The authenticity of products labeled as olive oils, and in particular as virgin olive oils, stands for a very important issue both in terms of its health and commercial aspects. In view of the continuously increasing interest in virgin olive oil therapeutic properties, the traditional methods of characterization and physical and sensory analysis were further enriched with more advanced and sophisticated methods such as HPLC-MS, HPLC-GC/C/IRMS, RPLC-GC, DEPT, and CSIA among others. The results of both traditional and "novel" methods were treated both by means of classical multivariate analysis (cluster, principal component, correspondence, canonical, and discriminant) and artificial intelligence methods showing that nowadays the adulteration of virgin olive oil with seed oil is detectable at very low percentages, sometimes even at less than 1%. Furthermore, the detection of geographical origin of olive oil is equally feasible and much more accurate in countries like Italy and Spain where databases of physical/chemical properties exist. However, this geographical origin classification can also be accomplished in the absence of such databases provided that an adequate number of oil samples are used and the parameters studied have "discriminating power."

Rice importance resides in its high consumption mainly in Asia and Africa and less in the EU. Several cultivars, both GM and non-GM, have established themselves in various regions depending mainly on the climatic and soil conditions. A high number of analytical, enzymic, and genomic analyses (instrumental) in conjunction with sensory analysis were applied (not always very successfully) towards detecting deliberate or non-deliberate rice adulteration. It was shown that the application of multivariate analysis to data obtained is very beneficial because it allows the effective discrimination of different origin, and/or cultivar rice. Although sensory analysis is based on a trained panel (subjective method), if this panel has been properly trained the adulteration results are comparable to those of the instrumental analysis obtained.

Adulteration of foods is a serious economic problem concerning most foodstuffs, and in particular meat products. Since high-priced meat demand premium prices, producers of meat-based products might be tempted to blend these products with lower cost meat. Moreover, the labeled meat contents may not be met. Both types of adulteration are difficult to detect and lead to deterioration of product quality. For the consumer, it is of outmost importance to guarantee both authenticity and compliance with product labeling. The purpose of this article is to review the state of the art of meat authenticity with analytical and immunochemical methods with the focus on the issue of geographic origin and sensory characteristics. This review is also intended to provide an overview of the various currently applied statistical analyses (multivariate analysis (MAV), such as principal component analysis, discriminant analysis, cluster analysis, etc.) and their effectiveness for meat authenticity.

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.