Sensing of Mycotoxin Producing Fungi in the Processing of Grains

Rasch, Coen R. N.; Kumke, Michael U.; Löhmannsröben, Hans‐Gerd

doi:10.1007/s11947-010-0364-y

Cited by 40 publications

(23 citation statements)

References 16 publications

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“…Recently, spectrally based noninvasive techniques were developed for evaluating chemical characteristics of cereals (Cozzolino et al 2013;Pojić and Mastilović 2013;Rasch et al 2010;Roa et al 2014;Shahin et al 2014), especially for its quality screening (Kawamura et al 2003). In particular, two techniques, spectroscopy and digital imaging, were used to provide information about the spectral profile and textural information of cereals.…”

Section: Introductionmentioning

confidence: 99%

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Wang

Liu

et al. 2014

Food Anal. Methods

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This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', lengthwidth ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice.

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Section: Introductionmentioning

confidence: 99%

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Wang

Liu

et al. 2014

Food Anal. Methods

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“…Furthermore, by using an excitation wavelength with a strong mycotoxin absorbance, the influence of the mycotoxin fluorescence emission onto the natural fluorescence spectrum of the food products can be maximized. When considering the absorption spectrum of aflatoxin B1, ochratoxin A and zearalenone, the strongest absorbances are observed within the 200-400-nm spectral region (Figure 3(a)) [9]. Specifically, aflatoxin B1 shows the strongest absorbance in the range between 200 and 275 nm and around 365 nm.…”

Section: Selection Of the Excitation Wavelengthsmentioning

confidence: 99%

“…However, toxic contaminants in food and feed products can hardly be optically detected due to the presence of large background fluorescent signals emitted by natural food constituents, like proteins, and the localized presence of the toxins. The current published fluorescence measurements only allow the identification of mycotoxins in homogeneous liquids, like beer or wine, in which no or very low background fluorescent elements are present [9,10]. Therefore, we investigate the use of fluorescence spectroscopy for the detection of mycotoxins in unprocessed, solid food products.…”

Section: Introductionmentioning

confidence: 99%

“…The Food and Agriculture Organization (FAO) estimates that 25% of the world's food crops are affected by mycotoxin-producing fungi. The accumulation of mycotoxins in food and feed products represents a major threat to human and animal health, because they can induce cancer, liver diseases, immune-system suppression, mutagenicity and nervous disorders [9]. Therefore, to decrease the exposure to mycotoxins, international regulations were established [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Improving Food Safety by Using One- and Two-Photon- Induced Fluorescence Spectroscopy for the Detection of Mycotoxins

Smeesters¹,

Meulebroeck²,

Thienpont³

2016

Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

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The presence of mycotoxins in food products is a major worldwide problem. Nowadays, mycotoxins can only be detected by the use of sample-based chemical analyses. Therefore, we demonstrate the use of one-and two-photon-induced fluorescence spectroscopy for the non-destructive detection of mycotoxins in unprocessed food products. We first explain our optical set-up, which is able to measure the localized oneand two-photon-induced fluorescence spectra. Following, as a case study, the detection of aflatoxin in maize kernels is discussed. We present our research methodology, from the characterization of the fluorescence of pure aflatoxin, to the study of the one-and two-photon-induced fluorescence spectra of maize kernels and the development of an optical detection criterion. During both one-and two-photon-induced fluorescence processes, the fluorescence of the aflatoxin influences the intrinsic fluorescence of the maize. Based on the fluorescence spectrum between 400 and 550 nm, a detection criterion to sense the contaminated kernels is defined. Furthermore, we successfully monitored the localized contamination level on the kernel's surface, showing both contaminated kernels with a high contamination in a limited surface area (a few square millimetres) and kernels with a low contamination spread over a large surface area (up to 20 mm 2 ). Finally, the extensibility of our research methodology to other fluorescent mycotoxins is discussed.

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“…DON is a colorless, low fluorescent-reactive substance (Rasch et al 2010) and is therefore difficult to detect. In the past, the gas chromatography-electron capture detector or the enzyme-linked immunosorbent assay method has been used to detect DON (Josephs et al 2004;Stroka et al 2004;Klötzel et al 2005), but these methods are time-consuming, difficult, and require expensive equipment and reagents.…”

Section: Introductionmentioning

confidence: 99%

Detection of Deoxynivalenol Using Fluorescence Excitation–Emission Matrix

Fujita

Tsuta

Kokawa

et al. 2010

Food Bioprocess Technol

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Deoxynivalenol (DON) is an acutely toxic mycotoxin produced in wheat and other cereal grains when exposed to fungi such as Fusarium spp. In this study, DON aqueous samples at seven concentration levels were examined using the excitation-emission matrix (EEM). EEM is a graph composed of an excitation wavelength axis, an emission wavelength axis, and a fluorescence intensity axis. It is acquired by measuring the fluorescence intensity of a sample at consecutive excitation and emission wavelengths. The EEM of DON solution showed fluorescent peaks, which were nonexistent in that of water, particularly in the excitation (Ex) and emission (Em) wavelength ranges: Ex 200-240 nm/Em 300 nm and Ex 250-300 nm/Em 400-500 nm. Since the EEM is a multidimensional data composed of many wavelength conditions (667 in this case), principal component analysis was applied to reduce the data to lower dimensions. By plotting the scores of the first and second principal components, it was shown that DON could be discriminated from water and the concentration of DON in the aqueous solution could be judged.

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Sensing of Mycotoxin Producing Fungi in the Processing of Grains

Cited by 40 publications

References 16 publications

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Improving Food Safety by Using One- and Two-Photon- Induced Fluorescence Spectroscopy for the Detection of Mycotoxins

Detection of Deoxynivalenol Using Fluorescence Excitation–Emission Matrix

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