zero-emission mode of transportation, an increasing number of Electric Vehicles (EV) have come into use in our daily lives. The EV charging station is an important component of the Smart Grid which is now facing the challenges of big data. This paper presents a data compression and reconstruction method based on the technique of Principal Component Analysis (PCA). The data reconstruction error Normalized Absolute Percent Error (NAPE) is taken into consideration to balance the compression ratio and data reconstruction quality. By using the simulated data, the effectiveness of data compression and reconstruction for EV charging stations are verified.

Download Full-text

Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽

10.3390/electronics8080870 ◽

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.

Download Full-text

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

Foods ◽

10.3390/foods8010038 ◽

2019 ◽

Vol 8 (1) ◽

pp. 38 ◽

Cited By ~ 2

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.

Download Full-text