Artificial Intelligence for detection and prevention of mold contamination in tomato processing

openIl presente elaborato si propone di analizzare l'uso dell'intelligenza artificiale attraverso il riconoscimento di immagini per rilevare la presenza di muffa nei pomodori durante il processo di essiccazione. La muffa nei pomodori rappresenta un rischio sia per la salute umana sia per l'industria alimentare, comportando, anche, una serie di problemi che vanno oltre l'aspetto estetico. Essa è causata principalmente da funghi che si diffondono rapidamente sulla superficie dei pomodori. Tale processo compromette così la qualità con la conseguente produzione di tossine che possono influire sulla salute umana. L'obiettivo sperimentale di questo lavoro è il problema dello spreco e della perdita di prodotto nell'industria alimentare. Quando i pomodori sono colpiti da muffe, infatti, diventano inadatti al consumo, con conseguente perdita di cibo. Lo spreco di pomodori a causa delle muffe rappresenta anche la perdita di preziose risorse, utili alla produzione, come terra, acqua, energia e tempo. Il proposito è testare, anche nella fase iniziale, la capacità di un algoritmo di rilevamento degli oggetti per identificare la muffa, e adottare misure preventive. L'analisi sperimentale ha previsto l'addestramento dell'algoritmo con un'ampia serie di foto, tra cui pomodori sani e rovinati di diversi tipi, forme e consistenze. Per etichettare le immagini e creare le epoche di addestramento è stato quindi utilizzato YOLOv7, l'algoritmo di rilevamento degli oggetti scelto, basato su reti neurali. Per valutare le prestazioni sono state utilizzate metriche di valutazione, tra cui “Precision” e “Recall”. L'ipotesi di applicazione dell'intelligenza artificiale in futuro sarà un grande potenziale per migliorare i processi di produzione alimentare, facilitando, così, l'identificazione delle muffe. Il rilevamento rapido delle muffe faciliterebbe la separazione tempestiva dei prodotti contaminati, riducendo così il rischio di diffusione delle tossine e preservando la qualità degli alimenti non contaminati. Questo approccio contribuirebbe a ridurre al minimo gli sprechi alimentari e le inefficienze delle risorse associate allo scarto di grandi quantità di prodotto. Inoltre, l'integrazione della computer vision nel contesto dell'HACCP (Hazard Analysis Critical Control Points) potrebbe migliorare i protocolli di sicurezza alimentare grazie a un rilevamento accurato e tempestivo. Questa tecnologia potrà offrire, dando priorità alla prevenzione, una promettente opportunità per migliorare la qualità, l'efficienza e la sostenibilità dei futuri processi di produzione alimentare.This study investigates the use of computer vision couples with artificial intelligence to detect mold in tomatoes during the drying process. Mold presence in tomatoes poses threats to human health and the food industry as it leads to several issues beyond appearance. It is primarily caused by fungi that spread rapidly over the tomato surface, compromising their quality, and potentially producing toxins that can harm human health. The experimental aim of this work focused on the issue of wastage and loss within the food industry. When tomatoes succumb to mold, they become unsuitable for consumption, resulting in a loss of food and resources. Considering that tomato production requires resources such as land, water, energy, and time, wasting tomatoes due to mold also represents a waste of these valuable resources. The goal was to evaluate the mold detection capabilities of an object detection algorithm, particularly in its early stages, to facilitate preventative measures. This experimental analysis entailed training the algorithm with an extensive array of images, encompassing a variety of healthy and spoiled tomatoes of different shapes, types, textures and drying stages. The chosen object detection algorithm, YOLOv7, is convolutional neural network-based and was utilized for image labeling and training epochs. Evaluation metrics, including precision and recall, were utilized to assess the algorithm's performance. The implementation of artificial intelligence in the future has significant potential for enhancing food production processes by streamlining mold identification. Prompt mold detection would expedite segregation of contaminated products, thus reducing the risk of toxin dissemination and preserving the quality of uncontaminated food. This approach could minimize food waste and resource inefficiencies linked to discarding significant product amounts. Furthermore, integrating computer vision in the HACCP (Hazard Analysis Critical Control Points) context could enhance food safety protocols via accurate and prompt detection. By prioritizing prevention, this technology offers a promising chance to optimize quality, efficiency, and sustainability of future food production processes

Challenges and Status on Design and Computation for Emerging Additive Manufacturing Technologies

The revolution of additive manufacturing (AM) has led to many opportunities in fabricating complex and novel products. The increase of printable materials and the emergence of novel fabrication processes continuously expand the possibility of engineering systems in which product components are no longer limited to be single material, single scale, or single function. In fact, a paradigm shift is taking place in industry from geometry-centered usage to supporting functional demands. Consequently, engineers are expected to resolve a wide range of complex and difficult problems related to functional design. Although a higher degree of design freedom beyond geometry has been enabled by AM, there are only very few computational design approaches in this new AM-enabled domain to design objects with tailored properties and functions. The objectives of this review paper are to provide an overview of recent additive manufacturing developments and current computer-aided design methodologies that can be applied to multimaterial, multiscale, multiform, and multifunctional AM technologies. The difficulties encountered in the computational design approaches are summarized and the future development needs are emphasized. In the paper, some present applications and future trends related to additive manufacturing technologies are also discussed

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

Novel Rapid Molecular Detection and Processing Approaches for the Control of Salmonella enterica Serovars in the Food Environment

The increase in Salmonella enterica outbreaks calls for an urgent need to rapidly detect and control Salmonella-associated contamination. Loop-mediated isothermal amplification (LAMP) assay is a novel method that can be completed within 90 min in a simple waterbath. Detection is by simple turbidity, fluorescence, or gel electrophoresis and is more specific than PCR. Reverse-transcriptase LAMP (RT-LAMP) targeting mRNA for the potential detection of live infectious Salmonella or recent contamination was used in this study and detection sensitivity to culture-based detection and RT-PCR assays was compared in pure culture, food products, and food processing environments. Our results showed detection limits of 101 and 102 CFU/ml for S. Typhimurium and 106 and 107 CFU/ml for S. Enteritidis by RT-PCR and RT-LAMP assays, respectively. Both assays targeted the specific Salmonella invA gene. Enrichment of 10 h was required for equivalent detection to culture-based methods for S. Typhimurium in pork products and 16 h for S. Enteritidis in liquid whole egg (LWE). For natural LWE and pork samples, 4-h non-selective enrichment followed by 16-h selective enrichment is recommended to ensure sensitive detection. Effective inactivation/control measures for foodborne pathogens include high intensity ultrasound (HIU, an attractive non-thermal microbial inactivation process). HIU is gaining popularity due to its low cost that also maintains product sensory and functionality attributes. The efficiency of HIU (20 kHz) for Salmonella inactivation alone or in combination with nisin (a broad range bacteriocin), in a food model (liquid whole egg, LWE) was studied. Significant S. Enteritidis reduction of 3.6 log CFU/ml in pure culture and 1.4 log CFU/25 ml in LWE were obtained after HIU treatment alone for 10 min (P\u3c0.05). Scanning electron micrographs revealed microbial structural damage after 5-min HIU. After 10-min HIU, LWE color became visually and instrumentally lighter along with a lower measured viscosity. However, no additional or synergistic antimicrobial effect was observed with nisin (100 and 1000 IU/ml) in combination with HIU. HIU shows great promise as an alternative non-thermal inactivation process for liquid foods. For use in hurdle approaches, further research on HIU combinations with other natural or generally recognized as safe antimicrobials is needed

Establishing a formulation design space for a generic clobetasol 17- propionate cream using the principles of quality by design

The pharmaceutical industry is global, is highly regulated and is able to achieve reasonable product quality but at high cost with maximum effort. Numerous challenges face the pharmaceutical industry and include a shrinking research pipeline, less innovation, outsourcing, investments, increasing research and development costs, long approval times, growth of the generic industry, failure to understand or analyze manufacturing failure and wastage as high at fifty percent for some pharmaceutical products. An efficient and flexible pharmaceutical sector should be able to consistently produce high quality pharmaceutical products at a reduced cost with minimal waste. As a result, Food and Drug Administration (FDA) and other agencies such as the International Conference on Harmonization (ICH) have embraced a “Quality by Design” (QbD) paradigm and this has become the “desired state” so as to shift manufacturing from being empirical to a science, engineering, and risk based approach. QbD is a systematic approach for the development of high quality pharmaceutical dosage forms that begins with predefined objectives based on the premise that quality must be built into and not tested into a product. QbD together with the establishment of a design space for dosage forms is a fairly new concept and there is limited published data on QbD concepts that report the entire process of identifying Critical Quality Attributes (CQA), design of a formulation and manufacturing process to meet product CQA, understanding the impact of material attributes and process parameters on product CQA, identification and controlling sources of variability in materials and processes that affect the CQA of a product and finally establishing, evaluating and testing a design space using both in vitro and in vivo approaches to assure that a product of consistent quality can always be produced. The objective of these studies was to implement a QbD approach to establish a design space for the development and manufacture of a safe, effective, stable generic formulation containing 0.05% w/w clobetasol 17-propionate (CP) that had similar in vitro and in vivo characteristics to an innovator product, Dermovate® (Sekpharma® Pty Ltd, Sandton, Gauteng, RSA). Such a product would pose a minimal risk of failure when treating severe skin disorders such as seborrhoeic dermatitis, extreme photodermatitis and/or severe psoriasis in HIV/AIDS patients in Southern Africa

COMPUTATIONAL ULTRASOUND ELASTOGRAPHY: A FEASIBILITY STUDY

Ultrasound Elastography (UE) is an emerging set of imaging modalities used to assess the biomechanical properties of soft tissues. UE has been applied to numerous clinical applications. Particularly, results from clinical trials of UE in breast lesion differentiation and staging liver fibrosis indicated that there was a lack of confidence in UE measurements or image interpretation. Confidence on UE measurements interpretation is critically important for improving the clinical utility of UE. The primary objective of my thesis is to develop a computational simulation platform based on open-source software packages including Field II, VTK, FEBio and Tetgen. The proposed virtual simulation platform can be used to simulate SE and acoustic radiation force based SWE simulations, including pSWE, SSI and ARFI. To demonstrate its usefulness, in this thesis, examples for breast cancer detections were provided. The simulated results can reproduce what has been reported in the literature. To statistically analyze the intrinsic variations of shear wave speed (SWS) in the fibrotic liver tissues, a probability density function (PDF) of the SWS distribution in conjunction with a lossless stochastic tissue model was derived using the principle of Maximum Entropy (ME). The performance of the proposed PDF was evaluated using Monte-Carlo (MC) simulated shear wave data and against three other commonly used PDFs. We theoretically demonstrated that SWS measurements follow a non-Gaussian distribution for the first time. One advantage of the proposed PDF is its physically meaningful parameters. Also, we conducted a case study of the relationship between shear wave measurements and the microstructure of fibrotic liver tissues. Three different virtual tissue models were used to represent underlying microstructures of fibrotic liver tissues. Furthermore, another innovation of this thesis is the inclusion of “biologically-relevant” fibrotic liver tissue models for simulation of shear wave elastography. To link tissue structure, composition and architecture to the ultrasound measurements directly, a “biologically relevant” tissue model was established using Systems Biology. Our initial results demonstrated that the simulated virtual liver tissues qualitatively could reproduce histological results and wave speed measurements. In conclusions, these computational tools and theoretical analysis can improve the confidence on UE image/measurements interpretation

Piper aduncum essential oil rich in dillapiole : development of hydrogel-thickened nanoemulsion and nanostructured lipid carrier intended for skin delivery

The essential oil extracted from the leaves of Piper aduncum, an aromatic plant from the Amazon region, is rich in dillapiole and presents anti-inflammatory activity. In this study, nanoemulsions (NE) and nanostructured lipid carriers (NLC), which are biocompatible nanostructured systems of a lipid nature, were prepared by high-pressure homogenization for the yet unexplored skin delivery of dillapiole. The addition of hydroxyethylcellulose produced hydrogel-thickened NE or NLC in view to improving the viscosity and skin adherence of the nanoformulations. Formulations were characterized with respect to dillapiole content, droplet size, polydispersity index, zeta potential, morphology, rheological behavior, bioadhesion, skin permeation profile, and in vitro irritancy (HETCAM). The formulations developed presented spherical, homogeneous nanometric particle size (around 130 nm), narrow polydispersity index (<0.3), and negative zeta potential (around 40 mV). Dillapiole content was slightly lower in NLC compared to NE since the production process involves heating. The hydrogels containing nanocarriers showed pseudoplastic behavior with bioadhesive characteristics. The developed formulations exhibited a controlled release profile, dillapiole delivery up to the dermis, the layer of interest for anti-inflammatory potential, and low irritant potential in the chorioallantoic membrane (HET-CAM). Both hydrogels-thickened NE and NLC seemed to be promising formulations for skin delivery of Piper aduncum essential oil