Mantenimento della qualità organolettica in alcuni ortofrutticoli tipici della regione Lazio minimamente processati

The research has the aim of enhancing the storability of some lightly processed foods typical of Lazio Region. The vegetables involved in this research are: artichoke, fennel, zucchini squash and chicory. Results shown reveal the importance of using first quality raw material, low temperature (5°C) through the entire process, and chlorinated water (100 ppm). In addition to this, a packaging using a low permeability plastic film, controlled atmosphere and a storage temperature of 4°C are crucial to slow down tissue browning, to reduce water loss and to maintain the organoleptic quality requested by the consumer.L'articolo è disponibile sul sito dell'editore http://www.soihs.it

Feasibility of computer vision as Process Analytical Technology tool for the drying of organic apple slices

Quality of a product and sustainability of its production depend on the cumulative impacts of each processing step in the food chain and their interplay. Various research studies evidenced that many drying systems operate inefficiently in terms of drying time, energy demand (e.g. fossil fuels), raw material utilisation and resulting product quality. Moreover, not all conventional drying processes are allowed in the organic sector (Reg. EC 834/2007; Reg. EC 889/2008). In recent years, non-invasive monitoring and control systems have shown a great potential for improvement of the quality of the resulting products. Thus, there is a need for smart processes which allow for simultaneous multi factorial control to guarantee high-value end products, enhance energy and resource efficiency by using innovative and reliable microcontrollers, sensors and embracing various R&D areas (e.g. computer vision, deep learning, etc.). The objective of this study was to evaluate the feasibility of computer vision (CV) as a tool in development of smart drying technologies to non-destructively forecast changes in moisture content of apple slices during drying. Usage of computer vision (CV) as Process Analytical Technology in drying of apple slices was tested. Samples were subjected to various anti-browning treatments at sub- and atmospheric pressures, and dried at 60°C up to a moisture content on dry basis (MCdb) of 0.18 g/g. CV-based prediction models of changes in moisture content on wet basis (MCwb) were developed and promising results were obtained (R2P > 0.99, RMSEP = 0.011÷0.058 and BIASP < 0.06 in absolute value), regardless of the anti-browning treatment. The proposed methodology lays the foundations for a scale-up smart-drying system based on CV and automation

The risk of musculoskeletal disorders due to repetitive movements of upper limbs for workers employed in hazelnut sorting

In the agro-industrial sector there are many activities whose urgent rhythms can cause a considerable exposure to bio-mechanical risk factors. In the hazelnut sorting, the workers are subject to several biomechanical risks, with repetitive movements, and operations that require a remarkable degree of strength. A thorough study of the workers' exposure to repetitive manual movements has been carried out, with the aim of setting up the necessary measures to reduce the risk factors. The aim of the research is to assess the risk of work-related musculo-skeletal disorders (WMSDs) due to repetitive work, for workers employed to hazelnut shells sorting. The research was carried out in an agricultural cooperative in the Viterbo's area. For risk assessment authors used a method (Occupational Repetitive Actions "OCRA" index according to ISO 11228- 3:2009, Ergonomics - Manual handling - Part 3: Handling of low loads at high frequency) which keeps into consideration several risk factors (such as repetitiveness, prehension force, posture). The risk was assessed for 16 female workers (in eight workplaces and in two different shifts) through this classification: workers with experience less than 1 year, from 1 to 10 years and more than 10 years. This classification is very important for knowing if the professional experience could be considered a "prevention measure" for the risk reduction. The results show a high risk level for the right and left limb. The factors which more have contributed to reach such risk level are the great number of movements and the lack of recovering time

Near-infrared spectroscopy is feasible to discriminate hazelnut cultivars

The study demonstrated the feasibility of the near infrared (NIR) spectroscopy use for hazelnut-cultivar sorting. Hazelnut spectra were acquired from 600 fruit for each cultivar sample, two diffuse reflectance spectra were acquired from opposite sides of the same hazelnut. Spectral data were transformed into absorbance before the computations. A different variety of spectral pretreatments were applied to extract characteristics for the classification. An iterative Linear Discriminant Analysis (LDA) algorithm was used to select a relatively small set of variables to correctly classify samples. The optimal group of features selected for each test was analyzed using Partial Least Squares Discriminant Analysis (PLS-DA). The spectral region most frequently chosen was the 1980-2060 nm range, which corresponds to best differentiation performance for a total minimum error rate lower than 1.00%. This wavelength range is generally associated with stretching and bending of the N-H functional group of amino acids and proteins. The feasibility of using NIR Spectroscopy to distinguish different hazelnut cultivars was demonstrated

Recognition of inlet wet food in drying process through a deep learning approach

Smart drying is one of the newest and most promising techniques. It is a multi- and inter-disciplinary sector which has potential to guarantee high value end-products by implementing innovative and reliable sensors, resources, tools and practices. Its recent developments embrace various R&D areas, such as computer vision (CV) and deep learning, which deal with allowing computers to understand digital images and videos better than humans. Conventional machine-learning techniques suffer several limitations, mainly due to their inability to process raw data. In fact, in the last few decades, machine learning required considerable domain expertise to mine raw data and extract features from which an algorithm could identify patterns in the input. Deep learning is a novel subfield of machine learning, which embraces methods that allow to discover patterns for detection or classification purposes by using raw data. Consequently, CV in combination with deep learning has the potential to be a powerful Process Analytical Technology tool useful for enhancing the understanding and control of critical process parameters that impact on quality of the final product. Deep learning was tested for its feasibility as CV tool for the analysis of inlet wet food to drying process. In details, convolutional neural networks (CNNs) were successfully applied for addressing the following tasks: (i) the semantic image segmentation of the inlet product (i.e., recognition between background and product pixels); (ii) the inlet product classification through its segmented image; (iii) the automated selection of optimal settings of drying process parameters. Results obtained not only represent a step forward in the development of smart dryers able to recognise the inlet wet product, and to set the proper process parameters on its own or as decision support system, but also lay the foundation for further researches on using a computer vision system as PAT tool for smart drying processes

Active Case Finding for Communicable Diseases in Prison Settings: Increasing Testing Coverage and Uptake among the Prison Population in the European Union/European Economic Area

Prison populations are disproportionally affected by communicable diseases when compared with the general community because of a complex mix of socioeconomic determinants and environmental factors. Tailored and adequate health care provision in prisons has the potential to reach vulnerable and underserved groups and address their complex needs. We investigated the available evidence on modalities and effectiveness of active case-finding interventions in prisons by searching PubMed, Embase, and the Cochrane Library for records on prison and active case finding with no language limit. Conference abstracts and unpublished research reports also were retrieved.We analyzed the findings by testing modality, outcomes, and study quality. The included 90 records-63 peer-reviewed, 26 from gray literature, and 1 systematic review-reported variously on viral hepatitis, human immunodeficiency virus, sexually transmitted infections, and tuberculosis. No records were retrieved for other communicable diseases. Provider-initiated opt-in testing was the most frequently investigated modality. Testing at entry and provider-initiated testing were reported to result in comparatively higher uptake ranges. However, no comparative studies were identified that reported statistically significant differences between testing modalities. Positivity rates among tested inmates ranged broadly but were generally high for all diseases. The evidence on active case finding in correctional facilities is limited, heterogeneous, and of low quality, making it challenging to draw conclusions on the effect of different testing modalities. Scale-up of provider-initiated testing in European correctional facilities could substantially reduce the undiagnosed fraction and, hence, prevent additional disease transmission in both prison settings and the community at large