Doctoral Degrees (Food Science)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/537

Browse

Browsing Doctoral Degrees (Food Science) by Author "Arendse, Ebrahiema"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Non-destructive measurement of pomegranate fruit quality
    (Stellenbosch : Stellenbosch University, 2017-12) Arendse, Ebrahiema; Opara, Umezuruike Linus; Fawole, Olaniyi Amos; Magwaza, Lembe Samukelo; Stellenbosch University. Faculty of AgriSciences. Dept. of Food Science.
    ENGLISH ABSTRACT: Pomegranate (Punica granatum L.) is an emerging fruit within the South African horticultural industry, which has experienced dramatic growth in annual production from 350 tonnes in the 2009 season to over 8000 tonnes in 2017. Literature shows that the fruit consists of considerable amount of sugars, organic acids, vitamins, mineral elements and possess potent pharmacological activities due to an array of phytochemical compounds found in the fruit. However, the fruit is highly susceptible to pest and disease infestation, including the development of physiological rind disorders during storage and shipping. The increased growth of the pomegranate industry has coincided with consumer demand for consistent supply of safe, nutritious and traceable fruit and processed products. Hence, non-destructive assessment of fruit quality and its processed products can contribute to the implementation of suitable management strategies to predict and control desired quality attributes. This will ensure delivery of high quality fruit and its derived products without the presence of defects in international and local markets. Therefore, the overall aim of this study was to develop non-destructive methods to predict external and internal quality attributes of pomegranate fruit. Section I of the thesis focuses on a critical review of non-destructive techniques for assessing the external and internal quality of fruit with thick rind. Thick rind fruits, such as pomegranate, have been reported to interfere with accurate measurement of internal quality using near-infrared spectroscopy. Hence, this review provides an overview of the issues related to quality measurement using non-destructive methods, including a concise summary of the current research and potential commercial applications. In section II (chapter 3), the feasibility of X-ray micro-computed tomography (μCT) as a non-destructive technique to characterise and quantify the internal structure of pomegranate fruit was investigated. μCT in combination with image analysis successfully characterised and quantified the volumes of the internal fruit components (arils, peel, kernel, juice content, air space). The calculated volume for total arils, peel, and air space were 162.45 ±16.21, 163.87 ±21.42 and 10.89 ±2.57 mL, respectively, which accounted for 48.04, 48.46 and 3.22% of the total fruit volume (338.19 ±22.4 mL). The calculated volume of juice content and kernels were 146.07 ±16.28 and 16.38 ±1.81 mL per fruit which were equivalent to an average of 89.92 and 10.08% of the total aril volume. Destructive validation results showed no significant difference with those obtained from the μCT-based non-invasive method. This study has demonstrated the potential use of μCT and associated image analysis as a promising tool for non-destructive characterization of the internal and external structure of pomegranate fruit. In chapter 4, the prospects of Fourier-transform near-infrared (FT-NIR) spectroscopy (FT-NIRS) and associated chemometric analysis were evaluated for the prediction of external and internal quality parameters of intact pomegranate fruit. Two diffuse reflectance spectral acquisition modes were assessed, namely, direct contact between the sample with an integrating sphere (IS) using the Multi-Purpose Analyser (MPA) and a contact-less measurement (distance 17 cm) using an optic fibre coupled emission head (EH) of the MATRIXTM-F analyser. Partial least squares (PLS) regression was used to construct calibration models over a spectral region of 800-2500 nm, and the results showed that optimal model performance was obtained using first derivative and second derivative spectral pre-processing methods. It was found that models obtained from the EH spectral data predicted fruit firmness, colour components (a* and C*), total soluble solids, titratable acidity, BrimA, total phenolics and vitamin C with high accuracy (RPD values ranging from 2.06 to 3.34), while the IS showed good prediction ability for h° colour component (RPD = 2.50), TSS:TA (RPD = 2.72) and total anthocyanin (RPD = 1.64). The results suggest that the contactless option of the MATRIX-F could be used to evaluate quality attributes of intact pomegranate fruit. In chapter 5, the development of calibration models by FT-NIRS for the evaluation of pomegranate aril quality was investigated using two different FT-NIR acquisition methods (IS and EH) over 800-2500 nm spectral region. Model development was based on pre-processing methods that yielded higher values of coefficient of determination (R2) and residual predictive deviation (RPD), lower root mean square error estimation (RMSEE) and root mean square error of prediction (RMSEP). The results showed that models based on the EH provided good prediction of TSS, pH, TA, BrimA, aril hue, total phenolic, total anthocyanin and vitamin C concentration, while those based on IS provided the best results for TSS:TA, firmness, arils redness (a*) and colour intensity (chroma). Furthermore, a follow-up study was conducted to compare near and mid infrared (MIR) spectrometers for predicting organoleptic and phytochemical quality attributes of pomegranate juice (chapter 6 (section II)). Three Fourier transform infrared (FT-IR) spectrometers (representing three different spectral acquisition modes) were assessed; namely, MPA FT-NIR spectrometer, Alpha-P FT-MIR spectrometer and WineScan FT-NIR/MIR spectrometer. Results obtained showed that spectral acquisition mode affected model ability to accurately predict various pomegranate quality attributes, with the WineScan in the NIR/MIR region outperforming the Alpha-P and MPA instruments. However, statistical comparison using Bland and Altman and Passing-Bablok analytical algorithms showed no statistical differences among the three spectrometers for the prediction of selected aril quality parameters. Section III of the thesis investigated the prospects for non-destructive detection and classification of pomegranate fruit affected by internal defects and postharvest rind scald. In chapter 7, the feasibility of μCT with a calibration function to differentiate between fruit fractions (albedo and arils) and detect the presence of false codling moth and blackheart disease in pomegranate fruit was assessed. A calibration function was implemented using different homogenous polymeric materials with a density ranging from 910 to 2150 kg m−3. The estimation of fruit density was successfully accomplished within the calibration range. The density of whole fruit (1070 ±20 kg m−3), arils (1120 ±40 kg m−3) and albedo 1040 ±30 kg m−3) were significantly higher compared to the larva of codling moth (940 ±40 kg m−3) inside the fruit. Furthermore, healthy fruit had significantly higher density (1070 ±20 kg m−3) compared to those with blackheart (870–1000 ±50 kg m−3). An increase in the severity of blackheart infestation was characterised by a decrease in density of affected fruit. The results of this study suggested that the use of X-ray μCT, in combination with a calibration function of polymers and image analysis, could be applied to non-destructively identify and differentiate between fruit fractions, and detect the presence of larva of false codling moth and blackheart in pomegranate fruit. The research reported in chapter 8 (section III) evaluated several biochemical markers associated with the development of husk scald (peel browning) and based on these markers, assess the feasibility of non-destructive discrimination of healthy and scalded affected fruit using Fourier transform near-infrared (FT-NIR) spectroscopy. The results suggest that enzymatic browning was the main cause of husk scald, phenolic compounds such as tannins acting as substrates for polyphenol oxidase and peroxidase activity. The severity of browning index increased with storage temperature and duration. FT-NIR reflectance spectroscopy spectral data and reference data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA) to discriminate between healthy and scalded fruit. Resulting in high classification accuracy (100%, 93% and 92.6% for healthy, severe and moderately scalded fruit, respectively). Therefore, this study has successfully demonstrated that biochemical markers associated with the development of husk scald could potentially be used to non-destructively discriminate between healthy and scalded fruit.