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.

Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates

“…Briefly, values were obtained for crude protein via total Kjeldahl nitrogen (AOAC 981.10), total fat via acid hydrolysis (AOAC 948.15), moisture via gravimetry at 105 °C (AOAC 950.46), and ash via incineration (AOAC 920.153); total carbohydrate content was estimated by difference (i.e., 100 − % protein − % fat − % moisture − % ash). These wet chemistry data were supplemented with WB proximate composition data estimated using near infrared (NIR) spectroscopy and species-specific NIR models we previously validated [ 48 ]. NIR models were also employed to obtain proximate composition and information on selected lipid-based characteristics (e.g., fatty acid classes) in fish fillets, extending the ‘general composition’ data layer (N = 28 variables).…”

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

“…Near infrared spectroscopy (NIR) has drawn considerable interest for many reasons, including its portability, speed, cost-effectiveness, suitability for in-line as well as on-line analysis, and the ability to concurrently measure different parameters for a large number of samples [151][152][153][154][155][156][157]. Several studies reported on the use of NIR for meat quality assessment and the analyses of meat properties in live animals or carcasses due to the capability of IR light to penetrate several layers of tissue [151][152][153][154][155][156][157][158][159]. Two published papers demonstrated that the short wavelength radiation in the NIR region of the electromagnetic spectrum can potentially be utilized to non-invasively assess meat quality [152,154].…”

“…There are a few other recent reports demonstrating the use of a NIR based analysis method for rapid and cost effective identification of crucial nutrients with high precision rates [161]. For example, recently, Miller et al [159] reported a NIR based method for quick assessments of various components of king salmon and greenshell mussels. Most of the demonstrated methods take minutes to prepare and analyze the samples as compared to the days required for the conventional wet-lab chemical assays.…”