Industrial applications of product design through the inversion of latent variable models

Jaeckle, Christiane; MacGregor, John F.

doi:10.1016/s0169-7439(99)00058-1

Cited by 88 publications

(114 citation statements)

References 4 publications

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“…O2-PLS should prove valuable in multiblock and hierarchical modeling where only the correlated variation is of interest. Also, O2-PLS could assist in the model inversion problems described by Jaeckle and MacGregor [26,27]. It is sometimes possible to construct a`virtual' response matrix Y in cases where Y initially is not available.…”

Section: Resultsmentioning

confidence: 99%

O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter

Trygg

Wold

2003

Journal of Chemometrics

309

270

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The O2-PLS method is derived from the basic partial least squares projections to latent structures (PLS) prediction approach. The importance of the covariation matrix (Y T X) is pointed out in relation to both the prediction model and the structured noise in both X and Y. Structured noise in X (or Y) is defined as the systematic variation of X (or Y) not linearly correlated with Y (or X). Examples in spectroscopy include baseline, drift and scatter effects. If structured noise is present in X, the existing latent variable regression (LVR) methods, e.g. PLS, will have weakened score±loading correspondence beyond the first component. This negatively affects the interpretation of model parameters such as scores and loadings. The O2-PLS method models and predicts both X and Y and has an integral orthogonal signal correction (OSC) filter that separates the structured noise in X and Y from their joint X±Y covariation used in the prediction model. This leads to a minimal number of predictive components with full score±loading correspondence and also an opportunity to interpret the structured noise. In both a real and a simulated example, O2-PLS and PLS gave very similar predictions of Y. However, the interpretation of the prediction models was clearly improved with O2-PLS, because structured noise was present. In the NIR example, O2-PLS revealed a strong water peak and baseline offset in the structured noise components. In the simulated example the O2-PLS plot of observed versus predicted Y-scores (u vs u hat ) showed good predictions. The corresponding loading vectors provided good interpretation of the covarying analytes in X and Y.

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Section: Resultsmentioning

confidence: 99%

O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter

Trygg

Wold

2003

Journal of Chemometrics

309

270

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“…Taking this idea one step further, an ideal response can be formulated which can then be traced back to ideal targets in a pathway. The latter type of problem has a close resemblance with the product design problem in process systems engineering [61].…”

Section: Structurementioning

confidence: 99%

Symbiosis of chemometrics and metabolomics: past, present, and future

Greef

Smilde

2005

Journal of Chemometrics

121

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“…The effectiveness of LVMs in such activities as process understanding, process monitoring and troubleshooting [16], process control [37][38][39][40], process design [41][42][43], product design [44], and optimization [45] has been proved in several industrial sectors.…”

Section: Latent Variable Models In the Pharmaceutical Industrymentioning

confidence: 99%

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Ottavian

Tomba²,

Barolo

2016

Methods in Pharmacology and Toxicology

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This chapter is intended to show how latent variable modeling techniques can be used to support\ud several pharmaceutical development and manufacturing activities by exploitation of historical databases\ud deriving from experiments, ongoing manufacturing processes or historical products already developed.\ud Basic theoretical concepts about latent variable modeling and latent variable model inversion are first\ud introduced. Then, some applications are reviewed to show how the pharmaceutical industry can benefit\ud from these modeling techniques to support decision-making activities in process development,\ud formulation design, process scale-up, product transfer, process control, and raw materials acceptability\ud assessment

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Industrial applications of product design through the inversion of latent variable models

Cited by 88 publications

References 4 publications

O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter

O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter

Symbiosis of chemometrics and metabolomics: past, present, and future

Advanced Process Decision Making Using Multivariate Latent Variable Methods

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