A sequential algorithm for multiblock orthogonal projections to latent structures

Worley, Bradley; Powers, Robert

doi:10.1016/j.chemolab.2015.10.018

Cited by 21 publications

(18 citation statements)

References 32 publications

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“…In most cases, the samples, data, and analysis are done separately. The NMR and MS data sets are not integrated into a single chemometrics model because, until recently, the field lacked software capable of handling data from multiple analytical sources [57, 67, 68]. Thus, any observed changes in the NMR and MS spectra are not statistically correlated and, importantly, the metabolites and pathways separately identified by NMR and MS may not be biologically related.…”

Section: Introductionmentioning

confidence: 99%

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Marshall

Powers

2017

Progress in Nuclear Magnetic Resonance Spectroscopy

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Metabolomics is undergoing tremendous growth and is being employed to solve a diversity of biological problems from environmental issues to the identification of biomarkers for human diseases. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths. NMR requires minimal sample handling without the need for chromatography, is easily quantitative, and provides multiple means of metabolite identification, but is limited to detecting the most abundant metabolites (≥ 1 μM). Conversely, mass spectrometry has the ability to measure metabolites at very low concentrations (femtomolar to attomolar) and has a higher resolution (∼103-104) and dynamic range (∼103-104), but quantitation is a challenge and sample complexity may limit metabolite detection because of ion suppression. Consequently, liquid chromatography (LC) or gas chromatography (GC) is commonly employed in conjunction with MS, but this may lead to other sources of error. As a result, NMR and mass spectrometry are highly complementary, and combining the two techniques is likely to improve the overall quality of a study and enhance the coverage of the metabolome. While the majority of metabolomic studies use a single analytical source, there is a growing appreciation of the inherent value of combining NMR and MS for metabolomics. An overview of the current state of utilizing both NMR and MS for metabolomics will be presented.

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Section: Introductionmentioning

confidence: 99%

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Marshall

Powers

2017

Progress in Nuclear Magnetic Resonance Spectroscopy

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188

136

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“…Importantly, these NMR and MS spectral changes are now identified as being statistically correlated. In addition to MB-PCA and MB-PLS, the data were jointly modeled using multiblock orthogonal projections to latent structures (MB-OPLS), which corroborated the MB-PLS analysis while better differentiating group separations [92]. By effectively integrating NMR and MS datasets, we could thoroughly analyze the metabolic changes to human dopaminergic cells resulting from treatments with toxins that were not achievable with just the NMR or MS data.…”

Section: Nmr and Ms Metabolomics Protocol To Investigate Pdmentioning

confidence: 99%

Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson’s Disease

et al. 2017

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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD occurrences. Exposure to environmental toxicants including pesticides and metals (e.g., paraquat (PQ) and manganese (Mn)) is also recognized as an important PD risk factor. Thus, aging, genetic alterations, and environmental factors all contribute to the etiology of PD. In fact, both genetic and environmental factors are thought to interact in the promotion of idiopathic PD, but the mechanisms involved are still unclear. In this study, we summarize our findings to date regarding the toxic synergistic effect between α-synuclein and paraquat treatment. We identified an essential role for central carbon (glucose) metabolism in dopaminergic cell death induced by paraquat treatment that is enhanced by the overexpression of α-synuclein. PQ “hijacks” the pentose phosphate pathway (PPP) to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. PQ also stimulated an increase in glucose uptake, the translocation of glucose transporters to the plasma membrane, and AMP-activated protein kinase (AMPK) activation. The overexpression of α-synuclein further stimulated an increase in glucose uptake and AMPK activity, but impaired glucose metabolism, likely directing additional carbon to the PPP to supply paraquat redox cycling.

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“…Compared with the above 2 methods, false variables, fault type, and unknown failure can be confirmed by multiblock method. However, other studies calculate the super scores and super loads of each block, which tend to show a profile for overall process variables instead of a profile for each subblock. Block scores represent subblock information and influence the monitoring results of subblocks, whereas super scores are calculated by super weights.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, computational loads are reduced, and the FAR of both output-related and output-irrelevant fault is improved.Instead of analyzing faults specifically, the above algorithm can only monitor faults; thus, process monitoring becomes complex, and the monitoring results are difficult to interpret with a large number of variables or data samples. For this reason, an improved PLS, 12 fuzzy positivistic C-means (FPCM) clustering, 13 and multiblock structure [14][15][16] have been proposed to diagnose fault. Improved PLS-based, input data are divided into key-performance-indicator-related subspace and key-performance-indicator-irrelevant subspace, and then, the corresponding monitoring statistics of the 2 subspaces are calculated to diagnose fault.…”

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confidence: 99%

Fault diagnosis of output‐related processes with multi‐block MOPLS

Sun

Zhang

Feng

et al. 2017

Journal of Chemometrics

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For fault diagnosis of output‐related processes, a relatively high false alarm rate (FAR) of output‐irrelevant faults exists because the output‐irrelevant variables are not removed completely by conventional approaches. A relatively large number of computational loads is thus required. Therefore, in this paper, a new fault diagnosis approach based on multiblock modified orthogonal projections to latent structures is proposed to complete fault diagnosis for complex chemical processes, particularly for the penicillin fermentation process. The main contributions are as follows: (1) Multiblock orthogonal projections to latent structures are applied to remove the noncorrelated variables of input blocks, which requires a relatively low computational load. In addition, block scores are obtained by block weights rather than super weights to better describe the character of each subblock. (2) Complete orthogonal decomposition between input block variables and output is explored to completely separate output‐related and output‐irrelevant variables to improve the accuracy of diagnosis. (3) A hierarchical diagnosis scheme, which is composed of block monitoring statistics and subblock monitoring statistics, is proposed to monitor and localize faults. Penicillin fermentation process is considered in this study, and the penicillin concentration‐relevant fault in each block and subblock is analyzed. The results of this study show that the proposed method has steadier performance on output‐related fault diagnoses and diagnoses faults more accurately.

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A sequential algorithm for multiblock orthogonal projections to latent structures

Cited by 21 publications

References 32 publications

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson’s Disease

Fault diagnosis of output‐related processes with multi‐block MOPLS

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