Background The development of statistical software has enabled food scientists to perform a wide variety of mathematical/statistical analyses and solve problems. Therefore, not only sophisticated analytical methods but also the application of multivariate statistical methods have increased considerably. Herein, principal component analysis (PCA) and hierarchical cluster analysis (HCA) are the most widely used tools to explore similarities and hidden patterns among samples where relationship on data and grouping are until unclear. Usually, larger chemical data sets, bioactive compounds and functional properties are the target of these methodologies. Scope and approach In this article, we criticize these methods when correlation analysis should be calculated and results analyzed. Key findings and conclusions The use of PCA and HCA in food chemistry studies has increased because the results are easy to interpret and discuss. However, their indiscriminate use to assess the association between bioactive compounds and in vitro functional properties is criticized as they provide a qualitative view of the data. When appropriate, one should bear in mind that the correlation between the content of chemical compounds and bioactivity could be duly discussed using correlation coefficients.

Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective

Melatonin is a tryptophan-derived molecule with pleiotropic activities. It is present in almost all or all organisms. Its synthetic pathway depends on the species in which it is measured. For example, the tryptophan to melatonin pathway differs in plants and animals. It is speculated that the melatonin synthetic machinery in eukaryotes was inherited from bacteria as a result of endosymbiosis. However, melatonin's synthetic mechanisms in microorganisms are currently unknown. Melatonin metabolism is highly complex with these enzymatic processes having evolved from cytochrome C. In addition to its enzymatic degradation, melatonin is metabolized via pseudoenzymatic and free radical interactive processes. The metabolic products of these processes overlap and it is often difficult to determine which process is dominant. However, under oxidative stress, the free radical interactive pathway may be featured over the others. Because of the complexity of the melatonin degradative processes, it is expected that additional novel melatonin metabolites will be identified in future investigations. The original and primary function of melatonin in early life forms such as in unicellular organisms was as a free radical scavenger and antioxidant. During evolution, melatonin was selected as a signaling molecule to transduce the environmental photoperiodic information into an endocrine message in multicellular organisms and for other purposes as well. As an antioxidant, melatonin exhibits several unique features which differ from the classic antioxidants. These include its cascade reaction with free radicals and its capacity to be induced under moderate oxidative stress. These features make melatonin a potent endogenously-occurring antioxidant that protects organisms from catastrophic oxidative stress.

https://www.mdpi.com/1420-3049/20/10/18886/pdf

Melatonin as a Potent and Inducible Endogenous Antioxidant: Synthesis and Metabolism.

Stir-bar sorptive extraction: A view on method optimisation, novel applications, limitations and potential solutions.

Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Sources and contamination routes of microbial pathogens to fresh produce during field cultivation: A review.

The consumption of fruit and vegetables continues to rise in the United States and European Union due to healthy lifestyle recommendations. Meanwhile, the rate of foodborne illness caused by the consumption of these products remains high in both regions, representing a significant public health and financial issue. This study addresses the occurrence of reported foodborne outbreaks associated with fresh fruits and vegetables consumption in the United States and European Union during the period 2004–2012, where data are available. Special attention is paid to those pathogens responsible for these outbreaks, the mechanisms of contamination, and the fresh produce vehicles involved. Norovirus is shown to be responsible for most of the produce-related outbreaks, followed by Salmonella. Norovirus is mainly linked with the consumption of salad in the United States and of berries in the European Union, as demonstrated by the Multiple Correspondence Analysis (MCA). Salmonella was the leading cause of mult...

Raquel M. Callejón

Papers

Reported Foodborne Outbreaks Due to Fresh Produce in the United States and European Union: Trends and Causes

Recent trends in the determination of biogenic amines in fermented beverages – A review

Volatile and sensory profile of organic red wines produced by different selected autochthonous and commercial Saccharomyces cerevisiae strains

Determination of amino acids in grape-derived products: a review.

Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis