In this paper, we present the implementation of a new class of optical pressure sensors in a robotic tactile-sensing system based on polydimethylsiloxane (PDMS). The sensor consists of a tapered optical fiber, where an optical signal goes across, embedded into a PDMS-gold nanocomposite material (GNM). By applying different pressure forces onto the PDMS-based nanocomposite, changes in the optical transmittivity of the fiber can be detected in real time due to the coupling between the GNM and the tapered fiber region. The intensity reduction of a transmitted light is correlated to the pressure force magnitude. Light intensity is converted into an electric signal by a system suitable for robotic implementation. High sensitivity using forces by applying weights of a few grams is proved. Sensitivity on the order of 5 g is checked. A detailed algorithm for the detection of roughness and shapes by means of a robotic finger is proposed.

Food fortification includes micronutrient directly added or by probiotics in food matter, making the final product more enticing to the consumers. Dairy products well represent a category of foods to fortify successfully. Fortification properly enhances cheeses, including micronutrients into the casein matrix. The process exalts either nutritional or technological product features. Food fortification occurs with the adding of primary or secondary compounds from the agrifood production chain. Using vegetable by-products encourages circular economy, generating an eco-sustainable and consumer health-promoting compounds class. Organic compounds of interest result from probiotic microorganism’s activity, offering micronutrients to “enrich” the profile of the exalted cheese. Allogeneic probiotics may originate from specialised insects-borne microbiota. The latter class offers new challenges to improve the nutritional and sensorial profiles of cheeses. The review focuses on food fortification frameworks; cheese fortification approaches; microencapsulation and stabilisation; cheese fortification with mineral elements; cheese fortification with essential micronutrients; fortification with by-products of the agrifood industry; fortification with microorganisms. The review considers the cheese matter a driver and an operating carrier for elements and organic compound as vitamins, carotenoids, polyphenols and essential fatty acids. Organic compounds of interest result from probiotic microorganisms’ activity are enzymes, antimicrobial compounds, short-chain fatty acids and some vitamins (A and D). Specialised insects-borne microbiota confers typical smells, flavours, consistency and nutritional attributes, respecting high hygienic-sanitary standards. The review aims to summarise in a single text the techniques, micronutrients and microorganisms most used for Market-Driven Cheese Fortification. Also, there is no review in the literature that includes all these aspects together. The paragraphs are concise dissertations on the nutraceutical, technological and sensory properties of the most widely used fortifiers in cheese. The information reported derived from an in-depth analysis of the literature from 1977 to 2020.

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new “proof of concept” of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.

In this paper are investigated the re-engineering approaches for the optimization of pasta production processes. A preliminary study of technologies and information systems architectures to be applied to the entire pasta supply chain has been carried out. In the first part of the paper is presented an overview concerning Industry 4.0 enabling technologies, besides, in the second part are discussed the engineered processes improving production quality. Concerning simulation process, is designed by means of modelling workflows the wheat storage process, by simulating the automatism of silos controlled and managed by volume sensors. Finally, following Industry 5.0 facilities have been applied image vision and artificial intelligence methodologies suitable for auto-adaptive quality check of pasta. The paper discusses different models about production processes, efficiency, risks, costs and benefits.

The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.