Classification Methods in Chemometrics

Marini, Federico

doi:10.2174/157341110790069592

Cited by 66 publications

(37 citation statements)

References 52 publications

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“…Until these assumptions, compute a classification model corresponds to calculating the regression vector between the data matrix and the dummy vector of responses. The result is a linear model that has proven to be statistically equivalent to the solution obtained to Linear Discriminant Analysis (Marini, 2010).…”

Section: Supervised Chemometric Techniquesmentioning

confidence: 87%

Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

Cruz

Cadena

Alvaro

et al. 2013

LWT - Food Science and Technology

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Section: Supervised Chemometric Techniquesmentioning

confidence: 87%

Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

Cruz

Cadena

Alvaro

et al. 2013

LWT - Food Science and Technology

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“…The PLSR is a new method of multivariate statistical data analysis, which makes a combination of the advantages of three analytical methods including principal component analysis, canonical correlation analysis and multiple linear regression analysis. (MARINI, 2010). This regression has been applied in analysis the change of total intramuscular fatty acids of cooked Hyla rabbit (XUE et al, 2016b), the composition of intramuscular phospholipid fatty acids of Inra rabbit during growth (XUE, 2016a), discriminating low-fat and full-fat yogurts (CRUZ et al, 2013), and the discrimination of Brazilian artisanal and inspected pork sausages (MATERA et al, 2014) and so on.…”

Section: Analysis Of Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

Cooking ways on composition of intramuscular phospholipid fatty acids of inra rabbit

Xue

2019

Cienc. Rural

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The effect of boiling, microwaving and aluminium (Al) foil-baking on composition of intramuscular phospholipid fatty acids of Inra rabbit was evaluated. Results showed that, the proportion of polyunsaturated fatty acids (PUFA) (e.g. C18:2n-6, C20:4n-6, C20:5n-3, C22:5n-3 and C22:6n-3) and monounsaturated fatty acids (MUFA) (e.g. C18:1n-7 and C18:1n-9) of treated longissimus dorsi muscle (LD) decreased, whilst the proportion of saturated (SFA)(e.g. C16:0 and C18:0) and n-6/n-3 value increased during cooking. Among the three treatments, microwaving can do better to stop the unsaturated fatty acids (UFA) being destroyed than boiling and Al foil-baking. However, boiling treatment did more serious damage to PUFA portion. Even so, the n-6/n-3 values of all of the cooked LD were within the recommended range. By analysis of partial least squares regression (PLSR), the microwaving treatment was more suitable in reserving UFA of intramuscular phospholipids from inra rabbit. RESUMO:Avaliou-se o efeito do cozimento da fervura, emprego de microondas e de preparo da carne envolvida em alumínio (Al) de três formas de cocção na composição de ácidos graxos fosfolipídicos intramusculares de coelho Inra. Os resultados mostraram que, a proporção de ácidos graxos poliinsaturados (durante o cozimento. Entre os três tratamentos, o micro-ondas parace ser o melhor por impedir a destruição dos ácidos graxos insaturados (UFA) do que a fervura e o cozimento em folha Al. No entanto, o tratamento de ebulição causou danos mais sérios à porção de PUFA. Mesmo assim, o valor n-6 / n-3 de todas as amostras de LD cozidas estava dentro da faixa recomendada. Por análise de regressão por mínimos quadrados parciais (PLSR), o tratamento com microondas foi mais adequado para preservar UFA de fosfolipídios intramusculares de Inra coelho. Palavras-chave: Inra coelho, formas de cozinhar, ácidos gordos fosfolipicos intramusculares, composição, regressão parcial por mínimos quadrados. FOOD TECHNOLOGY

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“…Classification methods consist of assigning the samples in a data set to a class or category based on the measurements performed on it. 30 There are several methods used for this purpose, e.g., soft independent modeling of class analogies (SIMCA), 31 which is a class modeling method, focuses on setting boundaries for a particular class and on the basis of those detect when a new sample is in or out of the class modeled. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), PLSDA, 22 and regularized discriminant analysis (RDA), 32 instead, are discriminant analysis methods, and aim at finding models that can help to distinguish among samples of two or more classes.…”

Section: Simplisma-based Scaling (Sbs)mentioning

confidence: 99%

Preprocessing Tools Applied to Improve the Assessment of Aldrin Effects on Prostate Cancer Cells Using Raman Spectroscopy

et al. 2017

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The study of pollutant effects on living organisms provides information about the possible biological and environmental response to a contaminant. Progression of prostate cancer may be related to exposure to pesticides or other chemical substances. In this work, the effect of the pesticide aldrin on human prostate cancer cells (DU145) is studied using Raman spectroscopy and chemometric techniques. Prostate cancer cell line DU145 has been exposed acutely the pesticide aldrin. Individual Raman spectra coming from control and treated cell populations have been acquired. Partial least squares discriminant analysis (PLSDA) has been used to assess differences among treated and control samples and to identify spectral biomarkers associated with pollutant stress. Some preprocessing methodologies have been tested in order to improve the capability of discrimination between fingerprints. Partial least squares discriminant analysis results suggest that the best normalization-scaling preprocessing combination is provided by Euclidean normalization (EN)-SIMPLISMA-based scaling (SBS). SIMPLISMA-based scaling has been proposed as a scaling method focused on the classification objective, which enhances variables with high relative variation among samples. The most relevant spectral variables related to aldrin effect on DU145 seem to be mainly related to lipids, proteins, and variations in nucleic acids.

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Classification Methods in Chemometrics

Cited by 66 publications

References 52 publications

Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

Cooking ways on composition of intramuscular phospholipid fatty acids of inra rabbit

Preprocessing Tools Applied to Improve the Assessment of Aldrin Effects on Prostate Cancer Cells Using Raman Spectroscopy

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