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)

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.     

Rapid compositional analysis of Atlantic salmon (Salmo salar) using visible‐near infrared reflectance spectroscopy

Rapid measurement of salmon flesh quality parameters (>400 samples day^?1) was demonstrated in the laboratory and remotely at industrial sites. Visible‐near infrared spectroscopy (VNIRS) was applied to predict astaxanthin (AX) and fat content in farmed Atlantic salmon. Fish were sampled from thirteen batches (1–6 kg whole weight, containing 2.3–16.3% fat and 1.2–12.5 μg g^?1 AX), and models validated on small (average ± SD: 1.4 ± 0.4 kg) and large fish (4.2 ± 0.9 kg). Both constituents were well predicted in minced Norwegian Quality Cutlet (NQC) samples (r² ≥ 0.86; standard error of prediction (SEP) ≤0.7% for fat and ≤0.7 μg g^?1 for AX). Comparable metrics were observed for AX prediction in whole NQCs (r² = 0.80–0.88; SEP 0.7 μg g^?1). Fat was better predicted in small fish than large fish for whole NQCs (r² = 0.82, SEP 1.0% cf r² = 0.59, SEP = 0.59%) and non‐destructive scanning through the skin of whole, gutted fish (r² = 0.77, SEP = 1.2% cf r² = 0.49, SEP = 1.5%). Models were also developed for screening polyunsaturated fatty acid (PUFA) concentrations, e.g. in NQCs for docosahexaenoic acid (r² = 0.73) and n‐3:n‐6 PUFA (r² = 0.89).     

Determination of Principal Components for Characterizing the Drying of Sea Cucumbers (Stichopus japonicus Selenka) Using Far Infrared Radiation Drying and Hot Air Drying

Principal component analysis (PCA) and multiple linear regression (MLR) were conducted to characterize the drying of sea cucumber (SC). Far infrared radiation drying (FIRD) and hot air drying (AD) were used to dehydrate SC. Thirteen variables—including the morphological, color, and textural properties of dried SC—were selected for statistical analysis. The analysis of these 13 variables yielded three principal components showing significant correlations. PCA showed that the first three components represented 86.2% of the total variation. The first principal component was primarily related to the morphological properties of SC. The principal components determined by the PCA were more strongly influenced by the morphological variables than by the drying method. Of the morphological variables, the weight of SC had the strongest influence on the drying time and rehydration ratio of FIRD and AD.