Bioprocess Fault Detection by Nonlinear Multivariate Analysis: Application of an Artificial Autoassociative Neural Network and Wavelet Filter Bank

Abstract

A nonlinear multivariate analysis, artificial autoassociative neural network (AANN), was applied to bioprocess fault detection. In an optimal production process of a recombinant yeast with a temperature controllable expression system, faults in test cases with faulty temperature sensors and plasmid instability of recombinant cells could be detected by the AANN. Since the raw data of measured variables included high-frequency noise, a wavelet filter bank (WFB) was applied to noise elimination before training of the AANN. The filtering performance of the WFB was compared with those of some classical first-order digital filters. The filtered signals at several resolution scales by the WFB were employed as the training data of the AANN. The computing time and summation of square of errors in training were compared, and the appropriate degree of the noise filtering and the density of the training data of the AANN were discussed. The performance of the feature capturing by the AANN was compared with that by a linear multivariate analysis, principal component analysis. A J index defined in this paper, using inputs and outputs of the AANN, was used for fault detection successfully. The output of the first unit of the trained AANN functioned effectively for the discrimination of the data in the abnormal cases from the data in the normal cases.

Application of near‐infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein

“…It has been applied to a variety of applications in the biopharmaceutical industry (Cunha et al, 2002;Gabrielsson et al, 2002;Gunther et al, 2007;Johnson et al, 2007;Kirdar et al, 2007;Kirdar et al, 2008;Shimizu et al, 1998;Vaidyanathan et al, 2001;Ü ndey and Ç inar, 2002;Ü ndey et al, 2003). Similarly, Near-infrared spectroscopy (NIR), as a fast and nondestructive analytical tool, has been gaining popularity in pharmaceutical analysis (Arnold et al, 2003;Bakeev, 2004;Card et al, 2008;Gemperline et al, 1989;Luypaert et al, 2007;Mates et al, 2007;Reich, 2005;Riley et al, 2001;Rosa et al, 2008).…”

Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application

“…More recently, MVDA has been applied to various applications in the biopharmaceutical industry (Gabrielsson et al, 2002; Johnson et al, 2007). Applications include near‐infrared spectral information from an antibiotic production process (Vaidyanathan et al, 2001), nonlinear multivariate analysis and artificial autoassociative neural network (AANN) for bioprocess fault detection (Shimizu et al, 1998), multivariate statistical process monitoring for processing of pharmaceutical granules (Ündey and Çinar, 2002), the assessment of seed inoculum quality from a manufacturing process (Cunha et al, 2002) and development of an integrated online multivariate statistical process monitoring, product attributes prediction and fault diagnosis framework for a fed‐batch penicillin fermentation (Ündey et al, 2003).…”

Control of cultivation processes for recombinant protein production: a review

“…As recombinant protein production processes are intrinsically non-linear, the use of principal components, which is a linear theory, might be problematic [83]. This restriction does not apply to non-linear alternatives to PCA.…”