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

Ethical consumption and brand detachment in the cosmetic sector: a study of Italian consumers- perception

In the past decade, the cosmetic market has become the focus of attention for its practices not always deemed ethical and sustainable. Indeed, this research aims at investigating the role of Italian consumers’ ethical behavior on brand detachment in the cosmetic sector, focusing on brands using chemical ingredients and testing on animals. Such relationships were further examined with the introduction of brand attitude as a moderator. A quantitative analysis has been adopted to test the hypotheses and an online questionnaire has been distributed, through which 310 Italian consumers were surveyed. The findings highlighted the influence of ethical consumer behavior on brand detachment, thus implying the impending need for cosmetic companies to consider their environmental and social impact, as this could accordingly affect consumer attachment to the brand. Still ,no significant interaction effect has been identified when including brand attitude in the correlation

An algorithm for Parkinson's disease speech classification based on isolated words analysis

Introduction Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effective follow-up of PD clinical condition, possibly performed in the home environment. Methods In this work, we performed a multi-level analysis, progressively combining features extracted from the entire signal, the voiced segments, and the on-set/off-set regions, leading to a total number of 126 features. Furthermore, we compared the performance of early and late feature fusion schemes, aiming to identify the best model configuration and taking advantage of having 25 isolated words pronounced by each subject. We employed data from the PC-GITA database (50 healthy controls and 50 PD patients) for validation and testing. Results We implemented an optimized k-Nearest Neighbours model for the binary classification of PD patients versus healthy controls. We achieved an accuracy of 99.4% in 10-fold cross-validation and 94.3% in testing on the PC-GITA database (average value of male and female subjects). Conclusion The promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application

Molecular Analysis of Prothrombotic Gene Variants in Venous Thrombosis: A Potential Role for Sex and Thrombotic Localization

Background: Requests to test for thrombophilia in the clinical context are often not evidence-based. Aim: To define the role of a series of prothrombotic gene variants in a large population of patients with different venous thromboembolic diseases. Methods: We studied Factor V Leiden (FVL), FVR2, FII G20210A, Methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C, beta-fibrinogen -455 G>A, FXIII V34L, and HPA-1 L33P variants and PAI-1 4G/5G alleles in 343 male and female patients with deep vein thrombosis (DVT), 164 with pulmonary embolism (PE), 126 with superficial vein thrombosis (SVT), 118 with portal vein thrombosis (PVT), 75 with cerebral vein thrombosis (CVT) and 119 with retinal vein thrombosis (RVT), and compared them with the corresponding variants and alleles in 430 subjects from the general population. Results: About 40% of patients with DVT, PE and SVT had at least one prothrombotic gene variant, such as FVL, FVR2 and FII G20210A, and a statistically significant association with the event was found in males with a history of PE. In patients with a history of PVT or CVT, the FII G20210A variant was more frequent, particularly in females. In contrast, a poor association was found between RVT and prothrombotic risk factors, confirming that local vascular factors have a key role in this thrombotic event. Conclusions: Only FVL, FVR2 and FII G20210A are related to vein thrombotic disease. Other gene variants, often requested for testing in the clinical context, do not differ significantly between cases and controls. Evidence of a sex difference for some variants, once confirmed in larger populations, may help to promote sex-specific prevention of such diseases

Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space

Parkinson’s disease (PD) is one of the most widespread neurodegenerative diseases worldwide, affected by a number of alterations, among which speech impairments that, interestingly, manifests up to 10 years before other major evidences (e.g. motor impairments). In this regard, we investigated the feasibility of a model based on the temporal evolution of speech attractors in the reconstructed phase space to identify hallmarks of PD identification and progression. To this end, the adopted dataset was made of vocal emissions of 46 de-novo and 54 mid-advanced People with PD, plus 113 healthy counterpart. A statistical analysis was applied to test the identified hallmarks effectiveness for diagnostic support, monitoring, and staging of the disease. According to the obtained results, the adopted approach of considering the temporal evolution of speech attractors in the reconstructed phase-space results effective to discriminate among the three groups of pathological or healthy voice

Tsunami risk perception in central and southern Italy

The Tsunami Alert Centre of the National Institute of Geophysics and Volcanology (CAT-INGV) has been promoting, since 2018, the study of tsunami risk perception in Italy. Between 2018 and 2021 a semi-structured questionnaire on the perception of tsunami risk was administered to a sample of 5842 citizens residing in 450 Italian coastal municipalities, representative of more than 12 million people. The survey was conducted with the computer-assisted telephone interviewing (CATI) methodology, described in Cerase et al. (2019), who published the results of the first pilot survey (about 1000 interviews). The large sample and the socio-demographic stratification give an excellent representation of the resident population in the surveyed Italian coastal municipalities. Moreover, in 2021 an optimized version of the questionnaire was also administered via Telepanel (a tool for collecting proportional and representative opinions of citizens) that was representative of the Italian population and included 1500 people distributed throughout the country. In this work we present the main results of the three survey phases, with a comparison among the eight surveyed regions and between the coastal regions and some coastal metropolitan cities involved in the investigations (Rome, Naples, Bari, Reggio Calabria, and Catania). Data analysis reveals heterogeneous and generally low tsunami risk perception. Some seaside populations, in fact, show a good perception of tsunami risk, while others, such as in Apulia and Molise, reveal a lower perception, most likely due to the long time elapsed since the last event and lack of memory. We do not find relevant differences related to the socio-demographic characteristics (age, gender) of the sample, whereas the education degree appears to affect people's perception. The survey shows that the respondents' predominant source of information on tsunamis is the television and other media sources (such as newspapers, books, films, internet), while the official sources (e.g., civil protection, local authorities, universities and research institutes) do not contribute significantly. Also, we find an interesting difference in people's understanding of the words tsunami and maremoto, the local term commonly used in Italy until the 2004 Sumatra–Andaman event, which should be taken into account in scientific and risk communication. The Telepanel survey, based on a nationwide sample, highlights a lower level of tsunami risk perception in comparison to average risk perception levels found in the coastal-municipality sample. Our results are being used to drive our communication strategy aimed at reducing tsunami risk in Italy, to activate dissemination and educational programs (data driven), to fill the data gap on tsunami risk perception in the North-Eastern Atlantic, Mediterranean and connected seas (NEAM) area, and to implement multilevel civil protection actions (national and local, top-down and bottom-up). Not least, outputs can address a better development of the UNESCO Tsunami Ready program in Italy.</p

Overall dietary variety and adherence to the Mediterranean diet show additive protective effects against coronary heart disease.

Abstract Background and aim Along with the increasing evidence of the cardioprotective effects of the Mediterranean Diet (MD), the scientific interest and advocacy of dietary variety as a potentially healthy eating habit gradually faded, until its complete oblivion in the latest European cardiovascular prevention guidelines. Our study aims to investigate whether dietary variety adds to the "Mediterranean-ness" of the diet in protecting against coronary heart disease (CHD). Methods and results In this case–control Italian study, data on eating habits were collected from 178 patients with CHD and 155 healthy controls, primarily males, frequency matched for age and gender, using the Food Frequency Questionnaire (FFQ) of the European Prospective Investigation into Cancer and Nutrition. Adherence to MD was estimated from FFQ by the Mediterranean Diet Score (MDS), an index developed by Trichopoulou (2003) ranging from 0 to 9, with higher scores indicating a stricter adherence. Overall dietary variety was computed from FFQ as a count of single food items consumed at least once a month. Associations between MDS or overall dietary variety and coronary status were evaluated by logistic regression models adjusted for BMI, physical activity, smoking, education, and caloric intake; the Odds Ratio (OR) for CHD for each 1.5-point increase in MDS was 0.76 [IC 95% 0.59; 0.98], whereas the OR for CHD for each 15-item increase in dietary variety was 0.62 [IC 95% 0.46; 0.84]. Remarkably, adherence to MD and overall dietary variety were independently associated with a significantly reduced chance of CHD. Conclusion Dietary Mediterranean-ness and overall dietary variety exhibit additive cardioprotective effects

Smartphone-Based Evaluation of Postural Stability in Parkinson’s Disease Patients During Quiet Stance

Background: Postural instability is one of the most troublesome motor symptoms of Parkinson’s Disease(PD).It impairs patients’quality of life and results in high risk of falls. The aim of this study is to provide a reliable tool for the automated assessment of postural instability. Methods: Data acquisition was performed on 42 PD patients and 7 young healthy subjects. They were asked to keep a quiet stance position for at least 30 s while wearing a waist-mounted smartphone. A total number of 414 features was extracted from both time and frequency domain, selected based on Pearson’s correlation, and fed to an optimized Support Vector Machine. Results: The implemented model was able to differentiate patients with mild postural instability from those with severe postural instability and from healthy controls, with 100% accuracy. Conclusion: This study demonstrated the feasibility of using inertial sensors embedded in commercial smartphones and proposed a simple protocol for accurate postural instability scoring. This tool can be used for early detection of PD motor signs, disease follow-up and fall prevention