Near infrared espectroscopy (NIRS) for determination of quality and freshness of quail eggs

Author(s)

Yasmin Lima Brasil

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia de Alimentos
• Defense date: 2021-08-12

Examining board members

Douglas Fernandes Barbin; Ana Paula Ayub da Costa Barbon; Juliana Azevedo Lima Pallone

• Advisor: Douglas Fernandes Barbin

Abstract

Eggs are consumed worldwide as an accessible source of essential nutrients in human nutrition. After laying, the egg is subject to physical and chemical changes that can result in losses in internal quality. Quail eggs have a higher nutritional value than chicken eggs. Egg quality is evaluated through complex, time-consuming, and invasive physical and chemical methods, which cause great economic loss for the industry. In this context, non-destructive techniques such as near infrared spectroscopy (NIRS) associated with chemometricsare an excellent alternative for the analysis and monitoring of parameters in foods, as this allows simple and fast measurements, simultaneous determination of multicomponents, with low cost. This project aimed to carry out a study on the potential of a portable spectrometer to analyze the quality and freshness of quail eggs during storage by determining the Haugh Unit (HU), Yolk Index (YI) and Index of Egg Quality (EQI). Mathematical pre-treatments such as mean centering, Savitzky-Golay derivative calculation (SG) and standard normal variable (SNV) were applied to the data to facilitate the interpretation of the spectra. Principal component analysis (PCA) was used as an exploratory tool for spectral data. The predictive models elaborated by means of support vector machine regression (SVMR) performed better than those elaborated by partial least squares regression (PLSR) for predicting HU, GI and EQI in intact quail eggs, demonstrating good predictive capacity, with RPD of 2.0 - 2.5 and RER >10. Partial least squares discriminant analysis (PLSDA) and support vector machine classification (SVMC) were used to discriminate between fresh and non-fresh quail eggs, with classification accuracy over than 80% of the samples, with accuracy between 79.3 – 85.4% and 79.6 – 85.3%, respectively. Results proved the effectiveness of NIR spectroscopy, in combination with multivariate analysis, to assess the quality and freshness of quail eggs through quantitative and qualitative approaches (AU)

• FAPESP's process: 19/11896-1 - Multivariate statistical analyses applied to NIR spectroscopy and digital image analyses for food products
• Grantee: Yasmin Lima Brasil
• Support Opportunities: Scholarships in Brazil - Master