An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

Workers’ Opinions on Using the Internet of Things to Enhance the Performance of the Olive Oil Industry: A Machine Learning Approach

Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees were employed in the development of the method. The data were collected from respondents with knowledge related to integrating new technologies into the industry. The results demonstrated that IoT implementation in olive oil companies significantly improved their performance. Moreover, it was found that there was a positive relationship between supply chain improvements via IoT implementation in olive oil companies and their performance.

Olive Fruit Selection through AI Algorithms and RGB Imaging

(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

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.

Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil

Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture.

Extra Virgin Olive Oil (EVOO): Quality, Safety, Authenticity, and Adulteration

The prevention and bioactivity effects associated with the so-called "Mediterranean diet" make olive oil the most consumed edible fat in the food intake of the Mediterranean basin [...].

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.