A comparison of three algorithms for chromatograms alignment

Nederkassel, A.M. van; Daszykowski, M.; Eilers, Paul H.C.; Heyden, Yvan Vander

doi:10.1016/j.chroma.2006.03.114

Cited by 155 publications

(100 citation statements)

References 13 publications

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“…The efficiency and simplicity of the alignment algorithm is attributed to using as landmarks multiple peptides identified with high probabilities (p>0.99 in these experiments). Thus, it is not necessary to use complicated nonlinear alignment algorithms [36,37] developed for applications where sample component identifications are not available at the alignment stage. In general, our alignment algorithm worked well for chromatograms having at least 10 −15 common identified peptides across the chomatogram to use as landmarks.…”

Section: Performance Of the Q-mend Algorithmmentioning

confidence: 99%

A New Algorithm Using Cross-Assignment for Label-Free Quantitation with LC−LTQ-FT MS

Andreev

Cao

et al. 2007

J. Proteome Res.

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A new algorithm is described for label-free quantitation of relative protein abundances across multiple complex proteomic samples. Q-MEND is based on the denoising and peak picking algorithm, MEND, previously developed in our laboratory. Q-MEND takes advantage of the high resolution and mass accuracy of the hybrid LTQFT MS mass spectrometer (or other high resolution mass spectrometers, such as a Q-TOF MS). The strategy, termed "cross-assignment", is introduced to increase substantially the number of quantitated proteins. In this approach, all MS/MS identifications for the set of analyzed samples are combined into a master ID list, and then each LC/ MS run is searched for the features that can be assigned to a specific identification from that master list. The reliability of quantitation is enhanced by quantitating separately all peptide charge states, along with a scoring procedure to filter out less reliable peptide abundance measurements. The effectiveness of Q-MEND is illustrated in the relative quantitative analysis of E.coli samples spiked with known amounts of non-E.coli protein digests. A mean quantitation accuracy of 7% and mean precision of 15% is demonstrated. Q-MEND can perform relative quantitation of a set of LC/MS datasets without manual intervention and can generate files compatible with the Guidelines for Proteomic Data Publication.

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Section: Performance Of the Q-mend Algorithmmentioning

confidence: 99%

A New Algorithm Using Cross-Assignment for Label-Free Quantitation with LC−LTQ-FT MS

Andreev

Cao

et al. 2007

J. Proteome Res.

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“…9 O pré-tratamento aplicado a fim de corrigir esses deslocamentos é o alinhamento dos picos dos cromatogramas. Várias técnicas de alinhamento foram propostas na literatura.…”

Section: Introductionunclassified

Uso de perfis cromatográficos de voláteis de cafés arábicas torrados para a diferenciação das amostras segundo o sabor, o aroma e a qualidade global da bebida

Ribeiro¹,

Augusto²,

Ferreira³

et al. 2010

Quím. Nova

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. In this work, the volatile chromatographic profiles of roasted Arabica coffees, previously analyzed for their sensorial attributes, were explored by principal component analysis. The volatile extraction technique used was the solid phase microextraction. The correlation optimized warping algorithm was used to align the gas chromatographic profiles. Fifty four compounds were found to be related to the sensorial attributes investigated. The volatiles pyrrole, 1-methyl-pyrrole, cyclopentanone, dihydro-2-methyl-3-furanone, furfural, 2-ethyl-5-methyl-pyrazine, 2-etenyl-n-methyl-pyrazine, 5-methyl-2-propionyl-furan compounds were important for the differentiation of coffee beverage according to the flavour, cleanliness and overall quality. Two figures of merit, sensitivity and specificity (or selectivity), were used to interpret the sensory attributes studied.Keywords: solid phase microextraction (SPME); coffee flavor; figures of merit. INTRODUÇÃOO aroma do café torrado é constituído por uma mistura complexa de compostos com poderes odoríferos distintos presentes em diferentes concentrações.1 De acordo com De Maria et al., 2 uma mesma substância volátil pode causar uma sensação odorífica agradável ou desagradável, dependendo de sua concentração no meio e das interações com outros compostos.Atualmente, a qualidade final da bebida do café é determinada pela "prova de xícara", realizada por provadores experientes. Dentre os atributos avaliados por estes profissionais encontram-se a qualidade global, o aroma e o sabor (bebida). Na "prova de xícara", considera-se o atributo "aroma" como a percepção olfativa dos gases liberados da infusão de café torrado e moído, enquanto o "sabor ou bebida" é considerado a combinação de sensações causadas pelos compostos químicos da bebida quando introduzida na boca. O atributo "qualidade global", por sua vez, é a percepção conjunta do sabor, do aroma e de outros atributos avaliados durante a análise.3 Estas definições acrescidas de resultados disponíveis na literatura indicam a elevada relação entre esses três atributos sensoriais da bebida. 4 Embora as pesquisas enfatizem que o aroma do café torrado tem um papel fundamental na qualidade da bebida, sendo considerado por muitos como sua impressão digital, são recentes os trabalhos que visam estabelecer essa relação. [4][5][6][7] Além disso, apenas alguns estudos são baseados na análise quimiométrica de dados cromatográficos com o propósito de tratar conjuntamente os constituintes voláteis do café e os resultados da "prova de xícara". 4,5,8 Os métodos quimiométricos são especialmente indicados para a aná-lise de cromatogramas de matrizes complexas, envolvendo centenas de compostos, da mesma forma que outros sinais instrumentais. A vantagem em se usar os perfís cromatográficos diretamente, ao invés de suas áreas de picos, é a possibilidade da extração de informações adicionais que não são computadas quando apenas as áreas são utilizadas como resposta analítica. Entretanto, para a aplicação das técnicas quimiométricas, os cromatogramas deve...

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“…However, due to several experiments issues, one may observe shifts between these peaks, which may be potentially harmful for subsequent analyzes. Most of solutions to peak alignment are based on time warping (see [46] for a comparison between three matching methods).…”

Section: Matching Identification and Learningmentioning

confidence: 99%

An overview of signal processing issues in chemical sensing

Duval

Duarte

Jutten

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This tutorial paper 1 aims at summarizing some problems, ranging from analytical chemistry to novel chemical sensors, that can be addressed with classical or advanced methods of signal and image processing. We gather them under the denomination of "chemical sensing". It is meant to introduce the special session "Signal Processing for Chemical Sensing" with a large overview of issues which have been and remain to be addressed in this application domain, including chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial nose, chromatography, mass spectrometry, etc. For enlarging and illustrating the points of view of this tutorial, the invited papers of the session consider other applications (NMR, Raman spectroscopy, recognition of explosive compounds, etc.) addressed by various methods, e.g. source separation, Bayesian, and exploiting typical chemical signal priors like positivity, linearity, unit-concentration or sparsity.

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A comparison of three algorithms for chromatograms alignment

Cited by 155 publications

References 13 publications

A New Algorithm Using Cross-Assignment for Label-Free Quantitation with LC−LTQ-FT MS

A New Algorithm Using Cross-Assignment for Label-Free Quantitation with LC−LTQ-FT MS

Uso de perfis cromatográficos de voláteis de cafés arábicas torrados para a diferenciação das amostras segundo o sabor, o aroma e a qualidade global da bebida

An overview of signal processing issues in chemical sensing

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