Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data

Kollenburg, Geert H. van; Bouman, Roel; Offermans, Tim; Gerretzen, Jan; Buydens, L.M.C.; Manen, Henk‐Jan van; Jansen, Jeroen J.

doi:10.1016/j.compchemeng.2021.107466

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…However, PLS-SEM has some mathematical restrictions and limitations. For example, one of the main limitations is unidimensionality, where PLS-PM assumes that each group of variables/indicators can be described with a single construct [89]. In our study, some groups of variables/indicators may be explained by other latent variables.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 93%

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The Impact of Integration of Industry 4.0 and Internal Organizational Forces on Sustaining Competitive Advantages and Achieving Strategic Objectives

et al. 2022

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Adopting and implementing the Industry 4.0 strategy to increase the overall performance of the organization became one of the main aims of organizations. However, ignoring the linkages between implementing strategic decisions and organizational internal factors/forces can endanger and shrink its performance, competitive advantages, and thus its strategic success. In this context, many companies failed to achieve the expected benefits of adopting the Industry 4.0 strategy. Therefore, the gained advantages of adopting the Industry 4.0 strategy should be sustained through perfect and comprehensive integration between Industry 4.0 concepts and the accompanying upgrades and changes in the organizational internal factors/forces. This will capitalize on organizations’ internal strengths and avoid weaknesses or turn them into strengths. In this paper, a conceptual model is proposed to investigate the relation between Industry 4.0 and internal organizational forces and examine their impacts on the sustainable competitive advantages of the organization. In the hypothesized model, three innovation capabilities (i.e., technological, economic, and commercial innovation) have been used to mediate the relation between the internal forces and the sustainable competitive advantages in parallel with Industry 4.0 adoption. The model and the proposed hypotheses have been simulated and tested using partial least squares structural equations modeling software called SmartPLS. The sample size used is 125 responses from different manufacturing fields. The results demonstrate the significant role that the internal organizational forces play in maintaining and sustaining the organization’s competitive advantages in combination with Industry 4.0.

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Section: Limitations and Future Research Directionsmentioning

confidence: 93%

“…The mathematical basis of PLS (i.e., nonparametric estimation procedure) enables it to analyze a small sample size [38]. However, PLS-SEM has some mathematical shortcomings and limitations, such as the unidimensionality assumption [89].…”

Section: Data Collection and The Analysis Methodsmentioning

confidence: 99%

The Impact of Integration of Industry 4.0 and Internal Organizational Forces on Sustaining Competitive Advantages and Achieving Strategic Objectives

et al. 2022

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“…While a component may not be exactly identified, the label it gets from the PARADISe analysis may still be used to identify the same component across different measurement stations. Work has been by some of the current authors to relate untargeted GC-MS measurements of different measuring stations along the Rhine using a statistical path model called Process PLS (van Kollenburg et al, 2021). The results of this approach are forthcoming.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Anthropogenic Origin of Unidentified Volatile Chemicals in the River Rhine

Ying

Kollenburg

Doel

et al. 2022

Water Air Soil Pollut

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Surface water of rivers like the Rhine is a highly relevant environmental and an important source of the Dutch drinking water. To improve protection of the environment and drinking water supply, it is important to have a continuous overview of the chemical composition of the river. Such an overview may be obtained with contemporary, untargeted analytical platforms like gas chromatography-mass spectrometry. Interpretation of such untargeted data is however challenged by the presence of many chemicals of natural origin. We developed a novel approach to screen for anthropogenic chemicals using non-parametric tests on the time trends of yet unidentified chemicals. The approach uses PARAFAC2 to extract unknown components present in GC–MS data and provides an assessment of whether such components may be anthropogenic. This significantly reduces screening efforts required by human laboratory staff. In total, out of twelve suspect unknown components, eleven were classified as anthropogenic, providing compelling evidence that studying unknown components can be highly valuable for regulatory bodies. This approach filters out many naturally occurring compounds, leaving more resources available for wet-lab identification of suspected anthropogenic chemicals.

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“…A combination between PCA and quadratic discriminant analysis (QDA) enables data classification and investigation of model quality parameters [ 32 , 33 ]. The information about the relation between a number of predictor variables and independent variables can be extracted by using PLS-R [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging

Ktash

Stefanakis

Wackenhut

et al. 2022

Sensors

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UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R2cv = 0.80 and low RMSECV = 0.01 g for the validation. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers.

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Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data

Cited by 11 publications

References 24 publications

The Impact of Integration of Industry 4.0 and Internal Organizational Forces on Sustaining Competitive Advantages and Achieving Strategic Objectives

The Impact of Integration of Industry 4.0 and Internal Organizational Forces on Sustaining Competitive Advantages and Achieving Strategic Objectives

Evaluating Anthropogenic Origin of Unidentified Volatile Chemicals in the River Rhine

Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging

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