scholarly journals CHEMOMETRIC-BASED ELECTRONIC NOSE APPLICATION TO PORK OIL AND OLIVE OIL USING THE ODOR PATTERN CLASSIFICATIONS

2018 ◽  
Vol 10 (2) ◽  
pp. 55
Author(s):  
Imam Tazi ◽  
Muthmainnah Muthmainnah ◽  
Suyono Suyono ◽  
Avin Ainur
Keyword(s):  
Discriminant Analysis ◽  
Olive Oil ◽  
Linear Discriminant Analysis ◽  
Pattern Classification ◽  
Discriminant Function ◽  
Electronic Nose ◽  
Chemical Sensors ◽  
Data Retrieval ◽  
Output Data ◽  
Linear Discriminant

<p class="abstrak">A chemometric-based electronic nose has designed for analyzing pork oil and olive oil  using the odor pattern classifications. The electronic nose (e-nose) built from a combination of several chemical sensors derived from a semiconductor. The data retrieval was done by vaporizing the sample, then being captured by the sensor and identified by the electronic nose (e-nose). The output data from the electronic nose is the voltage released by each sensor. The analyzed samples were 100% olive oil, 100% pork oil and a combination of olive oil and pork oil with a ratio of 50%: 50%. The result of pattern classification using linear discriminant analysis (LDA) method shows that each sample is clustered well with the percentage of first discriminant function value is 87,9% and second discriminant function is 12,1%.</p>

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.

Detection of Milk Fat Adulteration by Linear Discriminant Analysis of Fatty Acid Data

1994 ◽  
Vol 77 (5) ◽  
pp. 1326-1334 ◽  
Author(s):  
Franz Ulberth
Keyword(s):  
Fatty Acid ◽  
Discriminant Analysis ◽  
Olive Oil ◽  
Linear Discriminant Analysis ◽  
Palm Oil ◽  
Milk Fat ◽  
Classification Rule ◽  
Gas Liquid Chromatography ◽  
Data Set ◽  
Linear Discriminant

Abstract Analysis of the fatty acid (FA) profile of milk fat (MF) by gas-liquid chromatography is widely used to detect adulteration with foreign fats. On the basis of the FA spectra of 352 genuine Austrian MF samples collected over a 4-year period, the effectiveness of concentration ranges of the major FA of MF and of certain FA ratios to identify non-MF/MF mixtures was tested. FA ratios proved useful for the detection of coconut fat in MF and admixture of vegetable oils rich in linoleic acid down to a level of 2%. This approach failed to identify non-MF/MF blends containing beef tallow, lard, olive oil, or palm oil at a level less than 10% commingling. Linear discriminant analysis applied to FA data was successful in distinguishing pure MFfrom adulterated MF. Computer-simulated data were used to derive the discriminant functions. Saturated and un-saturated FA with 18 C atoms were the most useful discriminating variables selected by a stepwise variable selection procedure. More than 95% of a data set composed of pure MF, and non-MF/MF blends containing 3% of either tallow, lard, olive oil, or palm oil were correctly classified. The validity of the classification rule was also tested by 206 gravimetrically prepared fat mixtures. Mixtures containing &gt;3% foreign fat were detected in all cases.

The Research on the EC Individual Credibility Based on Linear Discriminant Analytic Hierarchy Process

2011 ◽  
Vol 219-220 ◽  
pp. 112-115
Author(s):  
Zun Qi Yang ◽  
Hai Lin
Keyword(s):  
Discriminant Analysis ◽  
Analytic Hierarchy Process ◽  
Linear Discriminant Analysis ◽  
Discriminant Function ◽  
Linear Discriminant Function ◽  
Simulation Test ◽  
Analytic Hierarchy ◽  
Linear Discriminant ◽  
Hierarchy Process

The paper gives the linear discriminant function (LDF) based on the theory of linear discriminant analysis (LDA) combined with computing weights of the different indicators by analytic hierarchy process (AHP) to predict a new e-commerce customer’s individual credibility level and analyzes the result of a simulation test to justify the theory in this research.

scholarly journals ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE

2014 ◽  
pp. 61-67
Author(s):  
A. Amari ◽  
N. El Bari ◽  
B. Bouchikhi
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Tin Dioxide ◽  
Principal Component ◽  
Support Vector ◽  
Current Pattern ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

An electronic nose based system, which employs an array of six inexpensive commercial gas sensors based on tin dioxide (Figaro Engineering Inc., Japan), has been used to analyse the freshness states of anchovies. Fresh anchovies were stored in a refrigerator at 4 ± 1°C over a period of 15 days. Electronic nose measurements need no sample preparation and the results indicated that the spoilage process of anchovies could be followed by using this technique. Conductance responses of volatile compounds produced during storage of anchovy were monitored and the result were analysed by multivariate analysis methods. In this paper principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different freshness states (fresh, moderated and non-fresh samples). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. Therefore, the support vector machines (SVM) method was applied to the new subset, with only the selected sensors, to confirm that a subset of a few sensors can be chosen to explain all the variance. The results obtained prove that the electronic nose can discriminate successfully different freshness state using LDA analysis. Some sensors have the highest influence in the current pattern file for electronic nose. Support vector machine (SVM) model, applied to the new subset of sensors show the good performance.

scholarly journals Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5177
Author(s):  
Toshikazu Shinba ◽  
Keizo Murotsu ◽  
Yosuke Usui ◽  
Yoshinori Andow ◽  
Hiroshi Terada ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Discriminant Analysis ◽  
Return To Work ◽  
Linear Discriminant Analysis ◽  
Discriminant Function ◽  
Low Frequency ◽  
Linear Discriminant ◽  
Frequency Components ◽  
Returning To Work

Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was scheduled, patients’ HRV was measured using a wearable electrocardiogram device. The outcome of the reinstatement was evaluated at one month after returning to work. HRV indices including high- and low-frequency components were calculated in three conditions within a session: initial rest, mental task, and rest after task. A linear discriminant function was made using the HRV indices of 30 MDD patients from our previous study to effectively discriminate the successful reinstatement from the unsuccessful reinstatement; this was then tested on 52 patients who participated in the present study. The discriminant function showed that the sensitivity and specificity in discriminating successful from unsuccessful returns were 95.8% and 35.7%, respectively. Sensitivity is high, indicating that normal HRV is required for a successful return, and that the discriminant analysis of HRV indices is useful for return-to-work screening in MDD patients. On the other hand, specificity is low, suggesting that other factors may also affect the outcome of reinstatement.

scholarly journals Cross-Validation in Linear Discriminant Analysis of Triacylglycerol Structural Data from Istrian Olive Oils

1999 ◽  
Vol 82 (6) ◽  
pp. 1489-1494 ◽  
Author(s):  
Luciana Gabrielli Favretto ◽  
Barbara Campisi ◽  
Luciano Favretto ◽  
Maria Stella Simonetti ◽  
Lina Cossignani ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Olive Oil ◽  
Linear Discriminant Analysis ◽  
Cross Validation ◽  
Positional Distribution ◽  
Virgin Olive Oil ◽  
Stereospecific Analysis ◽  
Structural Data ◽  
Climatic Conditions ◽  
Linear Discriminant

Abstract This paper describes the differentiation and classification of olive oil samples produced in the Istrian peninsula in 3 areas characterized by different climatic conditions: Capodistria and Parenzo and Pola. The triacylglycerol (TAG) fraction of 41 samples of virgin olive oil was analyzed. In particular, TAG stereospecific analysis was performed to obtain the positional distribution of the fatty acids in the glycerol backbone. The quantitative data were evaluated by using Linear Discriminant Analysis, and the results obtained showed a differentiation of the olive oil samples according to their geographical origin. A cross-validation procedure, the Leave-One-Out classifier, was applied to test the adequacy of the discriminant model.

Classification of vegetable oils by linear discriminant analysis of Electronic Nose data

1999 ◽  
Vol 384 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Yolanda González Martı́n ◽  
José Luis Pérez Pavón ◽  
Bernardo Moreno Cordero ◽  
Carmelo Garcı́a Pinto
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Vegetable Oils ◽  
Electronic Nose ◽  
Linear Discriminant

THE CONSTRUCTION OF THE LINEAR DISCRIMINANT ANALYSIS FUNCTION AND ITS EVALUATION TO DETERMINE EXTREME METEOROLOGICAL PHENOMENA (ICE DEPOSITS) IN UKRAINE

2020 ◽  
pp. 82-94
Author(s):  
S.I. Pyasetska ◽  
N.P. Grebenyuk ◽  
S.V Savchuk
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Discriminant Function ◽  
Winter Season ◽  
Correlation Coefficients ◽  
Linear Discriminant Function ◽  
Average Wind Speed ◽  
Linear Discriminant ◽  
Average Wind ◽  
Actual Formation

o predict the possibility of ice deposits on the territory of Ukraine in the winter season, an analogous approach is proposed using the construction of the equations of the linear discriminant function. For this, the correlation coefficients between 13 meteorological values (per day) at the start dates of ice deposits at all stations of Ukraine were calculated. Significant correlation coefficients were determined between individual meteorological variables, such as average air temperature, maximum, minimum average air humidity, average wind speed, and atmospheric pressure at sea level. It is these quantities that were used to construct the equations of the linear discriminant function and for the dates of the actual formation of ice deposits and the further forecast of its formation from a three-day lead time. As a result of the calculations for the winter season of 2001-2013 an equation of the linear discriminant function was obtained for the dates of the actual formation of ice deposits and a sufficiently high justification was obtained. Also, to predict the possible formation of ice deposits with a three-day lead time, a prognostic function of linear discriminant analysis was obtained to determine possible such deposits for the winter season of 2001-2010. On the example of the regional centers, a satisfactory assessment of the justification on an independent material for the winter season of 2011-2016 was obtained. Thus, in constructing linear discriminant functions to determine the possibility of such an adverse event as ice deposition, a number of conclusions were obtained: – The sufficiently high validity of the discriminant functions of extreme meteorological phenomena (ice deposits) for the winter season 2001-2013 was obtained. It ranges from 91 % (for the data set at selected dates with ice deposits) up to 90% (for an array of data at the date of extreme cold ). – A sufficiently high estimate of the validity of the independent material for the winter season 2014-2016 was obtained. It is up to 78 % (for an array of data on extreme cold dates and from 90 % ( for an array of data on selected dates with ice deposits) . – The prognostic function of linear discriminant analysis was obtained to determine possible (with 3-day timeliness) extreme meteorological phenomena (ice) during the winter season 2001-2010, using only meteorological values with statistically significant correlation, namely, the maximum air temperature; average humidity; and average wind speed. – Sufficiently significant and satisfactory validity of the prognostic functions of possible (with 3-day timeliness) extreme meteorological phenomena (ice deposits) for the winter season 2001-2010 was obtained.

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