The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies

Role of advanced cleaning and sanitation techniques in biofilm prevention on dairy equipment

Biofilm formation on dairy equipment is a persistent challenge in the dairy industry, contributing to product contamination, equipment inefficiency, and economic losses. Traditional methods such as manual cleaning and basic chemical sanitation are discussed as foundational approaches, followed by an in-depth investigation of cutting-edge technologies, including clean-in-place systems, high-pressure cleaning, foam cleaning, ultrasonic and electrochemical cleaning, dry ice blasting, robotics, nanotechnology-based agents, enzymatic cleaners, and oxidizing agents. Enhanced sanitation techniques, such as dry steam, pulsed light, acidic and alkaline electrolyzed water, hydrogen peroxide vapor, microbubble technology, and biodegradable biocides, are highlighted for their potential to achieve superior sanitation while promoting sustainability. The effectiveness, feasibility, and limitations of these methods are evaluated, emphasizing their role in maintaining dairy equipment hygiene and reducing biofilm-associated risks. Additionally, challenges, such as equipment compatibility, cost, and regulatory compliance, are addressed, along with insights into future directions and innovations, including automation, smart cleaning systems, and green cleaning solutions. This review provides a comprehensive resource for researchers, industry professionals, and policymakers aiming to tackle biofilm formation in dairy production systems and enhance food safety, operational efficiency, and sustainability.

Recent advances in screen-printed carbon electrodes for food additive analysis

Screen-printed carbon electrodes (SPCEs) are regarded as the actual and future sensing option for additive analysis in food samples; nonetheless, the sample preparation, selectivity, and detectability are key challenges to overcome for its technological development and wide application. In the present review, we inform, discuss, and compare some pivotal aspects associated with the fabrication of SPCEs, the presence of additives in foods, sample preparation, and voltammetric measurements of additives in food samples. Also, the proposed study has indicated that it is possible to develop suitable options for electroanalytical methodologies by using bare or modified SPCEs, which present affordable results in terms of selectivity, linear concentration range, and limit of detection for different classes of additives. Lastly, the review introduces challenging points that can be carefully evaluated for the next generation of SPCEs dedicated to additive analysis.

Immobilized enzymes: exploring its potential in food industry applications

The global demand for nutritious, longer-lasting food has spurred the food industry to seek eco-friendly solutions. Enzymes play a vital role in enhancing food quality by improving flavor, texture, and nutritional content. However, challenges like rapid deactivation and non-recoverability of free enzymes are addressed by immobilized enzymes, which enhance efficiency, quality, and sustainability in food processing. Immobilization methods include adsorption, covalent binding, entrapment, encapsulation and cross-liked enzyme aggregates, which enhancing their stability, reusability, and catalytic efficiency. Immobilization of enzyme such as pectinase, amylase, naringinase, cellulase, lactase, glucoamylase, xylanase, invertase, lipase, phytase, and protease have been utilized in fruit, vegetable, baking, dairy, brewing, and feed process due to their high thermostability, improved shelf life, food quality and safety. The catalytic efficiency of immobilized enzymes in detecting and quantifying various food components, contaminants, and quality indicators, also developed functional foods with nutraceuticals benefits, include prebiotic juices, lactose-free dairy products, poly unsaturated fatty acids rich foods, low-calorie sweeteners, fortified food and bioactive peptides.

Graphical abstract

Applications of Artificial Intelligence in Food Industry

With breakthroughs in artificial intelligence (AI) brought by the fourth industrial revolution, intelligent applications are providing innovative solutions across food industry [...].

Recent advances on artificial intelligence-based approaches for food adulteration and fraud detection in the food industry: Challenges and opportunities

Food adulteration is the deceitful practice of misleading consumers about food to profit from it. The threat to public health and food quality or nutritional valuable make it a major issue. Food origin and adulteration should be considered to safeguard customers against fraud. It has been established that artificial intelligence is a cutting-edge technology in food science and engineering. In this study, it has been explained how AI detects food tampering. Applications of AI such as machine learning tools in food quality have been studied. This review covered several food quality detection web-based information sources. The methods used to detect food adulteration and food quality standards have been highlighted. Various comparisons between state-of-the-art techniques, datasets, and outcomes have been conducted. The outcomes of this investigation will assist researchers choose the best food quality method. It will help them identify of foods that have been explored by researchers and potential research avenues.

AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance

This study aims to explore the transformative role of Artificial Intelligence (AI) in food manufacturing by optimizing production, reducing waste, and enhancing sustainability. This review follows a literature review approach, synthesizing findings from peer-reviewed studies published between 2019 and 2024. A structured methodology was employed, including database searches and inclusion/exclusion criteria to assess AI applications in food manufacturing. By leveraging predictive analytics, real-time monitoring, and computer vision, AI streamlines workflows, minimizes environmental footprints, and ensures product consistency. The study examines AI-driven solutions for waste reduction through data-driven modeling and circular economy practices, aligning the industry with global sustainability goals. Additionally, it identifies key barriers to AI adoption-including infrastructure limitations, ethical concerns, and economic constraints-and proposes strategies for overcoming them. The findings highlight the necessity of cross-sector collaboration among industry stakeholders, policymakers, and technology developers to fully harness AI's potential in building a resilient and sustainable food manufacturing ecosystem.

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is using deep learning-assisted visible near-infrared (vis-NIR) spectroscopy to monitor residual sugar and bacterial concentration in real-time, ensuring kombucha quality during production. The feature fingerprints of residual sugar and bacterial concentration were extracted by four variable selection algorithms and then reconstructed using serial and parallel processing methods. Based on these reconstructed features, Partial Least Squares (PLS) and Convolutional Neural Networks (1DCNN and 2DCNN) models were developed and compared. The experimental results showed that the 2DCNN model based on reconstruction features achieved superior performance. The RPDs of the residual sugar and bacterial concentrations models were 4.49 and 6.88, while the MAEs were 0.42 mg/mL and 0.04 (Abs), respectively. These results suggest that the proposed modeling strategy effectively supports quality control during kombucha production and provides a new perspective for spectral analysis.

Development and Optimization of a Real-Time Monitoring System of Small-Scale Multi-Purpose Juice Extractor

According to the concept of smart postharvest management, an information and communication technology sensor-based monitoring system is required in the juicing process to reduce losses and improve process efficiency. Such technologies are considered economically burdensome and technically challenging for small-scale enterprises to adopt. From this perspective, this study aimed to develop a smart monitoring system for the juicing processes in small-scale enterprises and to identify the optimal operating conditions based on the monitoring data. The system developed is equipped with two weight sensors attached to the twin-screw juice extractor, allowing for the automatic measurement of the weight of the raw material and the resulting juice product. The measured data are automatically transmitted and stored on a computer. Additionally, the system was designed to remotely control the speeds of the juicing and feeding screws, which are the primary controlling factors of the twin-screw juicer. Juice yield and processing time were optimized using carrots and pears. The optimal juicing and feeding speeds for pear yield were found to be 167.4 rpm and 1557 rpm, respectively; carrots achieved an optimal yield at a juicing speed of 502.2 rpm and feeding speed of 1211 rpm. In contrast, the processing time was minimized at juicing-feeding speeds of 6-6 and 7-5 for pears and carrots, respectively. Consequently, it was challenging to determine the optimal conditions for simultaneously optimizing the yield and processing time. This also suggests that the juicing process is affected by the properties of the fruits and vegetables being processed. By developing a system capable of accumulating the data necessary for the digitization of postharvest management and food processing, this research offers a valuable platform for the smart monitoring and optimization of the juicing process.

Transforming plant-based waste and by-products into valuable products using various "Food Industry 4.0" enabling technologies: A literature review

The last several years have seen unprecedented strain on food systems as a result of pandemics, climate change, population growth, and urbanization. Thus, academic and scientific communities now view global food security as a critical issue. However, food loss and waste are a major challenge when adopting food security and sustainability strategies, since a large proportion of food is lost or wasted along the food supply chain. In order to use resources efficiently and enhance food security and sustainability, food waste and by-products must be reduced and properly valorized. Plant-based food production generates various by-products which are generally rich in nutrients and bioactive compounds. Emerging technologies have been effectively employed to extract these valuable compounds with health benefits. Recently, Industry 4.0 technologies such as artificial intelligence, the Internet of Things, blockchain, robotics, smart sensors, 3D printing, and digital twins have a great deal of potential for waste reduction and by-products valorization in food industry. Reducing food waste not only benefits the environment, but also reduces greenhouse gas emissions and thus contributes to sustainable resource management. This review provides up-to-date information on the potential of Industry 4.0 for converting plant-based waste and by-products into valuable products. Recent studies showed that innovations in Industry 4.0 provide attractive opportunities to increase the effectiveness of manufacturing operations and improve food quality, safety and traceability. By leveraging Food Industry 4.0, companies can transform plant-based waste and by-products into valuable products and contribute to a more sustainable and efficient food production system.

Recent advances in fish cutting: From cutting schemes to automatic technologies and internet of things innovations

Fish-cutting products are widely loved by consumers due to the unique nutrient composition and flavor in different cuts. However, fish-cutting faces the issue of labor shortage due to the harsh working environment, huge workload, and seasonal work. Hence, some automatic, efficient, and large-scale cutting technologies are needed to overcome these challenges. Accompanied by the development of Industry 4.0, the Internet of Things (IoT), artificial intelligence, big data, and blockchain technologies are progressively applied in the cutting process, which plays pivotal roles in digital production monitoring and product safety enhancement. This review focuses on the main fish-cutting schemes and delves into advanced automatic cutting techniques, showing the latest technological advancements and how they are revolutionizing fish cutting. Additionally, the production monitoring architecture based on IoT in the fish-cutting process is discussed. Fish cutting involves a variety of schemes tailored to the specific characteristics of each fish cut. The cutting process includes deheading and tail removal, filleting, boning, skinning, trimming, and bone inspection. By incorporating sensors, machine vision, deep learning, and advanced cutting tools, these technologies are transforming fish cutting from a manual to an automated process. This transformation has significant practical implications for the industry, offering improved efficiency, consistent product quality, and enhanced safety, ultimately providing a modernized manufacturing approach to fish-cutting automation within the context of Industry 4.0.

Machine vision combined with deep learning-based approaches for food authentication: An integrative review and new insights

Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid and reliable analysis of food quality and safety for food authentication. Machine vision-based methods have emerged as promising solutions for the rapid and nondestructive analysis of food authenticity and quality. The Industry 4.0 revolution has introduced new trends in this field, including the use of deep learning (DL), a subset of artificial intelligence, which demonstrates robust performance and generalization capabilities, effectively extracting features, and processing extensive data. This paper reviews recent advances in machine vision and various DL-based algorithms for food authentication, including DL and lightweight DL, used for food authenticity analysis such as adulteration identification, variety identification, freshness detection, and food quality identification by combining them with a machine vision system or with smartphones and portable devices. This review explores the limitations of machine vision and the challenges of DL, which include overfitting, interpretability, accessibility, data privacy, algorithmic bias, and design and deployment of lightweight DLs, and miniaturization of sensing devices. Finally, future developments and trends in this field are discussed, including the development of real-time detection systems that incorporate a combination of machine vision and DL methods and the expansion of databases. Overall, the combination of vision-based techniques and DL is expected to enable faster, more affordable, and more accurate food authentication methods.

From Food Industry 4.0 to Food Industry 5.0: Identifying technological enablers and potential future applications in the food sector

Although several food-related fields have yet to fully grasp the speed and breadth of the fourth industrial revolution (also known as Industry 4.0), growing literature from other sectors shows that Industry 5.0 (referring to the fifth industrial revolution) is already underway. Food Industry 4.0 has been characterized by the fusion of physical, digital, and biological advances in food science and technology, whereas future Food Industry 5.0 could be seen as a more holistic, multidisciplinary, and multidimensional approach. This review will focus on identifying potential enabling technologies of Industry 5.0 that could be harnessed to shape the future of food in the coming years. We will review the state-of-the-art studies on the use of innovative technologies in various food and agriculture applications over the last 5 years. In addition, opportunities and challenges will be highlighted, and future directions and conclusions will be drawn. Preliminary evidence suggests that Industry 5.0 is the outcome of an evolutionary process and not of a revolution, as is often claimed. Our results show that regenerative and/or conversational artificial intelligence, the Internet of Everything, miniaturized and nanosensors, 4D printing and beyond, cobots and advanced drones, edge computing, redactable blockchain, metaverse and immersive techniques, cyber-physical systems, digital twins, and sixth-generation wireless and beyond are likely to be among the main driving technologies of Food Industry 5.0. Although the framework, vision, and value of Industry 5.0 are becoming popular research topics in various academic and industrial fields, the agri-food sector has just started to embrace some aspects and dimensions of Industry 5.0.

Unveiling the relationship between food unit operations and food industry 4.0: A short review

The fourth industrial revolution (Industry 4.0) is driving significant changes across multiple sectors, including the food industry. This review examines how Industry 4.0 technologies, such as smart sensors, artificial intelligence, robotics, and blockchain, among others, are transforming unit operations within the food sector. These operations, which include preparation, processing/transformation, preservation/stabilization, and packaging and transportation, are crucial for converting raw materials into high-quality food products. By incorporating advanced digital, physical, and biological innovations, Industry 4.0 technologies are enhancing precision, productivity, and environmental responsibility in food production. The review highlights innovative applications and key findings that showcase how these technologies can streamline processes, minimize waste, and improve food product quality. The adoption of Industry 4.0 innovations is increasingly reshaping the way food is prepared, transformed, preserved, packaged, and transported to the final consumer. The work provides a valuable roadmap for various sectors within agriculture and food industries, promoting the adoption of Industry 4.0 solutions to enhance efficiency, quality, and sustainability throughout the entire food supply chain.

Systematic Literature Review of Barriers and Enablers to Implementing Food Informatics Technologies: Unlocking Agri-Food Chain Innovation

Access to food products is becoming more and more complex due to population growth, climate change, political and economic instability, disruptions in the global value chain, as well as changes in consumption dynamics and food insecurity. Therefore, agri-food chains face increasingly greater challenges in responding to these dynamics, where the digitalization of agri-food systems has become an innovative alternative. However, efforts to adopt and use the technologies of the fourth industrial revolution (precision agriculture, smart agriculture, the Industrial Internet of Things, and the Internet of Food, among others) are still a challenge to improve efficiency in the links of production (cultivation), processing (food production), and final consumption, from the perspective of the implementation of Food Informatics technologies that improve traceability, authenticity, consumer confidence, and reduce fraud. This systematic literature review proposes the identification of barriers and enablers for the implementation of Food Informatics technologies in the links of the agri-food chain. The PRISMA methodology was implemented for the identification, screening, eligibility, and inclusion of articles from the Scopus and Clarivate databases. A total of 206 records were included in the in-depth analysis, through which a total of 34 barriers to the adoption of Food Informatics technologies (13 for the production link, 12 for the processing link, and 9 for the marketing link) and a total of 27 enablers (8 for the production link, 11 for the processing link, and 8 for the marketing link) were identified. Among the barriers analogous to the three links analyzed are privacy and information security and high investment and maintenance costs, while the analogous enablers are mainly government support.

Treatment of water extract of green tea during kale cultivation using a home vertical farming appliance conveyed catechins into kale and elevated glucosinolate contents

The growing interest in healthy diets has driven the demand for food ingredients with enhanced health benefits. In this study, we aimed to explore a method to enhance the bioactivity of kale using a home vertical farming appliance. Specifically, we investigated the effects of treating kale with a green tea water extract (GTE; 0.1-0.5 g/L in nutrient solution) for two weeks before harvest during five weeks of kale cultivation. GTE treatment did not negatively affect the key quality attributes, such as yield, semblance, or sensory properties. However, it led to the accumulation of bioactive compounds, epicatechin (EC) and epigallocatechin gallate (EGCG), which are typically absent in kale. In the control group, no catechins were detected, whereas in the GTE-treated group, the concentration of EC and EGCG were as high as 252.11 and 173.26 μg/g, respectively. These findings indicate the successful incorporation of catechins, known for their unique health-promoting properties, into kale. Additionally, GTE treatment enhanced the biosynthesis of glucosinolates, which are key secondary metabolites of kale. The total glucosinolate content increased from 9.56 μmol/g in the control group to 16.81 μmol/g in the GTE-treated group (treated with 0.5 g/L GTE). These findings showed that GTE treatment not only enriched kale with catechins, the primary bioactive compounds in green tea but also increased the levels of glucosinolates. This study, conducted using a home vertical farming appliance, suggests that bioactivity-enhanced kale can be grown domestically, providing consumers with a nutrient-fortified food source.

Implications of Russia-Ukraine war on land surface temperature and air quality: long-term and short-term analysis

The world is currently witnessing the military operations of Russia invading Ukraine, leading to missile bombing and shelling on various parts. Although the economic ill effects are more conspicuous and much talked about, the environmental impacts are grimmer and more devastating but ironically are less in the news. Hence, in this work, we focused on the environmental impact of the Russia-Ukraine war by quantifying the long-term (2001 to 2023) and short-term temperature changes using land surface temperature (LST) and air temperature (AT) as proxies and monitoring changes in air quality, mainly methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2), between 2021 and 2022. We used NASA MODIS FIRMS fire points from 24th February 2022 to 08th September 2023 to prepare the heat map for identifying the regions heavily devastated by bombing. Thus, parts of Kiev, Lviv, Luhansk, Odesa, Donetsk, Kherson, etc., in Ukraine were chosen for assessing the LST, AT variations during the peak season of war along with analysis of long-term and short-term changes. We used MODIS Terra LST and Emissivity, ERA 5 AT, CH4, CO2 from AIRS and CO from Sentinel 5P. The results of the LST showed an average increase of around 2.32 °C (2022-2023), 3.44 °C (2021 and 2022) in parts of Ukraine and an increase of about 2 °C from COVID time, whilst a decrease of about 1 °C during COVID. This increase in LST will cause enhanced warming, thus changing the regional climate in a shorter time frame. A consistent upward trend in CH4, CO and CO2 is seen from 2019 to 2023. These heat waves and pollution will grip Ukraine and cause menace due to the cumulative effect of heat waves, changing climate and the aftermaths of war. This would be catastrophic as it might lead to a widespread impact on human health, agricultural yield and infrastructure, to name a few.

Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.

Unveiling Digital Transformation: A Catalyst for Enhancing Food Security and Achieving Sustainable Development Goals at the European Union Level

The digital revolution is reshaping various aspects of society, including having a profound impact on food security and the advancement of Sustainable Development Goals (SDGs). This study investigates the relationship between digital transformation, quantified through the components of the Digital Economy and Society Index (DESI), and SDGs related to food (SDG1, SDG2, SDG3, and SDG10), along with the overall SDG Index score. The data used for investigation are sourced from reports issued by the European Commission concerning DESI, as well as the SDG reports for the period from 2017 to 2022. The paper elucidates how different components of digitalization, such as connectivity, digital skills, internet usage, and digital public services, influence the attainment of food security objectives and broader sustainable development targets using structural equation modeling and cluster analysis. The findings underscore the pivotal role of digital technologies in enhancing poverty alleviation, health and well-being, and, in particular, mitigating inequality. This study contributes to understanding the complex relationship between digital transformation and food security, offering insights for policymakers, practitioners, and stakeholders aiming to leverage technology for advancing SDGs and fostering a more equitable and sustainable future.

Emerging challenges and opportunities in innovating food science technology and engineering education

Progress in science, technology, innovation, and digital capabilities call for reassessing food science, technology, and engineering (FST&E) education and research programs. This survey targeted global professionals and students across food disciplines and nutrition. Its main objectives included assessing the status of FST&E higher education, identifying challenges and opportunities, and furnishing recommendations. Seven topics affecting the future of the FST&E curricula were evaluated by the panel as 'High' to 'Very high', namely: 'Critical thinking', followed by 'Problem-solving projects', 'Teamwork/collaboration', 'Innovation/Open innovation' and 'Multidisciplinary'. The importance of academic partnership/collaboration with the Food Industry and Nutrition Sciences was demonstrated. Significant positive roles of the food industry in collaboration and partnerships were found. Other essential food industry attributes were related to internships, education, strategy, and vision. Collaboration between FST&E and nutrition sciences indicated the high standing of this direction. The need to integrate or converge nutrition sciences and FST&E is emphasized, especially with the growing consumer awareness of health and wellness. The study provides insights into new education and learning opportunities and new topics for future curricula.

A Conceptual Model Relationship between Industry 4.0-Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

With the expected colonization of human daily life by artificial intelligence, including in industry productivity, the deployment of Industry 4.0 (I4) in the food agriculture industry (FAI) is expected to revolutionize and galvanize food production to increase the efficiency of the industry's production and to match, in tandem, a country's gross domestic productivity. Based on a literature review, there have been almost no direct relationships between the I4-Food-Agriculture (I4FA) Nexus and the agroecosystem. This study aimed to evaluate the state-of-the-art relationships between the I4FA Nexus and the agroecosystem and to discuss the challenges in the sustainable FAI that can be assisted by the I4 technologies. This objective was fulfilled by (a) reviewing all the relevant publications and (b) drawing a conceptual relationship between the I4FA Nexus and the agroecosystem, in which the I4FA Nexus is categorized into socio-economic and environmental (SEE) perspectives. Four points are highlighted in the present review. First, I4 technology is projected to grow in the agricultural and food sectors today and in the future. Second, food agriculture output may benefit from I4 by considering the SEE benefits. Third, implementing I4 is a challenging journey for the sustainable FAI, especially for the small to medium enterprises (SMEs). Fourth, environmental, social, and governance (ESG) principles can help to manage I4's implementation in agriculture and food. The advantages of I4 deployment include (a) social benefits like increased occupational safety, workers' health, and food quality, security, and safety; (b) economic benefits, like using sensors to reduce agricultural food production costs, and the food supply chain; and (c) environmental benefits like reducing chemical leaching and fertilizer use. However, more studies are needed to address social adaptability, trust, privacy, and economic income uncertainty, especially in SMEs or in businesses or nations with lower resources; this will require time for adaptation to make the transition away from human ecology. For agriculture to be ESG-sustainable, the deployment of I4FA could be an answer with the support of an open-minded dialogue platform with ESG-minded leaders to complement sustainable agroecosystems on a global scale.

Mathematical optimisation of extruded mixed plant protein-based meat analogues based on amino acid compositions

Developing meat analogues of superior amino acid (AA) profiles in the food industry is a challenge as plant proteins contain less of some essential AA than animal proteins. Mathematical optimisation models such as linear/non-linear programming models were used to overcome this challenge and create high-moisture meat analogues (HMMA) with AA profiles as close as possible to chicken breast meat. The effect on the physiochemical properties and specific mechanical energy (SME) of the HMMA was investigated. The AA content of HMMA was generally lower than chicken. Strong intermolecular bonds present in the globulin fraction could hinder protein acid hydrolysis of HMMA. Plant proteins also affect the HMMA colour as certain AA forms Maillard reaction products with higher browning intensity. Lastly, different characteristics of plant proteins resulted in different SME values under the same extrusion conditions. While mathematical programming can optimise plant protein combinations, fortification is required to match the AA profile of HMMA to an animal source.

Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

Phage Endolysins: Advances in the World of Food Safety

As antimicrobial resistance continues to escalate, the exploration of alternative approaches to safeguard food safety becomes more crucial than ever. Phage endolysins are enzymes derived from phages that possess the ability to break down bacterial cell walls. They have emerged as promising antibacterial agents suitable for integration into food processing systems. Their application as food preservatives can effectively regulate pathogens, thus contributing to an overall improvement in food safety. This review summarizes the latest techniques considering endolysins' potential for food safety. These techniques include native and engineered endolysins for controlling bacterial contamination at different points within the food production chain. However, we find that characterizing endolysins through in vitro methods proves to be time consuming and resource intensive. Alternatively, the emergence of advanced high-throughput sequencing technology necessitates the creation of a robust computational framework to efficiently characterize recently identified endolysins, paving the way for future research. Machine learning encompasses potent tools capable of analyzing intricate datasets and pattern recognition. This study briefly reviewed the use of these industry 4.0 technologies for advancing the research in food industry. We aimed to provide current status of endolysins in food industry and new insights by implementing these industry 4.0 strategies revolutionizes endolysin development. It will enhance food safety, customization, efficiency, transparency, and collaboration while reducing regulatory hurdles and ensuring timely product availability.

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

Consumer-oriented smart dynamic detection of fresh food quality: recent advances and future prospects

Since fresh foods include a significant amount of water, fat, and protein, it is more likely to become infected by microorganisms causing a major loss of quality. Traditional detection techniques are less able to meet customer expectations owing to the limitations of high cost, slow response time, and inability to permit dynamic monitoring. Intelligent non-destructive detection technologies have emerged in recent years, which offer the advantages of small size and fast response at low cost. However, dynamic monitoring of fresh food quality based on intelligent detection technologies on the consumer side has not been rigorously evaluated yet. This paper discussed the application of intelligent detection technologies based on the consumer side in the dynamic monitoring of fresh food freshness, microorganisms, food additives, and pesticide residues. Furthermore, the application of intelligent detection technologies combined with smartphones for quality monitoring and detection of fresh foods is evaluated. Moreover, the challenges and development trends of intelligent fresh food quality detection technologies are also discussed. Intelligent detection technologies based on the consumer side are designed to detect in real-time the quality of fresh food through visual color changes in combination with smartphones. This paper provides ideas and recommendations for the application of intelligent detection technologies based on the consumer side in food quality detection/monitoring and future research trends.

Birth of dairy 4.0: Opportunities and challenges in adoption of fourth industrial revolution technologies in the production of milk and its derivatives

Embracing innovation and emerging technologies is becoming increasingly important to address the current global challenges facing many food industry sectors, including the dairy industry. Growing literature shows that the adoption of technologies of the fourth industrial revolution (named Industry 4.0) has promising potential to bring about breakthroughs and new insights and unlock advancement opportunities in many areas of the food manufacturing sector. This article discusses the current knowledge and recent trends and progress on the application of Industry 4.0 innovations in the dairy industry. First, the "Dairy 4.0" concept, inspired by Industry 4.0, is introduced and its enabling technologies are determined. Second, relevant examples of the use of Dairy 4.0 technologies in milk and its derived products are presented. Finally, conclusions and future perspectives are given. The results revealed that robotics, 3D printing, Artificial Intelligence, the Internet of Things, Big Data, and blockchain are the main enabling technologies of Dairy 4.0. These advanced technologies are being progressively adopted in the dairy sector, from farm to table, making significant and profound changes in the production of milk, cheese, and other dairy products. It is expected that, in the near future, new digital innovations will emerge, and greater implementations of Dairy 4.0 technologies is likely to be achieved, leading to more automation and optimization of this dynamic food sector.

The big food view and human health from the prospect of bio-manufacturing and future food

The "big food view" has attracted widespread attention due to the view of sustainable nutrition and human health as part of sustainable development. The "big food view" starts from better meeting the people's needs for a better life. While ensuring the supply of grain, the effective supply of meat, vegetables, fruits, aquatic products and other foods also should be guaranteed. Using cell factories to replace the traditional food acquisition methods, establishing a new model of sustainable food manufacturing, will greatly reduce the demand for resources in food production, and improve the controllability of food production and manufacturing, and effectively avoid potential food safety and health risks. Cell factories can provide key technologies and supporting methods for the biological manufacturing of important food components, functional food ingredients and important functional nutritional factors, realizing a safer, nutritious, healthy and sustainable way of food acquisition. The combination of cell factory technology and other technologies meets the people's new dietary demand, and also supports that sustainable nutrition and human health as part of sustainable development. This paper focuses on the big food view and human health from the prospect of bio-manufacturing and future food, which aims to better meet people's dietary needs for increasingly diversified, refined, nutritious and ecological food through diversified food manufacturing.

Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof

As the main food source of the world's population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

Fermentation for Designing Innovative Plant-Based Meat and Dairy Alternatives

Fermentation was traditionally used all over the world, having the preservation of plant and animal foods as a primary role. Owing to the rise of dairy and meat alternatives, fermentation is booming as an effective technology to improve the sensory, nutritional, and functional profiles of the new generation of plant-based products. This article intends to review the market landscape of fermented plant-based products with a focus on dairy and meat alternatives. Fermentation contributes to improving the organoleptic properties and nutritional profile of dairy and meat alternatives. Precision fermentation provides more opportunities for plant-based meat and dairy manufacturers to deliver a meat/dairy-like experience. Seizing the opportunities that the progress of digitalization is offering would boost the production of high-value ingredients such as enzymes, fats, proteins, and vitamins. Innovative technologies such as 3D printing could be an effective post-processing solution following fermentation in order to mimic the structure and texture of conventional products.

Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm

Thermostatic bath calibration performance is usually checked for uniformity and stability to serve a wide range of industrial applications. Particularly challenging is the assessment at the limiting specification ends where the sensor system may be less effective in achieving consistency. An ensemble of eight sensors is used to test temperature measurement stability at various topological locations in a thermostatic bath (antifreeze) fluid at -20 °C. Eight streaks of temperature data were collected, and the resulting time-series were processed for normality, stationarity, and independence and identical distribution by employing regular statistical inference methods. Moreover, they were evaluated for autoregressive patterns and other underlying trends using classical Auto-Regressive Integrated Moving Average (ARIMA) modeling. In contrast, a continuous-time quantum walker algorithm was implemented, using an available R-package, in order to test the behavior of the fitted coefficients on the probabilistic node transitions of the temperature time series dataset. Tracking the network sequence for persistence and hierarchical mode strength was the objective. The quantum walker approach favoring a network probabilistic framework was posited as a faster way to arrive at simultaneous instability quantifications for all the examined time-series. The quantum walker algorithm may furnish expedient modal information in comparison to the classical ARIMA modeling and in conjunction with several popular stochastic analyzers of time-series stationarity, normality, and data sequence independence of temperature end-of-scale calibration datasets, which are investigated for temporal consistency.

Characteristics of the Beef Cheek Meat-Based Sausage Added with Snakehead (Channa striata) Gelatin

This study is aimed at determining the functional effect of snakehead fish gelatin as a binder on the characteristics and shelf life of beef cheek-based emulsion sausage compared with bovine commercial gelatin. The level of snakehead fish gelatin used was 0%, 1%, 2%, and 3%, while that of bovine commercial gelatin was 2% with a storage time of 0 to 28 days in the refrigerator (4 ± 2°C). Emulsion stability, cooking loss, proximate composition, texture profile, and microstructure of sausage were initially determined before storage; then, observations were made every seven days to determine the shelf life of sausages based on pH, antioxidant activity, and TBA reactivity. Characteristics such as emulsion stability, proximate composition, and texture profile were influenced by the treatment (p < 0.05). The gelatin level significantly affected the water holding capacity of sausages (p < 0.05), but the storage time did not (p > 0.05). On the other hand, the pH, TBA reactivity, and antioxidant activity of sausages were not only affected by gelatin level (p < 0.05) but also by storage time (p < 0.05). The sausage microstructure confirms the use of 2% snakehead fish gelatin to make sausages with properties similar to 2% commercial bovine gelatin. The byproduct of the snakehead fish industry can be used as a gelatin alternative to produce sausages. This gelatin has the potential as a binder, which can functionally improve sausage characteristics. This effectiveness can boost the water holding capacity of sausages, although it has not been effective in inhibiting fat oxidation which causes an increase in malonaldehyde levels.

Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.

Use of industry 4.0 technologies to reduce and valorize seafood waste and by-products: A narrative review on current knowledge

Fish and other seafood products represent a valuable source of many nutrients and micronutrients for the human diet and contribute significantly to global food security. However, considerable amounts of seafood waste and by-products are generated along the seafood value and supply chain, from the sea to the consumer table, causing severe environmental damage and significant economic loss. Therefore, innovative solutions and alternative approaches are urgently needed to ensure a better management of seafood discards and mitigate their economic and environmental burdens. The use of emerging technologies, including the fourth industrial revolution (Industry 4.0) innovations (such as Artificial Intelligence, Big Data, smart sensors, and the Internet of Things, and other advanced technologies) to reduce and valorize seafood waste and by-products could be a promising strategy to enhance blue economy and food sustainability around the globe. This narrative review focuses on the issues and risks associated with the underutilization of waste and by-products resulting from fisheries and other seafood industries. Particularly, recent technological advances and digital tools being harnessed for the prevention and valorization of these natural invaluable resources are highlighted.

Prospects for Plant-Based Meat: Current Standing, Consumer Perceptions, and Shifting Trends

Dietary habits have a substantial influence on both planet and individual health. High intake of animal products has significant negative effects on the environment and on human health; hence, a reduction in meat consumption is necessary. The transition towards plant-based meat (PBM) is one of the potential solutions for environmental and health issues. To achieve this goal, it is important to understand the dietary habits and demands of consumers. This review was designed with a focus on PBM alternatives, dietary shifts during the COVID-19 pandemic, the drivers of consumers' perceptions in various countries, and the measures that can promote the shift towards PBM. The PBM market is predicted to grow with rising awareness, familiarity, and knowledge in the coming years. Companies must focus on the categories of anticipated benefits to aid consumers in making the switch to a diet higher in PBM alternatives if they want to win over the target market.

Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect

Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.

Consumer acceptance of new food trends resulting from the fourth industrial revolution technologies: A narrative review of literature and future perspectives

The growing consumer awareness of climate change and the resulting food sustainability issues have led to an increasing adoption of several emerging food trends. Some of these trends have been strengthened by the emergence of the fourth industrial revolution (or Industry 4.0), and its innovations and technologies that have fundamentally reshaped and transformed current strategies and prospects for food production and consumption patterns. In this review a general overview of the industrial revolutions through a food perspective will be provided. Then, the current knowledge base regarding consumer acceptance of eight traditional animal-proteins alternatives (e.g., plant-based foods and insects) and more recent trends (e.g., cell-cultured meat and 3D-printed foods) will be updated. A special focus will be given to the impact of digital technologies and other food Industry 4.0 innovations on the shift toward greener, healthier, and more sustainable diets. Emerging food trends have promising potential to promote nutritious and sustainable alternatives to animal-based products. This literature narrative review showed that plant-based foods are the largest portion of alternative proteins but intensive research is being done with other sources (notably the insects and cell-cultured animal products). Recent technological advances are likely to have significant roles in enhancing sensory and nutritional properties, improving consumer perception of these emerging foods. Thus, consumer acceptance and consumption of new foods are predicted to continue growing, although more effort should be made to make these food products more convenient, nutritious, and affordable, and to market them to consumers positively emphasizing their safety and benefits.

Truths and myths about superfoods in the era of the COVID-19 pandemic

Nowadays, during the current COVID-19 pandemic, consumers increasingly seek foods that not only fulfill the basic need (i.e., satisfying hunger) but also enhance human health and well-being. As a result, more attention has been given to some kinds of foods, termed "superfoods," making big claims about their richness in valuable nutrients and bioactive compounds as well as their capability to prevent illness, reinforcing the human immune system, and improve overall health.This review is an attempt to uncover truths and myths about superfoods by giving examples of the most popular foods (e.g., berries, pomegranates, watermelon, olive, green tea, several seeds and nuts, honey, salmon, and camel milk, among many others) that are commonly reported as having unique nutritional, nutraceutical, and functional characteristics.While superfoods have become a popular buzzword in blog articles and social media posts, scientific publications are still relatively marginal. The reviewed findings show that COVID-19 has become a significant driver for superfoods consumption. Food Industry 4.0 innovations have revolutionized many sectors of food technologies, including the manufacturing of functional foods, offering new opportunities to improve the sensory and nutritional quality of such foods. Although many food products have been considered superfoods and intensively sought by consumers, scientific evidence for their beneficial effectiveness and their "superpower" are yet to be provided. Therefore, more research and collaboration between researchers, industry, consumers, and policymakers are still needed to differentiate facts from marketing gimmicks and promote human health and nutrition.

The fourth industrial revolution in the food industry-part II: Emerging food trends

The food industry has recently been under unprecedented pressure due to major global challenges, such as climate change, exponential increase in world population and urbanization, and the worldwide spread of new diseases and pandemics, such as the COVID-19. The fourth industrial revolution (Industry 4.0) has been gaining momentum since 2015 and has revolutionized the way in which food is produced, transported, stored, perceived, and consumed worldwide, leading to the emergence of new food trends. After reviewing Industry 4.0 technologies (e.g. artificial intelligence, smart sensors, robotics, blockchain, and the Internet of Things) in Part I of this work (Hassoun, Aït-Kaddour, et al. 2022. The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 1-17.), this complimentary review will focus on emerging food trends (such as fortified and functional foods, additive manufacturing technologies, cultured meat, precision fermentation, and personalized food) and their connection with Industry 4.0 innovations. Implementation of new food trends has been associated with recent advances in Industry 4.0 technologies, enabling a range of new possibilities. The results show several positive food trends that reflect increased awareness of food chain actors of the food-related health and environmental impacts of food systems. Emergence of other food trends and higher consumer interest and engagement in the transition toward sustainable food development and innovative green strategies are expected in the future.

Industry 4.0: A Chance or a Threat for Gen Z? The Perspective of Economics Students

Major transformations in the sphere of the economy that Industry 4.0 brings are also reflected in young people’s expectations regarding the development of their professional career. Existing social relations are being modified nowadays and new concepts of building them are being developed. The aim of the present article is to present the expectations, fears and hopes of young people related to the course of Industrial Revolution 4.0 in the context of their future life. For a simpler perception of the research objectives of students, the research was narrowed down to the topic of building relationships with robots, which are one of the pillars of Industry 4.0. The research methods are based on the literature studies and an experiment conducted among the students graduating from economic faculties and entering a strongly changing labour market. The experiment was qualitative. The students wrote a short essay on the topic of whether a friendship between a human and a robot is possible. One group of students was shown a short emotional clip about the relationship between the boy and the robot. Regardless of the attempt to influence the message with a film, both groups of students hardly noticed the negative effects of digitisation on building relationships and social trust. The relationship between human being and advanced technology will develop in the future, which will result in the emergence of new relationships between humans and artificial intelligence.

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

Application of a Cost-Effective Visible/Near Infrared Optical Prototype for the Measurement of Qualitative Parameters of Chardonnay Grapes

In this paper, a cost-effective visible/near infrared optical prototype was tested for grape maturity monitoring. The device was used to quantify the qualitative parameters of Chardonnay grapes, based on the combination of spectroscopic data and the creation of predictive models. The optical acquisitions were performed directly in the field through the use of 12 wavelengths in the vis/NIR range, i.e., 450, 500, 550, 570, 600, 610, 650, 680, 730, 760, 810 and 860 nanometers. The prediction of the qualitative parameters was carried out through a multivariate model, partial least square (PLS) regression technique and built knowing the real values of the parameters, i.e., total soluble solids (TSS), titratable acidity (TA) and pH measured through the reference laboratory analyses. Sampling included two harvest years. The most efficient model was the one for TSS evaluation that gave a R2 = 0.87 (independent test set validation). The results demonstrated that the optical device is able to provide useful information about the ripening parameters of Chardonnay grapes directly in the field in order to predict its correct maturation stage and, therefore, support operators in rapid and objective decision making. Overall, the use of the prototype promotes a sustainable approach and viticulture 4.0.