Use of near infrared spectroscopy to predict microbial numbers on Atlantic salmon

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. ► Near infrared spectroscopy could differentiate between fresh and stored salmon. ► Model constructed between the day 0 NIR spectra and bacterial numbers on day nine. ► May be possible to predict bacterial numbers using NIR spectroscopy.