Modeling, monitoring and control strategies for high temperature short time pasteurization systems — 3. Statistical monitoring of product lethality and process sensor reliability

Abstract Statistical process monitoring (SPM) is used in food processing industries to improve productivity and product quality. SPM can also provide information to operators on how close a process is to non-compliance to product safety limits, and carry out periodic checks of sensor accuracy at high frequency. Traditional SPM tools such as Shewhart charts are not appropriate for continuous food processes because of autocorrelation in data. Four alternative SPM techniques are presented and applied to high temperature short time (HTST) dairy pasteurization. The study attempted to achieve compliance of the HTST process operation with the recommended Pasteurized Milk Ordinance by providing a margin between the alarm limits of the monitoring chart and the safety limits. Monitoring of residuals and parameter change detection techniques are used for monitoring processes with autocorrelated variables. Hotelling's T2 and residuals of canonical variates techniques are used for monitoring multivariable processes.

[1]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[2]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[3]  D. Coleman.  Statistical Process Control—Theory and Practice , 1993 .

[4]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[5]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[6]  Layth C. Alwan,et al.  Time-Series Modeling for Statistical Process Control , 1988 .

[7]  D. Hawkins.  Multivariate quality control based on regression-adjusted variables , 1991 .

[8]  U. Desai,et al.  A realization approach to stochastic model reduction , 1985 .

[9]  Ali Cinar,et al.  Modeling, monitoring and control strategies for high temperature short time pasteurization systems — 1. Empirical model development , 1998 .

[10]  Emmanuel Yashchin,et al.  Performance of CUSUM control schemes for serially correlated observations , 1993 .

[11]  Douglas C. Montgomery,et al.  Some Statistical Process Control Methods for Autocorrelated Data , 1991 .

[12]  Frank B. Alt.  Multivariate Quality Control , 1984 .

[13]  H. Hotelling,et al.  Multivariate Quality Control , 1947 .

[14]  Nola D. Tracy,et al.  Multivariate Control Charts for Individual Observations , 1992 .

[15]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[16]  J. Tukey.  The Future of Data Analysis , 1962 .

[17]  T. Harris,et al.  Statistical process control procedures for correlated observations , 1991 .

[18]  A. R. Crathorne,et al.  Economic Control of Quality of Manufactured Product. , 1933 .

[19]  Ali Cinar,et al.  Modeling, monitoring and control strategies for high temperature short time pasteurization systems — 2. Lethality-based control , 1998 .

[20]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .