Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study

Abstract

Cereal seeds safety may be compromised by the presence of toxic contaminants, such as aflatoxins. Besides being carcinogenic, they have other adverse health effects on humans and animals. In this preliminary study, we used two non-invasive optical techniques, optical fiber fluorescence spectroscopy and multispectral imaging (MSI), for discrimination of maize seeds naturally contaminated with aflatoxin B1 (AFB1) from the uncontaminated seeds. The AFB1-contaminated seeds exhibited a red shift of the emission maximum position compared to the control samples. Using linear discrimination analysis to analyse fluorescence data, classification accuracy of 100% was obtained to discriminate uncontaminated and AFB1-contaminated seeds. The MSI analysis combined with a normalized canonical discriminant analysis, provided spectral and spatial patterns of the analysed seeds. The AFB1-contaminated seeds showed a 7.9 to 9.6-fold increase in the seed reflectance in the VIS region, and 10.4 and 12.2-fold increase in the NIR spectral region, compared with the uncontaminated seeds. Thus the MSI method classified successfully contaminated from uncontaminated seeds with high accuracy. The results may have an impact on development of spectroscopic non-invasive methods for detection of AFs presence in seeds, providing valuable information for the assessment of seed adulteration in the field of food forensics and food safety.

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

“…UV fluorescence has also been used for aflatoxin detection in other agricultural products. A recent study [ 18 ] using UV fluorescence spectra for maize kernel aflatoxin detection corroborated an earlier study [ 15 ] by finding a similar fluorescent shift associated with aflatoxin-contaminated kernels. The study reported a classification accuracy of 100% with principal component and linear discriminant analyses.…”

“…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.…”

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

“…The system is equipped with a 450 W high-pressure Xe lamp and a photomultiplier tube. After excitation at 337 nm, fluorescence emission spectra of pollen samples were recorded in the range from 350 to 800 nm with 1 nm spectral resolution using a quartz optical fiber (4 mm effective diameter) at a distance of 2 mm 58 , 59 . Both slits were fixed at 3 nm for excitation and emission beams, and the integration time was 0.1 s. Each sample representing bulk pollen of one class was measured 3 times.…”

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

“…The system is equipped with a 450 W high-pressure Xe lamp and a photomultiplier tube. After excitation at 337 nm, fluorescence emission spectra of pollen samples were recorded in the range from 350 to 800 nm with 1 nm spectral resolution using a quartz optical fiber (4 mm effective diameter) at a distance of 2 mm 48,49 . Both slits were fixed at 3 nm for excitation and emission beams, and the integration time was 0.1 s. Each sample representing bulk pollen of one class was measured 3 times.…”