1,721,389 research outputs found
Chemometrics in Food Chemistry
No full textThe issues related to food science and authentication are of particular importance for researchers, consumers and regulatory entities. The need to guarantee quality foodstuff - where the word "quality" encompasses many different meanings, including e.g. nutritional value, safety of use, absence of alteration and adulterations, genuineness, typicalness, etc. - has led researchers to look for increasingly effective tools to investigate and deal with food chemistry problems. As even the simplest food is a complex matrix, the way to investigate its chemistry cannot be other than multivariate. Therefore, chemometrics is a necessary and powerful tool for the field of food analysis and control. For food science in general and food analysis and control in particular, there are several problems for which chemometrics are of utmost importance. Traceability, i.e. the possibility of verifying the animal/botanical, geographical and/or productive origin of a foodstuff, is, for instance, one area where the use of chemometric techniques is not only recommended but essential: indeed, at present no specific chemical and/or physico-chemical markers have been identified that can be univocally linked to the origin of a foodstuff and the only way of obtaining reliable traceability is by means of multivariate classification applied to experimental fingerprinting results. Another area where chemometrics is of particular importance is in building the bridge between consumer preferences, sensory attributes and molecular profiling of food: by identifying latent structures among the data tables, bilinear modeling techniques (such as PCA, MCR, PLS and its various evolutions) can provide an interpretable and reliable connection among these domains. Other problems include process control and monitoring, the possibility of using RGB or hyperspectral imaging techniques to nondestructively check food quality, calibration of multidimensional or hyphenated instruments etc
Classification Methods in Chemometrics
No full textPattern recognition methods, i.e. the methods concentrating on the possibility of assigning an object to a class based on the result of a set of measurements are ubiquitous in chemometrics. In this paper, the main chemometric classification methods are discussed in terms of their nature, behavior, advantages and drawbacks. Both parametric and non parametric or discriminant and modeling techniques are illustrated together with a discussion of some applications to real world problems
Elsevier Chemometrics and Intelligent Laboratory Systems Award
No full textThe 2012 Chemometrics and Intelligent Laboratory Systems Award was delivered during the celebration of the Chemometrics in Analytical Chemistry meeting (in Budapest, 24-29 June, 2012), to Dr, Bahram Hemmateenejad and to Dr. Federico Marini for their contributions to the development of chemometrics in the last 5 years. The awardees were chosen by a selected committee of leading international scientists in the field of chemometrics based on their scientific papers and other contributions to the field of chemometrics
Artificial neural networks in food analysis: trends and perspectives
No full textArtificial neural networks are a family of non-linear Computational methods, loosely inspired by the human brain, that have found application in an increasing number of fields of analytical chemistry and specifically of food control. In this review, the main neural network architectures are described and examples of their application to solve food analytical problems are presented, together with some considerations about their uses and misuses
Non-linear class-modeling using artificial neural networks
No full textA new pattern recognition algorithm for classmodeling based on coupling an autoassociator artificial neural network with a SIMCA-like criterion is presented and discussed. The algorithm has been tested on different real and simulated datasets with promising results. © 2009 IEEE
Particle swarm optimization (PSO). A tutorial
No full textSwarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals. In particle swarm optimization (PSO) the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances. In the present paper, the potential of particle swarm optimization for solving various kinds of optimization problems in chemometrics is shown through an extensive description of the algorithm (highlighting the importance of the proper choice of its metaparameters) and by means of selected worked examples in the fields of signal warping, estimation robust PCA solutions and variable selection
The INTERNODES method for the treatment of non-conforming multipatch geometries in Isogeometric Analysis
No full textIn this paper we apply the INTERNODES method to solve second order elliptic problems discretized by Isogeometric Analysis methods on non-conforming multiple patches in 2D and 3D geometries. INTERNODES is an interpolation-based method that, on each interface of the configuration, exploits two independent interpolation operators to enforce the continuity of the traces and of the normal derivatives. INTERNODES easily handles both parametric and geometric NURBS non-conformity. We specify how to set up the interpolation matrices on non-conforming interfaces, how to enforce the continuity of the normal derivatives and we give special attention to implementation aspects. The numerical results show that INTERNODES exhibits optimal convergence rate with respect to the mesh size of the NURBS spaces and that it is robust with respect to jumping coefficients
Chapter 1 – Introduction Chemometrics in Food Chemistry
No full textThe chapter describes the motivation behind the book and introduces the role of chemometrics in food quality control and authentication. A brief description of the structure of the monograph is also provided
Validation of chemometric models - A tutorial
No full textIn this tutorial, we focus on validation both from a numerical and conceptual point of view. The often applied reported procedure in the literature of (repeatedly) dividing a dataset randomly into a calibration and test set must be applied with care. It can only be justified when there is no systematic stratification of the objects that will affect the validated estimates or figures of merits such as RMSE or R2. The various levels of validation may, typically, be repeatability, reproducibility, and instrument and raw material variation. Examples of how one data set can be validated across this background information illustrate that it will affect the figures of merits as well as the dimensionality of the models. Even more important is the robustness of the models for predicting future samples. Another aspect that is brought to attention is validation in terms of the overall conclusions when observing a specific system. One example is to apply several methods for finding the significant variables and see if there is a consensus subset that also matches what is reported in the literature or based on the underlying chemistry
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