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In-Line Process and Product Control Using Spectroscopy and Multivariate Calibration

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Abstract

The quality of a product is dynamic in nature and develops over time. We present a case study from the food industry in which the concept of measuring the end-product quality is extended to incorporate the shelf-life period of the product with practical examples showing the harm of ignoring the changes that occur in the product quality as a function of time. The article also addresses the use of in-line spectroscopy to relate the variations in the input parameters, such as the raw materials, and the process variables to the final product quality over the entire shelf-life of the product. We also discuss multivariate statistical process control and monitoring issues.

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... Kang and Albin (2000) [3] introduced semiconductor manufacturing applications. Sahni et al. (2005) [4] described a production process of food mayonnaise with profile. For these different applications, we need to use a variety of appropriate control charts to monitor the parameters. ...
... Kang and Albin (2000) [3] introduced semiconductor manufacturing applications. Sahni et al. (2005) [4] described a production process of food mayonnaise with profile. For these different applications, we need to use a variety of appropriate control charts to monitor the parameters. ...
Article
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In recent years, linear profile monitoring has become one of the popular research directions in SPC. Although the linear profile model is simple and widely applicable, it is too sensitive to small parameter changes, leading to an increase in false alarm rates. This paper presents a new control chart with the practical importance for linear profile. The control chart can be more tolerant for the small shifts comparing the conventional control chart with considering the practical importance, so as to ensure that the really important changes are detected. The simulation study shows that the new control chart can be used to detect the change of intercept and slope efficiently. Based on control chart provided by Kim (2003), we obtain the threshold for the control chart with tolerance under null hypothesis with nominal in control run length ( ARL 0 ) and also yield run length for the situations of out of control ( ARL 1 ).
... They also gave an example of designing a robust alternator, where the aim is to obtain a desired current profile over a range of speed. Jin and Shi (2001) Sahni et al. (2005) presents an example of a profile response from a mayonnaise production process in the food industry. Some of the examples of the profiles are shown in (Figures 1 and 2). ...
... uately using a polynomial model or using piecewise polynomial models. In this article, we restrict our attention to the types of non linear profiles that can be adequately modeled using lower order polynomials. Few examples of polynomial profile include -acceleration and deceleration profile of an air bag in automotives,Marklund and Nilsson (2003).Sahni et al. (2005) discuss a scenario where monitoring the viscosity of mayonnaise over time is of interest.In this study we investigate the changepoint approach for Phase I analysis of polynomial profiles and conclude the article with our comments on the Phase II aspect of profile monitoring using changepoint approach. The changepoint approach can be def ...
... Wärmefjord (2004) described the problem for the assembly process of the Saab automobile. Sahni et al. (2005) suggest how to monitor the raw materials and different process variables in food industry in order to assure the quality of the final product. ...
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The aim of sequential surveillance is on-line detection of an important change in an underlying process as soon as possible after the change has occurred. Statistical methods suitable for surveillance differ from hypothesis testing methods. In addition, the criteria for optimality differ from those used in hypothesis testing. The need for sequential surveillance in industry, economics, medicine and for environmental purposes is described. Even though the methods have been developed under different scientific cultures, inferential similarities can be identified. Applications contain complexities such as autocorrelations, complex distributions, complex types of changes, and spatial as well as other multivariate settings. Approaches to handling these complexities are discussed. Expressing methods for surveillance through likelihood functions makes it possible to link the methods to various optimality criteria. This approach also facilitates the choice of an optimal surveillance method for each specific application and provides some directions for improving earlier suggested methods.
... Marengo et al. (2005) considered an example involving organic pigments applied to cotton surfaces. Sahni et al. (2005), on the other hand, considered an example related to the shelf-life of a food product. Wang and Tsung (2005) used profile monitoring methods to detect changes in a Q-Q plot which refl ected the relationship between the current sample and a baseline sample. ...
Article
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Em muitas aplicações a qualidade de um processo ou de um produto é melhor caracterizada e descrita por uma relação funcional entre a variável resposta e uma ou mais variáveis explicativas. Monitoramento de perfis é usado para entender e checar a estabilidade desta relação ao longo do tempo. Cada vez que uma amostra é selecionada, uma coleção de pontos que pode ser representada por uma curva (ou perfil) é observada. Em algumas aplicações de calibração, o perfil pode ser representado adequadamente por um modelo de regressão linear simples, enquanto que em outras aplicações, modelos mais complicados serão necessários. Os objetivos deste artigo são: apresentar e resumir pesquisas recentes do uso de cartas de controle para monitorar perfis de processo e qualidade de produto e encorajar futuras pesquisas nesta área.
... Wärmefjord (2004) described the multivariate problem for the assembly process of the Saab automobile. Sahni et al. (2005) suggest that the raw material and different process variables in food industry should be analysed in order to assure the quality of the final product. Surveillance of several parameters (such as the mean and the variance) of a distribution is multivariate surveillance (see for example Knoth and Schmid (2002)). ...
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Multivariate surveillance is of interest in industrial production as it enables the monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance.Several types of multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have been proposed. Here a review of general approaches to multivariate surveillance is given with respect to how suggested methods relate to general statistical inference principles. Suggestions are made on the special challenges of evaluating multivariate sur-veillance methods.
... The vast majority of SPM methods were developed to handle scalar (0 th order tensor) sensor like data, either univariately [29][30][31] or multivariately [32][33][34][35]. More recently, profile monitoring emerged as a new SPM branch dedicated to functional relationships [36][37][38] or higher-order tensorial data structures such as: near-infrared (NIR) spectra [39][40][41], surface profilometry [37], grey-level images [42], colour and hyperspectral images [43][44][45][46][47], hyphenated instruments [48] among others, expanding the SPM domain to new processes/products. The one aspect shared by all methods proposed in the past and the fundamental premise established since the seminal work of Walter A. Shewhart [31], is that all common cause variation must be present in the reference historical Normal Operating Conditions (NOC) dataset collected to conduct Phase 1 analysis, i.e., in order to assess process stability and establish the control limits for the monitoring statistics. ...
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Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well represented in this reference dataset (using the terminology adopted by the founding father of process monitoring, Walter A. Shewhart). Otherwise, false alarms will inevitably occur during the implementation of the monitoring scheme. However, we argue and demonstrate in this article, that this assumption is often not met in modern industrial systems. Therefore, we introduce a new approach based on the rigorous mechanistic modeling of the dominant modes of common cause variation and the use of stochastic computational simulations to enrich the historical dataset with augmented data representing a comprehensive coverage of the actual operational space. We show how to compute the monitoring statistics and set their control limits, as well as to conduct fault diagnosis when an abnormal event is declared. The proposed method, called AGV (Artificial Generation of common cause Variability) is applied to a Surface Mount Technology (SMT) production line of Bosch Car Multimedia, where more than 17 thousand product variables are simultaneously monitored.
... The model structure presented in equation (2) is usually estimated through principal components regression (PCR) [26][27][28][29] or partial least squares (PLS). [30][31][32][33] This model structure finds wide applicability and success in chemometric problems, [34][35][36] soft sensor development, 37,38 large-scale process monitoring and predictive analysis, 20,39,40 and biosystems. 41 However, there is a mismatch between model structure in equation (2) and the structure of data collected in the present situation, as detailed next. ...
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The optimized operation of modern analytical instrumentation is a critical but complex task. It involves the simultaneous consideration of a large number of factors, both qualitative and quantitative, where multiple responses should be quantified and several goals need to be adequately pondered, such as global quantification performance, selectivity, and cost. Furthermore, the problem is highly case specific, depending on the type of instrument, target analytes, and media where they are dispersed. Therefore, an optimization procedure should be conducted frequently, which implies that it should be efficient (requiring a low number of experiments), as simple as possible (from experimental design to data analysis) and informative (interpretable and conclusive). The success of this task is fundamental for achieving the scientific goals and to justify, in the long run, the high economic investments made and significant costs of operation. In this article, we present a systematic optimization procedure for the prevalent class of situations where multiple responses are available regarding a family of chemical compounds (instead of a single analyte). This class of problems conducts to responses exhibiting mutual correlations, for which, furthermore, several goals need to be simultaneously considered. Our approach explores the latent variable structure of the responses created by the chemical affinities of the compounds under analysis and the orthogonality of the interpretable extracted components to conduct their simultaneous optimization with respect to different analysis goals. The proposed methodology was applied to a real case study involving the quantification of a family of analytes with impact on wine aroma.
... Multivariate problems for the assembly process of the Saab automobile were described in [40]. In food industry different raw materials and several process steps are used, and in [32] it is suggested that these be analyzed in order to assure the quality of the final product. During the last years there have been an increased need for and interest in continuous monitoring in many areas apart from industrial production. ...
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Multivariate surveillance is of interest in many areas such as industrial production, bioterrorism detection, spatial surveillance, and financial transaction strategies. Some of the suggested approaches to multivariate surveillance have been multivariate counterparts to the univariate Shewhart, EWMA, and CUSUM methods. Our emphasis is on the special challenges of evaluating multivariate surveillance methods. Some new measures are suggested and the properties of several measures are demonstrated by applications to various situations. It is demonstrated that zero-state and steady-state ARL, which are widely used in univariate surveillance, should be used with care in multivariate surveillance.
... Profiles are applicable in many other situations, such as performance testing, in which the response is a performance curve over a range of an independent variable like frequency or speed (Bisgaard & Steinberg 1997). Sahni et al. (2005) presented a non-linear profile to monitor a mayonnaise production process. Jin & Shi (2001) developed profiles as waveform signals and gave examples of force and torque signals collected from online sensors. ...
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In this paper, a non-parametric approach is first proposed to monitor simple linear profiles with non-normal error terms in Phase I and Phase II. In this approach, two control charts based on a transformation technique and decision on beliefs are designed in order to monitor the intercept and the slope, simultaneously. Then, some simulation experiments are performed in order to evaluate the performance of the proposed control charts in Phase II under both step and drift shifts in terms of out-of-control average run length (ARL1). Besides, the performance of the proposed control charts is compared to the ones of seven other existing schemes in the literature. Simulation results show that the proposed control charts outperform the other control charts in detecting both the small step and small drift shifts of intercept. However, they have a weaker performance compared to other control charts in detecting both small step and small drift shifts of the slope. At the end, a real example from an electronic industry is used to illustrate the implementation of the proposed method.
... The mixtures were the whole plots and the process variables the sub-plots. The design is presented and discussed more thoroughly in Sahni et al. 22 . Note that this is a crossed split-plot situation leading to OLS = GLS. ...
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Split-plot designs are frequently needed in practice because of practical limitations and issues related to cost. This imposes extra challenges on the experimenter, both when designing the experiment and when analysing the data, in particular for non-replicated cases. This paper is an overview and discussion of some of the most important methods for analysing split-plot data. The focus is on estimation, testing and model validation. Two examples from an industrial context are given to illustrate the most important techniques. Copyright © 2006 John Wiley & Sons, Ltd.
... See Bisgaard and Steinberg (1997) for an example. Sahni et al. (2005) discuss an example from a mayonnaise production process where the quality characteristic being monitored is a non-linear profile. Nair et al. (2002) present an example from injection moulding where the response of interest is the compression strength of foam measured over different levels of compression. ...
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... Gained more information from the data. Alsaleh (2007); Beardsell and Dale (1999); Bidder (1990); Hersleth and Bjerke (2001); Hung and Sung (2011);Jha, Michela, and Noori (1999); Matsuno (1995); Rohitratana and Boon-itt (2001); Sanigar (1990) (2000); Narinder, Aastveit, and Naes (2005); Negiz et al. (1998);Orr (1999); Srikaeo and Hourigan (2002); Van Der Spiegel, Luning, Boer, Ziggers, and Jongen (2005) Business ...
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  • Milliken G. A.