Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

• Published in: Journal of the science of food and agriculture Vol. 78; no. 3; pp. 329 - 336
• Main Authors: Bechmann, I.E, Jensen, H.S, Boknaes, N, Warm, K, Nielsen, J
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Ltd 01.11.1998; Wiley; Published for the Society of Chemical Industry by Elsevier Applied Science
• Subjects: Biological and medical sciences; cod (fish); color; duration; fish; Fish and seafood industries; Food industries; food quality; frozen fish; frozen storage; Fundamental and applied biological sciences. Psychology; geographical variation; multivariate analysis; odors; partial least squares regression; physical properties; physicochemical properties; postmortem changes; prediction; principal component analysis; quality; regression analysis; seasonal variation; stage of rigor; temperature; texture; Arctic Ocean; Denmark; Barents Sea; Europe; Origin; Edible fish; Seafood; Temperature effect; Multivariate analysis; Fishing; Optimization; PLS regression; Refrigerated storage; Quality; Cod; Frozen product; Season; Principal component analysis
• ISSN: 0022-5142; 1097-0010
• DOI: 10.1002/(SICI)1097-0010(199811)78:3<329::AID-JSFA121>3.0.CO;2-E

Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage, temperature, place of catch, season for catching and state of rigor) were varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod. PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised by ones and zeroes only. These results illustrate the application of multivariate analysis as an effective strategy for improving the quality of frozen fish products.