scholarly journals Simple Amperometric Biosensor for Sucrose Concentration Measurement Based on Principal Component Analysis

2021 ◽  
Vol 2049 (1) ◽  
pp. 012048
Author(s):  
Vira Annisa Rosandi ◽  
Tetty Marta Linda ◽  
Beny Agustirandi ◽  
Lazuardi Umar
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Sucrose Concentration ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Amperometric Biosensor ◽  
Yeast Cells ◽  
Cellular Respiration ◽  
Respiration Activity

Abstract Sucrose is a type of sugar that is widely used in various types of foods and beverages. In Indonesia, sucrose consumption reaches 2.8 million tons on average per year. Effects of consuming too much sucrose can increase the risk of various diseases such as diabetes, dental caries and obesity. The level of maximum amount of sucrose that is safe for the body equal to 10% of the total energy or the equivalent of 50 g/person/day, so that the required detection system and the identification of the sucrose concentration. In this work, the identification process was carried out using an amperometric biosensor based on the yeast Saccharomyces cerevisiae as a bioreceptor. Measurements were made by immobilizing yeast cells and analyte samples into the biosensor electrodes and observed based on cellular respiration activity which was expressed as a parameter of dissolved oxygen (DO). The biosensor response is generated in the form of an output potential value, then processed using principal component analysis (PCA) to produce a sucrose concentration classification point with a percentage of variance of the two main components of 98.77% which states that the sensor is able to identify sucrose concentrations.

scholarly journals Variability in size and shape in muscovy duck with age: Principal component analysis

2014 ◽  
Vol 30 (1) ◽  
pp. 125-136 ◽  
Author(s):  
D.M. Ogah ◽  
M. Kabir
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Dependence Structure ◽  
Muscovy Duck ◽  
Body Measurements ◽  
Male And Female ◽  
Size And Shape

Body weight and six linear body measurements, body length (BL), breast circumference (BCC), thigh length (TL), shank length (SL), total leg length (TLL) and wing length were recorded on 150 male and female muscovy ducklings and evaluated at 3, 5, 10, 15 and 20 weeks of age. Principal component analysis was used to study the dependence structure among the body measurements and to quantify sex differences in morphometric size and shape variations during growth. The first principal components at each of the five ages in both sexes accounted between 71.54 to 92.95% of the variation in the seven measurements and provided a linear function of size with nearly equal emphasis on all traits. The second principal components in all cases also accounted for between 6.7 to 16.17% of the variations in the dependence structure of the system in the variables as shape, the coefficient for the PCs at various ages were sex dependent with males showing higher variability because of spontaneous increase in size and shape than females. Contribution of the general size factor to the total variance increase with age in both male and female ducklings, while shape factor tend to be stable in males and inconsistent in females.

scholarly journals Principal component analysis of the exterior traits in dairy cows

2017 ◽  
Vol 19 (79) ◽  
Author(s):  
S. Kramarenko ◽  
N. Kuzmicheva ◽  
A. Kramarenko
Keyword(s):  
Principal Component Analysis ◽  
Dairy Cows ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Chest Circumference ◽  
State Enterprise ◽  
Body Measurements ◽  
Generalized Variance ◽  
Diagonal Length

The present study was undertaken to study the relationship between different body measurements and to develop unobservable factors (latent) to define which of these measurements best represent body conformation in the dairy cows. Biometrical observations were recorded on 109 Red Steppe dairy cows randomly selected from State Enterprise «Breeding reproducer «Stepove» (Mykolayiv region, Ukraine) during the 2001–2014. Principal Component Analysis (PCA) was used to account for the maximum portion of variation present in the original set of variables (body traits in cow) with a minimum number of composite variables through STATISTICA software. Most of the pairwise phenotypic correlations among the exterior traits in dairy cows were positive and significant. The Pearson’s correlation coefficients of the body measurements ranged from 0.215 (chest depth – cannon circumference) to 0.889 (height at withers – rump height). In factor solution of the Principal Component Analysis, two (latent) which explained 48.5% of the generalized variance were extracted. The first principal component (PC1) explained general body confirmation and explained 33.5% variation. It was represented by significant positive loading for height at withers, rump height, diagonal length from point of shoulder to pin bone, chest depth, chest circumference etc.). The second principal component (PC2) accounted for an additional 15.0% of the generalized variance and was interpreted as an indicator of body shape (e.g., endomorphic vs. ectomorphic). It was represented by significant negative loadings for height at withers, rump height, diagonal length from point of shoulder to pin bone, but significant positive loadings for chest width, chest depth, chest circumference and cannon circumference. The study also revealed that factors extracted from the present investigation could be used in breeding programs of the dairy cattle.

scholarly journals Assessment of body weight and morphological traits of two breed of grower pigs using principal component analysis

2021 ◽  
Vol 48 (5) ◽  
pp. 1-11
Author(s):  
P.O. Akporhuarho ◽  
O. Iriakpe
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Body Length ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Head Length ◽  
Large White ◽  
Poids Corporel ◽  
The Mean

The study aimed at explaining objectively the relationship between morphologic traits of two breeds of pigs (Large-white and Duroc) using principal component analysis to determine the body size of grower pigs of two different breeds with a view of identifying components that best define body conformation. Body weight and five biometric variables namely head length, body length, body girth, ham length and ear length. The descriptive statistics showed that the mean body weight of Large-white was 13.14kg while the body measurements were 24.61cm, 71.35cm, 65.12cm, 43.13cm and 21.94cm for head length, body length, body girth, ham length and ear length respectively at 5 – 24 weeks of age. The mean body weight of Duroc was 12.87kg while the body measurements were 23.70cm, 57.93cm, 47.93cm, 22.90cm, 19.26cm for head length, body length, body girth, ham length and ear length respectively. The coefficient of correlation ranges from 0.08-0.424 and 0.01-0.402 for Large-white and Duroc respectively. The association between and were the highest for Duroc, body length r=0.402 and Large-white, body girth 0.424. Two components were identified for Large-white while those of Duroc were three components. The ratios of variance were 53.55 and 71.07% for Large-white and Duroc, respectively. The first factor in each case accounted for the biggest percentage of the total variation, and was designated the general size, the other factors (indices of body shape) offer forms of variation independent of the general size. The principal component based regression models which were chosen for selecting animals for optimal balance accounted for 58 and 76% of the variation in the body weight for Large-white and Duroc respectively. The study concluded that the use of principal component analysis techniques tends to explore the interdependence in the original five parameters measured: head length, body length, body girth, ham length and ear length of Large-white and Duroc     L'étude explique objectivement la relation entre les traits morphologiques de deux races de porcs (gros blanc et de Duroc) à l'aide d'une analyse de composants principaux afin de déterminer la taille du corps des porcs de producteurs de deux races différentes en vue d'identifier les composants qui définissent le mieux la conformation corporelle. Poids corporel et cinq variables biométriques, nommément longueur de la tête, longueur du corps, circonférence du corps, longueur du jambon et longueur de l'oreille. Les statistiques descriptives ont montré que le poids corporel moyen de gros blanc était de 13,14 kg tandis que les mesures du corps étaient de 24,61 cm, 71,35 cm, 65,12 cm, 43,13 cm et 21,94 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement à 5 - 24 semaines. Le poids corporel moyen de Duroc était de 12,87 kg tandis que les mesures du corps étaient de 23,70 cm, 57,93 cm, 47,93 cm, 22,90 cm, 19,26 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement. Le coefficient de corrélation varie de 0,08 à 0,424 et de 0,01 à 0,402 pour les gros blancs et Duroc respectivement. L'association entre et étaient les plus élevées pour Duroc, la longueur du corps R = 0,402 et de gros blancs, la circonférence du corps 0,424. Deux composants ont été identifiés pour les gros blancs tandis que ceux de Duroc étaient trois composants. Les ratios de variance étaient respectivement de 53,55 et 71,07% pour les gros blancs et Duroc. Le premier facteur de chaque cas représentait le plus gros pourcentage de la variation totale et a été désigné la taille générale, les autres facteurs (indices de la forme du corps) offrent des formes de variation indépendantes de la taille générale. Les principaux modèles de régression basés sur les composants choisis pour sélectionner des animaux pour un solde optimal représentaient 58 et 76% de la variation du poids corporel pour les grands blancs et Duroc respectivement. L'étude a conclu que l'utilisation de techniques d'analyse des composants principaux a tendance à explorer l'interdépendance dans les cinq paramètres d'origines mesurées: longueur de la tête, longueur du corps, circonférence corporelle, longueur du jambon et longueur de l'oreille de grosse blanc et de Duroc

Morphological structuring using principal component analysis of Rampur-Bushair sheep under transhumance production in western Himalayan region, India

2017 ◽  
Author(s):  
Varun Sankhyan ◽  
Y. P. Thakur ◽  
Sanjeet Katoch ◽  
P. K. Dogra ◽  
Rakesh Thakur
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Adult Sheep ◽  
Body Conformation ◽  
Western Himalayan Region ◽  
Body Weights ◽  
Correlated Variable ◽  
Sufficient Reduction

Principal component analysis (PCA) was employed on 12 biometric traits of Rampur-Bushair sheep of Himachal Pradesh. Morphological and biometrical observations were recorded on 162 young and 566 adult animals. Multivariate statistics and principal component analysis revealed that body measurements except for peripheral traits were mostly positively and significantly correlated. The correlation among conformation traits ranged from -0.08 to 0.79 and “0.18 to 0.71 in young and adult sheep respectively. Three and four factors were extracted in young and adult sheep respectively, which accounted for 57% and 61% of variation. The principal component extracted contributed effectively to explain general body conformation. The regression analysis suggested that use of principal component was more appropriate than the use of original correlated variable in estimating body weights. Therefore, factor extracted could be helpful in breeding programme with sufficient reduction in the number of biometric traits to be recorded to explain the body conformation.

scholarly journals Using Principal Component Analysis to Solve a Class Imbalance Problem in Traffic Incident Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Changjiang Zheng ◽  
Shuyan Chen ◽  
Wei Wang ◽  
Jian Lu
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Class Imbalance ◽  
Principal Component ◽  
Component Analysis ◽  
Incident Detection ◽  
Least Squares Regression ◽  
Class Imbalance Problem ◽  
Significance Level ◽  
Traffic Incident

High imbalances occur in real-world situations when a detection system needs to identify the rare but important event of a traffic incident. Traffic incident detection can be treated as a task of learning classifiers from imbalanced or skewed datasets. Using principal component analysis (PCA), a one-class classifier for incident detection is constructed from the major and minor principal components of normal instances. Experiments are conducted with a real traffic dataset collected from the A12 highway in The Netherlands. The parameters setting, including the significance level, the percentage of the total variation explained, and the upper bound of the eigenvalues for the minor components, is discussed. The test results demonstrate that this method achieves better performance than partial least squares regression. The method is shown to be promising for traffic incident detection.

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