Revisions to Penicillium ser. Corymbifera: agents responsible for blue mould storage rot of various flower and vegetable bulbs

2005 ◽  
Vol 83 (11) ◽  
pp. 1422-1433 ◽  
Author(s):  
D.P. Overy ◽  
J.G. Valdez ◽  
J.C. Frisvad
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Secondary Metabolite ◽  
Partial Least Squares Regression ◽  
Phenotypic Expression ◽  
Chemical Diversity ◽  
Data Matrix ◽  
Phenotypic Traits ◽  
Least Squares Regression

Fifteen strains representing each Penicillium ser. Corymbifera taxa were compared using phenotypic and chemotaxonomic characters by cluster analysis and discriminant partial least squares regression. Variability in phenotypic expression of species strains resulted in a more fragmented classification compared with secondary metabolite expression. Although the observed phenotypic expression varied for strains cultured upon the same media, it was possible to classify strains into species groupings based only upon a few distinctive phenotypic traits. Data analysis of secondary metabolite profiles generated from HPLC-diode array dectection analysis gave reliable strain classification when more than one media type was employed. Depending on the species, Czapek yeast autolysate agar typically yielded the greatest chemical diversity; however, several metabolites (terrestric acid, corymbiferone, the corymbiferan lactones, and daldinin D) were only produced when strains were grown on either yeast extract sucrose or oatmeal agar. For the classification of strains based on a binary data matrix, application of the Yule coefficient gave the best clustering. Several secondary metabolites, of importance for the classification of ser. Corymbifera strains, were identified by discriminant-partial least squares regression analysis. A diagnostic key based on phenotypic, chemotaxonomic, and pathogenic traits is provided as an aid for species identification.

scholarly journals Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 378 ◽  
Author(s):  
Nerea Núñez ◽  
Xavi Collado ◽  
Clara Martínez ◽  
Javier Saurina ◽  
Oscar Núñez
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Partial Least Squares Regression ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Chromatographic Fingerprints ◽  
Sample Classification ◽  
Chemical Descriptors

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

scholarly journals Identification of phenolic and alcoholic compounds in wine spirits and their classification by use of multivariate analysis

2019 ◽  
Vol 84 (7) ◽  
pp. 663-677 ◽  
Author(s):  
Karla Hanousek-Cica ◽  
Martina Pezer ◽  
Jasna Mrvcic ◽  
Damir Stanzer ◽  
Jasna Cacic ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Higher Alcohols ◽  
Least Squares Regression ◽  
Phenol Composition ◽  
Ageing Period

During the ageing period wine spirits are changing their color, chemical composition and sensory characteristics. These changes should be simply monitored. The aim of this study was to develop partial least squares regression (PLS) models for higher alcohols and phenols in wine spirits as well as to show the feasibility of the NIR spectroscopy combined with chemometric tools to distinguish wine spirits and brandies with different ageing degree. To get the reference values, the usual methods for the analysis of spirits drinks were used. Ethanol, esters, acids, methanol and higher alcohols were studied. Wine spirits and brandies phenol composition was determined by liquid chromatography. Principal component analysis (PCA) was used to classify the wine spirits and brandies according to their phenolic and higher alcohols composition. Moreover, the Partial least squares regression (PLS regression) was used to calibrate and predict expected contents of higher alcohols and phenols in the wine spirits. Success of the classification of samples by ageing based on individual alcohols was 93.8 %, while success of the classification based on individual phenols raised to 100 %. This efficiency of the prediction was evaluated by use of linear discriminator analysis (LDA).

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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