A new approach based on hyperspectral imaging for farming system authentication of seeds

• Grant number: 19/04833-3
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Effective date (Start): June 15, 2019
• Effective date (End): December 14, 2019
• Field of knowledge: Agronomical Sciences - Food Science and Technology - Food Engineering
• Principal Investigator: Douglas Fernandes Barbin
• Grantee: Luis Jam Pier Cruz Tirado
• Supervisor: Baeten Vincent
• Host Institution: Faculdade de Engenharia de Alimentos (FEA). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Research place: Centre wallon de Recherches agronomiques (CRA-W), Belgium
• Associated to the scholarship: 18/02500-4 - Food analyses using NIR spectral imaging, BP.MS

Abstract

Seeds are an exceptional source of nutrients, so their consumption favors consumer health. In fact, the health concern has caused the market to express an increase in the consumption of organic products. In South America, between the seeds with organic production and large export volumes for Europe and the United States of America, there are Quinoa (Chenopodium quinoa) and Chia (Salvia hispanica). Quinoa is considered a superfood due to its high nutritional value, since it has high quality protein gluten-free. Chia seeds are vegetable source rich in essential fatty acids, phenolic compounds, protein and dietary fiber. To identify the farming system of different foods, isotopic techniques and high-efficiency spectrometry are traditionally used to identify specific markers (e.g. fatty acid profile). Generally, these analytical techniques are expensive, time-consuming and destructive to the sample. Therefore, despite their accuracy, they turn out to be unattractive to the food industry. Hyperspectral imaging system has several advantages over traditional analytical methods (e.g. mass spectrometry or high performance liquid chromatography), such as speed, chemical free, low cost of use and possible application for online processes for quality control and food authentication. Hyperspectral imaging provides spatial and spectral information, which is subject to multivariate analysis, to generate mathematical models capable of discriminating between foods based on their differences in chemical composition. For these reasons, this project aims to develop mathematical models based on information obtained from hyperspectral imaging to identify the farming system of Quinoa and Chia seeds. Thus, a new non-invasive and non-destructive authentication method for these seeds will be created and validated, which will undoubtedly favor the traceability of these products. (AU)

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)

CRUZ-TIRADO, J. P.; FERNANDEZ PIERNA, JUAN ANTONIO; ROGEZ, HERVE; FERNANDES BARBIN, DOUGLAS; BAETEN, VINCENT. Authentication of cocoa (Theobroma cacao) bean hybrids by NIR-hyperspectral imaging and chemometrics. FOOD CONTROL, v. 118, DEC 2020. (19/04833-3, 18/02500-4, 15/24351-2)

CRUZ-TIRADO, J. P.; DA SILVA MEDEIROS, MARIA LUCIMAR; BARBIN, DOUGLAS FERNANDES. On-line monitoring of egg freshness using a portable NIR spectrometer in tandem with machine learning. Journal of Food Engineering, v. 306, OCT 2021. (19/06846-5, 15/24351-2, 18/02500-4, 19/04833-3)

CRUZ-TIRADO, J. P.; OLIVEIRA, MARCIANO; DE JESUS FILHO, MILTON; GODOY, HELENA TEIXEIRA; MANUEL AMIGO, JOSE; BARBIN, DOUGLAS FERNANDES. Shelf life estimation and kinetic degradation modeling of chia seeds (Salvia hispanica) using principal component analysis based on NIR-hyperspectral imaging. FOOD CONTROL, v. 123, MAY 2021. (19/04833-3)

PERES, LOUISE M.; BARBON JUNIOR, SYLVIO; LOPES, JESSICA F.; FUZYI, ESTEFANIA M.; BARBON, ANA P. A. C.; ARMANGUE, JOEL G.; BRIDI, ANA M.. Meta-recommendation of pork technological quality standards. BIOSYSTEMS ENGINEERING, v. 210, p. 13-19, OCT 2021. (15/24351-2, 18/02500-4, 19/04833-3)