Revisión: aplicación de imágenes hiperespectrales en la determinación de inocuidad en productos hidrobiológicos
DOI:
https://doi.org/10.57063/ricay.v2i2.47Keywords:
Hyperspectral imaging, quality, safety, fish, hydrobiologicalAbstract
Hydrobiological products are important for their contribution to the human diet and international trade. Currently, the food industry seeks to implement non-destructive techniques to reduce losses in quality control activities, speed to obtain information and make decisions in real time in production. Hyperspectral imaging (Hsi) is a technique that has advantages due to its low cost, reliability of the results and the reduction of analysis losses in the food industry chain. The objective was to carry out an analysis of the scientific information on the applications of Hsi for the determination of safety in hydrobiological products. Compiling research articles in the following databases: Elsevier, Taylor and Francis, Wiley and Google Scholar; on publications in the years 2013 to 2021. Sixty-nine (69) research articles were obtained, of which forty-six (46) primary studies were referenced. The samples on Hsi application information focused on the following species: grass carp, salmon, rainbow trout, shrimp, tilapia, among others. In conclusion, the use of non-invasive technologies, such as Hsi, is in great demand in the food industry, as it is an efficient, fast and non-destructive technology.
References
Agrippi. (2014, May 14). Visión artificial para mejorar la calidad.
Anderssen, K. E., Stormo, S. K., Skåra, T., Skjelvareid, M. H., & Heia, K. (2020). Predicting liquid loss of frozen and thawed cod from hyperspectral imaging. In LWT (Vol. 133, p. 110093). https://doi.org/10.1016/j.lwt.2020.110093. DOI: https://doi.org/10.1016/j.lwt.2020.110093
Azarmdel, H., Mohtasebi, S. S., Jafari, A., & Muñoz, A. R. (2019). Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision. In Computers and Electronics in Agriculture (Vol. 162, pp. 613–629). https://doi.org/10.1016/j.compag.2019.05.005. DOI: https://doi.org/10.1016/j.compag.2019.05.005
Cheng, J.-H., & Sun, D.-W. (2014). Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: Current research and potential applications. In Trends in Food Science & Technology (Vol. 37, Issue 2, pp. 78–91). https://doi.org/10.1016/j.tifs.2014.03.006. DOI: https://doi.org/10.1016/j.tifs.2014.03.006
Cheng, J.-H., & Sun, D.-W. (2015). Data fusion and hyperspectral imaging in tandem with least squares-support vector machine for prediction of sensory quality index scores of fish fillet. In LWT - Food Science and Technology (Vol. 63, Issue 2, pp. 892–898). https://doi.org/10.1016/j.lwt.2015.04.039. DOI: https://doi.org/10.1016/j.lwt.2015.04.039
Cheng, J.-H., Sun, D.-W., Pu, H.-B., Chen, X., Liu, Y., Zhang, H., & Li, J.-L. (2015). Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets. Journal of Food Engineering, 161, 33–39. DOI: https://doi.org/10.1016/j.jfoodeng.2015.03.011
Cheng, J.-H., Sun, D.-W., Pu, H.-B., Wang, Q.-J., & Chen, Y.-N. (2015). Suitability of hyperspectral imaging for rapid evaluation of thiobarbituric acid (TBA) value in grass carp (Ctenopharyngodon idella) fillet. Food Chemistry, 171, 258–265. DOI: https://doi.org/10.1016/j.foodchem.2014.08.124
Cheng, J.-H., Sun, D.-W., Pu, H., & Zhu, Z. (2015). Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. In Food Chemistry (Vol. 185, pp. 245–253). https://doi.org/10.1016/j.foodchem.2015.03.111 DOI: https://doi.org/10.1016/j.foodchem.2015.03.111
Coelho, P. A., Soto, M. E., Torres, S. N., Sbarbaro, D. G., & Pezoa, J. E. (2013). Hyperspectral transmittance imaging of the shell-free cooked clam Mulinia edulis for parasite detection. Journal of Food Engineering, 117(3), 408–416. DOI: https://doi.org/10.1016/j.jfoodeng.2013.01.047
Dacal-Nieto, A., Vazquez-Fernandez, E., Formella, A., Martin, F., Torres-Guijarro, S., & Gonzalez-Jorge, H. (2009). A genetic algorithm approach for feature selection in potatoes classification by computer vision. In 2009 35th Annual Conference of IEEE Industrial Electronics. https://doi.org/10.1109/iecon.2009.5414871. DOI: https://doi.org/10.1109/IECON.2009.5414871
Dai, Q., Cheng, J.-H., Sun, D.-W., Pu, H., Zeng, X.-A., & Xiong, Z. (2015). Potential of visible/near-infrared hyperspectral imaging for rapid detection of freshness in unfrozen and frozen prawns. In Journal of Food Engineering (Vol. 149, pp. 97–104). https://doi.org/10.1016/j.jfoodeng.2014.10.001. DOI: https://doi.org/10.1016/j.jfoodeng.2014.10.001
Da-Wen, C. J.-H. &. (2015). Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis. LWT - Food Science and Technology, 62(2), 1060–1068. DOI: https://doi.org/10.1016/j.lwt.2015.01.021
Dowlati, M., Mohtasebi, S. S., Omid, M., Razavi, S. H., Jamzad, M., & de la Guardia, M. (2013). Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. In Journal of Food Engineering (Vol. 28, 119, Issue 2, pp. 277–287). https://doi.org/10.1016/j.jfoodeng.2013.05.023. DOI: https://doi.org/10.1016/j.jfoodeng.2013.05.023
Fazial, F. F., Tan, L. L., & Zubairi, S. I. (2018). Bienzymatic creatine biosensor based on reflectance measurement for real-time monitoring of fish freshness. Sensors and Actuators. B, Chemical, 269, 36–45. DOI: https://doi.org/10.1016/j.snb.2018.04.141
Fontanillo, J. A. P. (2005). El pescado en la dieta.
Guo, W., Li, X., & Xie, T. (2021, mayo 30). Method and system for nondestructive detection of freshness in Penaeus vannamei based on hyperspectral technology. Aquaculture, 538. https://doi.org/10.1016/j.aquaculture.2021.736512 DOI: https://doi.org/10.1016/j.aquaculture.2021.736512
Guzmán-Bermúdez, Y., Lozano-Gallardo, A., Gonzales-Rubio, R., Méndez, J., Torre, J. C.-L., & Siche, R. (2019). Prediction of the freshness of Sciaena delicious “lorna” using hyperspectral images. In Agroindustrial science (Vol. 9, Issue 1, pp. 99–107). https://doi.org/10.17268/agroind.sci.2019.01.13. DOI: https://doi.org/10.17268/agroind.sci.2019.01.13
He, H.-J., & Sun, D.-W. (2015a). Inspection of harmful microbial contamination occurred in edible salmon flesh using imaging technology. In Journal of Food Engineering (Vol. 150, pp. 82–89). https://doi.org/10.1016/j.jfoodeng.2014.10.012. DOI: https://doi.org/10.1016/j.jfoodeng.2014.10.012
He, H.-J., & Sun, D.-W. (2015b). Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging. In LWT - Food Science and Technology (Vol. 62, Issue 1, pp. 11–18). https://doi.org/10.1016/j.lwt.2015.01.036. DOI: https://doi.org/10.1016/j.lwt.2015.01.036
Huang, X., Xu, H., Wu, L., Dai, H., Yao, L., & Han, F. (2016). A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy. Analytical Methods, 8(14), 2929–2935 DOI: https://doi.org/10.1039/C5AY03005F
Ivorra, E., Girón, J., Sánchez, A. J., Verdú, S., Barat, J. M., & Grau, R. (2013). Detection of expired vacuum-packed smoked salmon based on PLS-DA method using hyperspectral images. In Journal of Food Engineering (Vol. 117, Issue 3, pp. 342–349). https://doi.org/10.1016/j.jfoodeng.2013.02.022. DOI: https://doi.org/10.1016/j.jfoodeng.2013.02.022
Ivorra, E., Sánchez, A. J., Verdú, S., Barat, J. M., & Grau, R. (2016). Shelf life prediction of expired vacuum-packed chilled smoked salmon based on a KNN tissue segmentation method using hyperspectral images. In Journal of Food Engineering (Vol. 178, pp. 110–116). https://doi.org/10.1016/j.jfoodeng.2016.01.008. DOI: https://doi.org/10.1016/j.jfoodeng.2016.01.008
Khoshnoudi-Nia, S., & Moosavi-Nasab, M. (2019). Comparison of various chemometric analysis for rapid prediction of thiobarbituric acid reactive substances in rainbow trout fillets by hyperspectral imaging technique. Food Science & Nutrition, 7(5), 1875–1883. DOI: https://doi.org/10.1002/fsn3.1043
Khoshtaghaza, M. H., Khojastehnazhand, M., Mojaradi, B., Goodarzi, M., & Saeys, W. (2016). Texture Quality Analysis of Rainbow Trout Using Hyperspectral Imaging Method. In International Journal of Food Properties (Vol. 19, Issue 5, pp. 974–983). https://doi.org/10.1080/10942912.2015.1042111. DOI: https://doi.org/10.1080/10942912.2015.1042111
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. In Information and Software Technology (Vol. 51, Issue 1, pp. 7–15). https://doi.org/10.1016/j.infsof.2008.09.009. DOI: https://doi.org/10.1016/j.infsof.2008.09.009
Lalabadi, H. M., Sadeghi, M., & Mireei, S. A. (2020). Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. In Aquacultural Engineering (Vol. 90, p. 102076). https://doi.org/10.1016/j.aquaeng.2020.102076. DOI: https://doi.org/10.1016/j.aquaeng.2020.102076
Ma, J., Sun, D.-W., Qu, J.-H., & Pu, H. (2017). Prediction of textural changes in grass carp fillets as affected by vacuum freeze drying using hyperspectral imaging based on integrated group wavelengths. In LWT - Food Science and Technology (Vol. 82, pp. 377–385). https://doi.org/10.1016/j.lwt.2017.04.040. DOI: https://doi.org/10.1016/j.lwt.2017.04.040
Manley, M. (2014). Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chemical Society Reviews, 43(24), 8200–8214 DOI: https://doi.org/10.1039/C4CS00062E
Marchant, I. M. (2019). MANUAL Y CONSERVACION DE ALIMENTOS. Obtenido http://www.inacap.cl/web/material-apoyo-cedem/profesor/Gastronomia/Manuales/Manual_Conservacion_de_Alimentos.pdf
Nations, F. A. A. O. of T. U., & Food and Agriculture Organization of the United Nations. (2018). El estado mundial de la pesca y la acuicultura 2018. In El estado mundial de la pesca y la acuicultura. https://doi.org/10.18356/37c4c7b4-es DOI: https://doi.org/10.18356/37c4c7b4-es
Omwange, K. A., Al Riza, D. F., Sen, N., Shiigi, T., Kuramoto, M., Ogawa, Y., Kondo, N., & Suzuki, T. (2020). Fish freshness monitoring using UV-fluorescence imaging on Japanese dace (Tribolodon hakonensis) fisheye. Journal of Food Engineering, 287(110111), 110111. DOI: https://doi.org/10.1016/j.jfoodeng.2020.110111
Peñuelas, R. A. C. (2020). Análisis de imágenes hiperespectrales y aplicaciones para la calidad de los alimentos. In Investigación y Ciencia de la Universidad Autónoma de Aguascalientes (Issue 79, pp. 101–102). https://doi.org/10.33064/iycuaa2020792988. DOI: https://doi.org/10.33064/iycuaa2020792988
Ponce-Corona, E., Sanchez, M. G., Fajardo-Delgado, D., Castro, W., De-la-Torre, M., & Avila-George, H. (2019). Detection of Vegetation Using Unmanned Aerial Vehicles Images: A Systematic Review. In 2019 8th International Conference On Software Process Improvement (CIMPS). https://doi.org/10.1109/cimps49236.2019.9082434. DOI: https://doi.org/10.1109/CIMPS49236.2019.9082434
Qin, J., Vasefi, F., Hellberg, R. S., Akhbardeh, A., Isaacs, R. B., Yilmaz, A. G., Hwang, C., Baek, I., Schmidt, W. F., & Kim, M. S. (2020). Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques. In Food Control (Vol. 114, p. 107234). https://doi.org/10.1016/j.foodcont.2020.107234. DOI: https://doi.org/10.1016/j.foodcont.2020.107234
Qu, J.-H., Cheng, J.-H., Sun, D.-W., Pu, H., Wang, Q.-J., & Ma, J. (2015). Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique. Lebensmittel-Wissenschaft + [i.e. Und] Technologie. Food Science + Technology. Science + Technologie Alimentaire, 62(1), 202–209. DOI: https://doi.org/10.1016/j.lwt.2015.01.018
Rahman, A., Kondo, N., Ogawa, Y., Suzuki, T., Shirataki, Y., & Wakita, Y. (2016). Classification of fresh and spoiled Japanese dace ( Tribolodon hakonensis ) fish using ultraviolet–visible spectra of eye fluid with multivariate analysis. In Engineering in Agriculture, Environment and Food (Vol. 9, Issue 1, pp. 64–69). https://doi.org/10.1016/j.eaef.2015.06.004. DOI: https://doi.org/10.1016/j.eaef.2015.06.004
Reis, M. M., Martínez, E., Saitua, E., Rodríguez, R., Pérez, I., & Olabarrieta, I. (2017). Non-invasive differentiation between fresh and frozen/thawed tuna fillets using near infrared spectroscopy (Vis-NIRS). In LWT (Vol. 78, pp. 129–137). https://doi.org/10.1016/j.lwt.2016.12.014. DOI: https://doi.org/10.1016/j.lwt.2016.12.014
Salcido, N. M. D. la F., De la Fuente Salcido, N. M., & Corona, J. E. B. (2010). Inocuidad y bioconservación de alimentos. In Acta Universitaria (Vol. 20, Issue 1, pp. 43–52). https://doi.org/10.15174/au.2010.76. DOI: https://doi.org/10.15174/au.2010.76
Shi, C., Qian, J., Han, S., Fan, B., Yang, X., & Wu, X. (2018). Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia (Oreochromis niloticus) pupil and gill color during storage at 4°C. Food Chemistry, 243, 134–140. DOI: https://doi.org/10.1016/j.foodchem.2017.09.047
Skjelvareid, M. H., Heia, K., Olsen, S. H., & Stormo, S. K. (2017). Detection of blood in fish muscle by constrained spectral unmixing of hyperspectral images. In Journal of Food Engineering (Vol. 212, pp. 252–261). https://doi.org/10.1016/j.jfoodeng.2017.05.029. DOI: https://doi.org/10.1016/j.jfoodeng.2017.05.029
Song, S., Liu, Z., Huang, M., Zhu, Q., Qin, J., & Kim, M. S. (2020). Detection of fish bones in fillets by Raman hyperspectral imaging technology. In Journal of Food Engineering (Vol. 272, p. 109808). https://doi.org/10.1016/j.jfoodeng.2019.109808 DOI: https://doi.org/10.1016/j.jfoodeng.2019.109808
Sun, D.-W. (2010). Hyperspectral Imaging for Food Quality Analysis and Control. Elsevier. Taheri-Garavand, A., Fatahi, S., Banan, A., & Makino, Y. (2019). Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches. In Computers and Electronics in Agriculture (Vol. 159, pp. 16–27). https://doi.org/10.1016/j.compag.2019.02.023. DOI: https://doi.org/10.1016/j.compag.2019.02.023
Tan, C., Huang, Y., Feng, J., Li, Z., & Cai, S. (2018). Freshness assessment of intact fish via 2D 1H J-resolved NMR spectroscopy combined with pattern recognition methods. In Sensors and Actuators B: Chemical (Vol. 255, pp. 348–356). https://doi.org/10.1016/j.snb.2017.08.060. DOI: https://doi.org/10.1016/j.snb.2017.08.060
Tappi, S., Rocculi, P., Ciampa, A., Romani, S., Balestra, F., Capozzi, F., & Dalla Rosa, M. (2017). Computer vision system (CVS): a powerful non-destructive technique for the assessment of red mullet (Mullus barbatus) freshness. European Food Research and Technology = Zeitschrift Fur Lebensmittel-Untersuchung Und -Forschung. A, 243(12), 2225–2233. DOI: https://doi.org/10.1007/s00217-017-2924-0
Velioğlu, H. M., Temiz, H. T., & Boyaci, I. H. (2015). Differentiation of fresh and frozen-thawed fish samples using Raman spectroscopy coupled with chemometric analysis. Food Chemistry, 172, 283–290. DOI: https://doi.org/10.1016/j.foodchem.2014.09.073
Wang, C., Yu, Z., Zhao, X., Lu, H., & Wang, Q. (2021). Rapid response to amine vapor based on fluorescent light-up sensor for real-time and visual detection of crawfish and fish freshness. Dyes and Pigments, 189(109228), 109228. DOI: https://doi.org/10.1016/j.dyepig.2021.109228
Wang, X., Shan, J., Han, S., Zhao, J., & Zhang, Y. (2019). Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging. In Analytical Letters (Vol. 52, Issue 12, pp. 1845–1859). https://doi.org/10.1080/00032719.2019.1571077. DOI: https://doi.org/10.1080/00032719.2019.1571077
Wei, W., Yan, Y., Zhang, X. P., Liu, Y., Lu, Y., Shi, W. Z., & Xu, C. H. (2018, Diciembre 5). Enhanced chemical and spatial recognition of fish bones in surimi by Tri-step infrared spectroscopy and infrared microspectroscopic imaging. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 205, 186-192. https://doi.org/10.1016/j.saa.2018.07.031. DOI: https://doi.org/10.1016/j.saa.2018.07.031
Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering - EASE ’14. https://doi.org/10.1145/2601248.2601268. DOI: https://doi.org/10.1145/2601248.2601268
Wu, D., & Sun, D.-W. (2013). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta, 111, 39–46. DOI: https://doi.org/10.1016/j.talanta.2013.03.041
Wu, D., Sun, D.-W., & He, Y. (2012). Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. In Innovative Food Science & Emerging Technologies (Vol. 16, pp. 361–372). https://doi.org/10.1016/j.ifset.2012.08.003. DOI: https://doi.org/10.1016/j.ifset.2012.08.003
Xie, T., Li, X., Zhang, X., Hu, J., & Fang, Y. (2021). Detection of Atlantic salmon bone residues using machine vision technology. Food Control, 123(107787), 107787. DOI: https://doi.org/10.1016/j.foodcont.2020.107787
Xu, T., Wang, X., Huang, Y., Lai, K., & Fan, Y. (2019). Rapid detection of trace methylene blue and malachite green in four fish tissues by ultra-sensitive surface-enhanced Raman spectroscopy coated with gold nanorods. In Food Control (Vol. 106, p. 106720). https://doi.org/10.1016/j.foodcont.2019.106720. DOI: https://doi.org/10.1016/j.foodcont.2019.106720
Yu, X., Wang, J., Wen, S., Yang, J., & Zhang, F. (2019). A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei). Biosystems Engineering, 178, 244–255. DOI: https://doi.org/10.1016/j.biosystemseng.2018.11.018
Zhang, H., Zhang, S., Chen, Y., Luo, W., Huang, Y., Tao, D., Zhan, B., & Liu, X. (2020). Non-destructive determination of fat and moisture contents in Salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural features. In Journal of Food Composition and Analysis (Vol. 92, p. 103567). https://doi.org/10.1016/j.jfca.2020.103567. DOI: https://doi.org/10.1016/j.jfca.2020.103567
Zhang, W., Cao, A., Shi, P., & Cai, L. (2021). Rapid evaluation of freshness of largemouth bass under different thawing methods using hyperspectral imaging. In Food Control (Vol. 125, p. 108023). https://doi.org/10.1016/j.foodcont.2021.108023. DOI: https://doi.org/10.1016/j.foodcont.2021.108023
Zhang, Y., Luo, Q., Ding, K., Liu, S. G., & Shi, X. (2021). A smartphone-integrated colorimetric sensor of total volatile basic nitrogen (TVB-N) based on Au@MnO2 core-shell nanocomposites incorporated into hydrogel and its application in fish spoilage monitoring. Sensors and Actuators. B, Chemical, 335(129708), 129708. DOI: https://doi.org/10.1016/j.snb.2021.129708
