Academic literature on the topic 'PLSDA/PCA'

This paper aims to provide a stable instrumental method for provenance discrimination of Anji-White tea by its distinctive taste. 180 authentic and 60 counterfeit white tea samples were collected for specific geographical origins detection; all of them were measured by electronic tongue coupled with 7 independent sensors. Therefore, chemometrics methods, principal component analysis (PCA), and partial least squares discriminant analysis (PLSDA) were performed in classification. The PCA distribution shows that, in provenance analysis, PCA is a simple and reliable tool for small sample sets, but for sets with large objects, PCA seems powerless in classification. Therefore, PLSDA was applied to develop a classification model. The prediction sensitivity and specificity of PLSDA, respectively, reached 0.917 and 0.950. This study demonstrates the potential of combining electronic tongue system and chemometrics as an effective tool for specific geographical origins detection in Anji-White tea.

The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties. The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

The study explores the profile of carvedilol active ingredients by NIR, 1H-NMR and LC-MS Q-TOF and data were analysed by PCA, cluster analysis and PLSDA. Two different groups of manufacturers based on the geographical area are classified.

Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information ofHibiscus mutabilisL. andBerberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines.

Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.

Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.

Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism of Angelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future.

Bupleurum scorzoneri folium Willd.(BSFW) is a traditional Chinese medicine which is widely distributed in China. To evaluate the quality of BSFW from different habitats, samples from 5 different areas in China were determined by UPLC/MS. The chemical data were dealed with hierarchical clustering, PCA, SPCA, PLSDA and SPLSDA using R software. The results show that these pattern recognition methods can fully reflect the chemical composition of different areas of BSFW, which make it possible to control the quality.

Watercore and sugar content are internal qualities which are impossible for exterior determination. Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectroscopy (NIR) technique. A total of 93 samples of Asian pear variety "SH-078" were used. For sugar content, spectrum of each fruit was measured in the short wavelength region (700–1100 nm) in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer. Prediction equation was performed by multiple linear regression. The result showed Standard Error of Prediction (SEP) = 0.58°Bx, and Bias=0.11. The result from t-test showed that sugar content predicted by NIR was not significantly different from the value analyzed by refractometer at 95% confidence. For watercore disorder, NIR measurement was performed over the short wavelength range (700–850 nm) in the transmission mode. The first derivative spectra were correlated with internal qualities. Then principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to perform discrimination models. The accuracy of the PCA model was greater than the PLSDA one. The scores from PC1 were separated into two boundaries, one predicted rejected pears with 100% classification accuracy, and the other was accepted pears with 92% accuracy. The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.

La recherche de nouveaux composés pour prévenir ou atténuer le vieillissement de la peau est une priorité des recherches actuelles dans les cosmétiques. Dans ce contexte, le venin de frelon asiatique (Vespa velutina nigrithorax) a été étudié comme une source particulière de molécules potentiellement bioactives d’intérêt dermacosmétique. La première étude a tout d’abord porté sur la mise en œuvre d’un protocole fiable d’extraction et récupération du venin. Puis, la fraction peptidique et petites molécules a été sélectionnée afin d’évaluer, en comparaison avec le venin brut, la présence de molécules actives vis-à-vis d’une activité antioxydante, anti-microbienne (C. acnes) et inhibitrice enzymatique (tyrosinase, élastase, collagénase) in-tubo et in-cellulo. Ces études ont conduit à identifier par UHPLC-ESI-QTOF-HRMS/MS, dans le venin brut, une molécule responsable de l’activité anti-oxydante sur kératinocytes HaCaT. Dans une seconde étude, une approche peptidomique basée sur une méthode UHPLC-QTOF-HRMS et MS/MS suivie par un traitement statistique (PCA, PLS-DA) a été appliquée sur l’étude différentielle de profil peptidique du venin, en fonction de la période de collecte, des castes et du comportement. Ces derniers ont pour but d’évaluer l’influence de différents facteurs sur le patrimoine moléculaire de ces venins. Parallèlement, en troisième étude, une approche de criblage d’interaction Ligand/enzyme par spectrométrie de masse sur les enzymes élastase et tyrosinase immobilisées a été développée. Cette méthode a pour objectif de mettre en évidence la présence d’inhibiteurs ou de substrats dans des fractions plus ou moins complexes. On a montré que deux peptides présents dans le venin de frelon étaient capables d’interagir avec l’enzyme élastase en tant que substrat. La séquence peptidique de ces peptides a été partiellement obtenue par séquençage de novo
The search for new compounds to prevent or attenuate skin aging is a priority in current research in cosmetics. In this context, Asian Hornet venom (Vespa velutina nigrithorax) has been studied as a particular source of potentially bioactive molecules for dermacosmetic interest.The first study focused on the implementation of a reliable venom extraction and sampling protocol. Then, the peptide - small molecules fraction was selected to evaluate, in comparison with crude venom, the presence of active molecules with respect to antioxidant, anti-microbial (C. acnes) and enzyme inhibition (tyrosinase, elastase, collagenase) activity in-tubo and in-cellulo. These studies led to the identification in crude venom, by UHPLC-ESI-QTOF-HRMS/MS, of one molecule responsible for antioxidant activity on HaCaT keratinocytes.In a second study, a peptidomic approach based on UHPLC-ESI-QTOF-HRMS/MS followed by statistical processing (PCA, PLS-DA) was applied to the differential study of venom, according to the collection period, castes and behavior. The latter aims at evaluating the influence of these different factors on the venom molecular heritage. At the same time, in a third study, a ligand/enzyme interaction screening approach by mass spectrometry on solid-supported elastase enzymes was developed. The aim of this method is to detect the presence of inhibitors or substrates in more or less complex fractions. Two hornet venom peptides presenting in the hornet venom were identified to be capable of interacting with the enzyme elastase. Their peptide sequences were then partially obtained by de novo sequencing

The present Ph.D. thesis, primarily conceived to support and reinforce the relation between academic and industrial worlds, was developed in collaboration with Shell Global Solutions (Amsterdam, The Netherlands) in the endeavour of applying and possibly extending well-established latent variable-based approaches (i.e. Principal Component Analysis - PCA - Partial Least Squares regression - PLS - or Partial Least Squares Discriminant Analysis - PLSDA) for complex problem solving not only in the fields of manufacturing troubleshooting and optimisation, but also in the wider environment of multivariate data analysis. To this end, novel efficient algorithmic solutions are proposed throughout all chapters to address very disparate tasks, from calibration transfer in spectroscopy to real-time modelling of streaming flows of data. The manuscript is divided into the following six parts, focused on various topics of interest: Part I - Preface, where an overview of this research work, its main aims and justification is given together with a brief introduction on PCA, PLS and PLSDA; Part II - On kernel-based extensions of PCA, PLS and PLSDA, where the potential of kernel techniques, possibly coupled to specific variants of the recently rediscovered pseudo-sample projection, formulated by the English statistician John C. Gower, is explored and their performance compared to that of more classical methodologies in four different applications scenarios: segmentation of Red-Green-Blue (RGB) images, discrimination of on-/off-specification batch runs, monitoring of batch processes and analysis of mixture designs of experiments; Part III - On the selection of the number of factors in PCA by permutation testing, where an extensive guideline on how to accomplish the selection of PCA components by permutation testing is provided through the comprehensive illustration of an original algorithmic procedure implemented for such a purpose; Part IV - On modelling common and distinctive sources of variability in multi-set data analysis, where several practical aspects of two-block common and distinctive component analysis (carried out by methods like Simultaneous Component Analysis - SCA - DIStinctive and COmmon Simultaneous Component Analysis - DISCO-SCA - Adapted Generalised Singular Value Decomposition - Adapted GSVD - ECO-POWER, Canonical Correlation Analysis - CCA - and 2-block Orthogonal Projections to Latent Structures - O2PLS) are discussed, a new computational strategy for determining the number of common factors underlying two data matrices sharing the same row- or column-dimension is described, and two innovative approaches for calibration transfer between near-infrared spectrometers are presented; Part V - On the on-the-fly processing and modelling of continuous high-dimensional data streams, where a novel software system for rational handling of multi-channel measurements recorded in real time, the On-The-Fly Processing (OTFP) tool, is designed; Part VI - Epilogue, where final conclusions are drawn, future perspectives are delineated, and annexes are included.
La presente tesis doctoral, concebida principalmente para apoyar y reforzar la relación entre la academia y la industria, se desarrolló en colaboración con Shell Global Solutions (Amsterdam, Países Bajos) en el esfuerzo de aplicar y posiblemente extender los enfoques ya consolidados basados en variables latentes (es decir, Análisis de Componentes Principales - PCA - Regresión en Mínimos Cuadrados Parciales - PLS - o PLS discriminante - PLSDA) para la resolución de problemas complejos no sólo en los campos de mejora y optimización de procesos, sino también en el entorno más amplio del análisis de datos multivariados. Con este fin, en todos los capítulos proponemos nuevas soluciones algorítmicas eficientes para abordar tareas dispares, desde la transferencia de calibración en espectroscopia hasta el modelado en tiempo real de flujos de datos. El manuscrito se divide en las seis partes siguientes, centradas en diversos temas de interés: Parte I - Prefacio, donde presentamos un resumen de este trabajo de investigación, damos sus principales objetivos y justificaciones junto con una breve introducción sobre PCA, PLS y PLSDA; Parte II - Sobre las extensiones basadas en kernels de PCA, PLS y PLSDA, donde presentamos el potencial de las técnicas de kernel, eventualmente acopladas a variantes específicas de la recién redescubierta proyección de pseudo-muestras, formulada por el estadista inglés John C. Gower, y comparamos su rendimiento respecto a metodologías más clásicas en cuatro aplicaciones a escenarios diferentes: segmentación de imágenes Rojo-Verde-Azul (RGB), discriminación y monitorización de procesos por lotes y análisis de diseños de experimentos de mezclas; Parte III - Sobre la selección del número de factores en el PCA por pruebas de permutación, donde aportamos una guía extensa sobre cómo conseguir la selección de componentes de PCA mediante pruebas de permutación y una ilustración completa de un procedimiento algorítmico original implementado para tal fin; Parte IV - Sobre la modelización de fuentes de variabilidad común y distintiva en el análisis de datos multi-conjunto, donde discutimos varios aspectos prácticos del análisis de componentes comunes y distintivos de dos bloques de datos (realizado por métodos como el Análisis Simultáneo de Componentes - SCA - Análisis Simultáneo de Componentes Distintivos y Comunes - DISCO-SCA - Descomposición Adaptada Generalizada de Valores Singulares - Adapted GSVD - ECO-POWER, Análisis de Correlaciones Canónicas - CCA - y Proyecciones Ortogonales de 2 conjuntos a Estructuras Latentes - O2PLS). Presentamos a su vez una nueva estrategia computacional para determinar el número de factores comunes subyacentes a dos matrices de datos que comparten la misma dimensión de fila o columna y dos planteamientos novedosos para la transferencia de calibración entre espectrómetros de infrarrojo cercano; Parte V - Sobre el procesamiento y la modelización en tiempo real de flujos de datos de alta dimensión, donde diseñamos la herramienta de Procesamiento en Tiempo Real (OTFP), un nuevo sistema de manejo racional de mediciones multi-canal registradas en tiempo real; Parte VI - Epílogo, donde presentamos las conclusiones finales, delimitamos las perspectivas futuras, e incluimos los anexos.
La present tesi doctoral, concebuda principalment per a recolzar i reforçar la relació entre l'acadèmia i la indústria, es va desenvolupar en col·laboració amb Shell Global Solutions (Amsterdam, Països Baixos) amb l'esforç d'aplicar i possiblement estendre els enfocaments ja consolidats basats en variables latents (és a dir, Anàlisi de Components Principals - PCA - Regressió en Mínims Quadrats Parcials - PLS - o PLS discriminant - PLSDA) per a la resolució de problemes complexos no solament en els camps de la millora i optimització de processos, sinó també en l'entorn més ampli de l'anàlisi de dades multivariades. A aquest efecte, en tots els capítols proposem noves solucions algorítmiques eficients per a abordar tasques dispars, des de la transferència de calibratge en espectroscopia fins al modelatge en temps real de fluxos de dades. El manuscrit es divideix en les sis parts següents, centrades en diversos temes d'interès: Part I - Prefaci, on presentem un resum d'aquest treball de recerca, es donen els seus principals objectius i justificacions juntament amb una breu introducció sobre PCA, PLS i PLSDA; Part II - Sobre les extensions basades en kernels de PCA, PLS i PLSDA, on presentem el potencial de les tècniques de kernel, eventualment acoblades a variants específiques de la recentment redescoberta projecció de pseudo-mostres, formulada per l'estadista anglés John C. Gower, i comparem el seu rendiment respecte a metodologies més clàssiques en quatre aplicacions a escenaris diferents: segmentació d'imatges Roig-Verd-Blau (RGB), discriminació i monitorització de processos per lots i anàlisi de dissenys d'experiments de mescles; Part III - Sobre la selecció del nombre de factors en el PCA per proves de permutació, on aportem una guia extensa sobre com aconseguir la selecció de components de PCA a través de proves de permutació i una il·lustració completa d'un procediment algorítmic original implementat per a la finalitat esmentada; Part IV - Sobre la modelització de fonts de variabilitat comuna i distintiva en l'anàlisi de dades multi-conjunt, on discutim diversos aspectes pràctics de l'anàlisis de components comuns i distintius de dos blocs de dades (realitzat per mètodes com l'Anàlisi Simultània de Components - SCA - Anàlisi Simultània de Components Distintius i Comuns - DISCO-SCA - Descomposició Adaptada Generalitzada en Valors Singulars - Adapted GSVD - ECO-POWER, Anàlisi de Correlacions Canòniques - CCA - i Projeccions Ortogonals de 2 blocs a Estructures Latents - O2PLS). Presentem al mateix temps una nova estratègia computacional per a determinar el nombre de factors comuns subjacents a dues matrius de dades que comparteixen la mateixa dimensió de fila o columna, i dos plantejaments nous per a la transferència de calibratge entre espectròmetres d'infraroig proper; Part V - Sobre el processament i la modelització en temps real de fluxos de dades d'alta dimensió, on dissenyem l'eina de Processament en Temps Real (OTFP), un nou sistema de tractament racional de mesures multi-canal registrades en temps real; Part VI - Epíleg, on presentem les conclusions finals, delimitem les perspectives futures, i incloem annexos.
Vitale, R. (2017). Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90442
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