Papers

2024

1. Fuentes, S., Ortega Farias, S., Carrasco-Benavides, M., Tongson, E., and Gonzalez Viejo, C. 2024. Actual evapotranspiration and Energy Balance Estimation from Vineyards using micro-meteorological data and machine learning modeling. Agricultural Water Management.
2. Wang, W., Taylor, A., Tongson, E., Edwards J., Vaghefi, N., Ades, P.K., Crous, P.W., Taylor, P. 2024. Identification and pathogenicity of Colletotrichum species associated with twig dieback of citrus in Western Australia. Plant Pathology.
3. Ramos-Parra, P.A., De Anda-Lobo, I.C., Gonzalez Viejo, C., Villarreal-Lara R., Clorio-Carrillo, J.A., Marin-Obispo, L.M., Obispo-Fortunato, D.J., Escobedo-Avellaneda, Z., Fuentes, S., Pere-Carrillo, E. and Hernande-Brenes, C. 2024. Consumer Insights into the At-Home Liking of Commercial Beers: Integrating Nonvolatile and Volatile Flavor Chemometrics. Food Science & Nutrition. 
4. Shi, L., Liu, Z., Gonzalez Viejo, C., Ahmadi, F., Dunshea, F.R., and Suleria, H. 2024. Comparison of phenolic composition in Australian-grown date fruit (Phoenix dactylifera L.) seeds from different varieties and ripening stages. Food Research International. p.114096.
5. Carrasco-Benavides, M.,  Espinoz, S., Umemura, K., Ortega-Farias, S., Baffico-Hernande, A., Neira Roman, J., Avila-Sanchez, C., and Fuentes, S. Evaluation of Thermal-Based Physiological Indicators for Determining Water Stress Thresholds in Drip- Irrigated 'Regina' Cherry Trees. Irrigation Science.
6. Tavan, M., Wee, B., Fuentes, S., Pang, A., Brodie, G., Gonzalez Viejo, C., and Gupta, D. 2024. Biofortification of kale microgreens with selenate-selenium using two delivery methods: Selenium-rich soilless medium and foliar application. Scientia Horticulturae, 323, 112522
7. Gonzalez Viejo, C., Harris, N., and Fuentes, S. 2024. Assessment of Changes in Sensory Perception, Biometrics and Emotional Response for Space Exploration by Simulating Microgravity Positions. Food Research International, 175, p.113827.

2023

1. Wu, H, Gonzalez Viejo, C., Fuentes, S., Dunshea, F.R, and Suleria H. 2023. Evaluation of spontaneous fermentation impact on the physicochemical properties and sensory profile of green and roasted arabica coffee by digital technologies. Food Research International, 176 (2024): 113800. 
2. Harris, N., Gonzalez Viejo, C., Barnes, C., Pang, A., and Fuentes, S. 2023. Wine Quality Assessment for Shiraz Vertical Vintages based on Digital Technologies and Machine Learning Modeling. Food Bioscience, p.103354
3. Biju, S., Fuentes, S., and Gupta, D. 2023. Novel insights into the mechanism(s) of silicon-induced drought stress tolerance in lentil plants revealed by RNA sequencing analysis. BMC Plant Biology, 23(1), p.498.
4. Gonzalez Viejo, C., Torrico, D.D. and Fuentes, S. 2023. Novel Contactless Sensors for Food, Beverage and Packaging Evaluation. Editorial. Sensors, 23(19), p.8082
5. Fuentes, S., Tongson, E., Gonzalez Viejo, C. 2023. New Developments and Opportunities for AI in Viticulture, Pomology and Soft Fruit Research: A Mini-Review and Invitation to Contribution Articles. Frontiers Horticulture, 2, p.1282615.
6. Ashfaq, W., Brodie, G., Fuentes, S., Pang, A., Gupta. D.  2023. Silicon improves root system and canopy physiology in wheat under drought stress. Plant and Soil. p. 1-18.
7. Fuentes, S., Harris, N., Tongson, E., Hernandez-Brenes, C., Valiente-Banuet, J., and Gonzalez Viejo, C. 2023. Non-Invasive Wine Authentication Method using Near-Infrared Spectroscopy through the Bottle. Acta Horticulturae. 
8. Fuentes, S., Tongson, E., Dutton, J., Mattioli, F., Hernandez-Brenes, C., Valiente-Banuet, J., Villarrea-Lara, R., De Anda-Lobo, I., and Gonzalez Viejo, C. 2023. Perception and Acceptability of Artificial Intelligence Applications in Viticulutre and Wine by Consumers from Mexico and Australia. Acta Horticulturae. 
9. Aznan, A., Gonzalez Viejo, C., Pang, A. and Fuentes, S. 2023. Review of Technology Advances to Assess Rice Quality Traits and Consumer Perception. J. Food Research International, 172, 113015. 
10. Mora-Poblete, F., Heidari, P., and Fuentes, S. 2023. Editorial: Integrating Advanced High-throughput Technologies to Improve Plant Resilience to Environmental Challenges. Frontiers in Plant Science, 14, p.121869.
11. Biju, S., Fuentes, S., and Gupta, D. 2023. Regulatory role of silicon on photosynthesis, gas-exchange and yield related traits of drought-stressed lentil under controlled and field conditions. Silicon. p.1-16
12. Cao, X., Wu, H., Gonzalez Viejo, C., and Suleria, H. 2023. Effects of Postharvest Processing on Aroma Formation in Roasted Coffee - A Review. International Journal of Food Science & Technology. 58(3) 1007-1027.
13. Gonzalez Viejo, C., Hernandez-Brenes, C., Villarreal-Lara, R., De Anda-Lobo, I., Ramos-Parra, P.A., Perez-Carrillo, E., Clorio-Carrillo, J.A., Tongson, E., and Fuentes, S. 2023. Effects of Different Beer Compounds on Biometrically Assessed Emotional Responses in Consumers. Fermentation, 9(3), 269.
14. Wu, H., Gonzalez Viejo, C., Fuentes, S., Dunshea, F.R., and Suleria, H.A.R. 2023. The impact of wet fermentation on coffee quality traits and volatile compounds using digital technologies.  Fermentation. 9(1), p.68.
15. Harris, N., Gonzalez Viejo, C., Barnes, C., and Fuentes, S. 2023. Non-invasive digital technologies to assess wine quality traits and provenance through the bottle. Fermentation, 9(1). p.10
16. Shin, M-Y., Gonzalez Viejo, C., Tongson, E., Wieche, T., Taylor, P.W.J., and Fuentes, S. 2023. Early detection of Verticillium wilt of potatoes using near-infrared spectroscopy and machine learning modeling. Computers and Electronics in Agriculture, 204. p.107567

2022

1. Cao, X., Wu, H., Gonzalez Viejo, C., and Suleria, H. 2022. Effects of Postharvest Processing on Aroma Formation in Roasted Coffee - A Review. International Journal of Food Science and Technology.
2. Aznan, A., Gonzalez Viejo, C., Pang, A., and Fuentes, S. Rapid Detection of Rice Adulteration using a Low-Cost  Electronic Nose and Machine Learning Modelling. Engineering Proceedings, 27(1), 1.
3. Ashfaq, W., Brodie, G., Fuentes, S., and Gupta, D. 2022. Infrared thermal imaging and morpho-physiological indices used for wheat genotypes screening under drought and heat stress. Plants. 11(23), p.3269.
4. Aznan, A., Gonzalez Viejo, C., Pang, A., and Fuentes, S. Rapid Detection of Rice Adulteration using Low-Cost Digital Sensing Devices and Machine Learning. Sensors, 22(22) 8655.
5. Feng, H., Gonzalez Viejo, C., Vaghefi, N., Taylor, P.W.J., Tongson, E., and Fuentes, S. 2022. Early detection of Fusarium oxysporum infection of processing tomatoes (Solanum lycopersicum) and pathogen-soil interac-tions using a low-cost portable electronic nose and machine learning modeling. Sensors, 22
6. Ho Dac, H., Gonzalez Viejo, C., Lipovetzky, N., Tongson, E., Dunshea, F.R., and Fuentes, S. 2022. Livestock Identification using Deep Learning for Traceability. Sensors, 22(21), 8256.
7. Gonzalez Viejo, C., Harris, N., and Fuentes, S. 2022. Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling. Fermentation, 8(10), p.516.  Selected as Journal Issue Cover
8. Fuentes, S., and Chang, J. 2022. Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture. Sensors. 22(20), 7898.
9. Fuentes, S., Gonzalez Viejo, C., Tongson, E., Dunshea, F.R., Ho Dac, H., and Lipovetzki, N. 2022. Animal Biometric Assessment Using Non-Invasive Computer Vision and Machine Learning are Good Predictors of Dairy Cows Age and Welfare: The Future of Automated Veterinary Support Systems. Journal of Agriculture and Food Research, 100388
10. Mehta, A., Serventi, L., Kumar, L., Gonzalez Viejo, C., Fuentes, S., and Torrico, D.D. 2022. Influence of expectations and emotions raised by packaging characteristics on orange juice acceptability and choice. Food Packaging and Shelf Life, 33, p.100926.
11. Carrasco-Benavides, M., Gonzalez Viejo, C., Tongson, E., Baffico-Hernandez, A., Avila-Sanchez, C., Mora, M., and Fuentes S. 2022.  Water status estimation of cherry trees using infrared thermal imagery coupled with supervised machine learning modeling. Computers and Electronics in Agriculture. 200, 107256
12. Asfaq, W., Gupta, D., Fuentes, S., and Brodie, G. 2022. Silicon’s role in regulating physiological and biochemical mechanisms of contrasting bread wheat cultivars under terminal drought and heat stress environments. Frontiers in Plant Science. p.2616.
13. Lopez-Olivari, R.A., Fuentes, S., Poblete-Echeverria, C., Quintulen, V., and Medin, L. 2022. Site-specific evaluation of canopy resistance models for estimating evapotranspiration over a drip-irrigated potato crop in southern Chile under water-limited conditions. Water. 14(13), p.2041
14. Rasekh, M., Karami, H., Fuentes, S., Kaveh, M., Rusinek, R., and Gankarz, M. 2022. Preliminary study on Non-Destructive Sorting Techniques for Pepper (Capsicum annuum L.) Using Odor Parameter. LWT. p.113667
15. Gonzalez Viejo, C., Tongson, E., and Fuentes, S. 2022. Novel Method to Conduct Remote Sensory Sessions and Biometrics During Isolation. Biology and Life Sciences Forum, 6(1) 88.
16. Fuentes S., Gonzalez Viejo C., Tongson E., Dunshea F.R. 2022. The Livestock Farming Digital Transformation: Implementation of new and emerging technologies using artificial intelligence. Animal Health Research Reviews, 1-13. doi:10.1017/S1466252321000177
17. Gonzalez Viejo, C., Fuentes, S., De Anda-Lobo, I.C., and Hernandez-Brenes, C. 2022. Remote sensory assessment of beer quality based on visual perception of foamability and biometrics compared to standard emotional responses from affective images. Food Research International. 156. 111341.
18. Gonzalez Viejo, C., and Fuentes, S. 2022. Digital detection of olive oil rancidity levels and aroma profiles using near-infrared spectroscopy, a low-cost electronic nose and machine learning modelling. Chemosensors. 10(5), 159.
19. Aznan, A., Gonzalez Viejo, C., Pang, A., and Fuentes, S. Rapid Assessment of Rice Quality Traits using a Low-Cost Digital Technologies. Foods. 11(9), 1181.
20. Fuentes, S. 2022. Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment. Foods. 11(9), 1192.
21. Gupta, M., Gonzalez Viejo, C., Fuentes, S., Torrico, D.D., Saturno, P.C., Gras S.L., Dunshea, F.R., and Cottrell, J.J. 2022. Digital technologies to assess yoghurt quality traits and consumers acceptability. J. Science of Food and Agriculture. https://doi.org/10.1002/jsfa.11911
22. Fuentes, S., Summerson, V., Tongson, E., and Gonzalez Viejo, C. 2022. Novel Digital Technologies to Assess Smoke Taint in Wine Using Non-Invasive Chemical Fingerprinting, a Low-Cost Electronic Nose, and Artificial Intelligence. Biology and Life Sciences Forum. 6(1), 56.
23. Gonzalez Viejo, C, Fuentes, S. 2022. Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling. Biology and Life Sciences Forum. 6(1), 46
24. Gonzalez Viejo C., and Fuentes S. 2022. Digital Assessment and Classification of Wine Faults Using a Low-cost Electronic nose, near-infrared spectroscopy and Machine Learning Modeling. Sensors, 22(6) 2303.
25. Gonzalez Viejo C., and Fuentes S. 2022. Editorial: Special Issue “Implementation of Digital Technologies on Beverage Fermentation”. Fermentation. 8(3) 127.
26. Liu C., Sharma C., Xu Q., Gonzalez Viejo C., Fuentes S., and Torrico D. 2022. Influence of Label Design and Country of Origin Information in Wines on Consumers’ Visual, Sensory, and Emotional Responses. Sensors. 22(6) 2158 https://doi.org/10.3390/s22062158
27. Carrasco-Benavides M., Ortega-Farias S., Gil P.M., Knopp D., Morales-Salinas L., Lagos O., De La Fuente D., Lopez-Olivari R., Fuentes S. 2022. Assessment of vineyard water footprint by using ancillary data and EEFlux satellite images. Examples in the Chilean central zone. Science of The Total Environment. 811, p.152452. doi.org/10.1016/j.scitotenv.2021.152452 
28. Summerson, V., Gonzalez Viejo C., and Fuentes, S. 2022. Non-destructive methods for assessing smoke-derived compounds. IVES Technical Reviews Vine and Wine. March 2022. https://doi.org/10.20870/IVES-TR.2022.5407

2021

1. Fuentes S., Gonzalez Viejo C., Torrico D.D., Dunshea F.R. 2021. Digital integration and automated assessment of eye-tracking and emotional response data using the BioSensory App to maximise packaging label analysis. Sensors. 21(22), p.7641. doi.org/10.3390/s21227641
2. Fuentes S., Gonzalez Viejo C., Summerson V., Hall C., Tang Y., Tongson E. 2021. Berry cell vitality assessment and the effect on wine sensory traits based on chemical fingerprinting, canopy architecture and machine learning modeling. Sensors. 21(21), p.7312. doi.org/10.3390/s21217312
3. Fuentes S., Gonzalez Viejo C., Tongson E., Lipovetzky N., Dunshea F.R. 2021. Biometric and physiological responses from dairy cows measured by visible remote sensing are good predictors of milk productivity and quality through artificial intelligence. Sensors. 21(20), p.6844. doi.org/10.3390/s21206844
4. Aznan, A., Gonzalez Viejo, C., Pang, A., & Fuentes, S. 2021. Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies. Sensors, 21(19), 6354. doi.org/10.3390/s21196354
5. Fuentes, S., & Tongson, E. J. 2021. Editorial: Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems”. Sensors, 21, 6383. doi.org/10.3390/s21196383
6. Fuentes, S., Tongson, E., Unnithan, R. R., & Gonzalez Viejo, C. 2021. Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling. Sensors, 21(17), 5948. doi.org/10.3390/s21175948
7. Fuentes S., Tongson E. 2021. Implementation of sensors and artificial intelligence for environmental hazards assessment in urban, agriculture and forestry systems. Sensors. 21, 6383. doi.org/10.3390/s21196383
8. Summerson V., Gonzalez Viejo C., Pang A., Torrico D.D., Fuentes S. 2021. Assessment of volatile aromatic compounds in smoke tainted Cabernet Sauvignon wines using a low-cost e-nose and machine learning modelling. Molecules. 26: 5108. doi.org/10.3390/molecules26165108
9. Kerr E.D., Caboche C.H., Pegg C., Phung T., Gonzalez Viejo C., Fuentes S., Howes M.T., Howell K., Schultz B.L. 2021. The post-transitional modification landscape of commercial beers. Scientific Reports. 11, 15890. doi.org/10.1038/s41598-021-95036-0. Top 100 cell and molecular biology Scientific Reports papers in 2021.
10. Gonzalez Viejo C., Fuentes S., Hernandez C. 2021. Smart detection of faults in beers using near-infrared spectroscopy, a low-cost electronic nose and artificial intelligence. Fermentation. 7, 117. doi.org/10.3390/fermentation7030117 Recognition as Outstanding Publication and Editors Choice Article.
11. Summerson V., Gonzaes Viejo C., Torrico D.D., Pang A., Fuentes S. 2021. Digital smoke taint detection in Pinot Grigio using an e-nose and machine learning algorithms following treatment with Activated carbon and cleaving enzyme. Fermentation. 7(3), p.119. doi.org/10.3390/fermentation7030119 Recognition as Outstanding Publication and Editors Choice Article.
12. Park. S., Ryu D., Fuentes S., Chung H., O’Connell M., Kim J. Dependence of CWSIBased Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard. 2021.  Remote Sensing. 13, 2775. doi.org/10.3390/rs13142775
13. Jorquera-Chavez, M., Fuentes, S., Dunshea, F. R., Warner, R. D., Poblete, T., Unnithan, R. R., ... & Jongman, E. C. 2021. Using imagery and computer vision as remote monitoring methods for early detection of respiratory disease in pigs. Computers and Electronics in Agriculture, 187, 106283. doi.org/10.1016/j.compag.2021.106283
14. Cameron W., Petrie P.R., Barlow E.W., Howell K., Patrick C.J. Fuentes S. 2021. A comparison of the effect of temperature on grapevine phenology between vineyard groups. Oeno One. (Accepted – In press). doi.org/10.20870/oeno-one.2021.55.2.4599
15. Biju S., Fuentes S., Gupta D. 2021. Silicon modulates nitro-oxidative homeostasis along with the antioxidant metabolism to promote drought stress tolerance in lentil plants. Physiologia Plantarum. (Accepted – In production). doi.org/10.1111/ppl.13437
16. Fuentes S., Tongson E., Gonzalez Viejo C. 2021. Novel digital technologies implemented in sensory science and consumer perception. Current Opinion in Food Science. 41: 99-106. doi.org/10.1016/j.cofs.2021.03.014
17. Park S., Ryu D., Fuentes S., Chung H., O’Connell M. and Kim J. 2021. Mapping very-high-resolution evapotranspiration from an unmanned aerial vehicle (UAV) imagery. International Journal of Geo-Information. 10(4), 211.. doi.org/10.3390/ijgi10040211
18. Gonzalez Viejo C., Tongson E., Fuentes S. 2021.  Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity. Sensors. 21(6), 2016; doi.org/10.3390/s21062016. Recognition as Outstanding Publication and Editors Choice Article.
19. Gonzalez Viejo, C., Zhang, H., Khamly, A., Xing, Y., & Fuentes, S. 2021. Coffee Label Assessment Using Sensory and Biometric Analysis of Self-Isolating Panelists through Videoconference. Beverages, 7(1), 5. doi.org/10.3390/beverages7010005 Recognition as Outstanding Publication and Editors Choice Article.
20. Cameron, W., Petrie, P. R., Barlow, E. W. R., Patrick, C. J., Howell, K., & Fuentes, S. 2021. Is advancement of grapevine maturity explained by an increase in the rate of ripening or advancement of veraison? Australian Journal of Grape and Wine Research. doi.org/10.1111/ajgw.12488
21. Summerson, V., Gonzalez Viejo, C., Pang, A., Torrico, D. D., & Fuentes, S. 2021. Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine. Beverages, 7(1), 7. doi.org/10.3390/beverages7010007 Recognition as Outstanding Publication and Editors Choice Article.
22. Tavan, M., Wee, B., Brodie, G., Fuentes, S., Pang, A., & Gupta, D. 2021. Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens. Frontiers Sustainability for Food Systems. 4: 622720. doi.org/10.3389/fsufs.2020.622720
23. Fuentes, S., Tongson, E., & Gonzalez Viejo, C. 2021. Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle. Sensors, 21(1), 295. doi.org/10.3390/s21010295
24. Torrico D.D., Sharma C., Dong W., Fuentes S., Gonzalez Viejo C., and Dunshea F.R. 2021. Virtual reality environments on the sensory acceptability and emotional responses of no-and full-sugar chocolate."  Food Science and Technology Journal. LWT 137: 110383. doi.org/10.1016/j.lwt.2020.110383

2019

1. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. Bubbles, Foam Formation, Stability and Consumer Perception of Carbonated Drinks: A Review of Current, New and Emerging Technologies for Rapid Assessment and Control. Foods. 8(12)., doi.org/10.3390/foods8120596
2. De Bei, R., Fuentes, S., & Collins, C. 2019. Vineyard variability: can we assess it using smart technologies? Original language of the article: English. IVES Technical Reviews, vine and wine. doi.org/10.20870/IVES-TR.2019.2544
3. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. Emerging technologies based on artificial intelligence to assess quality and consumer preference of beverages. Beverages. 5(4)., doi.org/10.3390/beverages5040062
4. Gunaratne T.M., Gonzalez Viejo C., Gunaratne N.M., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling. Foods. 8(10)., doi.org/10.3390/foods8100426
5. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. The Effect of Sonication on Bubble Size and Sensory Perception of Carbonated Water to Improve Quality and Consumer Acceptability. Beverages. 5(3)., doi.org/10.3390/beverages5030058
6. Condé B., Robinson A., Bodet A., Monteau A-C., Fuentes S., Scollary G, Smith T., Howell K.. 2019. Using Synchronous Fluorescence to Investigate Chemical Interactions Influencing Foam Characteristics in Sparkling Wines. Beverages. 5(3)., doi.org/10.3390/beverages5030054
7. Fuentes S., Tongson E.J., De Bei R., Gonzalez Viejo C., Ristic R., Tyerman S.D., Wilkinson K.L. 2019. Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach. Sensors. 19(15)., doi.org/10.3390/s19153335
8. Gunaratne N.M., Fuentes S., Gunaratne T.M., Torrico D.D., Ashman H., Francis C., Gonzalez Viejo C., Dunshea F.R. 2019. Consumers acceptability, eye fixations, and physiological responses: A study using eye tracking devices on novel and familiar chocolate packaging designs. Foods. doi.org/10.3390/foods8070253
9. Torrico D.D., Tam J., Fuentes S., Gonzalez Viejo C., Dunshea F.R. 2019. D-tagatose as sucrose substitute on the physico-chemical properties and acceptability of strawberry-flavored yogurt. Foods. 8(7)., doi.org/10.3390/foods8070256
10. Fuentes S., Chacon G., Torrico D.D., Zarate A., Gonzalez Viejo C. 2019. Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application. Sensors. 19(14)., doi.org/10.3390/s19143054
11. Gunaratne T.M., Fuentes S., Gunaratne N.M, Torrico D.D., Gonzalez Viejo C., Dunshea F.R. 2019. Physiological Responses to Basic Tastes for Sensory Evaluation of Chocolate Using Biometric Techniques. Foods. 8(7):243., doi.org/10.3390/foods8070243
12. Gunaratne N.M., Gonzalez Viejo C., Gunaratne T.M., Torrico D.D., Ashman H., Dunshea F.R., Fuentes S.. 2019. Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses. J-Multidisciplinary Scientific Journal. doi.org/10.3390/j2020015
13. De Bei R., Wang X., Papagiannis L., Cocco M., O’Brien P., Zito M., Ouyang J. Fuentes S., Gilliham M., Tyerman S., Collins C. 2019. Postveraison leaf removal does not consistently delay ripening in Sémillon and Shiraz in a hot Australian climate. American Journal of Enology and Viticulture 70, no. 4: 398-410. doi.org/10.5344/ajev.2019.18103
14. Jorquera-Chavez M., Fuentes S., Dunshea F.R, Jongman E., Warner R.D.. 2019. Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review. Meat Science. 156: 11-22. doi.org/10.1016/j.meatsci.2019.05.007
15. Gunaratne N.M., Fuentes S., Gunaratne T.M., Torrico D.D., Francis C., Ashman H., Gonzalez Viejo C., Dunshea F.R. 2019. Effects of packaging design on sensory liking and willingness to purchase: A study using novel chocolate packaging. Heliyon, doi.org/10.1016/j.heliyon.2019.e01696
16. Gonzalez Viejo C., Fuentes S., Torrico D.D., Godbole A., Dunshea F.R. 2019. Chemical characterization of aromas in beer and their effect on consumers liking. Food Chemistry. 293: 479-485. doi.org/10.1016/j.foodchem.2019.04.114
17. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S. 2019. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an Artificial Intelligence system. Beverages. 5(2): 33. doi.org/10.3390/beverages5020033
18. Wang X., De Bei R., Fuentes S., Collins C. 2019. Influence of Canopy Management Practices on Reproductive Performance of Semillon and Shiraz Grapevines in a Hot Climate. American Journal of Enology and Viticulture. 70(4): 360-372. doi.org/10.5344/ajev.2019.19007
19. *Tongson E.J., S. Fuentes, M. Carrasco-Benavides, M. Mora. 2019. Canopy architecture assessment of cherry trees by cover photography based on variable light extinction coefficient modelled using artificial neural networks. Acta Horticulturae doi.org/10.17660/ActaHortic.2019.1235.24
20. Jinru X, Fuentes S., Poblete-Echeverría C., Gonzalez Viejo C., Tongson E., Su B. 2019. Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible / near infrared spectroscopy. International Journal of Agricultural and Biological Engineering. 12(2), 123-131. doi.org/10.25165/j.ijabe.20191202.4637

2017

1. Torrico D.D., Fuentes S., Gonzalez Viejo C., Ashman H., Gunaratne N.M., Gunaratne T.M., Dunshea F.R. 2017. Images and chocolate stimuli affect physiological and affective responses of consumers: A cross-cultural study. Food Quality and Preference doi.org/10.1016/j.foodqual.2017.11.010
2. Torrico D.D., Fuentes S., Gonzalez Viejo C., Ashman H., Gurr P.A., Dunshea F.R. 2107. Analysis of thermochromic label elements and colour transitions using sensory acceptability and eye tracking techniques. LWT- Food Science and Technology. doi.org/10.1016/j.lwt.2021.111142
3. Condé B., Bouchard E., Culbert J.A., Wilkinson K.L., Fuentes S., Howell K. 2017. Soluble Protein and Amino Acid Content Affects the Foam Quality of Sparkling Wine. Journal of Agricultural and Food Chemistry 09/2017; 65(41)., doi.org/10.1021/acs.jafc.7b02675
4. Biju S., Fuentes S., Gupta D. 2017. Silicon improves seed germination and alleviates drought stress in lentil crops by regulating osmolytes, hydrolytic enzymes and antioxidant defense system. Plant Physiology and Biochemistry 09/2017; 119., doi.org/10.1016/j.plaphy.2017.09.001
5. Park S., Ryu D., Fuentes S., Chung H., Hernández-Montes E., O’Connell M. 2017. Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sensing; 9(828)., doi.org/10.3390/rs9080828
6. Zhang P., Wu X., Needs S., Liu D., Fuentes S., Howell K. 2017. The influence of apical and basal defoliation on the canopy structure and biochemical composition of Vitis vinifera cv. Shiraz grapes and wine. Frontiers in Chemistry; doi.org/10.3389/fchem.2017.00048
7. Gonzalez Viejo C., Fuentes S., Torrico D.D., Howell K., Dunshea F.R. 2017. Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and artificial neural networks modelling techniques. Journal of the Science of Food and Agriculture; doi.org/10.1002/jsfa.8506.
8. De Bei, R., Fuentes, S., Sullivan, W., Edwards, E., Tyerman, S., & Cozzolino, D. 2017. Rapid measurement of total non-structural carbohydrate concentration in grapevine trunk and leaf tissues using near infrared spectroscopy. Computers and Electronics in Agriculture, 136, 176-183. doi.org/10.1016/j.compag.2017.03.007
9. Condé, B. C., Fuentes, S., Caron, M., Xiao, D., Collmann, R., & Howell, K. S. 2017. Development of a robotic and computer vision method to assess foam quality in sparkling wines. Food Control, 71, 383-392. doi.org/10.1016/j.foodcont.2016.07.020

2015

1. *Conde, B.,  Peixoto, A. B., Howell, K., Xiao, Di, Fuentes, S. 2015. Assessment by image analysis of foamability and effervescence of sparkling wines during the prise de mousse and ageing process. Rev. Bras. Vitic. & Oenol. 7, 92-98.
2. Zhang, P., Howell, K., Krstic, M., Herderich, M., Barlow, E. W. R., & Fuentes, S. 2015. Environmental factors and seasonality affect the concentration of rotundone in Vitis vinifera L. cv. Shiraz wine. PloS one, 10(7), e0133137. doi.org/10.1371/journal.pone.0133137
3. *Park, S., Nolan, A., Ryu, D., Fuentes, S., Hernandez, E., Chung, H., & O’Connell, M. 2015. Estimation of crop water stress in a nectarine orchard using high-resolution imagery from unmanned aerial vehicle (UAV). Paper presented at the MODSIM2015, 21st International Congress on Modelling and Simulation.
4. *Nolan, A., Park, S., Fuentes, S., Ryu, D., & Chung, H. 2015. Automated detection and segmentation of vine rows using high resolution UAS imagery in a commercial vineyard. Paper presented at the MODSIM2015, 21st International Congress on Modelling and Simulation.
5. Zhang, P., Barlow, S., Krstic, M., Herderich, M., Fuentes, S., & Howell, K. 2015. Within-vineyard, within-vine, and within-bunch variability of the rotundone concentration in berries of Vitis vinifera L. cv. Shiraz. Journal of Agricultural and Food Chemistry, 63(17), 4276-4283. doi.org/10.1021/acs.jafc.5b00590
6. Poblete-Echeverría, C., Fuentes, S., Ortega-Farias, S., Gonzalez-Talice, J., & Yuri, J. A. 2015. Digital cover photography for estimating leaf area index (LAI) in apple trees using a variable light extinction coefficient. Sensors, 15(2), 2860-2872. doi.org/10.3390/s150202860
7. Medrano, H., Tomás, M., Martorell, S., Escalona, J.-M., Pou, A., Fuentes, S., Bota, J. 2015. Improving water use efficiency of vineyards in semi-arid regions. A review. Agronomy for Sustainable Development, 35(2), 499-517. doi.org/10.1007/s13593-014-0280-z

2013

1. Nguyen, T.-T., Fuentes, S., & Marschner, P. 2013. Effect of incorporated or mulched compost on leaf nutrient concentrations and performance of Vitis vinifera cv. Merlot. Journal of Soil Science and Plant Nutrition, 13(2), 485-497. doi.org/10.4067/S0718-95162013005000038
2. Bonada, M., Sadras, V., Moran, M., & Fuentes, S. 2013. Elevated temperature and water stress accelerate mesocarp cell death and shrivelling, and decouple sensory traits in Shiraz berries. Irrigation Science, 31(6), 1317-1331. doi.org/10.1007/s00271-013-0407-z
3. Fuentes, S., Mahadevan, M., Bonada, M., Skewes, M., & Cox, J. 2013. Night-time sap flow is parabolically linked to midday water potential for field-grown almond trees. Irrigation Science, 31(6), 1265-1276. doi.org/10.1007/s00271-013-0403-3
4. Bonada, M., Sadras, V. O., & Fuentes, S. 2013. Effect of elevated temperature on the onset and rate of mesocarp cell death in berries of Shiraz and Chardonnay and its relationship with berry shrivel. Australian Journal of Grape and Wine Research, 19(1), 87-94 doi.org/10.1111/ajgw.12010
5. Escalona, J. M., Fuentes, S., Tomás, M., Martorell, S., Flexas, J., & Medrano, H. 2013. Responses of leaf night transpiration to drought stress in Vitis vinifera L. Agricultural Water Management, 118, 50-58. doi.org/10.1016/j.agwat.2012.11.018
6. Conn, S.J., Hocking, B., Dayod, M., Xu, B., Athman, A., Henderson, S., Aukett, L., Conn, V., Shearer, M.K., Fuentes, S. and Tyerman, S.D., 2013. Protocol: optimising hydroponic growth systems for nutritional and physiological analysis of Arabidopsis thaliana and other plants. Plant Methods, 9(1), pp.1-11. doi.org/10.1186/1746-4811-9-4