Non-invasive classification of single and double-yolk eggs using Vis-NIR spectroscopy and multivariate analysis
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1. This study was conducted to develop an efficient technique for separating double-yolked (DY) from single-yolked (SY) light brown broiler eggs with comparable shape and size, that were hard to distinguish merely by their external characteristics, using Vis-NIR transmission spectroscopy combined with multivariate analysis.2. Spectroscopic transmission (200-900 nm) was measured after collecting the eggs, and the yolk number was verified by breaking the eggs after boiling. The absorbance of important spectral wavelengths sensitive to yolk amount were identified using feature selection techniques (Principal Component Analysis and Genetic Algorithm).3. Discriminant analysis (DA) and support vector machine (SVM) classifiers were used to develop classification models for DY and SY eggs using the selected important spectral wavelengths.4. When compared to alternative nonlinear techniques, the developed model applying linear discriminant analysis produced greater accuracies in the first (96%) and second (100%) experiments, implying lower inter-egg variability from spectral data and a linear relationship between classes. However, the position and orientation of yolks in DY eggs may limit the classification accuracy of the eggs.Keywords:
Yolk
Absorbance
In view of the mine ventilation system safety assessment problems,a principal component analysis(FDA)-Fisher discriminant analysis(FDA) model(PCA-FDA) is established.Firstly,evaluation indexes are selected in the safe and reliable,economic and reasonable,easy measurement principle.Combined with the theory of statistics,the:principal components are selected using SPSS software for learning samples by principal component analysis(PCA).Finally,Fisher diseriminant analysis is used to analyze the principal component.Then the model is used to discriminant the actual production of the mine ventilation system and compare with traditional Fisher discriminant analysis.The results show that SPSS PCA-FDA model can make discrimination results more accurate by eliminating the interaction between the sample variable indicators effectively.Computing time is reduced greatly,convenient for practical popularization and application.
Optimal discriminant analysis
Fisher kernel
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The combination of 1H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed 1H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85–97% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated.
Profiling (computer programming)
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Principal component analysis (PCA) is an effective statistical technique for face recognition because it can reduce the dimensions of a given unlabeled high-dimensional dataset while keeping its spatial characteristics as much as possible. However, since PCA only explains the covariance structure of all the data its most expressive components, it cannot represent the most important discriminant directions to separate sample groups. To solve this problem, in this paper we propose a new PCA method based on the linear discriminant analysis (LDA) space. From our theoretic analysis and numerical experiments, our new PCA method (we call it PCA-LDA) can work effectively and efficiently.
Optimal discriminant analysis
Multiple discriminant analysis
Sample (material)
Component analysis
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Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique that loses resolution between spectra of different proteins as more proteins are added to the data set due to large within-group variation. The supervised pattern recognition techniques discriminant principal component analysis (DPCA) and linear discriminant analysis (LDA), which aim to control within-group variation while maximizing between-group separation to enhance discrimination between groups, were compared with PCA using data sets of TOF-SIMS spectra of proteins adsorbed onto mica and PTFE substrates. DPCA and LDA quantitatively improved discrimination between groups and provided different information about the data than PCA. LDA was able to classify unknown samples with a misclassification rate lower than PCA or DPCA. Both unsupervised and supervised pattern recognition techniques are useful for the interpretation and classification of static TOF-SIMS spectra of adsorbed protein films.
Chemometrics
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image processing field is becoming more popular for the security purpose in now days.It has many sub fields and face recognition is one from them.Many techniques have been developed for the face recognition but in our work we just discussed two prevalent techniques PCA (Principal component analysis) and LDA (Linear Discriminant Analysis) and others in brief.These techniques mostly used in face recognition.PCA based on the eigenfaces or we can say reduce dimension by using covariance matrix and LDA based on linear Discriminant or scatter matrix.In our work we also compared the PCA and LDA.
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The nondestructive characterization of citrus varieties (Egyptian sweet orange, Lane Late navel orange, Australian orange, and Blood orange) was developed based on near-infrared diffuse reflectance spectroscopy (NIRDRS) together with principal component analysis (PCA) and Fisher linear discriminant analysis (FLDA). An experiment for the penetration of NIRDRS into the peel was designed and the effects of different spectral acquisition points were investigated. Pretreatments were used to eliminate the spectral interferences. As an unsupervised pattern recognition method, PCA was used to establish the characterization models. Furthermore, supervised pattern recognition based on PCA and FLDA was employed to enhance the accuracy. The results of the penetration experiments show that near-infrared light enters the citrus peel and is able to characterize the internal composition. Even with the optimized spectral pretreatment, accurate characterization of citrus varieties was not achieved by PCA. However, the accurate characterization of citrus varieties was provided by PCA-FLDA. The accuracies of four spectral acquisition points are 95%, while the characterization accuracies of six spectral acquisition points are 100% combined with optimized spectral pretreatment. Therefore, NIRDRS with PCA-FLDA is suitable for the rapid and nondestructive characterization of citrus varieties.
Characterization
Chemometrics
Citrus × sinensis
Spectral Analysis
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Combined with the sensory evaluation and TVBN,the electronic nose was used to detect the Penaeus vanmamei under 5℃,and established the freshness of Penaeus vanmamei discriminant model based on the electronic nose technology. The results showed that the data normalization method is determined by the model discriminate accuracy. In principal components analysis,the first three principal components accounted for 86. 974% of total variation,which can effectively used by fisher linear discriminant analysis. Principal component analysis-fisher linear discriminant model can determine the freshness of Penaeus vanmamei with high discriminant accuracy and stability.
Electronic Nose
Normalization
Penaeus
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Classification procedures are examined in the case when the dimensionality exceeds the sample size. Two particular suggestions are (i) Principal components analysis and (ii) Two-step discriminant analysis. Comparisons are made in the two sample and the several sample cases. Extensions to growth curve model are investigated using the two stage discriminant analysis.
Discriminant function analysis
Optimal discriminant analysis
Sample (material)
Multiple discriminant analysis
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