Rapid determination of potential aflatoxigenic fungi contamination on peanut kernels during storage by data fusion of HS-GC-IMS and fluorescence spectroscopy
Introduction
Aflatoxin (AF) is a group of toxic compounds produced as secondary metabolites by fungi A. flavus and A. parasiticus (Wu and Xu, 2020). AF contamination can occur in a wide variety of agricultural commodities, such as cereals, nuts and oil products etc. The occurrence of AFs in agricultural products would pose serious health issues for human and animals, as well as a large economic impact (Shukla et al., 2017). AFs mainly attack the liver, thereby causing serious damages including carcinogenicity, mutagenicity and immunosuppressive effects (Ben et al., 2020). Taking into account the potential risks of AFs, it is important to develop a rapid and sensitive analytical method to effectively detect AF for food safety. Peanut (Arachis hypogaea L.) is an important crop in China and other countries due to its nutritional and economic values. However, it is prone to be infected with A. flavus and A. parasiticus, resulting in aflatoxin contamination during the storage (Diao et al., 2014). Therefore, peanut kernel was used as a representative research object in this study.
Recently, various strategies have been developed to determine aflatoxin contamination and the presence of fungi in agricultural products. High-performance liquid chromatography (HPLC), thin layer chromatography (TLC) and enzyme-linked immunosorbent assay are conventional analytical methods for the determination of AFs (De Leon-Zapata et al., 2016; Gu et al., 2019; Tao et al., 2019). Traditional microbiological methods and diagnostic media were used for the identification of toxigenic fungi in a laboratory setting. These approaches have high sensitivity and good repeatability, whereas cumbersome sample processing, relative expensive reagents and expensive disposable columns such as immunoaffinity and multifunctional columns are required, which limit their application for rapid and on-site screening of AFs. They are also destructive to the test samples. Consequently, to satisfy the need for early quality control in the food industry and to provide peanut with low health risk, rapid and non-destructive techniques are needed.
Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) is an emerging technique, its application in food quality and safety has increased in recent years due to its multiple advantages (e.g. comparatively simple system setups and robustness) over other conventional analytical techniques (Arroyo-Manzanares et al., 2018). Ion mobility spectrometry (IMS) is a valuable field detection technology due to its speed and high sensitivity, but IMS cannot easily resolve interested analytes in mixtures. Coupling gas chromatography (GC) to IMS adds a separation capability to resolve complex matrices (Arroyo-Manzanares et al., 2019). So far, some studies have explored the application of HS-GC-IMS technology in the field of quality monitoring including olive, bread, honey and rice etc. (Chen et al., 2018; Contreras et al., 2019; Wang et al., 2019a,b; Pu et al., 2020; Gu et al., 2020). In the field of rapid microbiological diagnostic, Gallegos et al. (2017) first demonstrated the potential of HS-GC-IMS to differentiate lactic acid bacteria (LAB) strains widely used in cheese making, and identified relevant VOCs associated with the growth of LABs. Additionally, it is known that aflatoxins could emit natural and intrinsic fluorescence under certain UV conditions. Fluorescence emission is a phenomenon exhibited by some organic as well as inorganic substances (Yao et al., 2013a). The fluorescence emission can be recorded by a fluorescence spectroscopy. The already published fluorescence methods allow the identification of toxins in liquids, like beer or wine, and in grains and nuts (Rasch et al., 2010; Smeesters et al., 2015). Since peanut kernels contain various fluorescent protein, the elimination of background fluorescent elements through immunoaffinity column is required for conventional fluorescence analysis to ensure the accuracy of aflatoxin detection. This study attempted to perform minimal manual sample pretreatment without immunoaffinity separations to detect aflatoxigenic fungi contamination on peanut kernels, the potential characteristic information of aflatoxin was extracted and classification model was constructed with chemometrics. Notably, the purpose of fluorescence detection in this work was to combine HS-GC-IMS fingerprinting to obtain better classification and prediction results. Because HS-GC-IMS and fluorescence spectroscopy provide different types of information for aflatoxigenic fungi contamination on peanut kernels, synergistic effect may be presented when these two types of data are combined. The advantages of fusing information from different detection sources to comprehensively exploit the characteristic of samples have been reported in many researches (Liu et al., 2019; Song et al., 2020; Xu et al., 2019). To our best knowledge, no attempt has been made to examine the potential of using HS-GC-IMS and fluorescence spectroscopy to detect the activities of spoilage aflatoxigenic fungi in peanut kernels, and the potential for differentiating between aflatoxigenic and non-aflatoxigenic fungi.
In the present study, it was aimed to set up an emerging method for the early discriminative and quantitative evaluation of aflatoxigenic fungi contamination on peanut kernels. The orthogonal partial least squares discriminant analysis and partial least squares regression analysis using the data of HS-GC-IMS and fluorescence spectroscopy were carried out. Data fusion strategies were divided into three categories: data-level, feature-level using first 10 PCs and feature-level using the variables selected by the variable importance in projection (VIP). The specific objectives were: 1) to investigate the synergetic effect between HS-GC-IMS and fluorescence spectroscopy by using different data fusion strategies; 2) to discriminate peanut samples infected by aflatoxigenic and non-aflatoxigenic fungal species during different storage time; 3) to characterize major volatile organic compounds (VOCs) between aflatoxigenic and non-aflatoxigenic fungi at early storage period; 4) to quantitatively predict the colony counts of aflatoxigenic fungi contaminated peanut samples.
Section snippets
Sample preparation
The fungal species tested for this study were selected based on their frequency of occurrence on the surface of moldy peanut kernels. In particular, two potential AF-producing species (A. flavus and A. parasiticus) and three non-AF producing species (A. fumigatus, A. niger and R. stolonifer), isolated and identified from moldy peanut kernels, were used as inoculum separately for artificial laboratory inoculations. Five isolates were cultivated separately on potato dextrose agar (PDA) medium in
Fungal growth on peanut kernels during storage
Fungal growth in peanut kernels was monitored by viable colony counts. Peanuts are rich in starch, protein, lipid and minerals, which is a perfect substrate for fungal growth and potential aflatoxin contamination (Mupunga et al., 2017). With prolonged storage time, the total colony counts in CK group and fungi-infected groups increased proportionately, which indicated that the infection level continued to worsen, as expected (Table 1). It was noted that the CFUs level of CK group was
Conclusion
In this work, the performances of HS-GC-IMS, fluorescence spectra and their data fusion for the rapid and early detection of potential aflatoxigenic fungi contamination on peanut kernels were evaluated. From the results reported above, HS-GC-IMS could provide a superior classification and prediction results compared to fluorescence spectra. As for three data fusion methods, OPLS-DA and PLSR models established by feature-level fusion using first 10 PCs showed the best classification (96.7 %) and
CRediT authorship contribution statement
Shuang Gu: Conceptualization, Software, Investigation, Visualization, Methodology, Data curation, Writing - original draft, Writing - review & editing. Wei Chen: Validation, Data curation, Writing - original draft, Writing - review & editing. Zhenhe Wang: Formal analysis, Data curation, Software, Writing - review & editing. Jun Wang: Conceptualization, Project administration, Funding acquisition, Supervision, Writing - review & editing.
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgements
This research was supported by the National Key Research and Development Program of China through Project 2017YFD0400102.
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