Annual Review of Food Science and Technology - Volume 12, 2021

Carotenoids are versatile isoprenoids that are important in food quality and health promotion. There is a need to establish recommended dietary intakes/nutritional reference values for carotenoids. Research on carotenoids in agro-food and health is being propelled by the two multidisciplinary international networks, the Ibero-American Network for the Study of Carotenoids as Functional Foods Ingredients (IBERCAROT; http://www.cyted.org) and the European Network to Advance Carotenoid Research and Applications in Agro-Food and Health (EUROCAROTEN; http://www.eurocaroten.eu). In this review, considerations for their safe and sustainable use in products mostly intended for health promotion are provided. Specifically, information about sources, intakes, and factors affecting bioavailability is summarized. Furthermore, their health-promoting actions and importance in public health in relation to the contribution of reducing the risk of diverse ailments are synthesized. Definitions and regulatory and safety information for carotenoid-containing products are provided. Lastly, recent trends in research in the context of sustainable healthy diets are summarized.

Polyphenols are generally known for their health benefits and estimating actual exposure levels in health-related studies can be improved by human biomonitoring. Here, the application of newly available exposomic and metabolomic technology, notably high-resolution mass spectrometry, in the context of polyphenols and their biotransformation products, is reviewed. Comprehensive workflows for investigating these important bioactives in biological fluids or microbiome-related experiments are scarce. Consequently, this new era of nontargeted analysis and omic-scale exposure assessment offers a unique chance for better assessing exposure to, as well as metabolism of, polyphenols. In clinical and nutritional trials, polyphenols can be investigated simultaneously with the plethora of other chemicals to which we are exposed, i.e., the exposome, which may interact abundantly and modulate bioactivity. This research direction aims at ultimately eluting into atrue systems biology/toxicology evaluation of health effects associated with polyphenol exposure, especially during early life, to unravel their potential for preventing chronic diseases.

Glucosinolates (GSLs) are a class of sulfur-containing compounds found predominantly in the genus Brassica of the Brassicaceae family. Certain edible plants in Brassica, known as Brassica vegetables, are among the most commonly consumed vegetables in the world. Over the last three decades, mounting evidence has suggested an inverse association between consumption of Brassica vegetables and the risk of various types of cancer. The biological activities of Brassica vegetables have been largely attributed to the hydrolytic products of GSLs. GSLs can be hydrolyzed by enzymes; thermal or chemical degradation also breaks down GSLs. There is considerable variation of GSLs in Brassica spp., which are caused by genetic and environmental factors. Most Brassica vegetables are consumed after cooking; common cooking methods have a complex influence on the levels of GSLs. The variationof GSLs in Brassica vegetables and the influence of cooking and processing methods ultimately affect their intake and health-promoting properties.

Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications.

An increasing number of foodborne outbreaks, growing consumer desire for healthier products, and surging numbers of food allergy cases necessitate strict handling and screening of foods at every step of the food supply chain. Current standard procedures for detecting food toxins, contaminants, allergens, and pathogens require costly analytical devices, skilled technicians, and long sample preparation times. These challenges can be overcome with the use of biosensors because they provide accurate, rapid, selective, qualitative, and quantitative detection of analytes. Their ease of use, low-cost production, portability, and nondestructive measurement techniques also enable on-site detection of analytes. For this reason, biosensors find many applications in food safety and quality assessments. The detection mechanisms of biosensors can be varied with the use of different transducers, such as optical, electrochemical, or mechanical. These options provide a more appropriate selection of the biosensors for the intended use. In this review, recent studies focusing on the fabrication of biosensors for food safety or food quality purposes are summarized. To differentiate the detection mechanisms, the review is divided into sections based on the transducer type used.

Cocoa butter displays complex crystallization behavior and six crystal polymorphic forms. Although the crystal structure of cocoa butter has been studied extensively, the molecular interactions between cocoa butter triacylglycerols in relation to polymorphic transformations from metastable forms (forms III and IV) to stable crystal forms (forms V and VI) remain largely unknown. In this review, the triclinic polymorphism and melting profiles of the major triacylglycerols in cocoa butter—POP, POS, and SOS—are reviewed, and their binary and ternary phase behaviors in metastable (pseudoβ′) and stable (β2) crystal forms are discussed. We also attempt to clarify how the transformation of cocoa butter from form IV to V, as a critical step in the tempering of chocolate, is controlled by POS interactions with both POP and SOS. Moreover, we show how the crystal forms V and VI of cocoa butter are templated by crystal forms β3 and β1 of POS, respectively.

Large-amplitude oscillatory shear (LAOS) testing has been increasingly used over the past several decades to provide a fuller picture of food rheological behavior. Although LAOS is relatively easy to perform on a wide variety of foods, interpretation of the resulting data can be difficult, as it may not be possible to link the results to food components, microstructural features or changes, or physicochemical properties. Several analysis methods have been developed to address this issue, but there is currently no standard method for foods. In food research, LAOS has mainly been used to investigate connections between food microstructures and rheological behaviors, although there have been some studies on connections between food LAOS behaviors and processing or sensory behaviors. LAOS has the potential to be a valuable tool for investigating food structure–function–texture relationships, but much work remains to develop these relationships, particularly in the area of connecting LAOS to sensory attributes.