Color change of the snapper (Pagrus auratus) and Gurnard (Chelidonichthys kumu) skin and eyes during storage: effect of light polarization and contact with ice

Color of rainbow trout (Oncorhynchus mykiss) fillets by image and sensory analysis, and correlation with SalmoFan numbers

A total of 72 male and 50 female trout (Oncorhynchus mykiss) fillets were weighed (range 328-794 g, mean 546.2 ± 101.8 g; and range 426-994 g, mean 672.2 ± 106.1 g, respectively), the pictures of whom were taken in a light box, and image analysis was done to measure pixel colors, length, and view area of the fillets. Weight (W) was predicted using view area (V) obtained by image analysis using linear (W = A + BV), and power (W = AV^B ) equations. R² values were between 0.823 and 0.937. Although there was no difference between mean L* and a* values of male and female fillets, significant differences were found between mean b* values (p < 0.05). The colors of SalmoFan™ (SF) mini were also measured by image analysis and their mean L*, a*, b* values, and their entire color index (ECI) and reduced RGB values from 122 images were calculated. A total of 96 untrained panelists were asked to select the SF color of 5 representative fillets and to designate which point on the fillet image best described the SF color chosen. To predict SF numbers of the fillets by image analysis, four cases were considered: (1) whole fillet, (2) whole fillet with pixels a* > 25, (3) a rectangle along the length of the fillet to approximate panelists' selection, and (4) pixels in this rectangle with a* > 25. Mean L*a*b* values, mean reduced RGB values, and mean ECI of the four cases were used to predict fillet SF numbers. Different results obtained imply that image analysis can do repeatable and objective SF color classification of fillets, depending on the pixel selection method, and the color representation. PRACTICAL APPLICATION: Rainbow trout fillets can be assigned SalmoFan (SF) numbers using image analysis of the fillets. However, the selection of pixels and the color representation method affect the results. If these are standardized, SF numbers can be assigned objectively and automatically.

Image analysis-based quantification of the visual attributes of fish, with emphasis on color and visual texture

Images of Red Sea goatfish Parupeneus forsskali were taken in a light box to perform color and visual texture analyses. The average L* and b* values did not change significantly during storage of 7 days, but the a* values decreased (P < 0.05). The change of visual texture parameters energy and entropy (calculated based on histograms, and based on co-occurrence matrices [COM]), box counting-based fractal results, and texture change index (TCI) values are presented. The appearance of fish became “smoother” over time. The entropy values calculated by histograms decreased with storage (P < 0.05), and the maximum range was 0.395. That for COM-based entropies was 71.96. TCI also decreased with storage (P < 0.05) with a maximum range of 10.67. However, energy values increased during storage. The maximum range of the energy values calculated by histograms over time for any color channel was 0.0036. That for COM-based energies was 5.7. There was no observable change in fractal dimension. These image analysis-based parameters were compared with sensory analysis. A trained sensory panel evaluated the visual texture of a sub-set of images. The R2 values for equation fit between sensory score and texture features were, in increasing order: COM based energy (0.185), COM-based entropy (0.313), histogram-based energy (0.375), histogram-based entropy (0.386), TCI values (0.575). Since TCI correlated better with sensory values, it is recommended to be used in this type of visual texture evaluation.

Oxidation evaluation of free astaxanthin and astaxanthin esters in Pacific white shrimp during iced storage and frozen storage

BACKGROUND

In this paper, the changes in free astaxanthin (F-AST) and astaxanthin esters (AST-Es) in Litopenaeus vannamei during iced storage and frozen storage were investigated. The liquid chromatography-atmospheric pressure chemical ionization tandem mass spectrometry method was used to quantify the molecular species of AST-Es in shrimp during storage.

RESULTS

Based on the analysis of autoxidation products, apo-12-astaxanthinal and apo-13-astaxanthinone docosahexaenoic acid (DHA) ester were identified as the major oxidation products of F-AST and AST-Es in L. vannamei during storage. The total astaxanthin (T-AST) content decreased by 34.51% after 7 days in iced storage. In contrast, the content of T-AST decreased by 43.76% after 12 weeks in frozen storage. The content of F-AST decreased by 29.99% while 13-cis-astaxanthin increased after 3 days in iced storage, which indicated that degradation of AST was accompanied by isomerization. Total volatile basic nitrogen and T-AST content showed a significant negative correlation while in frozen storage, where the concentration of T-AST might be one indicator to evaluate shrimp freshness.

CONCLUSION

The correlation coefficients between phenol oxidase, lipoxygenase, apo-12-astaxanthinal, and apo-13-astaxanthinone DHA ester were all greater than 0.97 (P < 0.01). This correlation indicates that phenol oxidase and lipoxygenase were the main internal factors to improve oxygenation of astaxanthin in L. vannamei. These results provide a theoretical basis for further study of oxidation and the degradation mechanism in astaxanthin, as well as a new idea for the development and utilization of astaxanthin compounds in Pacific white shrimp. © 2018 Society of Chemical Industry.

Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.