Non-invasive measurement of quality attributes of processed pomegranate products

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Stellenbosch : Stellenbosch University
ENGLISH ABSTRACT: Pomegranate fruit has witnessed tremendous growth over the past decade in production, consumption, processing and research within South Africa. Currently, in order to provide value-addition and effective utilisation of pomegranate fruit parts, the edible portion has been processed by the food industry into various co-products such as juices, dried arils, seed oil and powders. The food processing industry is frequently confronted by new technological challenges to meet the increasing demand for quality assured processed products. This, however, has led to a shift in agribusiness reliance from subjective assessment of quality and authenticity to increasing adoption of objective, quantitative and non-invasive measurement. For pomegranates, non-invasive techniques such as X-ray computed tomography and infrared spectroscopy have successfully been used to evaluate postharvest rind disorders, quality attributes of whole fruit, and several of its co-products such as fresh arils and pomegranate juice. For processed agricultural and horticultural products, non-invasive techniques have been successfully used to evaluate and predict quality attributes related to juice, powders oils and minimally processed products. However, limited information on non-invasive techniques exist for evaluating different processed pomegranate co-products such as dried arils, powders and seed oil. Therefore, the aim of this research study was to develop non-invasive methods using infrared spectroscopy to predict the quality attributes of pomegranate co-products (dried arils and seed oil). Section I (Chapter 1) provides background information and the problem statement, including the aims and objectives of the research study. Chapter 2 provides a review of literature on non-invasive methods used to predict the quality attributes for different processed horticultural products with emphasis on juices, oils and powdered products and highlights potential research scientific gaps. Section II covers the application of infrared (FT-NIR and FT-MIR) spectroscopy in evaluating pomegranate co-products (dried arils and seed oil). In Chapter 3, Fourier-transform near infrared (FT-NIR) spectroscopy and associated chemometric analysis was used to evaluate quality attributes of dried pomegranate arils. This study compared two different regression techniques, namely partial least squares (PLS) and support vector machine (SVM), to develop calibration models over a spectral region of 800 – 2500 nm. Model development was based on pre-processing methods that yielded higher values of coefficient of determination (R2) and residual predictive deviation (RPD), and root mean square error of prediction (RMSEP). It was found that SVM could predict acidity (R2= 0.85, RMSEP = 0.04%, RPD = 2.50), redness (a ) colour attributes (R2 = 0.72, RMSEP = 1.82%, RPD = 1.71) and intensity (Chroma) (R2 = 0.70, RMSEP = 1.99%, RPD = 1.77). PLS regression also accurately predicted sensory attributes (pH, (R2 = 0.86, RMSEP = 0.13%, RPD = 2.38 and TSS:TA ratio, R2= 0.74, RMSEP = 1.68%, RPD = 1.68). These results suggest that SVM was better suited to evaluate the quality attributes of dried pomegranate arils. Chapter 4 (Section III) evaluated the quality of pomegranate seed oil by comparing two different spectrophotometers, namely; the Multipurpose Analyzer (MPA) in the FT-NIR spectral region of (12500 – 4000 cm1) and the Alpha ATR-FT-MIR in the spectral region of 4000 – 400 cm1. The MPA (FT-NIR) showed good prediction in the FT-NIR spectral region for total carotenoid content (R2 = 80.45, RMSEP = 0.0185 b-carotene/ mL oil, RPD = 2.28) and yellowness index (R2 = 53.19, RMSEP = 14.30%, RPD = 1.49). The Alpha (FT-IR) instrument in the FT-MIR spectral region provided good prediction for refractive index (R2 = 80.92, RMSEP = 0.0003%, RPD = 2.32) and prediction for peroxide value (R2 = 62.00, RMSEP = 3.88 meq O2/mL oil, RPD =1.62). In this study, FT-MIR spectroscopy provided better prediction statistics compared to than FT-NIR spectroscopy for evaluating the quality attributes of pomegranate oil. This research study has demonstrated that infrared spectroscopy and associated chemometric analysis has the ability to predict the quality attributes of pomegranate dried arils and seed oil.
Thesis (MEng)--Stellenbosch University, 2020.
Pomegranate industry -- South Africa, Pomegranate -- Quality control, Pomegranate (Punica granatum), UCTD, Processed foods -- Quality control, Infrared spectroscopy, X-ray microscopy