scholarly journals Saccharomyces cerevisiaeMixed Culture of Blackberry (Rubus ulmifoliusL.) Juice: Synergism in the Aroma Compounds Production

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Pedro Ulises Bautista-Rosales ◽  
Juan Arturo Ragazzo-Sánchez ◽  
Gabriela Ruiz-Montañez ◽  
Rosa Isela Ortiz-Basurto ◽  
Guadalupe Luna-Solano ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Synergistic Effect ◽  
Mixed Culture ◽  
Aromatic Compounds ◽  
Principal Component ◽  
Wine Industry ◽  
Different Strains ◽  
Factorial Discriminant Analysis

Blackberry (Rubussp.) juice was fermented using four different strains ofSaccharomyces cerevisiae(Vitilevure-CM4457, Enoferm-T306, ICV-K1, and Greroche Rhona-L3574) recognized because of their use in the wine industry. A medium alcoholic graduation spirit (<6GL°) with potential to be produced at an industrial scale was obtained. Alcoholic fermentations were performed at28C°, 200 rpm, and noncontrolled pH. The synergistic effect on the aromatic compounds production during fermentation in mixed culture was compared with those obtained by monoculture and physic mixture of spirits produced in monoculture. The aromatic composition was determined by HS-SPME-GC. The differences in aromatic profile principally rely on the proportions in aromatic compounds and not on the number of those compounds. The multivariance analysis, principal component analysis (PCA), and factorial discriminant analysis (DFA) permit to demonstrate the synergism between the strains.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

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.

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