icoshift: An effective tool for the alignment of chromatographic data

Tomasi, Giorgio; Savorani, Francesco; Engelsen, Søren B.

doi:10.1016/j.chroma.2011.08.086

Cited by 207 publications

(92 citation statements)

References 24 publications

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“…If this condition is not met, as it is often the case in real world experiments, the predictive ability of classification model and the chemical interpretation of the results can be compromised [29]. Misalignment problem can be overcome by using alignment algorithm; in particular, in the present study, the Interval Correlation Optimized Shifting (icoshift) algorithm was used for aligning HPLC-DAD data [30,31]. icoshift divides spectra into segments, and aligns these to the corresponding segments of a reference spectrum.…”

Section: Peak Alignmentmentioning

confidence: 93%

Geographical traceability of extra virgin olive oils from Sabina PDO by chromatographic fingerprinting of the phenolic fraction coupled to chemometrics

Nescatelli

Bonanni

Bucci

et al. 2014

Chemometrics and Intelligent Laboratory Systems

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Section: Peak Alignmentmentioning

confidence: 93%

Geographical traceability of extra virgin olive oils from Sabina PDO by chromatographic fingerprinting of the phenolic fraction coupled to chemometrics

Nescatelli

Bonanni

Bucci

et al. 2014

Chemometrics and Intelligent Laboratory Systems

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“…Correlation based alignment methods, such as recursive segment-wise peak alignment (VESELKOV et al, 2009) and interval correlated shifting (icoshift) (SAVORANI et al, 2010b) use the effi cient fast Fourier transform engine to optimize the algorithms to be able to handle large data sets in real time. The same methods can be used to align chromatographic data (TOMASI et al, 2011), but in chromatography, there has been a tradition of using the more fl exible and meta-parameter demanding Correlation Optimized Warping (COW) method (NIELSEN et al, 1998, TOMASI et al, 2004.…”

Section: Data Pre-processing Prior To Chemometric Analysismentioning

confidence: 99%

Trends in the application of chemometrics to foodomics studies

Khakimov¹,

Gürdeniz²,

Engelsen³

2015

Acta Alimentaria

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There is an ever-increasing trend in advanced food analysis and foodomics to use more and more sophisticated analytical platforms that generate large and complex data structures, which in turn require more and more sophisticated data analysis tools for converting data into information. The choice of multivariate chemometric methods is primarily determined by the design of the study, type of the data, and the conclusions sought. In order to validate multivariate models, scientists are required to have basic chemometric knowledge and to be familiar with the variance structure of the investigated data. This review outlines some of the key aspects of applying common chemometric methods used within foodomics and provides selected examples of current applications. The review aims to provide simple insight into various multivariate methods and to illustrate pros and cons of unsupervised and supervised methods. The main analytical platforms used in foodomics are briefl y discussed from the application point of view and the utilization of the generated data is illustrated. In addition, advanced data pre-processing tools, prior to multivariate analysis, are explained and relevant tools are demonstrated.

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“…43 Data sets 2 and 3 were mass drift corrected by using the icoshift algorithm 44,45 in the m/z range of 3000-10000. Mean-centering scaling was applied to the data before chemometric modelling.…”

Section: Computational Analysismentioning

confidence: 99%

“…474 (MathWorks, USA) with PLS Toolbox 7.9.3 (Eigenvector Research, Inc., USA). All data sets were normalized by Euclidian norm and baseline corrected using automatic Whittaker filter (λ = 100, p = 0.001).43 Data sets 2 and 3 were mass drift corrected by using the icoshift algorithm 44,45 in the m/z range of 3000-10000. Mean-centering scaling was applied to the data before chemometric modelling.…”

mentioning

confidence: 99%

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

Morais

Lima

2017

J. Braz. Chem. Soc.

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Mass spectrometry (MS) is a powerful technique that can provide the biochemical signature of a wide range of biological materials such as cells and biofluids. However, MS data usually has a large range of variables which may lead to difficulties in discriminatory analysis and may require high computational cost. In this paper, principal component analysis with linear discriminant analysis (PCA-LDA) and quadratic discriminant analysis (PCA-QDA) were applied for discrimination between healthy control and cancer samples (ovarian and prostate cancer) based on MS data sets. In addition, an identification of prostate cancer subtypes was performed. The results obtained herein were very satisfactory, especially for PCA-QDA. Selectivity and specificity were found in a range of 90-100%, being equal or superior to support vector machines (SVM)-based algorithms. These techniques provided reliable identification of cancer samples which may lead to fast and less-invasive clinical procedures.Keywords: mass spectrometry, classification, ovarian cancer, prostate cancer, QDA IntroductionMass spectrometry (MS) is an analytical technique that is used for determining the chemical composition of a given sample, to quantify compounds, 1 and to help elucidate molecular structures. 2,3 This technique has been increasingly utilized in biomedical and clinical research, 4 since it can overcome many limitations of classical immunoassays 5,6 and supports the development of fast and less-invasive clinical procedures. [7][8][9] MS is usually coupled with chromatography such as liquid chromatography (LC-MS) and gas chromatography (GC-MS). Other techniques such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) are often used in MS applications, including disease screening and diagnosis.5 Some examples of MS applications includes toxicology screening and toxic drug quantification using quadrupole MS/MS; 10 identification of inborn errors in metabolism or genetic defects in newborns for prenatal screening programs using electrospray tandem MS; 11 detection of drug-induced hepatotoxicity using MS-based metabolomics; 12 and identification and quantification of bleomycin in serum and tumor tissue by high resolution LC-MS. 13 MS-based techniques have been largely employed for cancer identification, such as for breast cancer, 14 prostate cancer, 15,16 ovarian cancer, 17 lung cancer, 18 and pancreatic cancer; 19 as well as for identifying many biomarkers. 18,[20][21][22][23][24] One of the main fields using MS data is metabolomics, which aims to identify and quantify small molecules involved in metabolic reactions. 25 Metabolomics studies have been applied in several areas, especially cancer. 26These analyses are typically performed in either targeted or untargeted approaches. 25 The target approach aims to identify and quantify specific metabolites or metabolite class; whereas in the untargeted analysis a new hypothesis for further tests is generated by ...

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icoshift: An effective tool for the alignment of chromatographic data

Cited by 207 publications

References 24 publications

Geographical traceability of extra virgin olive oils from Sabina PDO by chromatographic fingerprinting of the phenolic fraction coupled to chemometrics

Geographical traceability of extra virgin olive oils from Sabina PDO by chromatographic fingerprinting of the phenolic fraction coupled to chemometrics

Trends in the application of chemometrics to foodomics studies

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

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