PROXIMATE COMPOSITION AND FATTY ACID PROFILE OF SOUTH AFRICAN SARDINES (SARDINOPS SAGAX OCELLATUS) USING CONVENTIONAL TECHNIQUES AND NEAR INFRARED (NIR) SPECTROSCOPY BY ZIYANDA PHIKE Thesis presented in partial fulfillment of the requirements for the degree OF Master of Food Science and Technology In the Faculty of Applied Sciences At Cape Peninsula University of Technology Supervisor: Dr S. Henning, CPUT Bellville November 2020 DECLARATION I, the undersigned, declare that the work contained in this thesis is my own, original work, that it has not previously been submitted at any university for obtaining any qualification. Signed Date: 17 Feb 2021 i ABSTRACT Fish and fishery products form an essential part of a healthy human diet due to the high-quality proteins and omega-3 fatty acids associated with it. The South African (SA) sardine (Sardinops sagax ocellatus) is commercially important in South Africa’s purse seine fishery and the fish canning industry. Reasons being sardines are an important source of high-quality proteins and fatty acids and therefore used as a food source for humans and pets. Moreover, sardine fishery offers employment to a large SA workforce. The growing consumption of sardines has led to the fish canning industry investigating effective instrumental techniques of evaluating the quality of the respective products. Multivariate data analysis, in particular principal component analysis (PCA), was applied on acquired near infrared (NIR) spectra to investigate the correlation between SA sardines based on morphophysiological properties. Sample sets of whole (n=116), and homogenised (n=97) sardine samples were investigated based on sex, fat tage and gonad stage, using PCA. For the whole fish sample set, groupings of sex, fat and gonad stages were distinguishable. Homogenised fish samples could not be classified based on sex and gonad stages. Only the fat stages could be differentiated when using the homogenised fish samples. Homogenized sardine samples were scanned by NIR. Sub samples were used to determine percentage moisture, ash, protein and fat using conventional methods. The data sets from the NIR scans and from the chemical analysis were combined to develop calibration models to allow prediction of proximate composition. These results were used as reference values to build partial least square (PLS) regression models using the NIR data-spectra. Prediction statistics for 2 2 the respective validation sets were: R v = 0.22, SEP = 3.14%, RPD 1.15 for moisture, R v = 0.24, 2 2 SEP = 0.056%, RPD 1.07 for ash, R v = 0.61, SEP = 2.46%, RPD 1.47 for fat and R v = 0.22, SEP = 3.41%, RPD 1.07 for protein. The results indicated that no reasonable predictions could be made at the fast NIR scan rate. Additionally, the fatty acid composition of sardines was obtained using a conventional gas chromatography (GC) method. These results were used to build PLS regression models in combination with NIR spectra acquired from the respective sample sets. The developed PLS 2 models resulted in poor predictions for saturated fatty acids (SFA): R v = 0.48, SEP = 0.45% and 2 RPD = 0.65 and polyunsaturated fatty acids (PUFA): R v = 0.48, SEP = 0.44% and RPD = 1.47. Monounsaturated fatty acids (MUFA) had very poor prediction results with very little correlation 2 found between the predicted and reference values (R v = 0.38, SEC = 2.33). Likewise, total fatty 2 acids resulted in poor predictions (R v = 0.37, SEC = 2.03). NIR spectroscopy has potential to be ii used as an analytical technique to predict proximate and fatty acid composition of fish, however further improvement is still necessary before it can be considered for industrial use. iii iv ACKNOWLEDGEMENTS I would like to express my sincere gratitude to the following people and institutions that formed an integral part of the completion of this research study: Dr Suné St Clair Henning, my supervisor at the Cape Peninsula University of Technology, for her patience, support and assistance during the course of my studies; Dr Anina Guelpa for reviewing my work and providing feedback; My co-supervisor, Professor Manley for reviewing my work and providing feedback; Staff members and post graduate students at the Department of Food Science and Technology, CPUT; Mr Ndumiso Mshicileli and Malibongwe Menziwa at Agrifoods Technology Station for helping with fatty acids analysis; Staff members at the Department of Animal Science, Stellenbosch University: Lisa Uys, Beverley Ellis for the use of their facility for fat and protein analysis; Department of Agriculture, Forestry and Fisheries: Dr Carl Van der Lingen and Dumisani Ntiyantiya for the provision of fish samples and helping with morphophysiological analysis; Food & Beverage SETA and Wholesale & Retail SETA for financial assistance; National Research Foundation and Agrifoods Technology Station for their financial support through internships My siblings who always have my best interest at heart, to my mother for the years spent looking after my daughter when I couldn’t My sister, my friend Portia Griffiths for her constant moral support and for the happy distractions to rest my mind outside of my research. v DEDICATION Dedicated to the memory of my late father Lungiselo Phike (†05.05.2003) Dedicated to the memory of my late brother Thembela Phike (†19.04.2009) Dedicated to the memory of my late grandmother Nonkululeko Ndibeni (†03.10.2010) vi GLOSSARY Terms/Acronyms/Abbreviated Definition/Explanation NIR Near infrared PCA Principal component analysis PLS Partial least squares AOAC Association of official analytical chemists PUFA Polyunsaturated fatty acids MUFA Monounsaturated fatty acids SFA Saturated fatty acids PA Palmitic acid LA Lauric acid MA Myristic acid EPA Eicosapantaenoic acid DHA Docosahexaenoic acid ALA Alpha-linolenic acid DPA Docosapentaenoic acid PPARs Peroxisome proliferator-activated receptors SREBP Sterol regulatory element binding protein KCL Potassium chloride LVF Linear variable filter NMR Nuclear magnetic filter MDA Multivariate data analysis PC Principal component RMSEP Root mean square error of prediction RMSECV Root mean square error of cross-validation RPD Ratio performance deviation SD Standard deviation SECV Standard error of cross-validation SEC Standard error of calibration MSC Multiplicative scatter correction SG Savitzky Golay 2 R C Coefficient of determination for calibration 2 R V Coefficient of determination for validation vii DAFF Department of Agriculture, Forestry and Department of Agriculture Land Reform and Fisheries now renamed to Rural Development (DALRRD; 2020) CPUT Cape Peninsula University of Technology CL Caudal length Cm Centimeter PFTE Polytetrafluoroethylene EDTA Ethylene diamine tetraacetic acid HPLC High performance liquid chromatography GC Gas chromatography TLC Thin layer chromatography UV Ultraviolet FAME Fatty acid methyl esters SIMCA Soft independent modelling of class analogy PCA-R Principal component analysis regression viii TABLE OF CONTENT DECLARATION ............................................................................................................................ i ABSTRACT ................................................................................................................................. ii ACKNOWLEDGEMENTS ............................................................................................................ v DEDICATION............................................................................................................................... v GLOSSARY ............................................................................................................................... vii TABLE OF CONTENT ................................................................................................................ ix 1 CHAPTER ONE: MOTIVATION AND DESIGN OF THE STUDY ......................................... 1 1.1 Introduction ................................................................................................................... 1 1.2 Statement of the research problem ............................................................................... 4 1.3 Broad objective ............................................................................................................. 4 1.4 Specific objectives ........................................................................................................ 5 1.5 Hypothesis .................................................................................................................... 5 1.6 Delineations of the research ......................................................................................... 5 1.7 Significance of the study ............................................................................................... 6 1.8 Thesis Overview ........................................................................................................... 6 References ................................................................................................................................. 7 2 CHAPTER TWO ................................................................................................................ 13 2.1 Introduction ................................................................................................................. 13 2.2 Sardines ..................................................................................................................... 14 2.2.1 Distribution of sardine .........................................................................................