NIR spectral techniques and chemometrics applied to food processing

Author(s)

Amanda Teixeira Badaró

• Document type: Doctoral Thesis
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia de Alimentos
• Defense date: 2021-11-16

Examining board members

Douglas Fernandes Barbin; María del Carmen Alamar Gavidia; José Javier Benedito Fort; Angel Antonio Carbonell Barrachina; Wouter Saeys

• Advisor: Douglas Fernandes Barbin

Abstract

Fast, non-destructive and chemical-free techniques are in increasing demand in many fields of the industry. Near-infrared spectroscopy (NIRS) and NIR hyperspectral imaging (NIR-HSI) techniques have shown great potential in determining food quality parameters, authenticating food products, detecting food fraud, among many other applications. While in near infrared spectroscopy, the measurements are taken at specific points on the sample, detecting only a small portion; in hyperspectral imaging, spectral and spatial information are combined, making it a suitable choice for many food products, since they are very heterogeneous matrices. Therefore, this study aimed to review all the application of (dispersive) NIRS, Fourier Transform (FT) NIR, and HSI in assessing wheat flour and wheat-based products quality parameters, as well for the authentication and determination of composition of these products. Moreover, this work aimed to identify and classify different types of fibre samples added to the semolina and pasta produced by semolina-fibre formulations, and to monitor the cooking process of this fibre-enriched pasta by spectral techniques. In addition, this work had the aim of applying HSI to other powdered product, so the pectin content in orange peels was quantified. First, NIR spectra were acquired to compare the accuracy in the classification of fibre-enriched samples, to quantify the amount of these fibres and verify their distribution on semolina samples. Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were used for classification. Partial Least Squares Regression (PLSR) models applied to NIR-HSI spectra showed R2P between 0.85 and 0.98, and RMSEP between 0.5 and 1% of fibre content, and the models were used to construct the chemical maps to check the fibre distribution on the samples surface. Moreover, NIR-HSI together with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), was tested to investigate the ability for the evaluation, resolution and quantification of fibre distribution in enriched pasta. Results showed coefficient of determination of validation (R²V) between 0.28 and 0.89, % of lack of fit (LOF) <6%, variance explained over 99%, and similarity between pure and recovered spectra over 96% and 98% in models using pure flour and control as initial estimates, respectively. In addition, VIS/NIR-HSI in the transmission mode was tested as an objective alternative for the classification of pasta samples according to cooking time as way of automating the determination of pasta attributes. Linear Discriminant Analysis (LDA) showed values of sensitivity and specificity between 0.14 – 1.00 and 0.51 – 1.00, respectively, and non-error rate (NER) over 0.62. Partial Least Square Discriminant Analysis (PLSDA) showed values of sensitivity and specificity between 0.67 – 1.00 and 0.10 – 1.00, respectively, and NER over 0.80. The results of the first part of this work showed that NIR-HSI technique can be used for the identification and quantification of fibre added to semolina. Additionally, NIR-HSI and MCR-ALS are able to identify fibre in pasta. Hyperspectral imaging in the transmission mode demonstrated to be a suitable technique as an objective alternative for the classification of pasta samples according to the cooking time as a way of automating the determination of pasta attributes. Determination of pectin content in orange peels was investigated using NIR-HSI. LDA showed better discrimination results considering three groups: low (0–5%), intermediate (10–40%) and high (50–100%) pectin content. PLSR models based on full spectra showed higher precision (R2 > 0.93, RMSEP between 6.50 and 9.16% of pectin) than those based on few selected wavelengths (R2 between 0.92 and 0.94, RMSEP between 8.03 and 9.73% of pectin). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries (AU)

• FAPESP's process: 17/17628-3 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products
• Grantee: Amanda Teixeira Badaró
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)