A potential tool for predicting abalone meat quality Miriam Fluckiger

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Transcript A potential tool for predicting abalone meat quality Miriam Fluckiger

Near Infrared Reflectance Spectroscopy:
A potential tool for predicting abalone meat quality
Miriam Fluckiger
Supervisors: Louise Ward, Malcolm Brown & Natalie Moltschaniwskyj
Ph.D Candidate, Australian Seafood Cooperative Research Centre, University of
Tasmania, CSIRO Marine and Atmospheric Research
Evaluating meat quality
• Expensive and time consuming
• Subjective sensory assessments
• Chemically wasteful extractions
• Destructive sampling
NIRS – What is it and how does it work?
• Chemical bonds in different organic
molecules absorb infrared light at different
wavelengths
• The NIR instrument measures the amount
of reflected light giving rise to a spectrum
• Highly developed in grain and flour milling
industry
• Used in meat industry to predict meat
composition
NIRS and abalone meat quality
• Qualitative:
•Is NIRS a viable tool for discriminating
different treatment groups?
• eg. Diet "A" versus Diet "B“
• Quantitative:
•Is NIRS capable of measuring different
chemical components in the muscle
tissue of abalone ?
•eg. Glycogen and moisture
NIRS and abalone
• Foot of abalone scanned in three locations
AA
B
CC
Discriminating between
holding treatment
• 60 abalone collected from farm
• 30 abalone scanned with NIR probe on arrival
at lab (same day processing)
• 30 abalone held overnight in plastic lined
polystyrene boxes
• scanned with NIR 24 hours later
(next day processing)
PC 2
Discriminating holding treatments
PC 1
Discriminating between
species
• 80 frozen abalone obtained from grower
• 20 Greenlip
• 20 Blacklip
• 20 Hybrid
• 20 Greenlip x Hybrid
• Abalone thawed overnight and scanned with
NIR
PC 2
Discriminating species
PC 1
Discriminating between
freezing methods
• 12 abalone shucked and frozen by immersion in
brine/ice slurry
• Thawed and scanned with NIR
• 6 then steamed and scanned with NIR
• 12 abalone shucked and frozen in air at -20°C
• Thawed and scanned with NIR
•6 then steamed and scanned with NIR
• 12 abalone shucked and fresh meat scanned
PC 2
Discriminating freezing methods
PC 1
Developing a model for moisture
Spectral Data
Chemometric modelling
Model for moisture in abalone
Summary – where to from here?
• Further develop NIRS calibration models
• Can NIRS discriminate between abalone fed different
diets?
• Can NIRS be used to quantify taste-active
Components such as free amino
acids and glycogen?
Acknowledgements
Abalone sample providers:
• Great Southern Waters Abalone
Indented Head, Victoria
• Cold Gold Abalone
Dunalley, Tasmania
• Southern Australian Seafoods
Port Lincoln, South Australia
Thank you
Miriam Fluckiger
Ph.D Candidate
Australian Seafood CRC
University of Tasmania, NCMCRS
CSIRO Marine and Atmospheric Research
GPO Box 1538 Hobart, Tasmania 7001, Australia
Ph:(03) 62325224, Fax:(03) 62325000
[email protected]
Seasonal glycogen levels
Free amino acid concentrations (mg FAA/g wet weight)
Taste-active components in abalone
• Unique umami taste of abalone linked to
certain free amino acids (FAA) and nucleotides
• Glutamic acid & adenosine monophosphate
(AMP) intensify the savoury taste of abalone
• Glycogen & moisture content also contribute
to palatability