Autor(es)

Brasil, Yasmin Lima ^[1] ; Cruz-Tirado, J. P. ^[1] ; Barbin, Douglas Fernandes ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Food Engn, Rua Monteiro Lobato, 80, Cidade Univ, BR-13083862 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: FOOD CONTROL; v. 131, JAN 2022.
• Citações Web of Science: 3

Resumo

Quail eggs are one of the main natural sources of essential nutrients, presenting high amounts of protein, antioxidants, calcium, iron and phosphorus. However, its quality assessment demands laborious methods and chemicals, and there is currently no standard method do quantify its freshness. This work aimed to investigate the performance of a portable NIR spectrometer, in combination with machine learning, to estimate the freshness of quail eggs. Since there is no standard index to classify quail eggs, we compared Haugh Unit (HU), Yolk Index (YI) and the Egg Quality Index (EQI) as reference methods. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) were used to build prediction models, and Partial Least Squares-Discriminant Analysis (PLSDA) and Support Vector Machine Classification (SVMC) for the development of classification models. For the first time, we demonstrated that EQI, which is a parameter that measures egg freshness according to the quality of the yolk and the albumen, is the best way to express the freshness of quail eggs. The best prediction models were obtained for YI and EQI, using SVMR, with RPD = 2.0-2.5 and RER >10, indicating good predictive capacity. PLSDA and SVMC models showed similar performance, correctly classifying more than 80% of the samples. The results obtained demonstrate the potential of portable NIR spectrometer for monitoring quail eggs freshness during storage. (AU)

• Processo FAPESP: 20/09198-1 - Imagem hiperespectral e inteligência artificial para controle de qualidade de produtos baseados em proteína: isolados, microcápsulas e géis
• Beneficiário: Luis Jam Pier Cruz Tirado
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 15/24351-2 - Análise de imagens e espectroscopia de infravermelho próximo (NIR) na avaliação de qualidade e autenticação de alimentos
• Beneficiário: Douglas Fernandes Barbin
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 18/02500-4 - Análise de alimentos: uma abordagem utilizando imagens NIR
• Beneficiário: Luis Jam Pier Cruz Tirado
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 19/11896-1 - Análise multivariada combinada à espectroscopia por infravermelho próximo e imagens digitais aplicada a alimentos
• Beneficiário: Yasmin Lima Brasil
• Modalidade de apoio: Bolsas no Brasil - Mestrado