Identification Method of Egg Freshness by Principal Component and Discriminant Analysis

2017 ◽  
pp. 659-665
Author(s):  
Yinghao Xing ◽  
Qigen Tong ◽  
Xiujuan Zhi ◽  
Bin Du
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Identification Method ◽  
Egg Freshness

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Identification and Quantification of Metamizole in Traditional Herbal Medicines using Spectroscopy FTIR-ATR combined with Chemometrics

2021 ◽  
pp. 4413-4419
Author(s):  
Dharmastuti Cahya Fatmarahmi ◽  
Ratna Asmah Susidarti ◽  
Respati Tri Swasono ◽  
Abdul Rohman
Keyword(s):  
Discriminant Analysis ◽  
Herbal Medicine ◽  
Ftir Spectroscopy ◽  
Herbal Medicines ◽  
Principal Component ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Precision Data

The study aims to develop an effective, efficient, and reliable method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) combined with chemometric for identifying the synthetic drug in Indonesian herbal medicine known as Jamu. Jamu powders, Metamizole, and the binary mixture of Jamu and Metamizole were measured using FTIR-ATR at the mid-infrared region (4000-650 cm-1). The obtained spectra profiles were further analyzed by Principal Component Analysis, Partial Least Square Regression, Principal Component Regression, and Discriminant Analysis. Jamu Pegel Linu (JPL), Jamu Encok (JE), Jamu Sakit Pinggang (JSP), Metamizole (M), and adulterated Jamu by Metamizole were discriminated well on PCA score plot. PLSR and PCR showed the accuracy and precision data to quantify JPL, JE, and JSP, and each adulterated by M with R2 value > 0,995 and low value of RMSEC and RMSEP. Discriminant Analysis (DA) was successfully grouping Jamu and Metamizole without any misclassification. A combination of FTIR spectroscopy and chemometrics offered useful tools for detecting Metamizole in traditional herbal medicine.

scholarly journals Surface Electromyography Based Core Muscle Fatigue Analysis During Repetitive Plank Using Multivariate Dimensionality Reduction Methods in Boys Aged 12-14

2021 ◽  
Vol 21 (3) ◽  
pp. 253-263
Author(s):  
Abir Samanta ◽  
Sabyasachi Mukherjee
Keyword(s):  
Discriminant Analysis ◽  
Muscle Fatigue ◽  
Muscle Activity ◽  
Principal Component ◽  
Erector Spinae ◽  
P Value ◽  
Recruitment Strategies ◽  
Structure Matrix ◽  
Spine Stability ◽  
Wilks Lambda

The aims of the study were: 1. To analyse the discriminative power of neuromuscular components for classifying the pre and post muscle fatigued states. 2. To examine whether the modification of neural recruitment strategies become more/less heterogeneous due to fatigue. 3. To research the effect of Erector Spinae (ES) muscle activity collectively with Rectus Abdominis (RA) and External Oblique (EO) muscle activity to identify the reduced spine stability during fatiguing Plank.  Material and methods. Twelve boys (age – 12-14 years, height 148.75 ± 10 cm, body mass 38.9 ± 7.9 kg) participated in the study. Multivariate Discriminant Analysis (DA) and Principal Component Analysis (PCA) were applied to identify the changes in the pattern of the electromyographic signals during muscle fatigue. In DA the Wilks’ lambda, p-value, canonical correlation, classification percentage and structure matrix were used. To evaluate the component validity the standard limit for Kaiser-Meyer-Olkin (KMO) was set at ≥0.529 and the p-value of Bartlett’s test was ≤0.001. The eigenvalues ≥1 were used to determine the number of Principal Components (PCs). The satisfactory percentage of non-redundant residuals were set at ≤50% with standard value >0.05. The absolute value of average communality (x̄ h2) and component loadings were set at ≥0.6, ≥0.4 respectively.  Results. Standardized canonical discriminant analysis showed that pre and post fatigued conditions were significantly different (p = 0.000, Wilks’ lambda = 0.297, χ2 = 24.914, df=3). The structure matrix showed that the parameter that correlated highly with the discriminant function was ES ARV (0.514). The results showed that the classification accuracy was 95.8% between fatigued conditions. In PCA the KMO values were reduced [0.547Pre fatigue vs. 0.264Post fatigue]; the value of Bartlett’s sphericity test was in pre χ2 = 90.72 (p = 0.000) and post fatigue χ2 = 85.32 (p = 0.000); The Promax criterion with Kaiser Normalization was applied because the component rotation was non-orthogonal [Component Correlation Matrix (rCCM) = 0.520 Pre fatigue >0.3Absolute<0.357Post fatigue]. In pre fatigue two PCs (cumulative s2 – 80.159%) and post fatigue three PCs (cumulative s2 – 83.845%) had eigenvalues ≥1. The x̄ h2 increased [0.802 Pre fatigue vs. 0.838 Post fatigue] and the percentage of nonredundant residuals reduced [50% Pre fatigue vs. 44% Post fatigue] from pre to post fatigue.  Conclusions. The variability and heterogeneity increase in the myoelectric signals due to fatigue. The co-activity of antagonist ES muscle is significantly sensitive to identify the deteriorating spine stability during the fatiguing Plank. Highly correlated motor unit recruitment strategies between ES and RA, providing supportive evidence to the concept of shared agonist-antagonist motoneuron pool or “Common Drive” phenomenon during fatigue.

scholarly journals Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC) multivariate method

Genetika ◽  
2013 ◽  
Vol 45 (3) ◽  
pp. 963-977 ◽  
Author(s):  
Jasmin Grahic ◽  
Fuad Gasi ◽  
Mirsad Kurtovic ◽  
Lutvija Karic ◽  
Mirha Djikic ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Common Bean ◽  
Bosnia And Herzegovina ◽  
Principal Components ◽  
Morphological Characteristics ◽  
Principal Component ◽  
Discriminant Functions ◽  
Factor Loadings ◽  
Geographical Regions ◽  
High Level

In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H), thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo) and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA) showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC) created 3 discriminant functions (DF), whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn?t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina?s common been landraces could be useful in future breeding programs.

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%.

Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths

2020 ◽  
Vol 28 (4) ◽  
pp. 224-235
Author(s):  
Irina M Benson ◽  
Beverly K Barnett ◽  
Thomas E Helser
Keyword(s):  
Discriminant Analysis ◽  
Spatial Variability ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Otolith Chemistry ◽  
K Nearest Neighbor ◽  
Fisheries Science

Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.

scholarly journals Human Melanoma-Cell Metabolic Profiling: Identification of Novel Biomarkers Indicating Metastasis

2020 ◽  
Vol 21 (7) ◽  
pp. 2436 ◽  
Author(s):  
Mariangela Kosmopoulou ◽  
Aikaterini F. Giannopoulou ◽  
Aikaterini Iliou ◽  
Dimitra Benaki ◽  
Aristeidis Panagiotakis ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
High Resolution ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cell Lines ◽  
High Performance ◽  
Principal Component ◽  
Metastatic Tumor ◽  
Human Melanoma ◽  
Melanoma Metastasis

Melanoma is the most aggressive type of skin cancer, leading to metabolic rewiring and enhancement of metastatic transformation. Efforts to improve its early and accurate diagnosis are largely based on preclinical models and especially cell lines. Hence, we herein present a combinational Nuclear Magnetic Resonance (NMR)- and Ultra High Performance Liquid Chromatography-High-Resolution Tandem Mass Spectrometry (UHPLC-HRMS/MS)-mediated untargeted metabolomic profiling of melanoma cells, to landscape metabolic alterations likely controlling metastasis. The cell lines WM115 and WM2664, which belong to the same patient, were examined, with WM115 being derived from a primary, pre-metastatic, tumor and WM2664 clonally expanded from lymph-node metastases. Metabolite samples were analyzed using NMR and UHPLC-HRMS. Multivariate statistical analysis of high resolution NMR and MS (positive and negative ionization) results was performed by Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), while metastasis-related biomarkers were determined on the basis of VIP lists, S-plots and Student’s t-tests. Receiver Operating Characteristic (ROC) curves of NMR and MS data revealed significantly differentiated metabolite profiles for each cell line, with WM115 being mainly characterized by upregulated levels of phosphocholine, choline, guanosine and inosine. Interestingly, WM2664 showed notably increased contents of hypoxanthine, myo-inositol, glutamic acid, organic acids, purines, pyrimidines, AMP, ADP, ATP and UDP(s), thus indicating the critical roles of purine, pyrimidine and amino acid metabolism during human melanoma metastasis.

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