درجه‌بندی دانه‌های کاکائو آلوده به آفلاتوکسین با استفاده از طیف‌سنجی مادون قرمز نزدیک

نوع مقاله : مقاله پژوهشی

نویسندگان

• علی سعیدان ¹
• مهدی خجسته پور ¹
• محمودرضا گلزاریان ¹
• مرضیه معین فرد ²

¹ گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

² گروه علوم و صنایع غذایی،دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

https://doi.org/10.22067/ifstrj.2021.68475.1016

چکیده

آفلاتوکسین یک ترکیب شیمیایی سمی است که توسط قارچ‌های آسپرژیلوس فلاووس و آسپرژیلوس پارازیتکوس تولید می‌شود. این سموم قارچی می‌توانند باعث آلودگی گسترده محصولات کشاورزی شوند که به‌صورت بالقوه دارای خطرات زیادی برای سلامتی انسان و حیوان هستند. لذا تشخیص سریع و صحیح دانه‌های آلوده به آفلاتوکسین به لحاظ اقتصادی و ایمنی، از اهمیت بالایی برخوردار است. در این تحقیق از طیف‌سنجی مادون قرمز نزدیک به‌عنوان روشی غیرتخریبی و سریع، برای تشخیص دانه‌های کاکائو آلوده به آفلاتوکسین استفاده شد. دانه‌های کاکائو با دو غلظت سم (20و ppb 500) به‌صورت مصنوعی آلوده شدند و دانه‌های بدون آلودگی نیز به‌صورت سطحی با اتانول پاکسازی شدند. هر دو دسته دانه‌های آلوده و سالم با دستگاه طیف‌سنج و در دامنه 400 الی 2500 نانومتر مورد ارزیابی قرار گرفتند. مدل تجزیه و تحلیل تمایزی حداقل مربعات جزئی برای دسته‌بندی دانه‌های آلوده و غیرآلوده مورد استفاده قرار گرفت و پیش از آنالیز داده‌های طیفی، این طیف‌ها با مشتق مرتبه اول و دوم ساویتزی گولی مورد پیش تیمار قرار گرفتند. نتایج درجه‌بندی نشان داد که کمترین میزان خطای درجه‌بندی در حالتی بود که از مشتق مرتبه دوم به‌عنوان پیش تیمار استفاده شده بود و این مقادیر برای داده‌های کالیبراسیون، اعتبارسنجی متقابل و تست به‌ترتیب برابر 00/0، 02/0 و 00/0 گزارش شد. همچنین نتایج بررسی نمودار ضرایب تاثیر در هر دسته نشان داد که با افزایش غلظت سم در دانه های کاکائو از 20 به ppb 500، مقادیر ضرایب تاثیر کاهش پیدا کرد. در نهایت می‌توان گفت که روش تشخیص آلودگی آفلاتوکسین با استفاده از طیف‌سنجی مادون قرمز روشی کارا، غیرمخرب و سریع می‌باشد که می‌تواند جایگزین مناسبی برای روش‌های سنتی شود.

کلیدواژه‌ها

• آفلاتوکسین
• دانه کاکائو
• طیف‌سنجی مادون قرمز
• PLS_DA

موضوعات

• مهندسی مواد غذایی

عنوان مقاله [English]

Aflatoxin contaminated cocoa beans classification using near-infrared spectroscopy

نویسندگان [English]

• Ali Saeidan ¹
• Mehdi Khojastehpour ¹
• Mahmood Reza Golzarian ¹
• Marzieh Moeenfard ²

¹ Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad. Iran.

² Department of Food Science and Technology, Ferdowsi University of Mashhad, Iran.

چکیده [English]

Introduction: Due to the fact that the presence of high doses of aflatoxin in agricultural products such as cocoa beans is unacceptable in terms of national and international standards, appropriate quality control tests should prevent such products to entering in the process of processing cocoa beans. Conventional methods of detecting aflatoxins such as ELISA and HPLC are very time consuming, expensive and require expertise, so replacing these tests with non-destructive and rapid methods such as near-infrared spectroscopy can increase the detection efficiency. Brado et al. (Berardo et al., 2005) used infrared spectroscopy to evaluate and diagnose Fusarium verticillium, which produces fumonicin toxin in maize. Manvar et al. (Mohammadi Manvar, 2015) used transmission and reflection Infrared spectroscopy to detect aflatoxin levels in Iranian pistachios. Singh et al. (2012) used hyperspectral imaging in the range of 700-1100 nm to detect fungal contamination of Penicillium SPP, Aspergillus Glaucus, and Aspergillus Niger in wheat. Kandpal et al. (Kandpal et al., 2015) in a research work using hyperspectral imaging in the range of 700-1100nm classified grains of maize contaminated with aflatoxin toxin using PLS-DA into five groups. In current study, an attempt was made to detect the amount of aflatoxin in cocoa beans using infrared spectroscopy and to classify healthy and infected beans into groups.
 
Materials and Methods: In this research, 180 cocoa beans, each weighing 1 gram, were selected to do analyses. One mg of aflatoxin B1 powder (A. flavus, A 6636, Sigma-Aldrich, St. Louis, Mo USA) was prepared from Sigma Aldrich representative in Iran and by dissolving this powder in absolute ethanol and concentrations of 20µg/kg, 500µg/kg was obtained as mentioned. For cocoa bean spectroscopy, a near infrared spectrometer in Shiraz University Central Laboratory (NIRS XDS Rapid Content Analysis) was used, which has the ability to spectroscopy in the range of 400-2500 nm. PLS-DA method was used to classify aflatoxin-infected samples from healthy samples. All 180 experimental samples were divided into two groups of training (120 samples) and test (60 samples) and the constructed model was first calibrated with training values and then evaluated with test data. Due to the fact that some noise is always stored in the spectral data and in order to remove this noise, a series of mathematical pretreatment, including: first and second derivatives was used (Chen et al., 2013; Nicolai et al., 2007).
 
Results and Discussion: Comparing the average amount of infrared reflection spectrum, it is revealed that healthy grains have less reflection intensity than infected grains. Also, there are a number of local maximums and minimums where the difference in reflective intensity is more pronounced than elsewhere, and this phenomenon is due to the different concentrations of toxins in cocoa beans. After applying the second Savitzie Golay derivative pretreatment and performing PLS-DA classification using two latent variables, the distinction between classes can be clearly seen. The separation rate of the samples on the second LV is more specific, however, the second and first class samples in this LV have a closer score to each other. The peaks observed at 1440 nm and 1482 nm according to the first Everton O-H bond can be related to fungal contamination (Berardo et al., 2005; Sirisomboon et al., 2013). Also, the peak at 1838 nm is related to the tensile C-H bond, which can be related to the CH2 groups. According to the results obtained from the calibration, cross-validation and testing sections, it is determined that the degree of calibration error (ER) and the degree of error-free calibration (NER) in the pretreatment mode with the second-order derivative of Savitz Golay are the lowest and highest values, respectively. Also, in this pretreatment for the calibration model and testing, the specificity index for the first-, second- and third-class samples are equal to 1.00, which means that all classes are correctly classified. In the cross-validation model, the value of the specificity index for the third class (samples with 500 ppb contamination) is equal to 97%. This indicates that 97% of infected seeds are correctly classified in the third group and only 3% in the other groups are incorrectly classified.
 
Conclusion: The present study demonstrates the feasibility of near-infrared spectroscopy to identify and classify cocoa beans contaminated with aflatoxin. The results showed that the coefficients of independent variables (spectral wavelengths including 1440, 1482 and 1838 nm) decreased according to increasing in the concentration of toxin. Finally, it can be said that the method of detecting aflatoxin contamination using infrared spectroscopy is an efficient, non-destructive and fast method.

مراجع

1. Al-Holy, M. A., Lin, M., Cavinato, A. G., & Rasco, B. A. (2006). The use of Fourier transform infrared spectroscopy to differentiate Escherichia coli O157: H7 from other bacteria inoculated into apple juice. Food microbiology, 23(2), 162-168. https://doi.org/10.1016/j.fm.2005.01.017
2. Ardhana, M. M., & Fleet, G. H. (2003). The microbial ecology of cocoa bean fermentations in Indonesia. International journal of food microbiology, 86(1-2), 87-99. https://doi.org/10.1016/S0168-1605(03)00081-3
3. Ballabio, D., & Consonni, V. (2013). Classification tools in chemistry. Part 1: linear models. PLS-DA. Analytical Methods, 5(16), 3790-3798. https://doi.org/10.1039/C3AY40582F
4. Berardo, N., Pisacane, V., Battilani, P., Scandolara, A., Pietri, A., & Marocco, A. (2005). Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 53(21), 8128-8134. https://doi.org/10.1021/jf0512297
5. Broadent, J., & Oyeniran, J. (1968). A new look at mouldy cocoa. Paper presented at the Proceeding 1st International Biodeterioration Symposium.
6. Camps, C., & Christen, D. (2009). Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT-Food Science and Technology, 42(6), 1125-1131. https://doi.org/10.1016/j.lwt.2009.01.015
7. Chen, H., Song, Q., Tang, G., Feng, Q., & Lin, L. (2013). The combined optimization of Savitzky-Golay smoothing and multiplicative scatter correction for FT-NIR PLS models. International Scholarly Research Notices, 2013. http://dx.doi.org/10.1155/2013/642190
8. Copetti, M. V., Iamanaka, B. T., Pereira, J. L., Lemes, D. P., Nakano, F., & Taniwaki, M. H. (2012). Determination of aflatoxins in by-products of industrial processing of cocoa beans. Food Additives & Contaminants: Part A, 29(6), 972-978. https://doi.org/10.1080/19440049.2012.660657
9. Copetti, M. V., Iamanaka, B. T., & Taniwaki, M. H. (2013). Fungi and mycotoxin occurrence in cocoa Chocolate in Health and Nutrition (pp. 61-71): Springer. https://doi.org/10.1007/978-1-61779-803-0_5
10. Fernández-Ibañez, V., Soldado, A., Martínez-Fernández, A., & De la Roza-Delgado, B. (2009). Application of near infrared spectroscopy for rapid detection of aflatoxin B1 in maize and barley as analytical quality assessment. Food Chemistry, 113(2), 629-634. https://doi.org/10.1016/j.foodchem.2008.07.049
11. Hernández-Hierro, J., García-Villanova, R., & González-Martín, I. (2008). Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market. Analytica chimica acta, 622(1-2), 189-194. https://doi.org/10.1016/j.aca.2008.05.049
12. Kandpal, L. M., Lee, S., Kim, M. S., Bae, H., & Cho, B.-K. (2015). Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control, 51, 171-176. https://doi.org/10.1016/j.foodcont.2014.11.020
13. Khodabakhshian, R., Emadi, B., Khojastehpour, M., Golzarian, M.R. 2016. Determination of pomegranate ripeness and internal defects using VIS-NIR multispectral imaging. PhD dissertation,Ferdowsi University of Mashhad.
14. Magan, N., & Aldred, D. (2005). Conditions of formation of ochratoxin A in drying, transport and in different commodities. Food Additives and Contaminants, 22(s1), 10-16. https://doi.org/10.1080/02652030500412154
15. Mobli, H., Jamshidi, B., Azizi, A., & Sharifi, M. (2020). Microbial Contamination Assessment of Lettuce using NIR Hyperspectral Imaging: Case Study on Escherichia coli. Iranian Journal of Biosystems Engineering, 51(3), 599-610
16. Mohammadi Monavar, H., Mirzaee, S., Sepehr, B. 2016. Grading of aflatoxin contamination of pistachios by non-destructive near infrared spectroscopy NIR. Secound International Conference on Sustainable Development, Strategies and Challenges Focusing on Agriculture, Natural Resources, Environment and Tourism, Tabriz
17. Mounjouenpou, P., Gueule, D., Fontana-Tachon, A., Guyot, B., Tondje, P. R., & Guiraud, J.-P. (2008). Filamentous fungi producing ochratoxin A during cocoa processing in Cameroon. International Journal of Food Microbiology, 121(2), 234-241. https://doi.org/10.1016/j.ijfoodmicro.2007.11.017
18. Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., et al. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest biology and technology, 46(2), 99-118. https://doi.org/10.1016/j.postharvbio.2007.06.024
19. Pettersson, H., & Åberg, L. (2003). Near infrared spectroscopy for determination of mycotoxins in cereals. Food Control, 14(4), 229-232. https://doi.org/10.1016/S0956-7135(03)00011-2
20. Ribeiro, N. d. A., Bezerra, J., & Lopez, A. (1986). Micobiota na fermentação do cacau no estado da Bahia, Brasil. Revista Theobroma (Brasil) v. 16 (1) p. 47-55
21. Roelofsen, P. (1958). Fermentation, drying, and storage of cacao beans Advances in food research (Vol. 8, pp. 225-296): Elsevier. https://doi.org/10.1016/S0065-2628(08)60021-X
22. Sánchez-Hervás, M., Gil, J., Bisbal, F., Ramón, D., & Martínez-Culebras, P. (2008). Mycobiota and mycotoxin producing fungi from cocoa beans. International journal of food microbiology, 125(3), 336-340. https://doi.org/10.1016/j.ijfoodmicro.2008.04.021
23. Singh, C., Jayas, D., Paliwal, J., & White, N. (2012). Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging. International Journal of Food Properties, 15(1), 11-24. https://doi.org/10.1080/10942911003687223
24. Sirisomboon, C. D., Putthang, R., & Sirisomboon, P. (2013). Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice. Food Control, 33(1), 207-214. https://doi.org/10.1016/j.foodcont.2013.02.034
25. Sirisomboon, P., Tanaka, M., Fujita, S., & Kojima, T. (2007). Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy. Journal of food engineering, 78(2), 701-707. https://doi.org/10.1016/j.jfoodeng.2005.11.009
26. Zhang, H.-J., Wu, J.-H., Li-jun, Y. L., Hua, Y., YU, X.-q., WANG, X.-s., et al. (2007). Comparison of near infrared spectroscopy models for determining protein and amylose contents between calibration samples of recombinant inbred lines and conventional varieties of rice. Agricultural Sciences in China, 6(8), 941-948 https://doi.org/10.1016/S1671-2927(07)60132-1