Roberto Moscetti

Scopus Publications

Extending fresh-cut apple shelf-life with a high-barrier starch-based biofilm
Giuseppina Adiletta, Matteo Sambucci, Anna Maria Giusti, Shizuka Kibi, Daniela Tufi, Roberto Moscetti, Riccardo Massantini, Marco Valente, Paola Russo
European Food Research and Technology, 2026
Comparing deep and classical Chemometrics: can CNN enhance the accuracy of EVOO adulteration detection from spectral data?
Andrea Bandiera, Armando Camerlingo, Nico Sanna, Costantino Zazza, Alessandro Benelli, Riccardo Massantini, Roberto Moscetti
Food Control, 2026
Extra virgin olive oil (EVOO) has a high economic value and is therefore susceptible to adulteration with oils of lower quality and price. Spectroscopy, although not recognized as an official analysis methodology in EVOO, can be used in rapid screening to detect adulterants. Predictive models are usually developed with classical chemometrics, which require human knowledge in feature engineering. Deep chemometrics overcome human intervention by relying on neural networks. This study compares the use of PLS (Partial Least Squares) and CNN (Convolutional Neural Network) algorithm in combination with FT-NIR (1000-2500 nm) and Vis-NIR (380-900 nm) spectroscopy to predict EVOO adulteration using four different seed oils (peanut, maize, sunflower and soya). Adulterant concentrations at 0.5 % and 1.5 % were difficult to distinguish, as the subtle spectral changes were often masked by a low signal-to-noise ratio mainly due to high spectral similarity with the pure EVOO, instrumental noise, and intrinsic oil variability. The FT-NIR-based regressions generally showed minimal performance differences between PLS and CNN, regardless of the application of spectral pretreatment or data augmentation (RMSEP = 0.99-2.08 %), indicating that for this spectral range, the added complexity of CNN offered no significant advantage. Only the model obtained using CNN and FT-NIR for peanut adulteration was not able to converge. In contrast, the Vis-NIR models based on CNN significantly outperformed the PLS models, regardless of the use of pretreatment or data augmentation. Therefore, in the present study, deep chemometrics proved not to be a universal replacement for classical chemometrics, but rather a complementary tool that demonstrates its true value where the classical approach is less effective.
Predicting Forage Nutritional Quality With Near-Infrared Spectroscopy
Alessandro Benelli, Riccardo Primi, Chiara Evangelista, Raffaello Spina, Marco Milanesi, Daniele Pietrucci, Bruno Ronchi, Umberto Bernabucci, Roberto Moscetti
Journal of Sustainable Agriculture and Environment, 2025
The quality of green forage is crucial in pasture grazing, influencing both animal welfare, environmental sustainability, and production yield. Traditionally, the evaluation of forage composition requires time‐consuming and costly chemical analysis. In this context, near‐infrared spectroscopy (NIR) emerges as a promising alternative. This study adopted Fourier transform NIR (FT‐NIR) spectroscopy to predict nutritional characteristics of green forages. A total of 324 samples were collected from pastures in central Italy. Partial least squares (PLS) regression models were then developed, applying variable selection methods to improve PLS model accuracy. The interval PLS (iPLS) variable selection method gave the best results for fresh forage, while the genetic algorithm (GA) performed best for dried samples. The best results from the PLS models were obtained for dry matter (DM) and crude protein (CP). The DM model for fresh forage yielded an R2P of 0.96 and an RMSEP of 2.95 g 100 g−1 FW, while the CP model for dried forage yielded an R2P of 0.94 and an RMSEP of 1.84 g 100 g−1 DW, with a normalised root‐mean‐square error of cross‐validation (NRMSECV) of 3.8% and 5.6%, respectively. The results for the neutral detergent fibre (aNDF) were acceptable. NIR spectroscopy has proven to be a useful tool for assessing forage nutritional quality. Variable selection through iPLS also enabled the identification of “core” spectral regions for the development of compact and portable NIR sensors. Future research should further investigate sample preparation and moisture content effects and expand sampling to different geographical areas to enhance model robustness.
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, Pietro Ibba
Postharvest Biology and Technology, 2025
This study explored bioimpedance measurements and their derived parameters as potential indicators of dry matter content and potato variety. We investigated how bioimpedance correlates with the dry matter composition of potatoes,through destructive tests on tissue slices. Using the machine learning approach, we identified patterns and associations that can aid in predicting dry matter content, using regression models, and discriminating among potato varieties, focusing on classification models. In particular, four feature selection methods (correlation matrix, minimum-redundancy–maximum-relevance, neighborhood component analysis, and t-test) were evaluated against a baseline with all features. It was found that the best regression model for dry matter was a neural network regression model with t-test features, which achieved R 2 of 0.62 and RMSE of 2.3% in testing, while the best classification model for potato variety was a neural network classification model with correlation matrix features, achieving an F 1 score of 0.92. • Destructive bioimpedance spectroscopy for analyzing potato tuber variety and quality. • Machine Learning to optimize postharvest potato quality. • Neural Network predicts potato dry matter: R 2 = 0.62, RMSE = 2.3%. • Nine potato varieties classification: F 1 = 0.92.
Optimization on Banana Maturation Classification for Logistics Efficiency Using Computer Vision
Roberto Moscetti, Simone Zambelli, Eleonora Taormina, Andrea Bandiera, Alessandro Benelli, Massantini Riccardo
Lecture Notes in Civil Engineering, 2025
Deep Learning Versus Chemometrics: A Comparative Study in EVOO Adulteration Detection
Andrea Bandiera, Valentina Rosati, Domenico Capone, Alessandro Benelli, Eleonora Taormina, Riccardo Massantini, Roberto Moscetti
Lecture Notes in Civil Engineering, 2025
Smart Drying Technologies: A Case Study on Red Flesh Apple (Kissabel®) Using Computer Vision and Deep Learning
Alessandro Benelli, Flavia Pandolfi, Andrea Bandiera, Eleonora Taormina, Paola Russo, Giuseppina Adiletta, Riccardo Massantini, Roberto Moscetti
Lecture Notes in Civil Engineering, 2025
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, R. Moscetti
Postharvest Biology and Technology, 2024
Four pre-selected potato cultivars (Solanum tuberosum L.) from the “Patata dell’Alto Viterbese” PGI area underwent physico-chemical and enzymatic characterization to assess their suitability for specific processing line. Key chemical parameters (reducing sugar content (RSC), total phenol content (TPC), dry matter (DM), physical attributes (firmness), physico-chemical aspects (color, browning development), and enzymatic factors (polyphenol oxidase enzyme activity, PPO) were analyzed at harvest and monthly during a 6-month storage period over two production seasons (2020 and 2021). The results of one-way within-subject ANOVA and Principal Component Analysis (PCA) highlighted the superior attributes of the ‘Fontane’ cultivar for the processing industry, particularly for frying applications (e.g., chips, French fries, etc.). In comparison to other tested cultivars, ‘Fontane’ exhibited: i) the highest DM (> 200 g kg−1), ii) the lowest TPC and RSC, iii) the greatest firmness, iv) low susceptibility to browning, and v) high tolerance to Low-Temperature Sweetening (LTS). The ‘Jelly’ cultivar displayed favorable attributes for versatile use, suitable for both fresh market uses (roasting, homemade “gnocchi”, etc.) and industrial applications (fresh-cut or dried potato). It demonstrated high DM content (200 g kg−1) and firmness, the lowest PPO activity, good TPC, but higher RSC than ‘Fontane’. However, elevated RSC for ‘Jelly’ renders it less suitable for industrial frying. ‘Constance’ and ‘Gaudi’ exhibited positive traits for fresh market use such as boiling and steaming, with moderate to low DM content and firmness. Nonetheless, both cultivars displayed low tolerance to LTS, and ‘Gaudi’ exhibited the poorest processing properties, showing the highest susceptibility to browning.
Near-Infrared Spectroscopy to Predict Nutritional Factors of Green Forage
Alessandro Benelli, Chiara Evangelista, Raffaello Spina, Riccardo Primi, Daniele Pietrucci, Marco Milanesi, Giovanni Chillemi, Umberto Bernabucci, Roberto Moscetti
2024 IEEE International Workshop on Metrology for Agriculture and Forestry Metroagrifor 2024 Proceedings, 2024
Green forage quality is crucial for efficient breeding. Therefore, it would be extremely important to provide a method for a rapid and cost-effective analysis of the nutritional factors of green forage. The application of Fourier-Transformed Near-Infrared (FT-NIR) spectroscopy in combination with chemometric techniques could meet the identified requirements. The objective of the present study was to develop optimized partial least squares (PLS) models for the prediction of nutritional factors of green forage, namely dry matter, crude protein, crude ash, ether extract, neutral detergent fiber, acid detergent fiber, acid detergent lignin, and crude fiber. The elected PLS models resulted from a combination of chemometrics techniques applied to FT-NIR absorbance spectra acquired on fresh and $\\mathbf{dried}/ \\mathbf{milled}$ samples of green forage. The most accurate prediction results were obtained for dry matter in the fresh samples (coefficient of determination of cross-validation, $\\mathbf{R}^{2}\\mathbf{CV}=0.95$; root-mean-square error of cross-validation, RMSECV $=3.84\\mathbf{g}100\\mathbf{g}^{-1}$ fresh weight), and crude protein in the $\\mathbf{dried}/\\mathbf{milled}$ samples $(\\mathbf{R}^{2}\\mathbf{CV}$= 0.94, RMSECV = 1.99 g 100 $\\mathrm{g}^{-1}$ dry weight). Future developments will be further focused on improvement of prediction accuracy by applying deep learning techniques.
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, R. Moscetti
Future Foods, 2023
Potato, an important cash crop, is critically affected by supply chain losses that require attention of both research and industry to ensure sustainable production (SDG 12.3). Within potato supply chain management, dry matter (DM) determination is a key factor affecting the harvest quality, storability, food losses, designated user and tuber prices. Even though rapid methods like near-infrared spectroscopy (NIR) enable operators to carry out fast, reliable in situ measurements, the prediction results are often unsatisfactory due to inherent variability of tuber's composition. Thus, this study aims to evaluate Fourier Transformed-NIR based DM prediction in different potato tissues: i) periderm, ii) cortex, iii) outer medulla and iv) inner medulla of 12 different cultivars. The DM prediction models developed using standard chemometrics approaches (i.e., partial least squares, PLS; and interval-PLS, iPLS) showed that features selection by iPLS allowed for better performances. Among the evaluated tissues, the inner medulla had more precise and reliable measurements with RMSE and R2 prediction values of 0.93% and 0.90, respectively. This information can be used to develop more advanced systems inclusive of portable tools and advanced technologies for in situ evaluation to support operators and ensure better pre- and post-harvest management of potatoes.
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, Riccardo Massantini
Food Chemistry Advances, 2023
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, Roberto Moscetti
Computers and Electronics in Agriculture, 2023
Overnutrition is a significant component of food waste and has a large environmental impact
Silvio Franco, Marco Barbanera, Roberto Moscetti, Clara Cicatiello, Luca Secondi, Riccardo Massantini
Scientific Reports, 2022
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, R. Massantini
Biosystems Engineering, 2022
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, Riccardo Massantini
Food Control, 2022
Computer vision for the development of smart drying processes
Industrie Alimentari, 2022
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, Andrea Vannini
Journal of the Science of Food and Agriculture, 2022
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, P. Lugli
Scientific Reports, 2021
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, Riccardo Massantini
Foods, 2021
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, Riccardo Massantini
Foods, 2021
Use of photo catalytic oxidation for the post-harvest storage of fruit arid vegetables
Industrie Alimentari, 2021
Evaluating progress of chestnut quality: A review of recent developments
Riccardo Massantini, Roberto Moscetti, Maria Teresa Frangipane
Trends in Food Science and Technology, 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
Veronica Santarelli, Lilia Neri, Roberto Moscetti, Carla Daniela Di Mattia, Giampiero Sacchetti, Riccardo Massantini, Paola Pittia
Food and Bioprocess Technology, 2021
Feasibility of computer vision as Process Analytical Technology tool for the drying of organic apple slices
R. Moscetti, F. Raponi, M. Cecchini, D. Monarca, R. Massantini
Acta Horticulturae, 2021
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, R. Massantini
Journal of Food Engineering, 2021
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, Riccardo Massantini
Foods, 2021
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, Marek Kopecky, Dominika Średnicka-Tober, Renata Kazimierczak, Liina Talgre, Darja Matt, Eve Veromann, Roberto Mancinelli, Ewa Rembiałkowska
Renewable Agriculture and Food Systems, 2020
Computer Vision Technology for Quality Monitoring in Smart Drying System
Roberto Moscetti, Swathi Sirisha Nallan Chakravartula, Andrea Bandiera, Giacomo Bedini, Riccardo Massantini
2020 IEEE International Workshop on Metrology for Agriculture and Forestry Metroagrifor 2020 Proceedings, 2020
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, B. Sturm
Journal of Spectroscopy, 2020
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, Barbara Sturm
Foods, 2020
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, Riccardo Massantini
Italus Hortus, 2020
Application on-line NIR spectroscopy and other process analytical technology tools to the characterization of soy sauce desalting by electrodialysis
Roberto Moscetti, Riccardo Massantini, Marcello Fidaleo
Journal of Food Engineering, 2019
Effects of hot-water and steam blanching of sliced potato on polyphenol oxidase activity
Roberto Moscetti, Flavio Raponi, Danilo Monarca, Giacomo Bedini, Serena Ferri, Riccardo Massantini
International Journal of Food Science and Technology, 2019
Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible–NIR hyperspectral imaging
Roberto Moscetti, Barbara Sturm, Stuart OJ Crichton, Waseem Amjad, Riccardo Massantini
Journal of the Science of Food and Agriculture, 2018
Real-time monitoring of organic apple (var. Gala) during hot-air drying using near-infrared spectroscopy
Roberto Moscetti, Flavio Raponi, Serena Ferri, Andrea Colantoni, Danilo Monarca, Riccardo Massantini
Journal of Food Engineering, 2018
Monitoring and optimization of the process of drying fruits and vegetables using computer vision: A review
Flavio Raponi, Roberto Moscetti, Danilo Monarca, Andrea Colantoni, Riccardo Massantini
Sustainability Switzerland, 2017
Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy
Roberto Moscetti, Ron P. Haff, Serena Ferri, Flavio Raponi, Danilo Monarca, Peishih Liang, Riccardo Massantini
Food and Bioprocess Technology, 2017
Detection of pits and pit fragments in fresh cherries using near infrared spectroscopy
PS Liang, R Moscetti, R Massantini, D Light, Ron P Haff
Journal of Near Infrared Spectroscopy, 2017
Near-infrared spectroscopy for detection of hailstorm damage on olive fruit
Roberto Moscetti, Ron P. Haff, Danilo Monarca, Massimo Cecchini, Riccardo Massantini
Postharvest Biology and Technology, 2016
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, 2016
A comparative study on the effectiveness of ozonated water and peracetic acid in the storability of packaged fresh-cut melon
Rinaldo Botondi, Roberto Moscetti, Riccardo Massantini
Journal of Food Science and Technology, 2016
Management of winter cover crop residues under different tillage conditions affects nitrogen utilization efficiency and yield of eggplant (Solanum melanogena L.) in Mediterranean environment
Emanuele Radicetti, Roberto Mancinelli, Roberto Moscetti, Enio Campiglia
Soil and Tillage Research, 2016
Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin
Roberto Moscetti, Emanuele Radicetti, Danilo Monarca, Massimo Cecchini, Riccardo Massantini
Journal of the Science of Food and Agriculture, 2015
Hazelnut Quality Sorting Using High Dynamic Range Short-Wave Infrared Hyperspectral Imaging
Roberto Moscetti, Wouter Saeys, Janos C. Keresztes, Mohammad Goodarzi, Massimo Cecchini, Monarca Danilo, Riccardo Massantini
Food and Bioprocess Technology, 2015
"smart bio wine", a project for consumer Information,Business competitiveness and environmental sustainability
M. Cecchini, D. Monarca, A. Colantoni, F. Cossio, R. Massantini, et al.
Chemical Engineering Transactions, 2015
Effects of controlled atmosphere and novel antifungal pre-treatments on quality and storability of 'golden delicious' apple
M. Cecchini, D. Monarca, L. Carletti, E. Stella, R. Moscetti, R. Massantini, M. Contini
Acta Horticulturae, 2015
Feasibility of NIR spectroscopy to detect olive fruit infested by Bactrocera oleae
Roberto Moscetti, Ron P. Haff, Elisabetta Stella, Marina Contini, Danilo Monarca, Massimo Cecchini, Riccardo Massantini
Postharvest Biology and Technology, 2015
Review: Recent advances in the use of non-destructive near infrared spectroscopy for intact olive fruits
Elisabetta Stella, Roberto Moscetti, Ron P. Haff, Danilo Monarca, Massimo Cecchini, Marina Contini, Riccardo Massantini
Journal of Near Infrared Spectroscopy, 2015
Recent advances in the use of NIR spectroscopy for qualitative control and protection of extra virgin olive oil
Rivista Italiana Delle Sostanze Grasse, 2015
NIR spectroscopy is suitable to detect insect infested chestnuts
R. Moscetti, D. Monarca, M. Cecchini, R. Massantini, R.P. Haff, S. Saranwong
Acta Horticulturae, 2014
The evolution of the chestnut harvesting technique
D. Monarca, M. Cecchini, A. Colantoni, G. Menghini, R. Moscetti, R. Massantini
Acta Horticulturae, 2014
Quality maintenance and storability of chestnuts manually and mechanically harvested
D. Monarca, R. Moscetti, L. Carletti, M. Cecchini, A. Colantoni, E. Stella, C. Menghini, S. Speranza, R. Massantini, M. Contini, A. Manzo
Acta Horticulturae, 2014
Detection of mold-damaged chestnuts by near-infrared spectroscopy
Roberto Moscetti, Danilo Monarca, Massimo Cecchini, Ron P. Haff, Marina Contini, Riccardo Massantini
Postharvest Biology and Technology, 2014
Nondestructive detection of insect infested chestnuts based on NIR spectroscopy
Roberto Moscetti, Ron P. Haff, Sirinnapa Saranwong, Danilo Monarca, Massimo Cecchini, Riccardo Massantini
Postharvest Biology and Technology, 2014
Use of non destructive techniques for different hazelnut (Corylus avellana L.) cultivar kernel identification
Industrie Alimentari, 2014
Effect of alternative postharvest control treatments on the storability of 'Golden Delicious' apples
Roberto Moscetti, Letizia Carletti, Danilo Monarca, Massimo Cecchini, Elisabetta Stella, Riccardo Massantini
Journal of the Science of Food and Agriculture, 2013
Feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels
Roberto Moscetti, Ron P. Haff, Ben Aernouts, Wouter Saeys, Danilo Monarca, Massimo Cecchini, Riccardo Massantini
Journal of Food Engineering, 2013
The treatment of fruit and vegetables with ionizing irradiation
Industrie Alimentari, 2013
Use of ozone in sanitation and storage of fresh fruits and vegetables
Journal of Food Agriculture and Environment, 2013
Near-infrared spectroscopy is feasible to discriminate hazelnut cultivars
Elisabetta Stella, Roberto Moscetti, Letizia Carletti, Giuseppina Menghini, Francesco Fabrizi, Valerio Cristofori, Danilo Monarca, Massimo Cecchini, Riccardo Massantini
Journal of Agricultural Engineering, 2013
Influence of groundcover management systems and double harvest on quality and storage of fresh hazelnuts (Corylus avellana)
R. Massantini, M. Contini, R. Moscetti, D. Monarca, M. Cecchini, G. Menghini, M.L. Mordacchini Alfani
Acta Horticulturae, 2012
Dairy production in restricted environment and safety for the workers
Industrie Alimentari, 2012
Maintaining the quality of unripe, fresh hazelnuts through storage under modified atmospheres
R. Moscetti, M.T. Frangipane, D. Monarca, M. Cecchini, R. Massantini
Postharvest Biology and Technology, 2012
Effects of controlled atmospheres and low temperature on storability of chestnuts manually and mechanically harvested
Massimo Cecchini, Marina Contini, Riccardo Massantini, Danilo Monarca, Roberto Moscetti
Postharvest Biology and Technology, 2011
1-methylcyclopropene (1-MCP) effects on fruit and vegetable storage
Industrie Alimentari, 2009
The influence of cover crops and double harvest on storage of fresh hazelnuts (Corylus Avellana L.)
Advances in Horticultural Science, 2009
Quality maintenance of Catalonian chicory (Cichorium intybus) minimally processed and 1-metylcyclopropene (1-MCP) effect on the browning of the tissues
Industrie Alimentari, 2009
Zucchini (Cucurbita pepo L.) minimally processed packed in plastic film
Industrie Alimentari, 2009

Roberto Moscetti

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