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Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision

Year 2021, Volume: 18 Issue: 1, 157 - 168, 30.12.2020
https://doi.org/10.33462/jotaf.753185

Abstract

Aspergillus türleri tarafından üretilen aflatoksinler, gıda endüstrisinde, özellikle kuru fındık ve meyvelerde büyük önem taşımaktadır. Tarım ürünleri hasat, kurutma ve depolama gibi aşamalarda aflatoksinlere daha yatkındır. Aflatoksinli ürünlerin hızlı bir şekilde tanımlanması, gıda endüstrisi için büyük önem taşımaktadır. Gıda endüstrisi, daha karlı ve doğru sonuçlar elde etmek için insan gücü yerine görüntüleme teknolojilerini kullanmaya başladılar. Ayrıca aflatoksin kontaminasyonundan kaynaklanan ekonomik kayıplar ve hastalıklar da önemli bir sorun teşkil etmektedir. Bu çalışmanın amacı, aflatoksinli antep fıstıklarını sağlıklı olanlardan ayırmak için görüntü işleme tabanlı bir aflatoksinli antep fıstığı tanımlama sistemi geliştirmektir. Olası aflatoksin kontaminasyonunu gösteren parlak yeşilimsi sarı floresan (BGYF), kontamine antep fıstıklarının tanımlanması için ayırt edici bir faktör olarak araştırılmıştır. Toplam 100 adet antep fıstığı örneği (50 BGYF+ ve 50 BGYF-) değerlendirilmiştir. Çalışmada, antep fıstığı örneklerini BGYF+ ve BGYF- olarak sınıflandırmak amacıyla görüntüleme algoritmaları geliştirilmiştir. Her bir antep fıstığı örneğinin renk (L*, a* ve b*) ile kinetik (kroma, renk tonu açısı ve kahverengileşme indeksi) parametreleri analiz edilmiş ve aralarındaki farklar istatistiksel olarak belirlenmiştir. Renk ve kinetik parametreler de gruplandırılmış, görüntü işleme algoritmasını basitleştirmek amacıyla faktör analizi yöntemi kullanılarak birbirleriyle ilişkilendirilmiştir. İki grup arasındaki tüm renk ve kinetik parametreler için istatistiksel olarak anlamlı farklılıklar bulunmuştur. Faktör analizi sonuçlarına göre; kroma, a* ve kahverengileşme indeksi değerleri büyük ölçüde Faktör 1'de yer alırken, renk ton açısı ve b* değerleri ise Faktör 2'de yer almıştır. Geriye kalan L* değeri ise Faktör 3'de yer almıştır. Gelecek çalışmalarda, yeni bir eş zamanlı tespit etme ve ayırma sistemi geliştirmek amacıyla optimize edilmiş (daha etkili ve kullanışlı) bir görüntü işleme algoritması, istatistiksel analiz sonuçlarına dayalı olarak geliştirilecektir. Bu çalışmadan elde edilen sonuçlar daha ileri araştırmalar için de bir temel oluşturacaktır.

References

  • Ataş, M., Yardimci, Y. and Temizel, A. 2012. A new approach to aflatoxin detection in chili pepper by machine vision. Computers and Electronics in Agriculture, 87, 129–141.
  • Campbell, B. C., Molyneux, R. J. and Schatzki, T. F. 2003. Current research on reducing pre‐ and post‐harvest aflatoxin contamination of U.S. almond, pistachio, and walnut. Journal of Toxicology: Toxin Reviews 22(2-3), 225-266.
  • Dichter, C. R. 1984. Risk estimates of liver cancer due to aflatoxin exposure from peanuts and peanut products. Food and Chemical Toxicology, 22, 431-437.
  • Erdem, T., Ozluoymak, O. B. and Kizildag, N. 2018. Color change analysis of dried orange slices during hot air drying. Fresenius Environmental Bulletin, 27, 6064-6072.
  • Gloria, E. M. 2011. Aflatoxin contamination distribution among grains and nuts. In: aflatoxins-detection, measurement and control. Torres-Pacheco: 1st edn. InTech.
  • Güneş, A., Durmuş, E. and Kalkan, H. 2013. Detection of high aflatoxin risk figs with computer vision. In: 21st Signal Processing and Communications Applications Conference (SIU). Cyprus.
  • Hadavi, E. 2005. Several physical properties of aflatoxin-contaminated pistachio nuts: Application of BGY fluorescence for separation of aflatoxin-contaminated nuts. Food Additives & Contaminants, 22, 1144-1153.
  • Hepsag, F., Golge, O. and Kabak, B. 2014. Quantitation of aflatoxins in pistachios and groundnuts using HPLC-FLD method. Food Control, 38, 75-81.
  • Iamanaka, B. T., Castle de Menezes, H., Vicente, E., Leite, R. S. F. and Taniwaki, M. H. 2007. Aflatoxigenic fungi and aflatoxins occurrence in sultanas and dried figs commercialized in Brazil. Food Control, 18, 454-457.
  • Kalkan, H., Güneş, A., Durmuş, E. and Kuşçu, A. 2014. Non-invasive detection of aflatoxin-contaminated figs using fluorescence and multispectral imaging. Food Additives and Contaminants Part A, 31, 1414-1421.
  • Lizárraga-Paulín, E. G., Moreno-Martínez, E. and Miranda-Castro, S. P. 2011. Aflatoxins and their impact on human and animal health: An emerging problem. Aflatoxins – Biochemistry and Molecular Biology (pp. 255-282). InTech.
  • Lunadei, L., Ruiz-Garcia, L., Bodria, L. and Guidetti, R. 2013. Image-based screening for the identification of bright greenish yellow fluorescence on pistachio nuts and cashews. Food and Bioprocess Technology, 6, 1261-1268.
  • Marsh, P. B., Simpson, M. E., Ferretti, R. J., Merola, G. V., Donoso, J., Craig, G. O., Trucksess, M. W. and Work, P. S. 1969. Mechanism of formation of a fluorescence in cotton fiber associated with aflatoxin in the seeds at harvest. Journal of Agricultural and Food Chemistry, 17, 468-472.
  • McClure, W. F. and Farsaie, A. 1980. Dual-wavelength fiber-optic photometer measures fluorescence of aflatoxin-contaminated pistachio nuts. Transactions of the ASAE, 23, 204-207.
  • Nilüfer, D. and Boyacıoğlu, D. 2002. Comparative study of three different methods for the determination of aflatoxins in tahini. Journal of Agricultural and Food Chemistry, 50, 3375-3379.
  • Ozluoymak, O. B. and Guzel, E. 2018. Prediction of aflatoxin contamination on dried fig (ficus carica) samples by spectral image analysis in comparison with laboratory results. Fresenius Environmental Bulletin, 27, 681-689.
  • Özlüoymak, Ö. B. 2014. Development of a UV-based imaging system for real-time detection and separation of dried figs contaminated with aflatoxins. Tarim Bilimleri Dergisi — Journal of Agricultural Sciences, 20, 302-316.
  • Pearson, T. 1996. Machine vision system for automated detection of stained pistachio nuts. Food Science and Technology-Lebensmittel-Wissenschaft & Technologie, 29(3), 203-209.
  • Pearson, T. C. and Schatzki, T. F. 1998. Machine vision system for automated detection of aflatoxin-contaminated pistachios. Journal of Agricultural and Food Chemistry, 46, 2248-2252.
  • Sharifian, F., Modarres-Motlagh, A., Komarizade, M. H. and Nikbakht, A. M. 2013. Colour change analysis of fig fruit during microwave drying. International Journal of Food Engineering, 9, 107-114.
  • Steiner, W. E., Rieker, R. H. and Battaglia, R. 1988. Aflatoxin contamination in dried figs: distribution and association with fluorescence. Journal of Agricultural and Food Chemistry, 36, 88-91.

Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision

Year 2021, Volume: 18 Issue: 1, 157 - 168, 30.12.2020
https://doi.org/10.33462/jotaf.753185

Abstract

Aflatoxins produced by Aspergillus species have a great important in the food industry, especially in dried nuts and fruits. Agricultural products are prone to the aflatoxins during the stages like harvesting, drying and storage. Rapid identification of aflatoxin contaminated products is of great interest to the food industry. The food companies start using screening technologies instead of human labour to become more profitable and accurate. Moreover, economical losses and diseases resulting from aflatoxin contamination are a significant problem. The objective of this study was to develop an image processing based aflatoxin contaminated in-shell pistachio nut identification system in order to separate aflatoxin contaminated pistachio nuts from the healthies one. Bright greenish yellow fluorescence (BGYF), which indicates possible aflatoxin contamination, was investigated as a discriminating factor for identification of contaminated pistachio nuts. A total of 100 pistachio nut samples (50 BGYF+ and 50 BGYF-) were evaluated. In the study, imaging algorithms were developed in order to classify the pistachio nut samples as BGYF+ and BGYF-. The colour (L*, a* and b*) and kinetic (chroma, hue angle and browning index) parameters of each pistachio nut sample were analysed and differences between them were determined statistically. Colour and kinetic parameters were also grouped and associated each other by using factor analysis method to simplify the image processing algorithm. Statistically significant differences were found for all colour and kinetic parameters between two groups. According to the factor analysis results; chroma, a* and browning index values were substantially loaded on Factor 1, while hue angle and b* were substantially loaded on Factor 2. The remaining variable L* was substantially loaded on Factor 3. In future studies, an optimized (more effective and convenient) image processing algorithm for developing a new real-time determination and separation system will be enhanced based on the statistical analysis results. The results obtained from this study will form a basis for further investigations.

References

  • Ataş, M., Yardimci, Y. and Temizel, A. 2012. A new approach to aflatoxin detection in chili pepper by machine vision. Computers and Electronics in Agriculture, 87, 129–141.
  • Campbell, B. C., Molyneux, R. J. and Schatzki, T. F. 2003. Current research on reducing pre‐ and post‐harvest aflatoxin contamination of U.S. almond, pistachio, and walnut. Journal of Toxicology: Toxin Reviews 22(2-3), 225-266.
  • Dichter, C. R. 1984. Risk estimates of liver cancer due to aflatoxin exposure from peanuts and peanut products. Food and Chemical Toxicology, 22, 431-437.
  • Erdem, T., Ozluoymak, O. B. and Kizildag, N. 2018. Color change analysis of dried orange slices during hot air drying. Fresenius Environmental Bulletin, 27, 6064-6072.
  • Gloria, E. M. 2011. Aflatoxin contamination distribution among grains and nuts. In: aflatoxins-detection, measurement and control. Torres-Pacheco: 1st edn. InTech.
  • Güneş, A., Durmuş, E. and Kalkan, H. 2013. Detection of high aflatoxin risk figs with computer vision. In: 21st Signal Processing and Communications Applications Conference (SIU). Cyprus.
  • Hadavi, E. 2005. Several physical properties of aflatoxin-contaminated pistachio nuts: Application of BGY fluorescence for separation of aflatoxin-contaminated nuts. Food Additives & Contaminants, 22, 1144-1153.
  • Hepsag, F., Golge, O. and Kabak, B. 2014. Quantitation of aflatoxins in pistachios and groundnuts using HPLC-FLD method. Food Control, 38, 75-81.
  • Iamanaka, B. T., Castle de Menezes, H., Vicente, E., Leite, R. S. F. and Taniwaki, M. H. 2007. Aflatoxigenic fungi and aflatoxins occurrence in sultanas and dried figs commercialized in Brazil. Food Control, 18, 454-457.
  • Kalkan, H., Güneş, A., Durmuş, E. and Kuşçu, A. 2014. Non-invasive detection of aflatoxin-contaminated figs using fluorescence and multispectral imaging. Food Additives and Contaminants Part A, 31, 1414-1421.
  • Lizárraga-Paulín, E. G., Moreno-Martínez, E. and Miranda-Castro, S. P. 2011. Aflatoxins and their impact on human and animal health: An emerging problem. Aflatoxins – Biochemistry and Molecular Biology (pp. 255-282). InTech.
  • Lunadei, L., Ruiz-Garcia, L., Bodria, L. and Guidetti, R. 2013. Image-based screening for the identification of bright greenish yellow fluorescence on pistachio nuts and cashews. Food and Bioprocess Technology, 6, 1261-1268.
  • Marsh, P. B., Simpson, M. E., Ferretti, R. J., Merola, G. V., Donoso, J., Craig, G. O., Trucksess, M. W. and Work, P. S. 1969. Mechanism of formation of a fluorescence in cotton fiber associated with aflatoxin in the seeds at harvest. Journal of Agricultural and Food Chemistry, 17, 468-472.
  • McClure, W. F. and Farsaie, A. 1980. Dual-wavelength fiber-optic photometer measures fluorescence of aflatoxin-contaminated pistachio nuts. Transactions of the ASAE, 23, 204-207.
  • Nilüfer, D. and Boyacıoğlu, D. 2002. Comparative study of three different methods for the determination of aflatoxins in tahini. Journal of Agricultural and Food Chemistry, 50, 3375-3379.
  • Ozluoymak, O. B. and Guzel, E. 2018. Prediction of aflatoxin contamination on dried fig (ficus carica) samples by spectral image analysis in comparison with laboratory results. Fresenius Environmental Bulletin, 27, 681-689.
  • Özlüoymak, Ö. B. 2014. Development of a UV-based imaging system for real-time detection and separation of dried figs contaminated with aflatoxins. Tarim Bilimleri Dergisi — Journal of Agricultural Sciences, 20, 302-316.
  • Pearson, T. 1996. Machine vision system for automated detection of stained pistachio nuts. Food Science and Technology-Lebensmittel-Wissenschaft & Technologie, 29(3), 203-209.
  • Pearson, T. C. and Schatzki, T. F. 1998. Machine vision system for automated detection of aflatoxin-contaminated pistachios. Journal of Agricultural and Food Chemistry, 46, 2248-2252.
  • Sharifian, F., Modarres-Motlagh, A., Komarizade, M. H. and Nikbakht, A. M. 2013. Colour change analysis of fig fruit during microwave drying. International Journal of Food Engineering, 9, 107-114.
  • Steiner, W. E., Rieker, R. H. and Battaglia, R. 1988. Aflatoxin contamination in dried figs: distribution and association with fluorescence. Journal of Agricultural and Food Chemistry, 36, 88-91.
There are 21 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ömer Barış Özlüoymak 0000-0002-6721-0964

Emin Güzel 0000-0002-1827-9674

Publication Date December 30, 2020
Submission Date June 15, 2020
Acceptance Date November 12, 2020
Published in Issue Year 2021 Volume: 18 Issue: 1

Cite

APA Özlüoymak, Ö. B., & Güzel, E. (2020). Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision. Tekirdağ Ziraat Fakültesi Dergisi, 18(1), 157-168. https://doi.org/10.33462/jotaf.753185
AMA Özlüoymak ÖB, Güzel E. Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision. JOTAF. December 2020;18(1):157-168. doi:10.33462/jotaf.753185
Chicago Özlüoymak, Ömer Barış, and Emin Güzel. “Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision”. Tekirdağ Ziraat Fakültesi Dergisi 18, no. 1 (December 2020): 157-68. https://doi.org/10.33462/jotaf.753185.
EndNote Özlüoymak ÖB, Güzel E (December 1, 2020) Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision. Tekirdağ Ziraat Fakültesi Dergisi 18 1 157–168.
IEEE Ö. B. Özlüoymak and E. Güzel, “Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision”, JOTAF, vol. 18, no. 1, pp. 157–168, 2020, doi: 10.33462/jotaf.753185.
ISNAD Özlüoymak, Ömer Barış - Güzel, Emin. “Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision”. Tekirdağ Ziraat Fakültesi Dergisi 18/1 (December 2020), 157-168. https://doi.org/10.33462/jotaf.753185.
JAMA Özlüoymak ÖB, Güzel E. Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision. JOTAF. 2020;18:157–168.
MLA Özlüoymak, Ömer Barış and Emin Güzel. “Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision”. Tekirdağ Ziraat Fakültesi Dergisi, vol. 18, no. 1, 2020, pp. 157-68, doi:10.33462/jotaf.753185.
Vancouver Özlüoymak ÖB, Güzel E. Determination of Colour and Kinetic Parameter Differences Between Aflatoxin Contaminated and Uncontaminated Pistachio Nuts Using Machine Vision. JOTAF. 2020;18(1):157-68.