In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

No abstract

For the construction of a reliable decision area in the soft independent modeling by class analogy (SIMCA) method, it is necessary to analyze calibration data revealing the objects of special types such as extremes and outliers. For this purpose, a thorough statistical analysis of the scores and orthogonal distances is necessary. The distance values should be considered as any data acquired in the experiment, and their distributions are estimated by a data‐driven method, such as a method of moments or similar. The scaled chi‐squared distribution seems to be the first candidate among the others in such an assessment. This provides the possibility of constructing a two‐level decision area, with the extreme and outlier thresholds, both in case of regular data set and in the presence of outliers. We suggest the application of classical principal component analysis (PCA) with further use of enhanced robust estimators both for the scaling factor and for the number of degrees of freedom. A special diagnostic tool called extreme plot is proposed for the analyses of calibration objects. Extreme objects play an important role in data analysis. These objects are a mandatory attribute of any data set. The advocated dual data‐driven PCA/SIMCA (DD‐SIMCA) approach has demonstrated a proper performance in the analysis of simulated and real‐world data for both regular and contaminated cases. DD‐SIMCA has also been compared with robust principal component analysis, which is a fully robust method. Copyright © 2013 John Wiley & Sons, Ltd.

No abstract

No abstract

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

In the projection methods (PCA, PLS) two distance measures are of importance. They are the score distance (SD, a.k.a. leverage) and the orthogonal distance (OD, a.k.a. the residual variance). This paper shows that both distance measures can be modeled by the x 2 -distribution. Each model includes a scaling factor that can be described by an explicit equation. Moreover, the models depend on an unknown number of degrees of freedom, which have to be estimated using a training dataset. Such modeling is further applied to classification within the SIMCA framework, and various acceptance areas are built for a given significance level. A triangular area, constructed using the sum of the normalized SD and OD, is deemed to be the most practical. This theoretical notion is supported by three examples. The first is based on a simulated dataset, while the other two employ real world data.

The role of chemometrics in process analytical technology (PAT) solutions development is presented in the review on the basis of publications from 1993 to 2011. Main areas of application, stages of implementation, instruments, and chemometric methods used for the PAT implementations are reviewed. Generally speaking, PAT is considered to be an approach applicable not only in pharmaceutical industry but also in any production area such as food industry and biotechnology. PAT is claimed to be a new flexible manufacturing concept that accounts for variability and adapts the process to fit it. Copyright © 2012 John Wiley & Sons, Ltd.