Pros and cons of hazard- versus risk-based approaches to food safety regulation

Mehta, Jyotigna; Rietjens, Ivonne M.C.M.

doi:10.1016/b978-0-12-819470-6.00024-x

Cited by 2 publications

(2 citation statements)

References 60 publications

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“…As Generation Z matures, their influence on the food and beverage industry is steadily increasing; they exhibit an openness to diverse cultures and actively seek out culinary experiences from various regions [17]. Factors influencing Generation Z's food preferences include stress, healthy beliefs, food cravings, body/image security, among others.…”

Section: The Traits and Dietary Preferences Of Generation Zmentioning

confidence: 99%

Comfort food for Generation Z: a case study in Taiwan

Lin

2024

J. Ethn. Food

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The Generation Z cohort, born between 1995 and 2010, represents a substantial portion of the global population, experiencing their formative years in an era characterized by pervasive globalization. Comfort foods serve as tangible expressions of both individual and national dietary cultures. This study reveals that the comfort foods of Generation Z also reflect the locality and globalization of dietary habits. Exploring the comfort foods of Generation Z in Taiwan, this research categorizes them into four classifications: main meals, desserts and snacks, beverages, and fruits and vegetables. Among Taiwan’s Generation Z, approximately one-third of their comfort food choices hold specific cultural significance, reflecting the dietary transformations and historical trajectory of Taiwan over the past century. The study also identifies key experiences and recollections associated with Generation Z’s comfort foods, including enhanced mood, ritualistic engagement, and emotional solace; while leisure, hunger, and cravings are contextual factors influencing the consumption of comfort foods.

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Section: The Traits and Dietary Preferences Of Generation Zmentioning

confidence: 99%

Comfort food for Generation Z: a case study in Taiwan

Lin

2024

J. Ethn. Food

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“…Tis shift acknowledges the importance of proactively identifying and mitigating risks to food safety. While these approaches have contributed to improved food safety globally, there is a growing recognition of the need for increased efciency, precision, and accuracy in food safety practices [34]. As a result, researchers are turning to mathematical microbial modeling to complement existing methods [35].…”

Section: Introductionmentioning

confidence: 99%

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Taiwo,

Onyeaka,

Oladipo

et al. 2024

International Journal of Microbiology

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Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

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Pros and cons of hazard- versus risk-based approaches to food safety regulation

Cited by 2 publications

References 60 publications

Comfort food for Generation Z: a case study in Taiwan

Comfort food for Generation Z: a case study in Taiwan

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

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