Differentiation of toxigenic fungi using hyperspectral imagery

Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert L.

doi:10.1007/s11694-008-9055-z

Cited by 38 publications

(15 citation statements)

References 27 publications

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“…In earlier studies about using several optimal wavelengths to establish models, Yao et al separated five fungi by using only three bands [18], while Sun et al fitted growth curves for different kinds of fungi by characteristic wavelength in region of visible spectrum [7]. The authors pointed out that the characteristic wavelengths in visible region had good performance, because these wavelengths were associated with the variations of fungal color and texture during culture time.…”

Section: Optimal Wavelengths Svm Modelsmentioning

confidence: 99%

“…For example, there have been some publications that have reported the use of hyperspectral imaging to detect fungi plated on the culture medium. Yao et al identified five types of toxigenic fungi on day 5 of growth using a hyperspectral image, and all five fungi can be easily separated by only three wavelength bands (743, 458 and 541 nm) [18]. Jin et al classified toxigenic and atoxigenic A. flavus using a hyperspectral image under UV light and halogen light source [19].…”

Section: Introductionmentioning

confidence: 99%

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Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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Visible/near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA) was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC 2 to PC 6 ) gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM) classifier were applied to the full spectral range. SVM models built by PC 2 to PC 6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm) and seven (461, 538, 542, 742, 753, 756, 919 nm) wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength's SVM models on every pixel of the hyperspectral image.

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Section: Optimal Wavelengths Svm Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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“…In that study, halogen and UV light sources were used, and it was found that the classification obtained under UV light was more accurate than under halogen light. Yao et al (2008) prepared toxigenic fungi for mold separation and revealed that two types of fungi could be classified using HSI. Fiore et al (2010) detected fungiinoculated maize kernels using Vis/NIR HSI with a spectral range of 400~1,000 nm in the reflectance mode, while Shahin and Symons (2011) used HSI for the detection of Fusarium in damaged maize kernels.…”

Section: Application In Grainsmentioning

confidence: 99%

Spectroscopic Techniques for Nondestructive Detection of Fungi and Mycotoxins in Agricultural Materials: A Review

Min¹,

Cho

2015

Journal of Biosystems Engineering

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Purpose: Fungal secondary metabolite (mycotoxin) contamination in foods can pose a serious threat to humans and animals. Spectroscopic techniques have proven to be potential alternative tools for early detection of mycotoxins. Thus, the aim of this review is to provide an overview of the current developments in nondestructive food safety testing techniques, particularly regarding fungal contamination testing in grains, focusing on the application of spectroscopic techniques to this problem. Methods: This review focuses on the use of spectroscopic techniques for the detection of fungi and mycotoxins in agricultural products as reported in the literature. It provides an overview of the characteristics of the main spectroscopic methods and reviews their applications in grain analysis. Results: It was found that spectroscopy has advantages over conventional methods used for fungal contamination detection, particularly when combined with chemometrics. These advantages include the rapidness and nondestructive nature of this approach. Conclusions: While spectroscopy offers many benefits for the detection of mycotoxins in agricultural products, a number of limitations exist, which must be overcome prior to widespread adoption of these techniques.

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“…Recently, as a more readily accessible imaging solution, a visible and nearinfrared (VNIR) hyperspectral imaging technique was developed for detection of Campylobacter and non-Campylobacter organisms grown in Petri dishes [8]. Similarly, VNIR hyperspectral imaging was studied for fungi detection [9].…”

Section: Introductionmentioning

confidence: 99%

Detection of Campylobacter colonies using hyperspectral imaging

Yoon

Lawrence

Line

et al. 2010

Sens. & Instrumen. Food Qual.

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The presence of Campylobacter in foods of animal origin is the leading cause of bacterially induced human gastroenteritis. Isolation and detection of Campylobacter in foods via direct plating involves lengthy laboratory procedures including enrichments and microaerobic incubations, which take several days to a week. The incubation time for growing Campylobacter colonies in agar media usually takes 24-48 h. Oftentimes the problem is the difficulty of visually differentiating Campylobacter colonies from non-Campylobacter contaminants that frequently grow together with Campylobacter on many existing agars. In this study, a new screening technique using non-destructive and non-contact hyperspectral imaging was developed to detect Campylobacter colonies in Petri dishes. A reflectance spectral library of Campylobacter and non-Campylobacter contaminants was constructed for characterization of absorption features in wavelengths from 400 to 900 nm and for developing classification methods. Blood agar and Campy-Cefex agar were used as culture media. The study found that blood agar was the better culture medium than Campy-Cefex agar in terms of Campylobacter detection accuracy. Classification algorithms including single-band thresholding, band-ratio thresholding and spectral feature fitting were developed for detection of Campylobacter colonies as early as 24 h of incubation time. A band ratio algorithm using two bands at 426 and 458 nm chosen from continuum-removed spectra of the blood agar bacterial cultures achieved 97-99% of detection accuracy. This research has profound implications for early detection of Campylobacter colonies with high accuracy. Also, the developed hyperspectral reflectance imaging protocol is applicable to other pathogen detection studies.

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Differentiation of toxigenic fungi using hyperspectral imagery

Cited by 38 publications

References 27 publications

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Spectroscopic Techniques for Nondestructive Detection of Fungi and Mycotoxins in Agricultural Materials: A Review

Detection of Campylobacter colonies using hyperspectral imaging

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