Internet of things (IoT) results in massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supplychain modernization, social media (for open innovation and sentiment analysis) in food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes.

Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf‐stable food products. The conventional culture‐based methods for microbial detection are time and labor‐intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.

Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near‐Infrared (NIR) spectroscopy has been identified as a fast and cost‐effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.

Microbiome science has greatly expanded our understanding of the diverse composition and function of gut microorganisms over the past decades. With its rich microbial composition, the microbiome hosts numerous functionalities essential for metabolizing food ingredients and nutrients, resulting in the production of active metabolites that affect food fermentation or gut health. Most of these processes are mediated by microbial enzymes such as carbohydrate-active enzymes and amino acid metabolism enzymes. Metatranscriptomics enables the capture of active transcripts within the microbiome, providing invaluable functional insights into metabolic activities. Given the inter-kingdom complexity of the microbiome, metatranscriptomics could further elucidate the activities of fungi, archaea, and bacteriophages in the microbial ecosystem. Despite its potential, the application of metatranscriptomics in food and nutrition sciences remains limited but is growing. This review highlights the latest advances in food science (e.g., flavour formation and food enzymology) and nutrition science (e.g., dietary fibres, proteins, minerals, and probiotics), emphasizing the integration of metatranscriptomics with other technologies to address key research questions. Ultimately, metatranscriptomics represents a powerful tool for uncovering the microbiome activity, particularly in relation to active metabolic processes.