The potential of a near infrared spectroscopy (NIR) method to detect as well as predict microbial spoilage on Atlantic salmon (Salmo salar) was investigated. Principal component analysis (PCA) of the NIR spectra showed clear separation between the fresh salmon fillets and those stored for nine days at 4°C indicating that NIR could detect spoilage. A partial least squares regression (PLS) prediction model for total aerobic plate counts after nine days was established using the NIR spectra collected when the fish was fresh to predict the number of bacteria that would be present nine days later. The calibration equation was good (R² = 0.95 and RMSE = 0.12 log cfu/g) although the error of the validation curve was larger (R² = 0.64 and RMSE = 0.32 log cfu/g). These results indicate that with further model development, it may be possible to use NIR to predict bacterial numbers, and hence shelf-life, in Atlantic salmon and other seafood.

Item Type:	Refereed Article
Keywords:	NIR spectroscopy, PLS model, spoilage, seafood
Research Division:	Biological Sciences
Research Group:	Microbiology
Research Field:	Microbiology not elsewhere classified
Objective Division:	Manufacturing
Objective Group:	Processed food products and beverages (excl. dairy products)
Objective Field:	Processed fish and seafood products
UTAS Author:	Tito, NB (Miss Nthabiseng Tito)
UTAS Author:	Rodemann, T (Dr Thomas Rodemann)
UTAS Author:	Powell, SM (Dr Shane Powell)
ID Code:	74215
Year Published:	2012
Web of Science® Times Cited:	49
Deposited By:	Agricultural Science
Deposited On:	2011-11-15
Last Modified:	2018-06-25
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