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Microwave drying (MWD) is an efficient dielectric drying method in food, with advantages such as volumetric heating, fast drying, safety, and good product quality. As a key indicator of a dryer's market value, energy efficiency is of concern to sellers and dryer manufacturers. This paper systematically reviewed the quantification methods and influencing factors of energy efficiency of microwave drying in food application from different perspectives. Mechanisms and possible improvements of these factors are highlighted. Future trends in improving the energy efficiency of MWD are proposed. Energy consumption of MWD depends on a variety of factors such as equipment structure, drying conditions (microwave power, frequency, temperature, and air velocity), material properties, and combined/hybrid drying technologies. The drying system can be effectively improved if these parameters are adjusted appropriately and taking the processing cost into consideration. Although a good product can be obtained by pretreatment or combined/hybrid drying method, it may consume more energy. Future research should develop artificial intelligence, renewable energy, and computational fluid dynamics technology to pave the way for large-scale application of MWD and reduce energy consumption. [ABSTRACT FROM AUTHOR]

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers. [ABSTRACT FROM AUTHOR]

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0. [ABSTRACT FROM AUTHOR]

The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditions of an individual or a population. Large volumes of data are generated daily in the health sector. In this sense, Artificial Intelligence (AI) tools have been increasingly used, for example, the application of Machine Learning (ML) algorithms to extract useful information, find patterns, and predict diseases. This systematic review aimed to identify studies that used ML algorithms to assess food intake in different populations. A literature search was conducted using five electronic databases, and 36 studies met all criteria and were included. According to the results, there has been a growing interest in the use of ML algorithms in the area of nutrition in recent years. Also, supervised learning algorithms were the most used, and the most widely used method of nutritional assessment was the food frequency questionnaire. We observed a trend in using the data analysis programs, such as R and WEKA. The use of ML in nutrition is recent and challenging. Therefore, it is encouraged that more studies are carried out relating these themes for the development of food reeducation programs and public policies. [ABSTRACT FROM AUTHOR]

The gut microbiome comprises a variety of microorganisms whose genes encode proteins to carry out crucial metabolic functions that are responsible for the majority of health-related issues in human beings. The advent of the technological revolution in artificial intelligence (AI) assisted synthetic biology (SB) approaches will play a vital role in the modulating the therapeutic and nutritive potential of probiotics. This can turn human gut as a reservoir of beneficial bacterial colonies having an immense role in immunity, digestion, brain function, and other health benefits. Hence, in the present review, we have discussed the role of several gene editing tools and approaches in synthetic biology that have equipped us with novel tools like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR-Cas) systems to precisely engineer probiotics for diagnostic, therapeutic and nutritive value. A brief discussion over the AI techniques to understand the metagenomic data from the healthy and diseased gut microbiome is also presented. Further, the role of AI in potentially impacting the pace of developments in SB and its current challenges is also discussed. The review also describes the health benefits conferred by engineered microbes through the production of biochemicals, nutraceuticals, drugs or biotherapeutics molecules etc. Finally, the review concludes with the challenges and regulatory concerns in adopting synthetic biology engineered microbes for clinical applications. Thus, the review presents a synergistic approach of AI and SB toward human gut microbiome for better health which will provide interesting clues to researchers working in the area of rapidly evolving food and nutrition science. [ABSTRACT FROM AUTHOR]