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Catfish (Pangasius hypothalamus) is the most widely cultivated fishery commodity which can be sold in fresh form. In addition, catfish can also be processed into smoked catfish so that it has a higher selling value. Smoked catfish usually arrives abroad such as Malaysia, but currently it has stopped because the shelf life of smoked catfish can only last for 14 days. Currently, physical quality identification of smoked catfish is done manually and chemical quality is done destructively. Efforts are needed to determine smoked catfish using artificial intelligence such as image processing, artificial neural networks, and near infrared (NIR). This method has been widely used in identifying the physical and chemical properties of fresh and livestock fishery products and processed products from each. This state of the art aims to describe the development of fishery and livestock product quality control technology that is currently being developed, specifically in image processing, artificial neural networks, and NIR which are used to identify the physical quality and chemical content of fishery and livestock products and their processed products


Jaringan syaraf tiruan Mutu NIR Pengolahan citra Produk daging

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  1. Adawiyah R,. 2007. Pengawetan dan Pengolahan Ikan. PT Bumi Aksara. Jakarta.
  2. Ayu, D.F. R. Efendy, Y. Nopiani, E. Saputra, dan S. Haryani. 2022. Pendugaan umur simpan ikan patin salai menggunakan metode akselerasi dengan kemasan HDPE dan teknik pengemasan aluminium foil. Jurnal Teknologi dan Industri Pertanian Indonesia. 72-80. https://
  3. Badan Pusat Statistik Provinsi Riau. 2019. Produksi budidaya perikanan menurut provinsi dan subsektor.BPS. Pekanbaru.
  4. Badan Standardisasi Nasional. 2013. SNI 01-2725-2013 Persyaratan Mutu Ikan Asap. Jakarta.
  5. Balaban, M.Ö., dan Z. Alçiçek. 2016. Use of polarized light in image analysis: Application to the analysis of fish eye color during storage. LWT-Food Science and Technology. 365-371.
  6. Bao, G. J. Niu, S. Li, L. Zhang, and Y. Luo. 2022. Effects of ultrasound pretreatment on the quality, nutrients and volatile compounds of dry-cured yak meat. Ultrasonics Sonochemistry. 1-10.
  7. Barbin, D.G., S.M. Mastelini, S. Barbon Jr, G.F.C. Campos, A.P.A.C. Barbon, and M. Shimokomaki. 2016. Digital image analyses as an alternative tool for chicken quality assessment. Biosystems Engineering. 85-93.
  8. Barbin, D. F., A.T. Badar, D.C.B. Honorato, E.Y. Ida, and M. Shimokomaki. 2019. Identification of turkey meat and processed products using near infrared spectroscopy. Food Control, 106816. https://doi:10.1016/j.foodcont.2019.106816.
  9. Boschetti, L., M. Ottavian, P. Facco, M. Barolo, L. Serva, S. Balzan, and E. Novelli. 2013. A correlative study on data from pork carcass and processed meat (Bauernspeck) for automatic estimation of chemical parameters by means of near-infrared spectroscopy. Meat Science, 95(3), 621–628. https://doi:10.1016/j.meatsci.2013.06.001.
  10. Chau, A., M. Whitworth, C. Leadley, and S. Millar. 2009. Innovative sensors to rapidly and non-destructively determine fish freshness. Seafish Industrial Authority, Report No. CMS/REP/110284/1.
  11. Chen, Y., K. Cai, Z. Tu, W. Nie, T. Ji, B. Hu, C. Chen, and S. Jiang. 2018. Prediction of benzo[a ]pyrene content of smoked sausage using back-propagation artificial neural network. Journal of the Science of Food and Agriculture. https://doi:10.1002/jsfa.8801.
  12. Cheng, J.-H., D.-W. Sun, X.-A. Zeng, and H.-B Pu. 2014. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innovative Food Science & Emerging Technologies, 21, 179–187. http://doi:10.1016/j.ifset.2013.10.013.
  13. Cheng, J.-H., D.-W. Sun, H.-B. Pu , X. Chen, Y. Liu, H. Zhang, and J.-L. Li. 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. http://doi:10.1016/j.jfoodeng.2015.03.011.
  14. Cluff, K., G.K. Naganathan, J. Subbiah, R. Lu, C.R. Calkins, and A. Samal. 2008. Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region. Sensing and Instrumentation for Food Quality and Safety. 2: 189–196.
  15. Dutta, M.K., A. Issac, N. Minhas, and B. Sarkar. 2016. Image processing based method to assess fish quality and freshness. Journal of Food Engineering. 50-58.
  16. Dowlati, M., S.S. Mohtasebi, M. Omid, S.H. Razavi, M. Jamzad, and M. de la Guardia. 2013. Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. Journal of Food Engineering. 277-287.
  17. Elmasry, G., D.F. Barbin, D-W. Sun, and P. Allen. 2012. Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview. Critical Reviews in Food Science and Nutrition. 689-711.
  18. Fauzi dan A. Muchtar. 2011. Pengasapan ikan menggunakan lemari asap skala rumah tangga. Jurnal Perikanan dan Kelautan. 16(1): 22-26.
  19. Gao, F., Xu, L., Zhang, Y., Yang, Z., Han, L., & Liu, X. (2018). Analytical Raman spectroscopic study for discriminant analysis of different animal-derived feedstuff: Understanding the high correlation between Raman spectroscopy and lipid characteristics. Food Chemistry, 240, 989–996. https://doi:10.1016/j.foodchem.2017.07.143.
  20. He, H-J., D. Wu, and D-W. Sun. 2013. Non-destructive Spectroscopic and Imaging Techniques for Quality Evaluation and Assessment of Fish and Fish Products. Food Science and Nutrition. 1-80.
  21. Hu, Y., H. Yu, K. Dong, S. Yang, X. Ye, and S. Chen. 2014. Analysis of the tenderisation of jumbo squid (Dosidicus gigas) meat by ultrasonic treatment using response surface methodology. Food Chemistry. 219-225. http://doi:10.1016/j.foodchem.2014.01.085.
  22. Iqbal, A., N. A. Valous, F. Mendoza, D.-W. Sun, and P. Allen. 2010. Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. Meat Science, 84(3), 455–465. http://doi:10.1016/j.meatsci.2009.09.016.
  23. Isamu, K.T., Hari P. dan Sudarmadji S. Y. 2012. Karakteristik fisik, kimia dan organoleptik ikan cakalang (Katsuwonus pelamis) asap di Kendari. Jurnal Teknologi Pertanian. 13(2): 105-110.
  24. Jiang, S., D. Zhao, Y. Nian, J. Wu, M. Zhang, Q. Li, and C. Li. 2021. Ultrasonic treatment increased functional properties and in vitro digestion of actomyosin complex during meat storage. Food Chemistr. 1-11. http://doi:10.1016/j.foodchem.2021.129398.
  25. Karimi, S., J. Feizy, F. Mehrjo, and M. Farrokhnia. 2016. Detection and quantification of food colorant adulteration in saffron sample using chemometric analysis of FT-IR spectra. RSC Advances, 6(27), 23085–23093. https://doi:10.1039/c5ra25983e.
  26. Kong, S.G. 2003. Inspection of poultry skin tumor using hyperspectral fluorescence imaging. Proceedings of SPIE-The International Society for Optical Engineering. 5132: 455–463.
  27. Kong, S.G., Y.-R. Chen, I. Kim, and M.S. Kim. 2004. Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. Applied Optics. 43(4): 824–833.
  28. Kumar, Y., & Chandrakant Karne, S. (2017). Spectral analysis: A rapid tool for species detection in meat products. Trends in Food Science & Technology, 62, 59–67. https://doi:10.1016/j.tifs.2017.02.008.
  29. Lalabadi, H.M., M. Sadeghi, and S.A Mireei. 2020. Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. Aquacultural Engineering. 1-9 http://doi:10.1016/j.aquaeng.2020.10207.
  30. Liu, D., X-A. Zeng, and D-W Sun. 2013. NIR Spectroscopy and Imaging Techniques for Evaluation of Fish Quality-A Review. Applied Spectroscopy Reviews. 609-628.
  31. Mabood, F., R. Boqué, A.Y. Alkindi, A. Al-Harrasi, I.S. Al Amri, S. Boukra, F. Jabeen, J. Hussain, G. Abbas, Z. Naureen, Q. M.I. Haq, H.H. Shah, A. Khan, S.K. Khalaf, and I. Kadim. 2020. Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis. Meat Science, 108084. http://doi:10.1016/j.meatsci.2020.108084.
  32. Mathiassen, J.R., E. Misimi, M. Bondø, E. Veliyulin, and S.O. Østvik. 2011. Trends in application of imaging technologies to inspection of fish and fish products. Trends in Food Science & Technology. 257-275. http://doi:10.1016/j.tifs.2011.03.006.
  33. Mendez, J., L. Mendoza, Cruz-Tirado, J. P., R. Quevedo, and R. Siche. 2019. Trends in application of NIR and hyperspectral imaging for food authentication. Scientia Agropecuaria, 10(1), 143–161. https://doi: 10.17268/sci.agropecu.2018.01.16.
  34. Merkin, G. V., L.H. Stien, K. Pittman, and R. Nortvedt. 2013. Digital image analysis as a tool to quantify gaping and morphology in smoked salmon slices. Aquacultural Engineering, 54, 64–71. http://doi:10.1016/j.aquaeng.2012.11.003.
  35. Nakariyakul, S. and D.P. Casasent. 2007b. Fusion algorithm for poultry skin tumor detection using hyperspectral data. Applied Optics. 46(3): 357–364.
  36. Noghabi, M.S., M. Kaviani, and R. Niazmdand. 2014. Modeling of Oxidation Stability of Canola Oil Using Artificial Neural Networks during Deep Fat Frying of Potatoes. Journal of Food Processing and Preservation, 39(6), 1006–1015. https://doi:10.1111/jfpp.12314.
  37. Peng, Y. and J. Wu. 2008. Hyperspectral scattering profiles for prediction of beef tenderness. ASABE Annual International Meeting, RI, Paper No. 080004.
  38. Pirsaheb, M., E.-N. Dragoi, and Y. Vasseghian. 2020. Polycyclic Aromatic Hydrocarbons (PAHs) Formation in Grilled Meat products—Analysis and Modeling with Artificial Neural Networks. Polycyclic Aromatic Compounds, 1–17. http://doi:10.1080/10406638.2020.1720750.
  39. Pszczola, D. E. 1995. Highlights Production and Users of Smoke Based Flavours. Food Technology. 49 (1) : 70-74.
  40. Putra, M.R., M. Sukmiwati, dan N.I. Sari. 2017. Karakteristik Mutu Ikan Patin Asap (Pangasius sp) dengan Metode Pengasapan Tradisional dan Cair. Jurnal Online Mahasiswa Fakultas Perikanan dan Kelautan. Universitas Riau.
  41. Qiao, J., Ngadi, M.O., Wang, N., Gariepy, C., and Prasher, S.O. 2007. Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering. 83(1): 10–16.
  42. Rady, A., & Adedeji, A. (2018). Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Science, 136, 59–67. https://doi:10.1016/j.meatsci.2017.10.014.
  43. Rezende-de-Souza, J.H., V.F. de Moraes-Neto, G.Z. Cassol, M.C.D.S Camelo, and L.K. Savay-da-Silva. 2022. Use of colorimetric data and artificial neural networks for the determination of freshness in fish. Food Chemistry Advances. 1-7. http://doi:10.1016/j.focha.2022.100129.
  44. Rodiah, N. S., Utomo, B. S. B., Basmal, J, dan Hastarini, E. 2016. Pemurnian minyak ikan patin dari hasil samping pengasapan ikan. Jurnal Kelautan dan Perikanan. 11(2): 171-182.
  45. Rohman, A. 2019. The employment of Fourier transform infrared spectroscopy coupled with chemometrics techniques for traceability and authentication of meat and meat products. Journal of Advanced Veterinary and Animal Research, 6(1), 9–17. https:// doi: 10.5455/javar.2019.f306.
  46. Segtnan, V.H., M. Høy, F. Lundby, B. Narum, and J.P. Wold. 2009. Fat distributional analysis in salmon fillets using non-contact near infrared interactance imaging: A sampling and calibration strategy. Journal of Near-Infrared Spectroscopy. 17(5): 247–253.
  47. Segtnan, V.H., M. Høy, O. Sørheim, A. Kohler, F. Lundby, J.P. Wold, and R. Ofstad. 2009. Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using x-ray computed tomography and NIR interactance imaging. Journal of Agricultural and Food Chemistry. 57: 1705–1710.
  48. Sinanoglou, V.J., D. Cavouras, D. Xenogiannopoulos, C. Proestos, and P. Zoumpoulakis. 2018. Quality Assessment of Pork and Turkey Hams Using FT-IR Spectroscopy, Colorimetric, and Image Analysis. Foods. 2-16. http://doi:10.3390/foods7090152.
  49. Sulistijowati, R., O. Suhara, J. Nurhajati, E. Afrianto, dan Z. Udin. 2011. Mekanisme dan Faktor-Faktor Keberhasilan Pengasapan Ikan. Unpad Press. Bandung.
  50. Swastawati, F., T. Surti, T.W. Agustini, dan P.H. Riyadi. 2013. Karakteristik Kualitas Ikan Asap yang Diproses Menggunakan Metode dan Jenis Ikan Berbeda. Jurnal Aplikasi Teknologi Pangan. Vol.2 No.3. Hal 126-132.
  51. Wieja, K., P. Kiełczyński, P. Szymański, M. Szalewski, A. Balcerzak, and S. Ptasznik. 2021. Identification and investigation of mechanically separated meat (MSM) with an innovative ultrasonic method. Food Chemistry. 1-9 http://doi:10.1016/j.foodchem.2020.128907.
  52. Xing, W., X. Liu, C. Xu, M.S. Farid, K. Cai, H. Zhou, C. Chen, and B. Xu. 2022. Application of artificial neural network to predict benzo[a]pyrene based on multiple quality of smoked sausage. Food Science and Technology. 1-9.
  53. Yang, L., T. Wu, Y. Liu, J. Zou, Y. Huang, V. Babu V., S., and L. Lin. 2018. Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy. Journal of Spectroscopy, 2018, 1–10. https://doi:10.1155/2018/2413874.