Maize Authentication: Quality Control Methods and Multivariate Analysis (Chemometrics)

Abstract

Maize is one of the most important cereals because of its numerous applications in processed foods where it is the major or minor component. Apart from maize authenticity issues related to cultivar and geographical origin (national and/or international level), there is another important issue related to genetically modified maize. Various objective parameters such as fatty acids, phenolic compounds, pigments, heavy metals were determined in conjunction with subjective (sensory analysis) in order to identify the maize authenticity. However, the implementation of multivariate analysis (principal component analysis, cluster analysis, discriminant analysis, canonical analysis) is of great importance toward reaching valid conclusions on authenticity issues. This review summarized the most important finding of both objective and subjective evaluations of maize in five comprehensive tables in conjunction with the discussion.

The volatile fraction profiling of fresh tomatoes and triple concentrate tomato pastes as parameter for the determination of geographical origin

“…Moreover, the introduction of foodstuff coming from foreign country may represent an unfair competition for producers that can affect the regional economy and even national economy. In order to protect both the consumers and the national production, a decree was issued by Italy in 2006 that established the obligation from 1 January 2008 to state clearly in the label the origin (Region or State) of fresh tomato used in the preparation of sauces ( Several papers have been published for food products authentication purpose and a great number of different analytical techniques and parameters have been used (Arvanitoyannis, Vaitsi, & Mavromatis, 2008;Arvanitoyannis & Vlachos, 2009;Luykx & van Ruth, 2008;Peres, Barlet, Loiseau, & Montet, 2007;Tzouros & Arvanitoyannis, 2001).…”

Food Analysis: Present, Future, and Foodomics

“…The detection of multiple genetically engineered crops [64] is still an important topic in which the effect of food processing on plant DNA degradation and PCR-based analysis of transgenic foods has also been studied [65]. These DNA-based methods also allow the authenticity determination of meat and meat products [66], the identification of animal species in food products [67], as well as the authentication of maize [68].…”

A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry

“…In the recent years, advances in chemometric techniques in quality control of food products have gained considerable attention from groups worldwide (Arvanitoyannis & Vlachos 2009;Barbosa, Batista, Varrique, Coelho, Campiglia, Barbosa Jr., 2014;Drivelos & Georgiou, 2012;Fabani, Raverac, Wunderlin, 2013). In this context, several machine-learning techniques have been proposed such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Randon Forest (RF) (Aguiar, Batista, Rodrigues, Silva, Campiglia, Barbosa & Barbosa , 2012;Alcazar, Jurado, 5 Palacios-Morillo, de Pablos & Martín, 2012;Batista et al 2012;Bereton & Loyd, 2010Jurado, Alcázar, Palacios-Morillo & de Pablos, 2012.…”