Detection of Aflatoxin B1 in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies

Abstract

To study the dynamic changes of nutrient consumption and aflatoxin B1 (AFB1) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB1 contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with RC2 = 0.95 and RV2 = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB1 quantitatively.

Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality

“…and A. flavus enhanced the brightness of the seeds (Figure 2C). This behavior contributed to the increase in reflectance (Fonseca de Oliveira et al, 2022); ii) the formation of mycelia and toxicogenic compounds alters the chemical composition of infected seeds (Zhang et al, 2022), which can reduce light absorption and favor reflection enhancement in the spectral range (Figure 3C); iii) fungal colonization intensifies the production of anthocyanins as a plant response to tissue deterioration (Liu et al, 2018;Fonseca de Oliveira et al, 2022). This pigment, is also produced by fungi (Bu et al, 2020;Sicilia et al, 2021) and its accumulation in the contaminated seeds (Figure 6) may have contributed to the lower reflectance found (Figure 3C).…”

“…Their ability to assess multi-angle organic realities in the electromagnetic spectrum brings promising exploratory power for fungal detection (Boelt et al, 2018). The main reason for this is that the mycelia resulting from fungal colonization changes the surface and chemical composition of the seeds (Zhang et al, 2022). This event generates easily perceptible changes in the spectral range (Franca-Silva et al, 2020;Rego et al, 2020).…”

“…Also, the reflectance property depends on parameters such as the texture and chemical composition of the fungal colonies and the seeds (Barboza da Silva et al, 2021a). The development of the fungus depends on the consumption of reserves in the embryo (Zhang et al, 2022), which physically modifies the integument with the formed mycelia. These chemical and structural changes in the seed generate variations in reflectance (Ziyaee et al, 2021) and in textural parameters such as smoothness, roughness and regularity that can be detected autonomously (Rego et al, 2020).…”

A Low-Cost, Portable Device for Detecting and Sorting Aflatoxin-Contaminated Maize Kernels

“…One of the more recent developments in the detection of aflatoxin contamination is the use of spectral technology, including the use of fiber-optic spectrometry [ 14 ], fluorescence hyperspectral imaging [ 15 ], multispectral imaging [ 11 , 16 ], fluorescence spectroscopy [ 17 ], fluorescence spectroscopy and multispectral imaging [ 18 ], ultraviolet–visible–near-infrared spectra [ 19 ], and hyperspectral and microscopic imaging [ 20 ]. A fluorescence hyperspectral study [ 15 ] found that there exists a fluorescence shift toward longer wavelengths in the blue-green spectral region in fungal-infected maize kernels with high aflatoxin content.…”