Application of the needle algorithm for exploratory analysis and resolution of HPLC-DAD data

Juan, Anna de; Bogaert, B. van den; Sánchez, F. Cuesta; Massart, D.L.

doi:10.1016/0169-7439(96)00009-3

Cited by 46 publications

(15 citation statements)

References 9 publications

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“…These will be close to the true subspace, but may have some problems with collinearity. Other more systematic methods, such as SIMPLISMA [ 22 ] and the needle-search method [ 23 ], can also be used. Whichever method is used, it is advisable to run the algorithm several times from different starting points to ensure consistency in the generated profiles.…”

Section: Introductionmentioning

confidence: 99%

Multivariate curve resolution of time course microarray data

et al. 2006

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Background: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.

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Section: Introductionmentioning

confidence: 99%

Multivariate curve resolution of time course microarray data

et al. 2006

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“…Another approach is to use p profiles selected randomly from X, which is close to the true subspace, but may have problems with colinearity. Other more systematic methods such as the needle-search method [17] and SIMPLISMA [27] exist and can be readily used. In this work, we have implemented SIMPLISMA, random positive values, and random profiles as starting points to ensure consistency in the profiles generated.…”

Section: Multivariate Curve Resolution Via Alternating Least Squares mentioning

confidence: 99%

“…There are several methods for implementing MCR, [17,19] although alternating least squares (MCR-ALS) is the most popular due to its ease of implementation. The basic algorithm for MCR-ALS has been reported in several references [16,20 -22,25,26] and is not repeated here except for the two ALS Eqns (2) and (3), which form the core of the algorithm, i.e.…”

Section: Multivariate Curve Resolution Via Alternating Least Squares mentioning

confidence: 99%

Analysis of time course ¹H NMR metabolomics data by multivariate curve resolution

Karakach

Knight

Lenz

et al. 2009

Magnetic Reson in Chemistry

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Modeling NMR-based metabolomics data often involves linear methods such as principal component analysis (PCA) and partial least squares (PLS). These methods have the objective of describing the main variance in the data and maximum covariance between the predictor variables and some response variable respectively. If the experiment is designed to investigate temporal biological fluctuations, however, the factors obtained become difficult to interpret in a biological context. Moreover, when these methods are applied to analyze data, an implicit assumption is made that the measurement errors exhibit an iid-normal distribution, often limiting the extent of the information recovered. A method for the linear decomposition of NMR-based metabolomics data by multivariate curve resolution (MCR), which has been used elsewhere for time course transcriptomics applications, is introduced and implemented via a weighted alternating least squares (ALS) approach. Measurement of error information is incorporated in the modeling process, allowing the least squares projections to be performed in a maximum likelihood fashion. As a result, noise heteroscedasticity resulting from pH-induced peak shifts can be modeled, eliminating the need for binning/bucketing. The utility of the method is demonstrated using two sets of temporal NMR metabolomics data, HgCl(2)-induced nephrotoxicity in rat, and fish (Japanese medaka, Oryzias latipes) embryogenesis. Profiles extracted for the nephrotoxicity data exhibit strong correlations with metabolites consistent with temporal fluctuations in glucosuria. The concentration of metabolites such as acetate, glucose, and alanine exhibit a steady increase, which peaks at Day 3 post dose and returns to basal levels at Day 8. Other metabolites including citrate and 2-oxoglutarate exhibit the opposite characteristics. Although the fish embryogenesis data are more complex, the profiles extracted by the algorithm display characteristics that depict temporal variation consistent with processes associated with embryogenesis.

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“…One approach is the use of partial vector profiles which are iteratively refined to form key set of unique factors. The approach is the so called needle search strategy (or partial target) described extensively elsewhere 47,57 . For example the value 1 is set at each of the rows of R t keeping the rest at 0.…”

Section: Overview Of Target Factor Analysis (Tfa)mentioning

confidence: 99%

A Combined Study of Headspace Volatiles using Human Sensory, Mass Spectrometry and Chemometrics

McAdam¹,

Bishop

Digard

et al. 2020

Sci Rep

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Smokeless tobacco products (Stps) are widely used in certain parts of the world, yet there is limited understanding of how they are consumed, particularly the impact of chemosensory characteristics on their use. in order to develop an understanding of the drivers of Stp use and product acceptability we conducted both human sensory panel testing and chemical analyses on a range of Stps. free-sorting paired odour testing using sensory panellists identified similarities and clear differences between eleven different STPs. Headspace volatiles, analysed by headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS), identified 20 to 70 components depending upon the STP. Key differences in headspace volatiles were found between STPs. For example, the headspace of Skoal Bandits Wintergreen was dominated by methyl salicylate, while Marlboro Spice consists of a more complex profile including pinene, nicotine, eugenol and cymene. Chemometric target factor Analysis (tfA) and Hierarchical cluster Analysis (HcA) of chemistry and sensory data was used to deduce chemical drivers of sensory perceptions. the chemometric strategy used showed that headspace analysis is a complementary screening tool to sensory analysis in classification studies. This study is generic with applications across various product sectors that require routine human sensory panel evaluation.

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Application of the needle algorithm for exploratory analysis and resolution of HPLC-DAD data

Cited by 46 publications

References 9 publications

Multivariate curve resolution of time course microarray data

Multivariate curve resolution of time course microarray data

Analysis of time course ¹H NMR metabolomics data by multivariate curve resolution

A Combined Study of Headspace Volatiles using Human Sensory, Mass Spectrometry and Chemometrics

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Application of the needle algorithm for exploratory analysis and resolution of HPLC-DAD data

Cited by 46 publications

References 9 publications

Multivariate curve resolution of time course microarray data

Multivariate curve resolution of time course microarray data

Analysis of time course 1H NMR metabolomics data by multivariate curve resolution

A Combined Study of Headspace Volatiles using Human Sensory, Mass Spectrometry and Chemometrics

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Analysis of time course ¹H NMR metabolomics data by multivariate curve resolution