Application of a Fourier transform—near infrared reflectance spectroscopy method for the rapid proximate analysis of the greenshell mussel ( Perna canaliculus ) and king (Chinook) salmon ( Oncorhynchus tshawytscha )

Abstract

Greenshell™ mussel (GSM, Perna canaliculus) and king (Chinook) salmon (Oncorhynchus tshawytscha) are New Zealand's two major aquaculture species generating $380 million NZD in exports during the 2017–18 financial year. This study addresses the development and validation of a method based on Fourier transform—near infrared reflectance spectroscopy (FT-NIRs) to determine proximate composition for both species to aid breeding-, production- and consumer decisions. Rapid measurements of GSM (n = 176) were taken by FT-NIRs and analysed by traditional wet chemistry ‘reference methods’ to develop calibration models for proximate composition (protein, moisture, fat, ash and carbohydrate). The predictive models for moisture (r2 = 0.98, root mean square error of cross validation (RMSECV) = 0.314, residual prediction deviation (RPD = 6.47), protein (r2 = 0.91, RMSECV = 0.295, RPD = 3.01)) and carbohydrate (r2 = 0.87, RMSECV = 0.440, RPD = 2.78) in GSM performed well. Additional models based on 90 portions of salmon were developed to predict moisture (r2 = 0.98, RMSECV = 1.02, RPD = 7), protein (r2 = 0.96, RMSECV = 0.347, RPD = 5.08), fat (r2 = 0.99, RMSECV = 1.09, RPD = 5.98) and ash (r2 = 0.72, RMSECV = 0.05, RPD = 1.9). The predictive FT-NIRs and reference methods were tested for short-term and intermediate precision, which demonstrated that the repeatability of the predictive models was comparable to the reference methods. Proximate analysis of GSM and king salmon using FT-NIRs was quick (minutes for sample preparation and analysis rather than days) and all components were assessed simultaneously. This provides a low-cost short turn-around method suitable for industry and research applications.