Sensory properties of frozen herring (Clupea harengus) from different catch seasons and locations.

UNLABELLED: Freezing of herring (Clupea harengus) for human consumption is increasing in the Nordic herring industry, either onboard the fishing vessels or right after landing. The quality of frozen herring as a raw material does not only depend on the frozen storage conditions applied, but also on compositional features, something which in turn can vary with season and catching ground. To unravel the link between biological variations, basic muscle composition, and sensory properties of frozen herring, a unique herring raw material was caught by commercial fishing vessels at three locations: around Iceland, outside the Norwegian coast, and in Kattegat/Skagerrak. The samplings were done according to a specific scheme and conducted over several seasons and 2 years. The herring was converted into butterfly fillets, packed in cardboard boxes, frozen, and then stored at -20 °C or -80 °C for up to 18 mo. The sensory quality was characterized by objective sensory profiling. It was shown that two generalized sensory variables could be defined from a principal component analysis of the sensory data. Except for the expected pronounced effect from storage time, the most distinct variation followed the lipid content, which in turn varied with season. An unexpected conclusion was that catching location only had a minor affect on the changes in sensory quality of herring during frozen storage. Knowledge about how season and catching location affect herring during frozen storage will be useful for optimizing the utilization of herring for frozen storage for human consumption. PRACTICAL APPLICATION: The results of this study constitute important information for the herring processing industry when handling raw materials with different geographical and seasonal history. The reported information on a highly equal sensory quality of geographically diverse raw materials will give consumers and retailers valuable knowledge on the changes in eating quality to expect after long-term frozen storage under conditions similar to household freezers or refrigerated counters in supermarkets.

Cod and rainbow trout as freeze-chilled meal elements.

BACKGROUND: 'Meal elements' are elements of a meal, e.g. portions of pre-fried meat, sauces, frozen fish or pre-processed vegetables typically prepared industrially. The meal elements are distributed to professional satellite kitchens, where the staff can combine them into complete meals. Freeze-chilling is a process consisting of freezing and frozen storage followed by thawing and chilled storage. Combining the two would enable the manufacturer to produce large quantities of frozen meal elements to be released into the chill chain according to demand. We have studied the influence of freeze-chilling on the quality attributes of cod and rainbow trout portions. Sensory profiling and chemical analyses were used to determine the changes in quality after slow thawing and subsequent chill storage and to find the high-quality shelf life. RESULTS: Cod had a consistent and high sensory quality during the first 6 days of chilled storage, and the corresponding time for rainbow trout was 10 days. After this period the sensory quality decreased and chemical indicators of spoilage were seen to increase. CONCLUSION: The consistent quality during storage and the high-quality shelf life are practically applicable and cod and rainbow trout seem potential candidates for freeze-chilled meal elements.

Using a cross-model loadings plot to identify protein spots causing 2-DE gels to become outliers in PCA.

The multivariate method PCA is an exploratory tool often used to get an overview of multivariate data, such as the quantified spot volumes of digitized 2-DE gels. PCA can reveal hidden structures present in the data, and thus enables identification of potential outliers and clustering. Based on PCA, we here present an approach for identification of protein spots causing 2-DE gels to become outliers. The approach can potentially obviate analytical exclusion of entire 2-DE gels.

Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples.

One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified 'autoscaling' of the full data set based on within-group standard deviations is introduced and shown to be advantageous in revealing potential group-dependent proteins additional to those found by prefiltering.

Combination of statistical approaches for analysis of 2-DE data gives complementary results.

Five methods for finding significant changes in proteome data have been used to analyze a two-dimensional gel electrophoresis data set. We used both univariate (ANOVA) and multivariate (Partial Least Squares with jackknife, Cross Model Validation, Power-PLS and CovProc) methods. The gels were taken from a time-series experiment exploring the changes in metabolic enzymes in bovine muscle at five time-points after slaughter. The data set consisted of 1377 protein spots, and for each analysis, the data set were preprocessed to fit the requirements of the chosen method. The generated results were one list from each analysis method of proteins found to be significantly changed according to the experimental design. Although the number of selected variables varied between the methods, we found that this was dependent on the specific aim of each method. CovProc and P-PLS focused more on getting the minimum necessary subset of proteins to explain properties of the samples. These methods ended up with less selected proteins. There was also a correlation between level of significance and frequency of selection for the selected proteins.

Peak alignment and robust principal component analysis of gas chromatograms of fatty acid methyl esters and volatiles.

Gas chromatograms of fatty acid methyl esters and of volatile lipid oxidation products from fish lipid extracts are analyzed by multivariate data analysis [principal component analysis (PCA)]. Peak alignment is necessary in order to include all sampled points of the chromatograms in the data set. The ability of robust algorithms to deal with outlier problems, including both sample-wise and element-wise outliers, and the advantages and drawbacks of two robust PCA methods, robust PCA (ROBPCA) and robust singular value decomposition when analysing these GC data were investigated. The results show that the usage of ROPCA is advantageous, compared with traditional PCA, when analysing the entire profile of chromatographic data in cases of sub-optimally aligned data. It also demonstrates how choosing the most robust PCA (sample or element-wise) depends on the type of outliers present in the data set.

Multivariate analysis of 2-DE protein patterns--practical approaches.

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.

Quantitative relationship between trimethylamine oxide aldolase activity and formaldehyde accumulation in white muscle from gadiform fish during frozen storage.

The accumulation of formaldehyde and the resulting deterioration of seafood products during frozen storage are primarily caused by the enzymatic activity of trimethylamine oxide aldolase (TMAOase). A screening of muscle samples from 24 species showed TMAOase activity in only the nine gadiform species that were analyzed. Enzyme activities in the major white muscle of gadiform fish showed large variations between species as well as between individuals. A frozen storage experiment showed a similarly large variation in the rate of formaldehyde accumulation, which could be accounted for by the endogenous white muscle in situ TMAOase activity. This TMAOase activity also correlated with the rate of insolubilization of otherwise high ionic strength soluble protein. A simple model describing the accumulation of free formaldehyde during frozen storage of gadiform fish is proposed. The model is based on a storage time-dependent decay of substrate-saturated white muscle TMAOase activity.

Extracting information from two-dimensional electrophoresis gels by partial least squares regression.

Two-dimensional gel electrophoresis (2-DE) produces large amounts of data and extraction of relevant information from these data demands a cautious and time consuming process of spot pattern matching between gels. The classical approach of data analysis is to detect protein markers that appear or disappear depending on the experimental conditions. Such biomarkers are found by comparing the relative volumes of individual spots in the individual gels. Multivariate statistical analysis and modelling of 2-DE data for comparison and classification is an alternative approach utilising the combination of all proteins/spots in the gels. In the present study it is demonstrated how information can be extracted by multivariate data analysis. The strategy is based on partial least squares regression followed by variable selection to find proteins that individually or in combination with other proteins vary informatively in relation to the experimental conditions. Finding of such coherent protein patterns leads to identification of potential relations between the involved proteins, and will be useful for focusing further investigation of proteins that relate to the chosen experimental conditions.