Research Grants 15/24351-2 - Agronomical Sciences, Food Science and Technology - BV FAPESP
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Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products

Grant number: 15/24351-2
Support type:Research Grants - Young Investigators Grants
Duration: March 01, 2017 - February 28, 2021
Field of knowledge:Agronomical Sciences - Food Science and Technology
Principal Investigator:Douglas Fernandes Barbin
Grantee:Douglas Fernandes Barbin
Home Institution: Faculdade de Engenharia de Alimentos (FEA). Universidade Estadual de Campinas (UNICAMP). Campinas, SP, Brazil
Associated scholarship(s):17/17628-3 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products, BP.DD
18/02500-4 - Food analyses using NIR spectral imaging, BP.MS


In recent years, the concept of process analytical technology (PAT) in food quality control has been the focus of attention from researchers and producers. Quality control in the food industry is traditionally accomplished through chemical, physical and microbiological test. Some of these methods are slow, laborious and destructive. Nowadays it is possible to replace most of the drawbacks of these methods through indirect fast and non-destuctive techniques. Image analyses and NIR spectroscopy technique are fast methods with a wide range of applications due to its speed, simplicity and safety as well as its ability to measure multiple parameters simultaneously, avoiding lengthy sample preparation procedures. In addition, the combination of these techniques as in hyperspectral systems is currently the state of the art in this field. These techniques offer a number of advantages over traditional methods of quality assessment, including ease of adaptation for online systems and the possibility of simultaneous determination of several attributes. However, there are few studies on the subject in emerging countries. This project aims to develop the technology for image analyses and NIR spectroscopy in the classification and evaluation of physical, chemical and sensory characteristics of food products. The project aims to measure physical and chemical attributes (pH, color, chemical composition, etc) of fresh poultry meat, and processed poultry products; and rapid classification and authentication of samples and prediction of the attributes measured by imaging and spectral information of samples. (AU)

Articles published in Agência FAPESP about the research grant
Algorithms and digital images are used to find out whether papayas are ripe 

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BARBIN, DOUGLAS FERNANDES; MACIEL, LEONARDO FONSECA; VIDIGAL BAZONI, CARLOS HENRIQUE; RIBEIRO, MARGARETH DA SILVA; SALES CARVALHO, ROSEMARY DUARTE; BISPO, ELIETE DA SILVA; SPINOLA MIRANDA, MARIA DA PUREZA; HIROOKA, ELISA YOKO. Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE, v. 55, n. 7, p. 2457-2466, JUL 2018. Web of Science Citations: 0.
PERES, LOUISE MANHA; BARBON, JR., SYLVIO; FUZYI, ESTELANIA MAYUMI; BARBON, ANA PAULA A. C.; BARBIN, DOUGLAS FERNANDES; MAEDA SAITO, PRISCILA TIEMI; ANDREO, NAYARA; BRIDI, ANA MARIA. Fuzzy approach for classification of pork into quality grades: coping with unclassifiable samples. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 150, p. 455-464, JUL 2018. Web of Science Citations: 0.
SANTANA, EVERTON J.; GERONIMO, BRUNA C.; MASTELINI, SAULO M.; CARVALHO, RAFAEL H.; BARBIN, DOUGLAS F.; IDA, ELZA I.; BARBON, JR., SYLVIO. Predicting poultry meat characteristics using an enhanced multi-target regression method. BIOSYSTEMS ENGINEERING, v. 171, p. 193-204, JUL 2018. Web of Science Citations: 0.
SANTOS PEREIRA, LUIZ FERNANDO; BARBON, JR., SYLVIO; VALOUS, NEKTARIOS A.; BARBIN, DOUGLAS FERNANDES. Predicting the ripening Of papaya fruit with digital imaging and random forests. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 145, p. 76-82, FEB 2018. Web of Science Citations: 1.

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