Application of NIR Reflectance Spectroscopy Combined with Chemometrics for Non-destructive Detection of Aflatoxin B1 in Individual Maize Kernels
DOI:
https://doi.org/10.52151/jae2025623.1954Keywords:
forward selection method, linear discriminant analysis, optimal wavelengths, partial least square regression, principal component analysis, quadratic discriminant analysisAbstract
Maize is a potential host for several fungi, some of which produce mycotoxins. Maximum mycotoxin levels in food vary depending on the type of food, consumer sensitivity, and country regulations. Aflatoxin-B1 (AFB1) is a harmful mycotoxin that causes illness and economic losses in humans, animals, and crops. A visible-near infrared spectroradiometer (350-2500 nm) was used in this study to detect maize kernels with varying AFB1 contamination levels (control, 25, 40, 70, and 200 ppb). Spectral reflectance and chemometrics were used to predict the AFB1 content in maize kernels. Principal component analysis (PCA) and forward selection method were utilized for data exploration; linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were employed to develop classification models; and partial least square regression (PLSR) was used for AFB1 quantification. The Kernels germ orientation influenced the classification performance. In training, cross-validation, and testing, the LDA model preceded with PCA had the lowest error rates when the kernels were kept at germ-up orientation. Throughout training, cross-validation and testing of the PLSR model, R2 values were around 0.85. Forward selection method has provided seven optimal wavelengths from the group of 2051 spectral data. The R2 and RMSE of the PLSR model using optimal wavelength data were also close to the results obtained from full spectrum data. The RPD value for the optimal wavelength model during testing was around 2.5, indicating good predictive performance. Results showed that AFB1 can be identified and quantified using spectral data. Spectroscopy is a feasible solution for automatic, non-destructive detection without affecting the products.
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Amigo, J. M., Babamoradi, H., & Elcoroaristizabal, S. (2015). Hyperspectral image analysis. A tutorial. Analytica Chimica Acta, 896, 34-51. https://doi.org/10.1016/j.aca.2015.09.030
Anonymous. (2000). Approved methods of the American association of cereal chemists (Vol. 1). American Association of Cereal Chemists. Approved Methods Committee. 39-70 A, 44-15 A, and 46-10. St. Paul, MN: The Association.
Anonymous. (2022a). Rapid Alert System for Food and Feed. Available at: https://webgate.ec.europa.eu/rasff-window/screen/search (accessed on 15 January 2022).
Anonymous. (2022b). Food Safety and Standards (Contaminants, Toxins and Residues) Regulations. Available at: https://www.fssai.gov.in/upload/uploadfiles/files/Compendium_Contaminants_Regulations_20_08_2020.pdf (accessed on 10 February 2022).
Ballabio, D. (2015). A MATLAB toolbox for Principal Component Analysis and unsupervised exploration of data structure. Chemometrics and Intelligent Laboratory Systems, 149, 1–9. https://doi.org/10.1016/j.chemolab.2015.10.003
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
Bartolić, D., Mutavdžić, D., Carstensen, J. M., Stanković, S., Nikolić, M., Krstović, S., & Radotić, K. (2022). Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study. Scientific Reports, 12(1), 4849. https://doi.org/10.1038/s41598-022-08352-4
Batista, L. R., Chalfoun, S. M., Prado, G., Schwan, R. F., & Wheals, A. E. (2003). Toxigenic fungi associated with processed (green) coffee beans (Coffea arabica L.). International Journal of Food Microbiology, 85(3), 293-300. https://doi.org/10.1016/S0168-1605 (02) 00539-1
Berardo, N., Pisacane, V., Battilani, P., Scandolara, A., Pietro, A., & Marocco, A. (2005). Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy. Journal of Agriculture and Food Chemistry, 53, 8128–8134. https://doi.org/10.1021/jf0512297
Chakraborty, S. K., Mahanti, N. K., Mansuri, S. M., Tripathi, M. K., Kotwaliwale, N., & Jayas, D. S. (2021). Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging. Journal of Food Science and Technology, 58(2), 437-450. https://doi.org/10.1007/s13197-020-04552-w
Chakraborty, S. K., & Mansuri, S. M. (2025). Trends and approaches in hyperspectral imaging for detection of fungal contamination in food crops. In: Pathak, H., Lakra, W. S., Gopalakrishnan, A., & Bansal, K. C. (Eds), Advances in agri-food systems (Volume II, pp. 245-265). Springer Nature, Singapore. https://doi.org/10.1007/978-981-96-0763-1_15
Chu, X., Wang, W., Yoon, S.C., Ni, X., & Heitschmidt, G. W. (2017). Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging. Biosystems Engineering, 157, 13–23. https://doi.org/10.1016/j.biosystemseng.2017
Ciurczak, E. W., Igne, B., Workman Jr, J., & Burns, D. A. (Eds.). (2021). Handbook of near-infrared analysis (4th Edition). CRC press, Boca Raton. https://doi.org/10.1201/b22513
Dabhi, M. N., & Dhudashia, R. D. (2024). Effect of packaging material on infestation level in stored groundnut pods. Journal of Agricultural Engineering (India), 61(4), 509-524. https://doi.org/10.52151/jae2024614.1868
Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A. A., Bonifazi, G., & Fanelli, C. (2010). Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. International Journal of Food Microbiology, 144(1), 64-71. https://doi.org/10.1016/j.ijfoodmicro.2010.08.001
Delwiche, S. R., & Hareland, G. A. (2004). Detection of scab‐damaged hard red spring wheat kernels by near‐infrared reflectance. Cereal Chemistry, 81(5), 643-649. https://doi.org/10.1094/CCHEM.2004.81.5.643
Dowell, F. E., Pearson, T. C., Maghirang, E. B., Xie, F., & Wicklow, D. T. (2002). Reflectance and transmittance spectroscopy applied to detecting fumonisin in single corn kernels infected with Fusariumverticillioides. Cereal Chemistry Journal, 79(2), 222–226. https://doi.org/10.1094/cchem.2002.79.2.222
Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K., & Prasanna, B. M. (2022). Global maize production, consumption and trade: trends and R & D implications. Food Security, 14(5), 1295-1319. https://doi.org/10.1007/s12571-022-01288-7
Fan, S., Li, J., Xia, Y., Tian, X., Guo, Z., & Huang, W. (2019). Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method. Postharvest Biology and Technology, 151, 79-87. https://doi.org/10.1016/j.postharvbio.2019.02.001
Folarin-Ottun, O. (2018). Non-destructive Detection of Aflatoxin B1 in Beans. Master in Science Thesis, McGill University, Québec, Canada.
Hamad, R., Chakraborty, S. K., Kate, A., & Mansuri, S. M. (2024). Understanding the changes in millable oil content and fatty acid composition of infrared radiation treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging. Infrared Physics & Technology, 137, 105128. https://doi.org/10.1016/j.infrared.2024.105128
Jaimez, J., Fente, C. A., Vazquez, B. I., Franco, C. M., Cepeda, A., Mahuzier, G., & Prognon, P. (2000). Application of the assay of aflatoxins by liquid chromatography with fluorescence detection in food analysis. Journal of Chromatography A, 882(1-2), 1-10. https://doi.org/10.1016/S0021-9673 (00) 00212-0
Jha, S. N. (2007). Nondestructive determination of sweetness of orange juice using portable near infrared spectroscopy. Journal of Agricultural Engineering (India), 44(3), 10-14. https://doi.org/10.52151/jae2007443.1274
Karoui, R., Hammami, M., Rouissi, H., & Blecker, C. (2011). Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems. Food Chemistry, 127(2), 743-748. https://doi.org/10.1016/j.foodchem.2010.12.135
Lee, K.M., Davis, J., Herrman, T. J., Murray, S. C., & Deng, Y. (2015). An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize. Food Chemistry, 173, 629–639. https://doi.org/10.1016/j.foodchem.2014.10.099
Mansuri, S. M., Chakraborty, S. K., Mahanti, N. K., & Pandiselvam, R. (2022). Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling. Food Control, 139, 109077. https://doi.org/10.1016/j.foodcont.2022.109077
Măruţoiu, C., Puiu, S., Moise, M. I., Soran, L., Măruţoiu, O. F., & Boboş, L. (2004). Optimization of the separation of some aflatoxins by thin-layer chromatography. JPC–Journal of Planar Chromatography–Modern TLC, 17(5), 372-374. https://doi.org/10.1556/JPC.17.2004.5.10
Mishra, G., Panda, B. K., Ramirez, W. A., Jung, H., Singh, C. B., Lee, S. H., & Lee, I. (2021). Research advancements in optical imaging and spectroscopic techniques for non-destructive detection of mold infection and mycotoxins in cereal grains and nuts. Comprehensive Reviews in Food Science and Food Safety, 20(5),4612-4651.https://doi.org/10.1111/1541-4337.12801
Mishra, G., Panda, B. K., Ramirez, W. A., Jung, H., Singh, C. B., Lee, S. H., & Lee, I. (2022). Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. LWT, 155, 112954. https://doi.org/10.1016/j.lwt.2021.112954
Ordaz, J. J., Fente, C. A., Vázquez, B. I., Franco, C. M., & Cepeda, A. (2003). Development of a method for direct visual determination of aflatoxin production by colonies of the Aspergillus flavus group. International Journal of Food Microbiology, 83(2), 219-225. https://doi.org/10.1016/S0168-1605 (02) 00362-8
Pearson, T. C., Wicklow, D. T., Maghirang, E. B., Xie, F., & Dowell, F. E. (2001). Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy. Transactions of the ASAE, 44(5), 1247-1254. https://doi.org/10.13031/2013.6418
Putthang, R., Sirisomboon, P., & Sirisomboon, C. D. (2019). Shortwave near-infrared spectroscopy for rapid detection of aflatoxin B1 contamination in polished rice. Journal of Food Protection, 82(5), 796–803. https://doi.org/10.4315/0362-028X.JFP-18-318
Ravikanth, L., Jayas, D. S., White, N. D., Fields, P. G., & Sun, D. W. (2017). Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food and Bioprocess Technology, 10 (1), 1-33. https://doi.org/10.1007/s11947-016-1817-8
Reuss, C., Green, J., Willis, J., Osborne, T., & Wesley, I. (2005). Near infrared spectroscopic changes in stored grain. In: Proceedings of the 12th international conference on near infrared spectroscopy (ICNIRS): NIR in action- making a difference (pp. 670-673), Auckland – New Zealand, 10-15 April 2005.
Romero, I. (2010). PCA based noise reduction in ambuilatory ECG’s. In: 2010 Computing in Cardiology, Belfast, UK. pp. 677-680.
Senthilkumar, T., Singh, C. B., Jayas, D. S., & White, N. D. G. (2012). Detection of fungal infection in canola using near-infrared hyperspectral imaging. Journal of Agricultural Engineering (India), 49(1), 21-27. https://doi.org/10.52151/jae2012491.1464
Shahin, M. A., & Symons, S. J. (2012). Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging. Journal of Food Measurement & Characterization, 6(1-4), 3–11. https://doi.org/10.1007/s11694-012-9126-z
Simas, M. M., Botura, M. B., Correa, B., Sabino, M., Mallmann, C. A., Bitencourt, T. C., & Batatinha, M. J. (2007). Determination of fungal microbiota and mycotoxins in brewers grain used in dairy cattle feeding in the State of Bahia, Brazil. Food Control, 18(5), 404-408. https://doi.org/10.1016/j.foodcont.2005.11.007
Sirisomboon, C. D., Wongthip, P., & Sirisomboon, P. (2019). Potential of near infrared spectroscopy as a rapid method to detect aflatoxins in brown rice. Journal of Near Infrared Spectroscopy, 27 (3), 232-240. https://doi.org/10.1177/0967033519835051
Sserumaga, J. P., Ortega-Beltran, A., Wagacha, J. M., Mutegi, C. K., & Bandyopadhyay, R. (2020). Aflatoxin-producing fungi associated with pre-harvest maize contamination in Uganda. International Journal of Food Microbiology, 313, 108376. https://doi.org/10.1016/j.ijfoodmicro.2019.108376
Stuart, B. H. (2004). Infrared spectroscopy: Fundamentals and applications. John Wiley & Sons, Ltd, The Atrium, England. https://doi.org/10.1002/0470011149
Su, W. H., Yang, C., Dong, Y., Johnson, R., Page, R., Szinyei, T., Hirsch, C. D., & Steffenson, B. J. (2021). Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening. Food Chemistry, 343, 128507. https://doi.org/10.1016/j.foodchem.2020.128507
Sun, D. W. (Ed.). (2010). Hyperspectral imaging for food quality analysis and control. Academic Press, Elsevier. https://doi.org/10.1016/C2009-0-01853-4
Virág, I., Szalay, K., Szőke, C., Milics, G., Marton, L., & Neményi, M. (2011). Analysing symptoms of Fusarium ear rot on maize (Zea mays L.) using an ex situ hyperspectral examination method. Acta Agronomica Hungarica, 59(3), 231-240. https://doi.org/10.1556/AAgr.59.2011.3.6
USDA. (2004). Grain Inspection Handbook-Book II: Grain Grading Procedures. United States Department of Agriculture (USDA), Agricultural Marketing Service, Federal Grain Inspection Service, Washington, D.C.
Wang, W., Heitschmidt, G. W., Ni, X., Windham, W. R., Hawkins, S., & Chu, X. (2014). Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging. Food Control, 42, 78-86. https://doi.org/10.1016/j.foodcont.2014.01.038
Williams, P. J., Bezuidenhout, C., & Rose, L. J. (2019). Differentiation of maize ear rot pathogens, on growth media, with near infrared hyperspectral imaging. Food Analytical Methods, 12(7), 1556-1570. https://doi.org/10.1007/s12161-019-01490-y
Wu, Q., Xie, L., & Xu, H. (2018). Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques. Food Chemistry, 252, 228–242. https://doi.org/10.1016/j.foodchem.2018.01.076
Yuan, D., Jiang, J., Qiao, X., Qi, X., & Wang, W., (2020). An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts. Journal of Food Engineering, 280, 109915. https://doi.org/10.1016/j.jfoodeng.2020.109915
Zhang, Q., Jia, F. G., Liu, C. H., Sun, J. K., & Zheng, X. Z. (2014). Rapid detection of aflatoxin B1 in paddy rice as analytical quality assessment by near infrared spectroscopy. International Journal of Agricultural and Biological Engineering, 7(4), 127–133.
Zheng, M. Z., Richard, J. L., & Binder, J. (2006). A review of rapid methods for the analysis of mycotoxins. Mycopathologia, 161(5), 261-273. https://doi.org/10.1007/s11046-006-0215-6
