Spatio-temporal patterns of Aspergillus flavus infection and aflatoxin B1 biosynthesis on maize kernels probed by SWIR hyperspectral imaging and synchrotron FTIR microspectroscopy

Lu, Yao; Jia, Baoping; Yoon, Seung-Chul; Zhuang, Hong; Ni, Xinzhi; Guo, Bao‐Zhu; Gold, Scott E.; Fountain, Jake C.; Glenn, Anthony E.; Lawrence, Kurt C.; Zhang, Haicheng; Guo, Xiaohuan; Zhang, Feng; Wang, Wei

doi:10.1016/j.foodchem.2022.132340

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…Corn Fungal infection, FUM, AFLA B 1 (Chu et al, 2020;Chu et al, 2017;Del Fiore et al, 2010;Firrao et al, 2010;Kandpal et al, 2015;Kimuli, Wang, Jiang, et al, 2018;Kimuli, Wang, Lawrence, et al, 2018;Lu et al, 2022;Parrag et al, 2020;Tao et al, 2020;Wang et al, 2014;Williams et al, 2010;Williams et al, 2012;Yang et al, 2020;Zhao et al, 2017;Zhu et al, 2016) Wheat Fungal infection, DON, OTA, ergosterol (Alisaac et al, 2019;Barbedo et al, 2017;Bauriegel et al, 2011;Berman et al, 2007;Dammer et al, 2011;Delwiche et al, 2010;Delwiche et al, 2019;Femenias, Bainotti, et al, 2021;Liang et al, 2020;Nadimi et al, 2021;Ropelewska & Zapotoczny, 2018;Senthilkumar et al, 2016;Shao et al, 2020;Shi et al, 2020;Singh et al, 2012;Singh et al, 2007;Zhao et al, 2020) Rice Fungal infection (Siripatrawan & Makino, 2015;Wu et al, 2020) Oats DON (Tekle et al, 2015) Peanuts Fungal contamination (He et al, 2021;Jiang et al, 2016;Qi et al, 2019;Qiao et al, 2017) Barley DON…”

Section: Target Contaminants Referencementioning

confidence: 99%

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Freitag

Sulyok

Logan

et al. 2022

Comp Rev Food Sci Food Safe

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Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner.The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.

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Section: Target Contaminants Referencementioning

confidence: 99%

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Freitag

Sulyok

Logan

et al. 2022

Comp Rev Food Sci Food Safe

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“…Lu et al. ( Lu et al., 2022 ) ingeniously combined HSI with deep convolutional generative adversarial networks to predict the oil content of individual corn kernels. Yu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al (Zhang et al, 2022) Used HSI to detect hybrid wheat seed purity. Lu et al (Lu et al, 2022) ingeniously combined HSI with deep convolutional generative adversarial networks to predict the oil content of individual corn kernels. Yu et al (Yu et al, 2016) effectively measured fat content in peanuts (R 2 p = 0.84 and SEP = 1.88) and Ma et al (Ma et al, 2021) further devised a streamlined model for the non-destructive assessment of protein content in rice, achieving notable success (R 2 p = 0.8011 and RMSEP = 0.52).…”

Section: Introductionmentioning

confidence: 99%

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

Zhu,

Han,

Liu

et al. 2024

Front. Plant Sci.

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Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed’s nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the Rp2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

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“…Recent spectroscopy and hyperspectral imaging studies have been conducted using grains to detect mycotoxins in maize [ 9 , 16 , 17 , 18 ], rather than ground samples. There are pros and cons to using grain and ground samples, depending on purpose.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

Kim¹,

Baek

Lee

et al. 2023

Toxins

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Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.

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Spatio-temporal patterns of Aspergillus flavus infection and aflatoxin B1 biosynthesis on maize kernels probed by SWIR hyperspectral imaging and synchrotron FTIR microspectroscopy

Cited by 15 publications

References 34 publications

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

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