Malcolm R. Brown

Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS).

Near infrared reflectance spectroscopy (NIRS) was used to predict glycogen concentrations in the foot muscle of cultured abalone. NIR spectra of live, shucked and freeze-dried abalones were modelled against chemically measured glycogen data (range: 0.77-40.9% of dry weight (DW)) using partial least squares (PLS) regression. The calibration models were then used to predict glycogen concentrations of test abalone samples and model robustness was assessed from coefficient of determination of the validation (R2(val)) and standard error of prediction (SEP) values. The model for freeze-dried abalone gave the best prediction (R2(val) 0.97, SEP=1.71), making it suitable for quantifying glycogen. Models for live and shucked abalones had R2(val) of 0.86 and 0.90, and SEP of 3.46 and 3.07 respectively, making them suitable for producing estimations of glycogen concentration. As glycogen is a taste-active component associated with palatability in abalone, this study demonstrated the potential of NIRS as a rapid method to monitor the factors associated with abalone quality.

Physicochemical Factors of Abalone Quality: A Review

Abstract Abalone meat has long been held in high regard for its unique sensory properties of texture and flavor, as well as its appearance. From a physicochemical viewpoint, the concentrations of certain free amino acids (especially glycine and glutamate) and the nucleotide AMP have been implicated as major factors characterizing the taste of abalone, and there seems to be a strong interaction (synergism) between them. The texture of abalone meat is related to the distribution of protein within the foot, and there is a good correlation between the collagen content and the toughness of abalone. These physicochemical factors, which largely define quality, may be influenced by species, season, diet, physiological condition and genetic factors. Protocols for handling and transport, and processing also influence quality; lactic acid is considered a useful post-mortem indicator of “freshness” in abalone meat. This review focuses on the abovementioned physicochemical factors and their link to abalone quality, and briefly discusses market related aspects and objective methods used for assessing quality attributes in abalone.

Assessing near-infrared reflectance spectroscopy for the rapid detection of lipid and biomass in microalgae cultures

With intensification of interest in microalgae as a source of biomass for biofuel production, rapid methods are needed for lipid screening of cultures. In this study, near-infrared reflectance spectroscopy (NIRS) was assessed as a method for analysing lipid (specifically, total fatty acid methyl esters (FAME) obtainable from processing) and biomass in late logarithmic and stationary phase cultures of the green alga Kirchneriella sp. and the eustigmatophyte Nannochloropsis sp. Culture samples were filtered, scanned by NIRS and chemically analysed; by combining these sets of information, models were developed to predict total biomass, FAME content and FAME as a percentage of dry weight in samples. Chemically derived (actual) and NIRS-predicted data were compared using the coefficient of determination (R2) and the ratio of the standard deviation (SD) of actual data to the SD of NIRS prediction (RPD). For Kirchneriella sp. samples, models gave excellent prediction (R2 ≥ 0.96; RPD ≥ 4.8) for all parameters. For Nannochloropsis sp., the model metrics were less favourable (R2 = 0.84–0.94; RPD = 2.5–4.2), though sufficient to provide estimations that could be useful for screening purposes. This technique may require further validation and comparison with other species, but this study shows the potential of the NIRS as a rapid screening method (e.g. up to 200 sample analyses per day) for estimating FAME or other microalgal constituents and encourages further investigation.

Application of Near-Infrared Reflectance Spectroscopy for the Rapid Chemical Analysis of Sydney Rock Oyster (Saccostrea Glomerata) and Pacific Oyster (Crassostrea gigas)

ABSTRACT Near-infrared IR reflectance spectroscopy (NIRS) was applied to the compositional analysis of oysters (Crassostrea gigas and Saccostrea glomerata). Homogenized meat samples of 332 oysters were scanned by NIRS, subsamples were analyzed chemically, and, by combining the sets of information, calibration models were developed to allow prediction of proximate composition (moisture, protein, glycogen, and fat). Predicted and actual (chemically measured) data in independent validation sample sets were compared using R 2 and the ratio of the SE of chemical data to the SE of NIRS prediction (RPD). For S. glomerata, models gave excellent prediction for all components (R 2 = 0.95–0.97, RPD = 2.7–5.5). Prediction within the C. gigas validation set was generally less precise, but still very good for all components (R 2 = 0.92–0.96, RPD = 2.7–4.8). With a smaller subset of samples (n = 48), prediction models were also developed for estimating concentration of polyunsaturated fatty acid and long-chain polyunsaturated fatty acid (R 2 = 0.94 and 0.93, respectively). The major advantages of the methodology are its speed—250—300 samples can be analyzed simultaneously for all components each day—and cost-effectiveness when a large number of samples (e.g., several hundred or more per year) are analyzed. Therefore, the method is ideally suited to applications requiring the rapid analysis of many individuals, such as selective breeding programs for which chemical compositional data can provide information on traits associated with oyster condition or quality.

Sensory and Physicochemical Assessment of Wild And Aquacultured Green and Black Lip Abalone (Haliotis laevigata and Haliotis rubra)

ABSTRACT Abalone is a highly valued food product in many countries, in large part a result of its unique sensory properties. Wild and cultured abalone both attract premium prices, but generally this is not based on sensory characteristics. Yet, abalone aquaculture is developing to provide an alternative to a dwindling supply of wild abalone, and this provides an opportunity to optimize the sensory properties if they are better understood. In most natural food products, farming practices and growing environment are responsible for the sensory properties of the final product; therefore, the comparison of both wild and aquacultured abalones sensory characteristics could contribute to a better understanding of the impact of the growing and farming practices on the sensory properties. Our study focused on the development of a descriptive sensory analysis methodology to measure abalone sensory properties, and the observation of differences among the abalone sampled. Wild and aquacultured abalone were prepared according to a standardized cooking protocol. A sensory panel of trained assessors developed and defined a descriptive vocabulary and a method of assessment, and then quantified the sensory properties of abalone. A vocabulary of 16 terms describing aroma, texture, flavor, and aftertaste of the abalone was developed. Very significant differences were found between abalone sourced from the wild and aquacultured abalone from different sources. The wild-caught blacklip abalone, which were larger in size, were perceived as more firm, springy, and chewy, but also rated significantly higher in aroma, flavor, and aftertaste impact as well as earthy and metallic flavors. Significant differences in sensory properties were also found between cultured abalone fed different diets. Compositional analysis showed significant differences between abalone in their content of glycogen (range, 4.8%–23.2% of dry weight (DW)), moisture (69.4%–73.7% live weight), and taste-active free amino acids, especially glycine (3.4–18.2 mg/g DW) and glutamate (1.0–3.6 mg/g DW). Correlations were found between sensory attributes and some chemical compounds. This study indicates that growing conditions as well as growing techniques may have a large influence on abalone sensory characteristics. However, because the design of the study was not balanced for key growth or production variables, additional studies are required to identify and quantify which factors were most influential. The descriptive sensory method developed was successful in measuring the sensory properties of abalone and can now be applied more broadly.