Roberto Moscetti

@unitus.it

Associate Professor at Deptment for Innovation in Biological, Agro-Food and Forest Systems
University of Tuscia



              

https://researchid.co/rmoscetti

RESEARCH, TEACHING, or OTHER INTERESTS

Food Science, Artificial Intelligence, Spectroscopy, Computer Vision and Pattern Recognition

61

Scopus Publications

1981

Scholar Citations

27

Scholar h-index

41

Scholar i10-index

Scopus Publications

  • Bioimpedance-based prediction of dry matter content and potato varieties through supervised machine learning methods
    Ciro Allará, Roberto Moscetti, Giacomo Bedini, Manuela Ciocca, Alessandro Benelli, Paolo Lugli, Luisa Petti, and Pietro Ibba
    Elsevier BV

  • Potatoes (Solanum tuberosum L.) grown at “Patata dell'alto Viterbese” PGI have different quality characteristics and storage responses
    G. Bedini, Ron P. Haff, A. Benelli, A. Bandiera, E. Taormina, R. Massantini, and R. Moscetti
    Elsevier BV

  • Prediction of potato dry matter content by FT-NIR spectroscopy: Impact of tuber tissue on model performance
    G. Bedini, S.S. Nallan Chakravartula, M. Nardella, A. Bandiera, R. Massantini, and R. Moscetti
    Elsevier BV

  • Impact of traditional and innovative malaxation techniques and technologies on nutritional and sensory quality of virgin olive oil – A review
    Marco Nardella, Roberto Moscetti, Giacomo Bedini, Andrea Bandiera, Swathi Sirisha Nallan Chakravartula, and Riccardo Massantini
    Elsevier BV

  • Computer vision-based smart monitoring and control system for food drying: A study on carrot slices
    Swathi Sirisha Nallan Chakravartula, Andrea Bandiera, Marco Nardella, Giacomo Bedini, Pietro Ibba, Riccardo Massantini, and Roberto Moscetti
    Elsevier BV

  • Overnutrition is a significant component of food waste and has a large environmental impact
    Silvio Franco, Marco Barbanera, Roberto Moscetti, Clara Cicatiello, Luca Secondi, and Riccardo Massantini
    Springer Science and Business Media LLC
    AbstractFood waste and obesity and overweight conditions are both linked to the unsustainability of current food systems. This article argues that overnutrition should be considered a form of food waste and it provides a first estimation of the quantity of food over-consumed in Italy. This is done by calculating the excess calories consumed by obese and overweight people and converting them into food quantities by comparison with a typical Italian diet. The total quantity of food consumed in excess by Italian citizens due to overnutrition is calculated as 1.553 million tonnes per year, which is comparable to the current national household food waste assessments. The environmental impact arising from production and consumption of this food accounts for 6.15 Mt of CO2-eq per year, as estimated by a Life Cycle Analysis conducted on the 46 food categories which compose the typical Italian diet. Overnutrition in the South-Islands regions of Italy exerts the largest impact (31.6%), followed by the North-West (26.6%), the Centre (22.2%), and the North-East (19.1%).

  • Monitoring the hot-air drying process of organically grown apples (cv. Gala) using computer vision
    F. Raponi, R. Moscetti, Swathi Sirisha Nallan Chakravartula, M. Fidaleo and R. Massantini


  • Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee
    Swathi Sirisha Nallan Chakravartula, Roberto Moscetti, Giacomo Bedini, Marco Nardella, and Riccardo Massantini
    Elsevier BV

  • Computer vision for the development of smart drying processes


  • Impact of ‘brown rot’ caused by Gnomoniopsis castanea on chestnut fruits during the post-harvest process: critical phases and proposed solutions
    Carmen Morales‐Rodriguez, Giorgia Bastianelli, Romina Caccia, Giacomo Bedini, Riccardo Massantini, Roberto Moscetti, Thomas Thomidis, and Andrea Vannini
    Wiley
    AbstractBACKGROUNDThe brown rot fungus, Gnomoniopsis castanea, is the main organism responsible for the outbreak of chestnut postharvest decay that is threatening the sustainability of the chestnut market in Europe. Currently, no specific strategy is available to mitigate the impact and remediate the high losses of fruits in postharvest storage. In the present study, the different phases of chestnut handling in a standard facility plant were analyzed by evaluating the amount of fruit rot and infection by G. castanea at each phase.RESULTSThe warm bath (48 °C) was identified as the critical phase, requiring strict parametrization to effectively inactivate G. castanea in fruits. Laboratory tests indicated that maintaining fruits at 50 °C for a maximum of 45 min provided optimal conditions to completely inactivate G. castanea inoculum during postharvest handling. However, the warm bath at 50 °C and over was not effective in inactivating the complex of fungal taxa responsible for contamination and development of molds. Higher temperatures and extended treatment times caused significant losses in fruit quality, as indicated by taste panel evaluation. Upscaling of postharvest facilities is discussed and critically evaluated.CONCLUSIONThe warm bath (50 °C for 45 min) is effective in completely inactivating G. castanea in fruits but did not reduce the impacts of the complex of molds responsible for external contamination and mycotoxin production. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

  • Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
    P. Ibba, C. Tronstad, Roberto Moscetti, T. Mimmo, G. Cantarella, L. Petti, Ø. Martinsen, S. Cesco and P. Lugli


  • Use of photo catalytic oxidation for the post-harvest storage of fruit arid vegetables


  • A review on high-power ultrasound-assisted extraction of olive oils: Effect on oil yield, quality, chemical composition and consumer perception
    Marco Nardella, Roberto Moscetti, Swathi Sirisha Nallan Chakravartula, Giacomo Bedini, and Riccardo Massantini
    MDPI AG
    The objective of this review is to illustrate the state of the art in high-power ultrasound (HPU) application for olive oil extraction with the most recent studies about the effects of HPU treatment on oil yield, quality, chemical composition, as well as on the consumer’s perception. All the examined works reported an increase in oil yield and extractability index through the use of HPU, which was ascribed to reduced paste viscosity and cavitation-driven cell disruption. Olive oil legal quality was generally not affected; on the other hand, results regarding oil chemical composition were conflicting with some studies reporting an increase of phenols, tocopherols, and volatile compounds, while others underlined no significant effects to even slight reductions after HPU treatment. Regarding the acceptability of oils extracted through HPU processing, consumer perception is not negatively affected, as long as the marketer effectively delivers information about the positive effects of ultrasound on oil quality and sensory aspect. However, only a few consumers were willing to pay more, and hence the cost of the innovative extraction must be carefully evaluated. Since most of the studies confirm the substantial potential of HPU to reduce processing times, improve process sustainability and produce oils with desired nutritional and sensory quality, this review points out the need for industrial scale-up of such innovative technology.

  • Chestnut cultivar identification through the data fusion of sensory quality and ft-nir spectral data
    Piermaria Corona, Maria Teresa Frangipane, Roberto Moscetti, Gabriella Lo Feudo, Tatiana Castellotti, and Riccardo Massantini
    MDPI AG
    The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other’s drawbacks, synergistically contributing to an excellent result.

  • Evaluating progress of chestnut quality: A review of recent developments
    Riccardo Massantini, Roberto Moscetti, and Maria Teresa Frangipane
    Elsevier BV

  • Combined Use of Blanching and Vacuum Impregnation with Trehalose and Green Tea Extract as Pre-treatment to Improve the Quality and Stability of Frozen Carrots
    Veronica Santarelli, Lilia Neri, Roberto Moscetti, Carla Daniela Di Mattia, Giampiero Sacchetti, Riccardo Massantini, and Paola Pittia
    Springer Science and Business Media LLC

  • Feasibility of computer vision as Process Analytical Technology tool for the drying of organic apple slices
    R. Moscetti, F. Raponi, M. Cecchini, D. Monarca, and R. Massantini
    International Society for Horticultural Science (ISHS)
    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.

  • Pine nut species recognition using NIR spectroscopy and image analysis
    R. Moscetti, D. Berhe, M. Agrimi, R. Haff, Pei-Shih Liang, S. Ferri, D. Monarca and R. Massantini


  • Stinging nettles as potential food additive: Effect of drying processes on quality characteristics of leaf powders
    Swathi Sirisha Nallan Chakravartula, Roberto Moscetti, Barbara Farinon, Vittorio Vinciguerra, Nicolò Merendino, Giacomo Bedini, Lilia Neri, Paola Pittia, and Riccardo Massantini
    MDPI AG
    Stinging nettle (Urtica dioica L.) is a ubiquitous, multi-utility, and under-utilized crop with potential health benefits owing to its nutritional and bioactive components. The objective of the work is to produce powders by drying wild stinging nettle leaves as a storable, low-cost functional additive to be used in bakery and ready-to-cook products. Convective drying (CD) and freeze-drying (FD) were applied on unblanched (U) or blanched (B) leaves, which were then milled to nettle powders (NPs). The obtained NPs were evaluated for selected physicochemical (moisture, color), techno-functional (flow indices, hygroscopicity), and phytochemical (pigments, phenols) characteristics as well as mineral contents. Blanching improved mass transfer and reduced the oxidative degradation of pigments during drying, but it caused a loss of total phenols content, antioxidant activity, and potassium content. As for the drying method, CD resulted in better flow properties (i.e., Carr Index and Hausner Ratio), while FD retained better the color, pigments, magnesium content, phenolic, and antioxidant parameters. Overall, the evaluated processing methods resulted in different technological properties that can allow for better evaluation of NPs as a food additive or ingredient. Among the NPs, blanched and freeze-dried powders despite showing inferior technological properties can be recommended as more suitable ingredients targeted f or food enrichment owing to better retention of bio-active components.

  • Knowledge and skills attractive for the employers of the organic sector: A survey across Europe
    Teresa Briz, Peter von Fragstein und Niemsdorff, Emanuele Radicetti, Roberto Moscetti, Eeva Uusitalo, Sari Iivonen, Ritva Mynttinen, Jan Moudry, Jan Moudry, Petr Konvalina,et al.
    Cambridge University Press (CUP)
    AbstractIn all countries, the organic sector of the agricultural industry is increasing, with Europe traditionally leading this trend. A survey of different stakeholders (employers) was carried out in 2015 in seven European countries to evaluate the employment market for the organic agricultural industry in Europe. Results indicate the willingness to employ qualified graduates. From the employers' perspective, the most desirable knowledge skills among the graduates of organic agricultural studies include plant production, food quality and plant protection. Further, the study revealed the work skills most desired by the employers are practical expertise, teamwork and problem-solving, and the most important method of learning is cooperation with enterprises (internships/training) in the organic agricultural sector.

  • Computer Vision Technology for Quality Monitoring in Smart Drying System
    Roberto Moscetti, Swathi Sirisha Nallan Chakravartula, Andrea Bandiera, Giacomo Bedini, and Riccardo Massantini
    IEEE
    Drying is one of the most viable and effective preservation technologies to improve the shelf-life of foods. Carrots are among the most consumed vegetables, owing to their nutritional profile as well as their wide use in dried foods, ready-to-eat and ready-to-use convenience products like snacks, meals, and soups. As for the dried products, the quality of produce depends on the timely recognition of the dehydration state. Traditional off-line analyses in combination with drying rates to identify the end-time of the process can fail in identifying process discrepancies and avoiding product degradation. The use of computer vision (CV) as a Process Analytical Technology (PAT) tool in the drying system can be of interest to monitor the drying process and product quality. The objective of this study was to study the drying behavior of carrot slices during drying at 35 °C for 36 h using a smart dryer augmented with computer vision system and load cell. The system developed was effective in measuring the weight, size, and color of the untreated (control) and pre-treated (blanched) carrot slices along the drying time. The image analysis and the weight loss of the slices enabled the prediction of relative moisture content (MC) using linear and thin-layer (Newton-Lewis) models in comparison. The applicability of the models was further evaluated by use of different pretreatments (i.e. blanched at 90 °C for 2 min or not blanched). The results showed promising prediction capability for the linear models, which was independent of time with a Root Mean Square Error (RMSE) similar to the thin-layer models, an adj. R2 > 0.99 as well as both Mean BIAS Error (MBE) and reduced Ȥ2 tending towards zero. The blanching treatment affected the model parameters but negligibly affected the model performances.

  • Comparison between Hyperspectral Imaging and Chemical Analysis of Polyphenol Oxidase Activity on Fresh-Cut Apple Slices
    Luna Shrestha, B. Kulig, R. Moscetti, R. Massantini, E. Pawelzik, O. Hensel and B. Sturm

    The assessment of the quality of fresh-cut apple slices is important for processing, storage, market value, and consumption. Determination of polyphenol oxidase activity (PPO) in apples is critical for controlling the quality of the final product (i.e., dried apples and juices). Hyperspectral imaging (HSI) is a nondestructive, noncontact, and rapid food quality assessment technique. It has the potential to detect physical and chemical quality attributes of foods such as PPO of apple. The aim of this study was to investigate the suitability of HSI in the visible and near-infrared (VIS-NIR) range for indirect assessment of PPO activity of fresh-cut apple slices. Apple slices of two cultivars (cv. Golden Delicious and Elstar) were used to build a robust detection algorithm, which is independent of cultivars and applied treatments. Partial least squares (PLS) regression using the 7-fold cross-validation method and method comparison analysis (Bland–Altman plot, Passing–Bablok regression, and Deming regression) were performed. The 95% confidence interval (CI) bands for the Bland–Altman analysis between the methods were −4.19 and 13.11, and the mean difference was 3.7e−12. The Passing–Bablok regression had a slope of 0.8 and an intercept of 7.6. The slope of the Deming regression was 0.8 within the CI bands of 0.56 and 1.10. These results show acceptable performance and no significant deviation from linearity. Hence, the results demonstrated the feasibility of HSI as an indirect alternative to the standard chemical analysis of PPO enzyme activity.

  • Feasibility of ft-nir spectroscopy and vis/nir hyperspectral imaging for sorting unsound chestnuts
    Giacomo Bedini, Giorgia Bastianelli, Swathi Sirisha Nallan Chakravartula, Carmen Morales-Rodríguez, Luca Rossini, Stefano Speranza, Andrea Vannini, Roberto Moscetti, and Riccardo Massantini
    Societa di Ortoflorofrutticoltura Italiana (SOI)
    Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

  • Optimisation of physical and chemical treatments to control browning development and enzymatic activity on fresh-cut apple slices
    Luna Shrestha, Boris Kulig, Roberto Moscetti, Riccardo Massantini, Elke Pawelzik, Oliver Hensel, and Barbara Sturm
    MDPI AG
    Optimisation of processing time and pre-treatments are crucial factors prior to apple drying to produce a high-quality product. The purpose of the present study was to test the utility of physical (hot-water, HWB and steam blanching, SB) and chemical (1% ascorbic acid, AA; and 1% citric acid, CA) treatments, alone or in combination in reducing surface discolouration as well as oxidative enzyme activity in apple slices (cv. Golden Delicious and Elstar) exposed to air at room temperature for 0, 30 and 60 min. The total colour change (ΔE) for Golden Delicious was equal to 2.38, 2.68, and 4.05 after 0, 30 and 60 min of air exposure, respectively. Dipping in AA solution (1% w/v) was found to be the best treatment to limit surface discolouration of both apple cultivars. The best heat treatments to inhibit polyphenol oxidase/peroxidase enzymes activity were 70 °C HWB for Golden Delicious and 60 °C HWB for Elstar slices, both in combination with a solution of 1% AA and 1% CA. The tested apple cultivars were found to require different treatments at minimum ambient air exposure to obtain the best surface colour condition.


RECENT SCHOLAR PUBLICATIONS

  • Bioimpedance-based prediction of dry matter content and potato varieties through supervised machine learning methods
    C Allar, R Moscetti, G Bedini, M Ciocca, A Benelli, P Lugli, L Petti, P Ibba
    Postharvest Biology and Technology 222, 113358 2025

  • Instrumentation Techniques and Analyses of Bioactive Compounds in Different Dried Fruit Products
    R Moscetti, A Benelli, P Ibba, C Mannozzi, F Melini, R Massantini
    Dried Fruit Products, 129-166 2025

  • Potatoes (Solanum tuberosum L.) grown at “Patata dell'alto Viterbese” PGI have different quality characteristics and storage responses
    G Bedini, RP Haff, A Benelli, A Bandiera, E Taormina, R Massantini, ...
    Postharvest Biology and Technology 214, 112991 2024

  • 6 InstrumentationTechniquesand
    R Moscetti, A Benelli, P Ibba, C Mannozzi, F Melini, R Massantini
    Dried Fruit Products, 129 2024

  • Prediction of potato dry matter content by FT-NIR spectroscopy: Impact of tuber tissue on model performance
    G Bedini, SSN Chakravartula, M Nardella, A Bandiera, R Massantini, ...
    Future Foods 8, 100241 2023

  • Impact of traditional and innovative malaxation techniques and technologies on nutritional and sensory quality of virgin olive oil–A review
    M Nardella, R Moscetti, G Bedini, A Bandiera, SSN Chakravartula, ...
    Food Chemistry Advances 2, 100163 2023

  • Computer vision-based smart monitoring and control system for food drying: A study on carrot slices
    SSN Chakravartula, A Bandiera, M Nardella, G Bedini, P Ibba, ...
    Computers and Electronics in Agriculture 206, 107654 2023

  • Monitoring the hot-air drying process of organically grown apples (cv. Gala) using computer vision
    F Raponi, R Moscetti, SSN Chakravartula, M Fidaleo, R Massantini
    Biosystems Engineering 223, 1-13 2022

  • Overnutrition is a significant component of food waste and has a large environmental impact
    S Franco, M Barbanera, R Moscetti, C Cicatiello, L Secondi, R Massantini
    Scientific Reports 12 (1), 8166 2022

  • Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee
    SSN Chakravartula, R Moscetti, G Bedini, M Nardella, R Massantini
    Food Control 135, 108816 2022

  • Computer vision for the development of smart drying processes.
    A Bandiera, G Bedini, SSN Chakravartula, R Massantini, R Moscetti
     2022

  • Impact of ‘brown rot’ caused by Gnomoniopsis castanea on chestnut fruits during the post‐harvest process: critical phases and proposed solutions
    C Morales‐Rodriguez, G Bastianelli, R Caccia, G Bedini, R Massantini, ...
    Journal of the Science of Food and Agriculture 102 (2), 680-687 2022

  • A review on high-power ultrasound-assisted extraction of olive oils: Effect on oil yield, quality, chemical composition and consumer perception
    M Nardella, R Moscetti, SS Nallan Chakravartula, G Bedini, R Massantini
    Foods 10 (11), 2743 2021

  • Chestnut cultivar identification through the data fusion of sensory quality and FT-NIR spectral data
    P Corona, MT Frangipane, R Moscetti, G Lo Feudo, T Castellotti, ...
    Foods 10 (11), 2575 2021

  • Evaluating progress of chestnut quality: A review of recent developments
    R Massantini, R Moscetti, MT Frangipane
    Trends in Food Science & Technology 113, 245-254 2021

  • Combined use of blanching and vacuum impregnation with trehalose and green tea extract as pre-treatment to improve the quality and stability of frozen carrots
    V Santarelli, L Neri, R Moscetti, CD Di Mattia, G Sacchetti, R Massantini, ...
    Food and Bioprocess Technology 14, 1326-1340 2021

  • Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
    P Ibba, C Tronstad, R Moscetti, T Mimmo, G Cantarella, L Petti, ...
    Scientific reports 11 (1), 11202 2021

  • Stinging nettles as potential food additive: effect of drying processes on quality characteristics of leaf powders
    SS Nallan Chakravartula, R Moscetti, B Farinon, V Vinciguerra, ...
    Foods 10 (6), 1152 2021

  • Pine nut species recognition using NIR spectroscopy and image analysis
    R Moscetti, DH Berhe, M Agrimi, RP Haff, P Liang, S Ferri, D Monarca, ...
    Journal of Food Engineering 292, 110357 2021

  • Use of photo catalytic oxidation for the post-harvest storage of fruit arid vegetables
    V Falconi, G Paolini, R Moscetti, S Crognale, E Carota, A Bandiera, ...
     2021

MOST CITED SCHOLAR PUBLICATIONS

  • Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee
    SSN Chakravartula, R Moscetti, G Bedini, M Nardella, R Massantini
    Food Control 135, 108816 2022
    Citations: 122

  • Nondestructive detection of insect infested chestnuts based on NIR spectroscopy
    R Moscetti, RP Haff, S Saranwong, D Monarca, M Cecchini, R Massantini
    Postharvest Biology and Technology 87, 88-94 2014
    Citations: 110

  • Optimisation of physical and chemical treatments to control browning development and enzymatic activity on fresh-cut apple slices
    L Shrestha, B Kulig, R Moscetti, R Massantini, E Pawelzik, O Hensel, ...
    Foods 9 (1), 76 2020
    Citations: 84

  • Monitoring and optimization of the process of drying fruits and vegetables using computer vision: A review
    F Raponi, R Moscetti, D Monarca, A Colantoni, R Massantini
    Sustainability 9 (11), 2009 2017
    Citations: 84

  • Effects of controlled atmospheres and low temperature on storability of chestnuts manually and mechanically harvested
    M Cecchini, M Contini, R Massantini, D Monarca, R Moscetti
    Postharvest Biology and Technology 61 (2-3), 131-136 2011
    Citations: 84

  • Management of winter cover crop residues under different tillage conditions affects nitrogen utilization efficiency and yield of eggplant (Solanum melanogena L.) in
    E Radicetti, R Mancinelli, R Moscetti, E Campiglia
    Soil and Tillage Research 155, 329-338 2016
    Citations: 82

  • Evaluating progress of chestnut quality: A review of recent developments
    R Massantini, R Moscetti, MT Frangipane
    Trends in Food Science & Technology 113, 245-254 2021
    Citations: 79

  • Feasibility of NIR spectroscopy to detect olive fruit infested by Bactrocera oleae
    R Moscetti, RP Haff, E Stella, M Contini, D Monarca, M Cecchini, ...
    Postharvest Biology and Technology 99, 58-62 2015
    Citations: 76

  • Detection of mold-damaged chestnuts by near-infrared spectroscopy
    R Moscetti, D Monarca, M Cecchini, RP Haff, M Contini, R Massantini
    Postharvest Biology and Technology 93, 83-90 2014
    Citations: 73

  • Use of ozone in sanitation and storage of fresh fruits and vegetables
    L Carletti, R Botondi, R Moscetti, E Stella, D Monarca, M Cecchini, ...
    Journal of Food, Agriculture and Environment 11 (3-4), 585-589 2013
    Citations: 69

  • Real-time monitoring of organic apple (var. Gala) during hot-air drying using near-infrared spectroscopy
    R Moscetti, F Raponi, S Ferri, A Colantoni, D Monarca, R Massantini
    Journal of food engineering 222, 139-150 2018
    Citations: 68

  • Hazelnut quality sorting using high dynamic range short-wave infrared hyperspectral imaging
    R Moscetti, W Saeys, JC Keresztes, M Goodarzi, M Cecchini, M Danilo, ...
    Food and bioprocess technology 8, 1593-1604 2015
    Citations: 66

  • Maintaining the quality of unripe, fresh hazelnuts through storage under modified atmospheres
    R Moscetti, MT Frangipane, D Monarca, M Cecchini, R Massantini
    Postharvest biology and technology 65, 33-38 2012
    Citations: 61

  • Feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels
    R Moscetti, RP Haff, B Aernouts, W Saeys, D Monarca, M Cecchini, ...
    Journal of Food Engineering 118 (1), 1-7 2013
    Citations: 59

  • Near-infrared spectroscopy for detection of hailstorm damage on olive fruit
    R Moscetti, RP Haff, D Monarca, M Cecchini, R Massantini
    Postharvest Biology and Technology 120, 204-212 2016
    Citations: 54

  • Real-time monitoring of organic carrot (var. Romance) during hot-air drying using near-infrared spectroscopy
    R Moscetti, RP Haff, S Ferri, F Raponi, D Monarca, P Liang, R Massantini
    Food and Bioprocess Technology 10, 2046-2059 2017
    Citations: 48

  • Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin
    R Moscetti, E Radicetti, D Monarca, M Cecchini, R Massantini
    Journal of the Science of Food and Agriculture 95 (13), 2619-2625 2015
    Citations: 47

  • Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
    P Ibba, C Tronstad, R Moscetti, T Mimmo, G Cantarella, L Petti, ...
    Scientific reports 11 (1), 11202 2021
    Citations: 46

  • Comparison between hyperspectral imaging and chemical analysis of polyphenol oxidase activity on fresh‐cut apple slices
    L Shrestha, B Kulig, R Moscetti, R Massantini, E Pawelzik, O Hensel, ...
    Journal of Spectroscopy 2020 (1), 7012525 2020
    Citations: 46

  • Yield and quality of eggplant (Solanum melongena L.) as affected by cover crop species and residue management
    E Radicetti, R Massantini, E Campiglia, R Mancinelli, S Ferri, R Moscetti
    Scientia Horticulturae 204, 161-171 2016
    Citations: 46