1,472 research outputs found

    Real-time monitoring system for shelf life estimation of fruit and vegetables

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    The control of the main environmental factors that influence the quality of perishable products is one of the main challenges of the food industry. Temperature is the main factor affecting quality, but other factors like relative humidity and gas concentrations (mainly C2H4, O2 and CO2) also play an important role in maintaining the postharvest quality of horticultural products. For this reason, monitoring such environmental factors is a key procedure to assure quality throughout shelf life and evaluate losses. Therefore, in order to estimate the quality losses that a perishable product can suffer during storage and transportation, a real-time monitoring system has been developed. This system can be used in all post-harvest steps thanks to its Wi-Fi wireless communication architecture. Several laboratory trials were conducted, using lettuce as a model, to determine quality-rating scales during shelf life under different storage temperature conditions. As a result, a multiple non-linear regression (MNLR) model is proposed relating the temperature and the maximum shelf life. This proposed model would allow to predict the days the commodities will reduce their theoretical shelf-life when an improper temperature during storage or in-transit occurs. The system, developed as a sensor-based tool, has been tested during several land transportation trips around Europe.The authors are grateful to Fruca Marketing S.L. for providing the lettuce used in this research, and to Transportes Directos del Segura SL and Transportes Mesa SL for the logistic support. We also are grateful to Miriam Montoya GĂłmez for the translation services

    DEVELOPMENT OF ASSESSMENT TOOLS OF PACKAGE/PRODUCT SYSTEMS FOR A SUSTAINABLE FOOD CHAIN

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    Post-harvest life of fresh produce is limited due to high metabolic activity and microbial spoilage. Modified atmosphere packaging (MAP) has proven to be one of the most effective techniques to extend the shelf life of fresh produce commercially. Obtaining of an optimum concentration of oxygen and carbon dioxide inside the package depends upon the product properties, the environmental conditions of the cold chain, the permeable film, some of which are subjected to natural variability during the food distribution chain. This variability may generate produce that is out of specification that will lead to food waste. Uncertainty analysis of this problem may lead to relevant interventions to prevent these losses. The hypothesis of this work was to create a mathematical model that predicts key quality factors for MAP packaged fresh products in the supply chain distribution, which will help to assess the food losses in relation to quality thresholds. The model developed simulated the respiration rate as function of O2 and CO2 concentration and produce temperature using Michaelis-Menten equations. The exchange of gases (O2, CO2) and water vapour between the fruit surface, package atmosphere and external atmosphere was modelled taking into account the process of transpiration and condensation. In the transpiration model, the fresh produce surface was assumed to be perfectly saturated and the energy of respiration was used to evaporate surface water. Temperature changes in the headspace due to metabolic heat, convective heat transfer and heat exchange by gas transmission through the package were accounted for. The quality attributes of fresh produce included weight loss and colour change (L, a, and b values) for mushroom, from Botrytis and its fermentative activity for strawberry and weight loss and spoilage for tomato. ii These conditions were simulated for real and variable i) export cold chain and ii) retail display storage to evaluate the effect of cold chain variability (temperature and relative humidity) on the quality of fresh produce and associated waste generation. The prediction of propagation of biological variance on the quality of fresh produce during storage was obtained using a mathematical model. Sensitivity analysis of the stochastic MAP model pointed out the influence of input parameters on the quality of fresh produce. The conclusions of the study showed that the toolbox developed is able to interpret cold chain data: 1) mathematical prediction of quality; 2) simulation of cold chain conditions allowing for different variability components; 3) estimation of waste generation kinetics based in all quality criteria and thresholds; 4) sensitivity analysis to identify the most sensitive technological parameters; and 5) identification of interventions that affect the benchmarked technological parameters

    Artificial Intelligence Decision Support System Based on Artificial Neural Networks to Predict the Commercialization Time by the Evolution of Peach Quality

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    Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical‐chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support Sys‐ tem (DSS) designed to predict the evolution of the quality of peaches, namely the storage time re‐ quired before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and val‐ idation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple predic‐ tion of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and con‐ sumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI‐based solutions for smart cities.This study is within the activities of project PrunusPĂłs—Otimização de processos de ar‐ mazenamento, conservação em frio, embalamento ativo e/ou inteligente, e rastreabilidade da qual‐ idade alimentar no pĂłscolheita de produtos frutĂ­colas (Optimization of processes of storage, cold conservation, active and/or intelligent packaging, and traceability of food quality in the postharvest of fruit products), Operation n.Âș PDR2020‐101‐031695 (Partner), Consortium n.Âș 87, Initiative n.Âș 175 promoted by PDR2020 and co‐financed by FEADER under the Portugal 2020 initiative.info:eu-repo/semantics/publishedVersio

    Dynamic simulation driven design and management of production facilities in agricultural/food industry

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    An industrial plant in the agro-food sector can be considered a complex system as it is composed of numerous types of machines and it is characterized by a strong variation (seasonality) in the agricultural production. Whenever the dynamic behavior of the plants during operation is considered, system and design complexities increase. Reliable operation of food processing farms is primarily dependent on perfect balance between variable supply and product storage at each given time. To date, the classical modus operandi of food processing management systems is carried out under stationary and average conditions. Moreover, most of the systems installed for agricultural and food industries are sized using average production data. This often results in a mismatch between the actual operation and the expected operation. Consequently, the system is not optimized for the needs of a specific company. Also, the system is not flexible to the evolution that the production process could possibly have in the future. Promising techniques useful to solve the above-described problems could possibly be borrowed from demand side management (DSM) in smart grid systems. Such techniques allow customers to make dynamically informed decisions regarding their energy demand and help the energy providers in reducing the peak load demand and reshape the load profile. DSM is successfully used to improve the energy management system and we conjecture that DSM could be suitably adapted to food processing management. In this paper we describe how DSM could be exploited in the intelligent management of production facilities serving agricultural and food industry. The main objective is, indeed, to present how methods for modelling and implementing the dynamic simulation used for the optimization of the energy management in smart grid systems can be applied to a fruit and vegetables processing plant through a suitable adaptation

    The potential use of non destructive optical-based techniques for early detection of chilling injury and freshness in horticultural commodities

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    The increasing concern and awareness of the modern consumer regarding food including fruits and vegetables, has been oriented the research in the food industry to develop rapid, reliable and cost effective methods for the evaluation of food products including the traceability of the product history in terms of storage conditions. Since the conventional destructive analysis methods are time consuming, expensive, targeted and labor intensive, non-destructive methods are gaining significant popularity. These methods are being utilized by the food industry for the early detection of fruits defects, for the classification of fruits and vegetables on the basis of variety, maturity stage, storage history and origin and for the prediction of main internal constituents. Since chilling injury (CI) occurrence is a major problem for chilling sensitive products, as tropical and sub-tropical fruit and vegetables, prompt detection of CI is still a challenge to be addressed. The incorrect management of the temperature during storage and distribution causes significant losses and wastes in the horticultural food chain, which can be prevented if the product is promptly reported to the correct temperature, before that damages become irreversible. For this reason, rapid and fast methods for early detection of CI are needed. In the first work of this thesis, non-destructive optical techniques were applied for the early detection of chilling injury in eggplants. Eggplant fruit is a chilling sensitive vegetable that should be stored at temperatures above 12°C. For the estimation of CI, fruit were stored at 2°C (chilling temperature) and at 12°C (safe storage temperature) for a time span of 10 days. CIE L*a*b* measurements, reflectance data in the wavelength range 360–740 nm, Fourier Transformed (FT)-NIR spectra (800–2777 nm) and hyperspectral images in the visible (400–1000 nm) and near infrared (900–1700 nm) spectral range were acquired for each fruit. Partial least square discriminant analysis (PLSDA), supervised vector machine (SVM) and k-nearest neighbor (kNN) were applied to classify fruit according to the storage temperature. According to the results, although CI symptoms started being evident only after the 4th day of storage at 2°C, it was possible to discriminate fruit earlier using FT-NIR spectral data with the SVM classifier (100 and 92% non-error-rate (NER) in calibration and cross validation, respectively, in the whole data set. Color data and PLSDA classification possessed relatively lower accuracy as compared to SVM. These results depicted a good potential of for the non-destructive techniques for the early detection of CI in eggplants. Similarly, in the second experimental part of the thesis, hyperspectral imaging in Vis-NIR and SWIR regions combined with chemometric techniques were used for the early estimation of chilling injury in bell peppers. PLSDA models accompanied by wavelength selection algorithms were used for this purpose, with accuracies ranging from 81% and 87% non-error-rate (NER) based on the wavelength ranges used and variables selected. PLSR models were developed for the prediction of days of cold storage resulting in RÂČCV = 0.92 for full range and RÂČCV = 0.79 using selected variables. Based on the results, it was concluded, that Vis-NIR hyperspectral imaging is a reliable option for on-line classification of fresh versus refrigerated fruit and for identifying early incidence of CI. Inspired by the results obtained from previous studies a third study regarded the use of nondestructive techniques for the estimation of freshness of eggplants using color, spectral and hyperspectral measurements. To this aim, fruit were stored at 12°C for 10 days. Fruit were left at room temperature (20°C) for 1 day after sampling which was done with a 2-day interval, simulating one-day of shelf life in the market. PLSR models were developed using the spectral and hyperspectral data and the storage days, allowing safe assessment of the freshness of the fruits along with the utilization of SPA for variable reduction. The results depicted strong correlation between storage days, FT-NIR spectra and the hyperspectral data in the Vis-NIR range with accuracies as high as RC> 0.98, RCV> 0.94, RMSEC < 0.4 and RMSECV< 0.8, followed by lower accuracies using color data. The results of this study may set the basis to develop a protocol allowing a rapid screening and sorting of eggplants according to their postharvest freshness either upon handling in a distribution center or even upon the reception in the retail market. In the last work, as a deeper investigation, the effect of temperature and storage time on the FTNIR spectra was statistically investigated using ANOVA-simultaneous component analysis (ASCA) on eggplant fruit as a crop model. Also in this case, fruit were stored at 2 and 12 °C, for 10 days. Sensorial analysis, electrolyte leakage (EL), weight loss and firmness were used, as the reference measurements for CI. ASCA model proved that both temperature, duration of storage, and their interaction had a significant effect on the spectral changes over time of eggplant fruit. Followed by ASCA, PLSDA was conducted on the data to discriminate fruit based on the storage temperature. In this case, only the WL significant in the ASCA approach for temperature were considered, allowing to reach 87.4±2.7% as estimated by a repeated double-cross-validation procedure. The outcomes of all these studied manifested a promising, non-invasive, and fast tool for the control of CI and the prevention of food losses due to the incorrect management of the temperature in the horticultural food chain

    Sensors for product characterization and quality of specialty crops—A review

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    This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow

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

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    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 ù 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 ù 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 ù 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 ù 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

    From hand holes to vent holes: what’s next in innovative horticultural packaging?

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    Inaugural lecture delivered on 2 February 2011.Umezuruike Linus Opara was born on 1 July 1961 in a rural subsistence farming and hunting village, Umunam, in Imerienwe, Imo State, Nigeria, where he lived and obtained his primary and secondary education. He attended Upe/Umunam CMS (Anglican) Primary School, Upe Primary School and Umunam Central Primary School, receiving most of the first three years of classes in nearby rubber plantations, tree shades and other makeshift shelters during the Nigerian Civil War. At the end of his primary education in 1974, he baby-sat for one year before attending Owerri Grammar School, Imerienwe. He completed the West African Examination Council School Certificate examination in 1980 and won the annual Senior Essay Competition of the School for his essay entitled 1980 – The year of changes, in which he prematurely and naively predicted a sudden end of apartheid in South Africa. After high school, he travelled to northern Nigeria and joined his parents in Yola, the capital city of present day Adamawa State in Nigeria, where he worked for two years at UTC (Nig.) Ltd, rising from the position of Sales Assistant to First Sales/Storekeeper. During this period, he used his weekends for self-study and in 1982 sat as an external candidate and passed both the General Certificate of Education examination and the Joint Admissions and Matriculation Board examination, and gained admission to study Agricultural Engineering at the University of Nigeria, Nsukka, in the same year. Based on his first-year results, he was awarded the University of Nigeria Foundation Undergraduate Scholarship for Academic Merit in 1983, which he successfully retained throughout his undergraduate studies. He graduated with a bachelor’s degree in Agricultural Engineering in 1987 with first-class honours (cum laude) and received the Department Prize for Best Graduating Student. He was an elected member of the University of Nigeria Students Union Senate (Upper House) and president of the National Association of Ngor-Okpala Local Government Students. In December 1987, he was awarded the prestigious Prize for Academic Excellence by the Mezie Owerri national community development organisation in Nigeria. For his National Youth Service Corps assignment, he spent one year as agricultural engineer at the National Centre for Agricultural Mechanization, Ilorin. He returned to the University of Nigeria in 1988 with a Federal Government Postgraduate Scholarship and completed his master’s degree in Agricultural Engineering (cum laude) in record time in 1989. The results of his BEng honours thesis on Nomograph models for selective agricultural mechanization and his MEng thesis on Computer-aided model for selective agricultural mechanization (CAM-SAM) provided major inputs for the Agricultural Mechanization Study component of the 1989–2004 National Agricultural Development Strategy of Nigeria, of which he was co-leading author with the late Prof UGN Anazodo and Dr Taiwo Abimbola. In 1988 he was awarded a New Zealand University Grant’s Committee PhD Scholarship reserved for local students who made first class. He commenced his PhD studies in Agricultural Engineering 3 4 at Massey University 1990 and completed in 1993. His dissertation on Studies on stem-end splitting in apples under the supervision of Prof Cliff Studman and Prof Nigel Banks provided the first scientific evidence linking the development of stem-end splitting with a precursor internal ring-cracking. Through the combination of engineering knowledge of the physico-chemical properties of fruit and horticultural science, industry guidelines were developed and disseminated on practical measures to predict and reduce the incidence of fruit-splitting damage. He subsequently held the position of postdoctoral researcher in the Department of Agricultural Engineering from 1993 to 1994. He joined Lincoln Technology in Hamilton briefly as Postharvest Research Engineer but returned to Massey in 1995 as lecturer in Postharvest Engineering, was promoted to senior lecturer in 1999 and to program director for Engineering Technology in 2001, and was a founding member of the Centre for Postharvest and Refrigeration Research. He held several management and administrative positions, including that of coordinator of the Agricultural Engineering programme and coordinator of the BApplSc (General) programme. In 1993 he was awarded the inaugural Dean’s Prize for Meritorious Contributions to the Affairs of the Faculty of Agricultural and Horticultural Sciences. He was an elected member of the Massey University Governing Council (1993–1997), representing all internal and extramural students, and served on several council committees, panels and other university-wide committees, including the University Disciplinary Appeals Committee, chaired by the chancellor, and the panel for the appointment of a new vice-chancellor (1994– 1995). He was also the residential community coordinator (1995–2001) responsible for mentoring and overseeing the welfare of students living in on-campus university accommodation. He was executive committee member of the Africa Association of New Zealand, president of the African Students Association, elected member of the Massey University Students’ Association Executive, and president of International Students. He is a chartered engineer (UK), currently chair of Section VI: Postharvest Technology and Process Engineering and executive committee member of the International Commission of Agricultural and Biosystems Engineering (CIGR), vice-chair of the Roots and Tuber section of the International Society for Horticultural Science, section chair for Engineering and Information Technology of the International Society for Food, Agriculture and Environment, and former vicepresident (Postharvest Technology and Biotechnology) of the Asian Association for Agricultural Engineering (AAAE). He is a life member of the AAAE and the American Society of Agricultural and Biological Engineers, and member of several international and national scientific societies. At the 80th anniversary of the CIGR and the World Congress in Quebec in 2010, he received the CIGR Presidential Citation for significant contributions to the advancement of agricultural engineering in Africa. He is founding editor-in-chief of the International Journal of Postharvest Technology and Innovation and member of the editorial board and regular reviewer for several international peer-reviewed journals. He has published over 60 articles in peer-reviewed journals and book chapters, co-edited three special issues of the International Journal of Engineering Education documenting recent advances in agricultural and biological engineering education, was the editor of two conference proceedings and made over 150 oral presentations at international conferences, including keynotes and invited lectures. Prior to joining Stellenbosch University, he worked at Sultan Qaboos University in Oman (2002– 2008), where he held the positions of associate professor of Agricultural Engineering, director of the Agricultural Experiment Station, assistant dean for Postgraduate Studies and Research, and acting dean during summer periods. During this period, he also developed a new research programme and courses in postharvest technology and received the university’s Distinguished Researcher Award in 2006. He also served in many university and national policy and advisory committees, including the university’s Academic Council (Senate), he was a member of the University Quality Audit Committee, which prepared the first quality audit report, and is a certified quality auditor of the Oman Accreditation Council. He is active in the international development arena, serving as visiting expert on postharvest technology at the headquarters of the Food and Agriculture Organization (FAO) of the United Nations (UN) in Rome (2000–2001), agricultural mechanisation expert in Iraq for the FAO/UN (2001–2002), FAO expert panel on microbial safety of green leafy vegetables (2008), FAO expert on postharvest and marketing systems and member of the technical panel that developed an agricultural development strategy for Timor-Leste (2009) as well as a member of the International Advisory Board of the USAID Horticulture Collaborative Research Support Program (Hort CRSP). Prof Opara holds the South African Research Chair in Postharvest Technology at Stellenbosch University, and his current research programmes focus on cold chain technologies, non-destructive technologies for quality measurement and mapping and reducing postharvest food losses. He is married to Gina and has two daughters, Ijeoma (15) and Okaraonyemma (13), who both enjoy playing the piano and watching their dad play football
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