Revisión: aplicación de imágenes hiperespectrales en la determinación de inocuidad en productos hidrobiológicos

Autores/as

  • Mónica Castro Barba Universidad Nacional de Frontera, Sullana, Piura, Perú.
  • Roberto Simón Seminario Sanz Universidad Nacional de Frontera, Sullana, Piura, Perú.

Palabras clave:

Imágenes hiperespectrales, calidad, inocuidad, pescado, hidrobiológicos

Resumen

Los productos hidrobiológicos son importantes por su aporte en la dieta humana y en el intercambio comercial internacional. Actualmente, la industria alimentaria busca implementar técnicas no destructivas para reducir pérdidas en las actividades de control de calidad, rapidez para obtener información y tomar decisiones en tiempo real en la producción. Las imágenes hiperespectrales (Hsi); es una técnica que posee ventajas por su bajo costo, confiabilidad de los resultados y la reducción de mermas por análisis en la cadena de la industria alimentaria. El objetivo fue realizar un análisis de la información científica sobre las aplicaciones de las Hsi para la determinación de inocuidad en productos hidrobiológicos. Recopilando artículos de investigación en las bases de datos: Elsevier, Taylor and Francis, Wiley y Google Académico; sobre las publicaciones en los años 2013 al 2021. Obteniéndose sesenta y nueve (69) artículos de investigación, de los que se referenciaron cuarenta y seis (46) estudios primarios. Las muestras sobre información de aplicaciones de Hsi se centraron en las especies: carpa herbívora, salmón, trucha arcoíris, camarón, tilapia, entre otros. Concluyendo que el uso de tecnologías no invasivas, como son las Hsi, generan una gran demanda en la industria alimentaria, al ser esta una tecnología eficiente, rápida y no destructiva.

Citas

Agrippi. (2014, May 14). Visión artificial para mejorar la calidad.

https://serviciosencalidadeinocuidad.wordpress.com/2014/05/14/vision-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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

Manley, M. (2014). Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chemical Society Reviews, 43(24), 8200–8214

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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2024-06-13

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Castro Barba, M., & Seminario Sanz, R. S. (2024). Revisión: aplicación de imágenes hiperespectrales en la determinación de inocuidad en productos hidrobiológicos. Revista De Investigación Científica De La UNF – Aypate, 2(2), 98–116. Recuperado a partir de https://aypate.revista.unf.edu.pe/index.php/aypate/article/view/47

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