Influencing factors and sensory/instrumental measurement of seafood quality
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2024
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Melbourne Authors
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School of Agriculture, Food and Ecosystem Sciences
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PhD thesis
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© 2024 Kieren Michael Watkins
Abstract
The sensory qualities of seafood are important to consumers and hence influence likelihood of consumption. The colour and texture of seafood are known to be important sensory factors, but without knowing their relative importance to each other and other factors, trade-offs cannot be made to maximise liking. The methods to measure the colour of seafood have not been standardised, and hence the results between studies can vary. Similarly, the methods for measuring the instrumental texture of seafood have not been standardised, including methods that predict sensory texture most accurately. While the effects of cooking on the texture of seafood have been attributed to protein denaturation, the effect of specific proteins lacks agreement in the literature which prevents development of methods to optimise the texture. Hence, the focus of this thesis is to understand the factors influencing the colour and texture of seafood, and how to measure them.
The importance of sensory and non-sensory factors of seafood to Australian consumers has been investigated by many researchers. Chapter 3 used Qualitative Multivariate Analysis to understand the relative importance of various factors of seafood for consumers. Non-sensory factors were found to be more important than sensory factors, in particular the price and accessibility. Flavour and texture of seafood were also important, which substantiates the focus of this thesis on texture. Seafood producers should generally focus on the important attributes of price, accessibility, flavour and texture to maximise consumer acceptance.
Seafood colour measurement is typically performed using colorimeters, which require repeat measurements on each sample due to the colour heterogeneity of the sample and the small aperture size of colorimeters. Chapter 4 investigated the optimal number of repeated measurements to improve precision. Fifteen repeat measurements were made on each sample of raw and cooked salmon, rockling and prawns, using two colorimeters, Minolta CR400 and Nix Pro 2, which were compared against machine-vision. Two analysis methods were used to determine the number of repeat measurements required for each device and species combination. Machine-vision was expected to require fewer repeat measurements than the colorimeters due to machine vision being unaffected by the sample colour heterogeneity, and both methods of analysis supported this hypothesis. Hence, automated machine-vision system is recommended to increase the precision of seafood colour measurement.
The colour of raw seafood and meat is known to vary based on the device, background colour, and sample thickness, however no investigations have been conducted on cooked seafood, or on the use of machine-vision. In Chapter 5, machine-vision was hypothesised to be less affected by background colour and sample thickness compared to the colorimeters. The hypothesis was tested using the same experiment as Chapter 4, where black and white tiles were used as different backgrounds, and thicknesses ranged from 1 to 5 cm. White backgrounds generally increased the L, a and b while black backgrounds decreased them, and increased thickness reduced the effect of background. Particularly in raw samples, the colour measured by machine-vision was more affected by background and thickness than for the colorimeters. Machine-vision may have been more affected by background due to edge loss in colorimeters, or the surrounding background affecting machine-vision.
Cooking causes protein denaturation and water loss, which affects the texture of seafood, but the specific effect of each protein in abalone, barramundi and scallops is contested or unknown. Chapter 6 contains an experiment where abalone, barramundi and scallops were cooked to temperatures up to 78 C. The texture was measured using Texture Profile Analysis (TPA) and Warner-Bratzler Shear Force (WBSF). Protein denaturation was measured using Differential Scanning Calorimetry (DSC) to determine the peak temperature of denaturation, and the total energy (cumulative enthalpy) required. Cumulative enthalpy predicted up to 65% of the variation in texture within species. The myosin/paramyosin/collagen denaturation contributed significantly to change in abalone texture as did myosin denaturation in barramundi, whereas the texture of scallops was less influenced by protein denaturation. The texture of these species can be manipulated by cooking to cause protein denaturation.
The texture of abalone is important to consumers, but literature methods to predict sensory texture have varying accuracies, and the optimum instrumental texture to maximise liking is unknown. This hinders development of abalone products to maximise consumer liking. In Chapter 7, abalone was cooked to 40, 55, 70 and 95 C and tested using consumers, and instruments (TPA, WBSF, Kramer, and pressed juice percent (PJP)). The sensory texture was evaluated for chewiness, firmness, juiciness and springiness on a Just About Right (JAR) scale and a hedonic scale. TPA hardness correlated with the chewiness JAR, and TPA hardness, WBSF and Kramer correlated with the firmness JAR. Ranges of instrumental values were defined to select abalone which were acceptable for consumer chewiness and firmness, and only the TPA hardness range met the threshold of 80% samples being acceptable to consumers. Research is required to identify instrumental methods to predict consumer sensory juiciness and springiness of abalone, but TPA hardness can be used to tentatively predict sensory chewiness and firmness of abalone.
Texture has been validated as an important characteristic of seafood, which supports the use of TPA hardness to predict the chewiness and firmness of abalone, and determine whether a product is likely to be acceptable to the consumer. Texture can be manipulated through cooking to the temperatures required to denature proteins. Machine-vision measures colour more precisely than colorimeters, but the accuracy of machine-vision is yet to be established in seafood. This thesis has provided guidance for measuring the colour and texture of seafood for researchers.
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Seafood, Colour, Texture, Water holding capacity, Sensory, Colorimeter, Machine-vision, Protein denaturation, Texture Profile Analysis, Warner-Bratzler Shear Force, Kramer Shear Force, Differential scanning calorimetry, Cumulative Enthalpy, Qualitative Multivariate Analysis, Abalone, Fish, Salmon, Barramundi, Prawns