The Process Chemometrics Approach to Process Monitoring and Fault Detection

Modelling of spectroscopic batch process data using grey models to incorporate external information

“…The common approach to data modelling of chemical data is purely data-driven, often based on empirical latent variable projection methods such as principal component analysis (PCA) [7][8][9][10][11] partial least squares (PLS) [12][13][14] or, more recently, three-way methods such as PARAFAC [15][16][17][18][19][20][21]. These models aim to explain significant variation in the data in terms of a much reduced number of latent factors (scores and loadings) which describe combinations of the process variables.…”

Wavelets for scrutinizing multivariate exploratory models? interpreting models through multiresolution analysis

“…Recently, many excellent papers related to process applications have been published. To mention just a few of them, we refer to References [20][21][22][23][24][25].…”

“…These two multivariate control charts form the basis for multivariate statistical process control (MSPC); see e.g. References [20][21][22][23][24][25][26].…”

Integrated framework of nonlinear prediction and process monitoring for complex biological processes

“…As the number of measured variables in biosystems increases, bioprocess monitoring with diagnosable statistical techniques becomes very important. However, high dimensionality, collinearity, and nonlinearity in experimental or historical data often make it difficult to apply statistical modeling and analysis techniques [1][2][3][4][5][6]. Traditionally, principal component analysis (PCA) and partial least squares (PLS) are used to statistically monitor a biological process.…”