Effect of non‐normality on the monitoring of simple linear profiles

In some statistical process control (SPC) applications, it is assumed that a quality characteristic or a vector of quality characteristics of interest follows a univariate or multivariate normal distribution, respectively. However, in certain applications this assumption may fail to hold and could lead to misleading results. In this paper, we study the effect of non‐normality when the quality of a process or product is characterized by a linear profile. Skewed and heavy‐tailed symmetric non‐normal distributions are used to evaluate the non‐normality effect numerically. The results reveal that the method proposed by Kimtextitet al. (J. Qual. Technol. 2003; 35 :317–328) can be designed to be robust to non‐normality for both highly skewed and heavy‐tailed distributions. Copyright © 2010 John Wiley & Sons, Ltd.     

Dipyridamole recognition and controlled release by uniformly sized molecularly imprinted nanospheres

We used novel synthetic conditions of precipitation polymerization to obtain uniformly sized molecularly imprinted nanospheres of dipyridamole for application in the design of new drug delivery systems. In addition, the morphology, drug release, and binding properties of molecularly imprinted polymers (MIPs) were studied, and the effects of morphology on other properties were investigated. The MIPs prepared by acetonitrile/chloroform (19:1, v/v) were uniformly sized nanospheres with an average mean diameter of approximately 88 nm at a wetted state, 50 nm at a dry state, and a polydispersity index of 0.062. The imprinted nanospheres showed excellent binding properties and had 62.7% of template binding compared with 17.1% of its blank polymer. The imprinted nanospheres with 67.5 (mg template/of polymer) of binding capacity had better imprinting efficiency than the 50.5% of binding capacity shown by irregularly shaped MIP particles that were prepared by chloroform. The molecular binding abilities of imprinted nanospheres in human serum were evaluated by HPLC analysis (binding about 77% of dipyridamole). Results from release experiments of MIPs showed a very slow, controlled, and satisfactory release of dipyridamole. The loaded drug was released up to 99% in 17 days for nanospheres and 22 days for irregularly shaped particles.     

Identifying change point of a non-random pattern on control chart using artificial neural networks

An important step in root cause analysis is the identification of the time when process first changed. The time when a disturbance first manifested itself into the process is referred to as change point. Identification of the change point could help process engineer to perform root cause analysis effectively. In this paper, an estimator for the change point of a normal process mean using artificial neural network (ANN) is proposed. Five patterns of change namely single step, linear trend, systematic, cyclic, and mixture are studied. Whenever possible, results are compared numerically to the results obtained by other methods proposed by different researchers. First the type of change to be recognized by an ANN-based pattern recognizer is identified and then the change point in the process mean is estimated. Results indicate satisfactory performance for the proposed method that could be used as an effective method for root cause analysis by process engineer.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Identifying the period of a step change in high‐yield processes

Quality control charts have proven to be very effective in detecting out‐of‐control states. When a signal is detected a search begins to identify and eliminate the source(s) of the signal. A critical issue that keeps the mind of the process engineer busy at this point is determining the time when the process first changed. Knowing when the process first changed can assist process engineers to focus efforts effectively on eliminating the source(s) of the signal. The time when a change in the process takes place is referred to as the change point. This paper provides an estimator for a period of time in which a step change in the process non‐conformity proportion in high‐yield processes occurs. In such processes, the number of items until the occurrence of the first non‐conforming item can be modeled by a geometric distribution. The performance of the proposed model is investigated through several numerical examples. The results indicate that the proposed estimator provides a reasonable estimate for the period when the step change occurred at the process non‐conformity level. Copyright © 2009 John Wiley & Sons, Ltd.     

Statistical monitoring of multivariate multiple linear regression profiles in phase I with calibration application

In some statistical process control applications, there are some correlated quality characteristics which can be modeled as linear functions of some explanatory variables. We refer to this structure as multivariate multiple linear regression profiles. When the correlation structure between quality characteristics is ignored and profiles are monitored separately then misleading results could be expected. Hence, developing methods to account for this multivariate structure is required. In this paper, we specifically focus on phase I monitoring of multivariate multiple linear regression profiles and develop four methods for this purpose. The performance of the developed methods is compared through simulation studies in terms of probability of a signal. In addition, a diagnostic scheme to find the out‐of‐control samples is developed. Finally, the application of the proposed methods is illustrated using a calibration application at the National Aeronautics and Space Administration (NASA) Langley Research Center. Copyright © 2009 John Wiley & Sons, Ltd.     

A strategy for the electro-organic synthesis of new hydrocaffeic acid derivatives

Electrochemical treatment processes can significantly contribute to the protection of the environment through the minimization of waste and toxic materials in effluents. From a pharmaceutical point of view and due to the existing resemblance between the electrochemical and biological reactions, it can be assumed that the oxidation mechanisms on the electrode and in the body share similar principles. In this paper, the application of electrochemical studies in the design of an environmentally friendly method was delineated for the new hydrocaffeic acid (HCA, 3,4-dihydroxy hydrocinnamic acid) derivatives synthesis at carbon electrodes in an undivided cell. In this cell, the EC mechanism reaction was involved, comprising two steps alternatively; (1) electrochemical oxidation and (2) chemical reaction. In particular, the electro-organic reactions of HCA, an important biological molecule, were studied in a water–acetonitrile (90:10 v/v) mixture in the presence of benzenesulfinic acid (3) and p-toluenesulfinic acid (4). The research included the use of a variety of experimental techniques, such as cyclic voltammetry, controlled-potential electrolysis and product spectroscopic identification.