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.

Institut National de la Recherche Agronomique 29450 Sizun France

Institute of Complex Systems South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses Faculty of Fisheries and Protection of Waters University of South Bohemia in České Budějovice Zámek 136 Nové Hrady 37 333 Czech Republic

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Weeranantanaphan J., Downey G., Allen P., Sun D. Review of near Infrared Spectroscopy in Muscle Food Analysis: 2005–2010. J. Near Infrared Spectrosc. 2011;19:61–104. doi: 10.1255/jnirs.924. DOI

Brinker A., Reiter R. Fish meal replacement by plant protein substitution and guar gum addition in trout feed, Part I: Effects on feed utilization and fish quality. Aquaculture. 2011;310:350–360. doi: 10.1016/j.aquaculture.2010.09.041. PubMed DOI

Gormley T. Note on consumer preference of smoked salmon colour. Ir. J. Agric. Food Res. 1992;31:199–202. doi: 10.2307/25562193A. DOI

Colihueque N., Parraguez M., Estay F., Diaz N. Skin Color Characterization in Rainbow Trout by Use of Computer-Based Image Analysis. N. Am. J. Aquac. 2011;73:249–258. doi: 10.1080/15222055.2011.581578. DOI

Yi X., Xu W., Zhou H., Zhang Y., Luo Y., Zhang W., Mai K. Effects of dietary astaxanthin and xanthophylls on the growth and skin pigmentation of large yellow croaker Larimichthys croceus. Aquaculture. 2014;433:377–383. doi: 10.1016/j.aquaculture.2014.06.038. DOI

Segade Á., Robaina L., Ferrer O., Romero G., Domínguez M. Effects of the diet on seahorse (Hippocampus hippocampus) growth, body colour and biochemical composition. Aquac. Nutr. 2014;21:807–813. doi: 10.1111/anu.12202. DOI

Costa D., Mattioli C., Silva W., Takata R., Leme F., Oliveira A., Luz R. The effect of environmental colour on the growth, metabolism, physiology and skin pigmentation of the carnivorous freshwater catfsh Lophiosilurus alexandri. J. Fish Biol. 2016;90:1–14. doi: 10.1111/jfb.13208. PubMed DOI

Erikson U., Misimi E. Atlantic salmon skin and fillet color changes effected by perimortem handling stress, rigor mortis, and ice storage. J. Food Sci. 2008;73:C50–C59. doi: 10.1111/j.1750-3841.2007.00617.x. PubMed DOI

Balaban M., Chombeau M., Cırban D., Gümüş B. Prediction of the weight of Alaskan pollock using image analysis. J. Food Sci. 2010;75:E552–E556. doi: 10.1111/j.1750-3841.2010.01813.x. PubMed DOI

Clydesdale F., Ahmed E. Colorimetry—Methodology and applications∗. C R C Crit. Rev. Food Sci. Nutr. 1978;10:243–301. doi: 10.1080/10408397809527252. PubMed DOI

Skonberg D., Hardy R., Barrows F., Dong F. Color and flavor analyses of fillets from farm-raised rainbow trout (Oncorhynchus mykiss) fed low-phosphorus feeds containing corn or wheat gluten. Aquaculture. 1998;166:269–277. doi: 10.1016/S0044-8486(98)00294-4. DOI

Macagnano A., Careche M., Herrero A., Paolesse R., Martinelli E., Pennazza G., Carmona P., D’Amico A., Natale D. A model to predict fish quality from instrumental features. Sens. Actuators B Chem. 2005;111–112:293–298. doi: 10.1016/j.snb.2005.06.028. DOI

Kalinowski C., Izquierdo M., Schuchardt D., Robaina L. Dietary supplementation time with shrimp shell meal on red porgy (Pagrus pagrus) skin colour and carotenoid concentration. Aquaculture. 2007;272:451–457. doi: 10.1016/j.aquaculture.2007.06.008. DOI

Mendoza F., Aguilera J. Application of image analysis for classification of ripening bananas. J. Food Sci. 2004;69:E471–E477. doi: 10.1111/j.1365-2621.2004.tb09932.x. DOI

Yam K., Papadakis S. A simple digital imaging method for measuring and analyzing color of food surfaces. J. Food Eng. 2004;61:137–142. doi: 10.1016/S0260-8774(03)00195-X. DOI

Saberioon M., Gholizadeh A., Cisar P., Pautsina A., Urban J. Application of machine vision systems in aquaculture with emphasis on fish: State-of-the-art and key issues. Rev. Aquac. 2017;9:369–387. doi: 10.1111/raq.12143. DOI

Zaťková I., Sergejevová M., Urban J., Vachta R., Štys D., Masojídek J. Carotenoid-enriched microalgal biomass as feed supplement for freshwater ornamentals: Albinic form of wels catfish (Silurus glanis) Aquac. Nutr. 2009;17:278–286. doi: 10.1111/j.1365-2095.2009.00751.x. DOI

Colihueque N. Analysis of the coloration and spottiness of Blue Back rainbow trout at a juvenile stage. J. Appl. Anim. Res. 2014;42:474–480. doi: 10.1080/09712119.2014.883317. DOI

Balaban M., Stewart K., Fletcher G., Alçiçek Z. Color Change of the Snapper (Pagrus auratus) and Gurnard (Chelidonichthys kumu) Skin and Eyes during Storage: Effect of Light Polarization and Contact with Ice. J. Food Sci. 2014;79:E2456–E2462. doi: 10.1111/1750-3841.12693. PubMed DOI

Wishkerman A., Boglino A., Darias M., Andree K., Estévez A., Gisbert E. Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole. Aquaculture. 2016;464:303–308. doi: 10.1016/j.aquaculture.2016.06.040. DOI

Wallat G., Lazur A., Chapman F. Carotenoids of Different Types and Concentrations in Commercial Formulated Fish Diets Affect Color and Its Development in the Skin of the Red Oranda Variety of Goldfish. N. Am. J. Aquac. 2017;67:42–51. doi: 10.1577/FA03-062.1. DOI

Luzuriaga D., Balaban M., Yeralan S. Analysis of visual quality attributes of white shrimp by machine vision. J. Food Sci. 1997;62:113–130. doi: 10.1111/j.1365-2621.1997.tb04379.x. DOI

Du C., Sun D. Learning techniques used in computer vision for food quality evaluation: A review. J. Food Eng. 2006;72:39–55. doi: 10.1016/j.jfoodeng.2004.11.017. DOI

Hu J., Li D., Duan Q., Han Y., Chen G., Si X. Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput. Electron. Agric. 2012;88:133–140. doi: 10.1016/j.compag.2012.07.008. DOI

Hernández-Serna A., Jiménez-Segura L. Automatic identification of species with neural networks. PeerJ. 2014;2:e563. doi: 10.7717/peerj.563. PubMed DOI PMC

Rossi F., Benso A., Carlo S., Politano G., Savino A., Acutis P. FishAPP: A mobile App to detect fish falsification through image processing and machine learning techniques; Proceedings of the IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR); Cluj-Napoca, Romania. 19–21 May 2016; pp. 1–6.

Liu Z., Cheng F., Hong H. Identification of Impurities in Fresh Shrimp Using Improved Majority Scheme-Based Classifier. Food Anal. Methods. 2016;9:3133–3142. doi: 10.1007/s12161-016-0497-3. DOI

Dutta M., Issac A., Minhas N., Sarkar B. Image processing based method to assess fish quality and freshness. J. Food Eng. 2016;177:50–58. doi: 10.1016/j.jfoodeng.2015.12.018. DOI

National Research Council . Nutrient Requirements of Fish and Shrimp. The National Academies Press; Washington, DC, USA: 2011.

Yang J., Yang J., Zhang D., Lu J. Feature fusion: Parallel strategy vs. serial strategy. Pattern Recognit. 2003;36:1369–1381. doi: 10.1016/S0031-3203(02)00262-5. DOI

Pavlidis M., Papandroulakis N., Divanach P. A method for the comparison of chromaticity parameters in fish skin: Preliminary results for coloration pattern of red skin Sparidae. Aquaculture. 2006;258:211–219. doi: 10.1016/j.aquaculture.2006.05.028. DOI

Erikson U., Shabani F., Beli E., Muji S., Rexhepi A. The impacts of perimortem stress and gutting on quality index and colour of rainbow trout (Oncorhynchus mykiss) during ice storage: A commercial case study. Eur. Food Res. Technol. 2018;244:197–206. doi: 10.1007/s00217-017-2944-9. DOI

Tang L., Tian L., Steward B. Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network. Trans. ASAE. 2003;46:1247–1254. doi: 10.13031/2013.13944. DOI

Trussell H., Saber E., Vrhel M. Color image processing: Basics and special issue overview. IEEE Signal Process. Mag. 2005;22:14–22. doi: 10.1109/MSP.2005.1407711. DOI

Westland S., Ripamonti C. Computational Colour Science Using MATLAB. John Wiley & Sons, Ltd.; Hoboken, NJ, USA: 2004.

Cheng H., Jiang X., Sun Y., Wang J. Color image segmentation: Advances and prospects. Pattern Recognit. 2001;34:2259–2281. doi: 10.1016/S0031-3203(00)00149-7. DOI

Xu Y., Wang X., Sun H., Wang H., Zhan Y. Study of monitoring maize leaf nutrition based on image processing and spectral analysis; Proceedings of the World Automation Congress; Kobe, Japan. 19–23 September 2010; Piscataway, NJ, USA: IEEE; 2010.

Tucker C. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979;8:127–150. doi: 10.1016/0034-4257(79)90013-0. DOI

Kawashima S., Nakatani M. An Algorithm for Estimating Chlorophyll Content in Leaves Using a Video Camera. Ann. Bot. 1998;81:49–54. doi: 10.1006/anbo.1997.0544. DOI

Karcher D., Richardson M. Quantifying Turfgrass Color Using Digital Image Analysis. Crop Sci. 2003;43:943–951. doi: 10.2135/cropsci2003.9430. DOI

Haidekker M. Advanced Biomedical Image Analysis. John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2010. Image Registration; pp. 350–385.

Hall-Beyer M. GLCM Texture: A Tutorial v. 1.0 through 2.7. [(accessed on 4 April 2017)]; Available online: http://hdl.handle.net/1880/51900.

Hsu C., Chang C., Lin C. A Practical Guide to Support Vector Classification. Department of Computer Science, National Taiwan University; Taipei, Taiwan: 2003.

Vapnik V. Statistical Learning Theory. John Wiley & Sons; Hoboken, NJ, USA: 1998.

Kuhn M. Building Predictive Models in R Using the caret Package. J. Stat. Softw. 2008;28 doi: 10.18637/jss.v028.i05. DOI

Breiman L. Bagging predictors. Mach. Learn. 1996;24:123–140. doi: 10.1007/BF00058655. DOI

Ho T. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 1998;20:832–844. doi: 10.1109/34.709601. DOI

Breiman L. Random forests. Mach. Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324. DOI

Díaz-Uriarte R., Andres S. Gene selection and classification of microarray data using random forest. BMC Bioinform. 2006;7:3. doi: 10.1186/1471-2105-7-3. PubMed DOI PMC

Liaw A., Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.

Akaike H. New look at the statistical model identification. IEEE Trans. Autom. Control. 1974;19:716–723. doi: 10.1109/TAC.1974.1100705. DOI

James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning: With Applications in R. Springer; Berlin/Heidelberg, Germany: 2013. DOI

Marschner I. glm2: Fitting generalized linear models with convergence problems. R J. 2011;3:12–15.

Venables W., Ripley B. Modern Applied Statistics with S-PLUS. 4th ed. Springer; Berlin/Heidelberg, Germany: 2002. p. 495.

Amigo J., Babamoradi H., Elcoroaristizabal S. Hyperspectral image analysis. A tutorial. Anal. Chim. Acta. 2015;896:34–51. doi: 10.1016/j.aca.2015.09.030. PubMed DOI

Bradley A. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997;30:1145–1159. doi: 10.1016/S0031-3203(96)00142-2. DOI

Robin X., Turck N., Hainard A., Tiberti N., Lisacek F., Sanchez J., Müller M. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:1–8. doi: 10.1186/1471-2105-12-77. PubMed DOI PMC

Ariana D., Guyer D., Shrestha B. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Comput. Electron. Agric. 2006;50:148–161. doi: 10.1016/j.compag.2005.10.002. DOI

Araújo S., Wetterlind J., Demattê J., Stenberg B. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. Eur. J. Soil Sci. 2014;65:718–729. doi: 10.1111/ejss.12165. DOI

Boser B., Guyon I., Vapnik V. A training algorithm for optimal margin classifiers; Proceedings of the Fifth Annual Workshop on Computational Learning Theory; Pittsburgh, PA, USA. 27–29 July 1992; New York, NY, USA: ACM; 1992. pp. 144–152.

Mountrakis G., Im J., Ogole C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011;66:247–259. doi: 10.1016/j.isprsjprs.2010.11.001. DOI

Chatzifotis S., Pavlidis M., Jimeno C., Vardanis G., Sterioti A., Divanach P. The effect of different carotenoid sources on skin coloration of cultured red porgy (Pagrus pagrus) Aquac. Res. 2005;36:1517–1525. doi: 10.1111/j.1365-2109.2005.01374.x. DOI

Ho A., O’Shea S., Pomeroy H. Dietary esterified astaxanthin effects on color, carotenoid concentrations, and compositions of clown anemonefish, Amphiprion ocellaris, skin. Aquac. Int. 2013;21:361–374. doi: 10.1007/s10499-012-9558-9. DOI

Rosenlund G., Obach A., Sandberg M., Standal H., Tveit K. Effect of alternative lipid sources on long-term growth performance and quality of Atlantic salmon (Salmo salar L.) Aquac. Res. 2001;32:323–328. doi: 10.1046/j.1355-557x.2001.00025.x. DOI

Choubert G., Mendes-Pinto M., Morais R. Pigmenting efficacy of astaxanthin fed to rainbow trout Oncorhynchus mykiss: Effect of dietary astaxanthin and lipid sources. Aquaculture. 2006;257:429–436. doi: 10.1016/j.aquaculture.2006.02.055. DOI

Nguyen T. The cholesterol-lowering action of plant stanol esters. Recent Adv. Nutr. Sci. 1999;129:2109–2112. doi: 10.1093/jn/129.12.2109. PubMed DOI

Regost C., Jakobsen J., Rørå A. Flesh quality of raw and smoked fillets of Atlantic salmon as influenced by dietary oil sources and frozen storage. Food Res. Int. 2004;37:259–271. doi: 10.1016/j.foodres.2003.12.003. DOI

Turchini G., Quinn G., Jones P., Palmeri G., Gooley G. Traceability and Discrimination among Differently Farmed Fish: A Case Study on Australian Murray Cod. J. Agric. Food Chem. 2009;57:274–281. doi: 10.1021/jf801962h. PubMed DOI

Shearer K., Kestin S., Warriss P. Farmed Fish Quality. Blackwell Science; Hoboken, NJ, USA: 2001. The effect of diet composition and feeding regime on the proximate composition of farmed fishes; pp. 31–41.

Dabbene F., Gay P., Tortia C. Traceability issues in food supply chain management: A review. Biosyst. Eng. 2014;120:65–80. doi: 10.1016/j.biosystemseng.2013.09.006. DOI

Føre M., Frank K., Norton T., Svendsen E., Alfredsen J., Dempster T., Eguiraun H., Watson W., Stahl A., Sunde L., et al. Precision fish farming: A new framework to improve production in aquaculture. Biosyst. Eng. 2017 doi: 10.1016/j.biosystemseng.2017.10.014. DOI

Dev S., Savoy F., Lee Y., Winkler S. Rough-set-based color channel selection. IEEE Geosci. Remote Sens. Lett. 2017;14:52–56. doi: 10.1109/LGRS.2016.2625303. DOI