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Tao F, Yao H, Hruska Z, Rajasekaran K, Qin J, Kim M, Chao K. Raman Hyperspectral Imaging as a Potential Tool for Rapid and Nondestructive Identification of Aflatoxin Contamination in Corn Kernels. J Food Prot 2024; 87:100335. [PMID: 39074611 DOI: 10.1016/j.jfp.2024.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.
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Affiliation(s)
- Feifei Tao
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA; USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
| | - Haibo Yao
- USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS 39762, USA.
| | - Zuzana Hruska
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA
| | - Kanniah Rajasekaran
- USDA-ARS, Food and Feed Safety Research Unit, Southern Regional Research Center, New Orleans, LA 70124, USA
| | - Jianwei Qin
- USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
| | - Moon Kim
- USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
| | - Kuanglin Chao
- USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
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Raki H, Aalaila Y, Taktour A, Peluffo-Ordóñez DH. Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review. Foods 2023; 13:11. [PMID: 38201039 PMCID: PMC10777928 DOI: 10.3390/foods13010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/27/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024] Open
Abstract
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.
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Affiliation(s)
- Hind Raki
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
| | - Yahya Aalaila
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
| | - Ayoub Taktour
- Materials Sciences and Nanotechnoloy (MSN), University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco;
| | - Diego H. Peluffo-Ordóñez
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
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Kim YK, Baek I, Lee KM, Kim G, Kim S, Kim SY, Chan D, Herrman TJ, Kim N, Kim MS. Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques. Toxins (Basel) 2023; 15:472. [PMID: 37505741 PMCID: PMC10467122 DOI: 10.3390/toxins15070472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 07/29/2023] Open
Abstract
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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Affiliation(s)
- Yong-Kyoung Kim
- Division of Safety Analysis, Experiment & Research Institute, National Agricultural Products Quality Management Service, Gimcheon 39660, Republic of Korea; (Y.-K.K.); (S.K.); (S.-Y.K.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd., Building 303 BARC-East, Beltsville, MD 20705, USA; (I.B.); (G.K.); (D.C.)
| | - Kyung-Min Lee
- Office of the Texas State Chemist, Texas A&M AgriLife Research, Texas A&M University System, College Station, TX 77841, USA; (K.-M.L.); (T.J.H.)
| | - Geonwoo Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd., Building 303 BARC-East, Beltsville, MD 20705, USA; (I.B.); (G.K.); (D.C.)
- Department of Bio-Industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501 Jinju-daero, Jinju-si 52828, Republic of Korea
| | - Seyeon Kim
- Division of Safety Analysis, Experiment & Research Institute, National Agricultural Products Quality Management Service, Gimcheon 39660, Republic of Korea; (Y.-K.K.); (S.K.); (S.-Y.K.)
| | - Sung-Youn Kim
- Division of Safety Analysis, Experiment & Research Institute, National Agricultural Products Quality Management Service, Gimcheon 39660, Republic of Korea; (Y.-K.K.); (S.K.); (S.-Y.K.)
| | - Diane Chan
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd., Building 303 BARC-East, Beltsville, MD 20705, USA; (I.B.); (G.K.); (D.C.)
| | - Timothy J. Herrman
- Office of the Texas State Chemist, Texas A&M AgriLife Research, Texas A&M University System, College Station, TX 77841, USA; (K.-M.L.); (T.J.H.)
| | - Namkuk Kim
- Division of Safety Analysis, Experiment & Research Institute, National Agricultural Products Quality Management Service, Gimcheon 39660, Republic of Korea; (Y.-K.K.); (S.K.); (S.-Y.K.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd., Building 303 BARC-East, Beltsville, MD 20705, USA; (I.B.); (G.K.); (D.C.)
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Liu S, Jiang S, Yao Z, Liu M. Aflatoxin detection technologies: recent advances and future prospects. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:79627-79653. [PMID: 37322403 DOI: 10.1007/s11356-023-28110-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 06/01/2023] [Indexed: 06/17/2023]
Abstract
Aflatoxins have posed serious threat to food safety and human health. Therefore, it is important to detect aflatoxins in samples rapidly and accurately. In this review, various technologies to detect aflatoxins in food are discussed, including conventional ones such as thin-layer chromatography (TLC), high performance liquid chromatography (HPLC), enzyme linked immunosorbent assay (ELISA), colloidal gold immunochromatographic assay (GICA), radioimmunoassay (RIA), fluorescence spectroscopy (FS), as well as emerging ones (e.g., biosensors, molecular imprinting technology, surface plasmon resonance). Critical challenges of these technologies include high cost, complex processing procedures and long processing time, low stability, low repeatability, low accuracy, poor portability, and so on. Critical discussion is provided on the trade-off relationship between detection speed and detection accuracy, as well as the application scenario and sustainability of different technologies. Especially, the prospect of combining different technologies is discussed. Future research is necessary to develop more convenient, more accurate, faster, and cost-effective technologies to detect aflatoxins.
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Affiliation(s)
- Shenqi Liu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Shanxue Jiang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China.
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China.
| | - Minhua Liu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
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Smeesters L, Kuntzel T, Thienpont H, Guilbert L. Handheld Fluorescence Spectrometer Enabling Sensitive Aflatoxin Detection in Maize. Toxins (Basel) 2023; 15:361. [PMID: 37368662 DOI: 10.3390/toxins15060361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/15/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
Aflatoxins are among the main carcinogens threatening food and feed safety while imposing major detection challenges to the agrifood industry. Today, aflatoxins are typically detected using destructive and sample-based chemical analysis that are not optimally suited to sense their local presence in the food chain. Therefore, we pursued the development of a non-destructive optical sensing technique based on fluorescence spectroscopy. We present a novel compact fluorescence sensing unit, comprising both ultraviolet excitation and fluorescence detection in a single handheld device. First, the sensing unit was benchmarked against a validated research-grade fluorescence setup and demonstrated high sensitivity by spectrally separating contaminated maize powder samples with aflatoxin concentrations of 6.6 µg/kg and 11.6 µg/kg. Next, we successfully classified a batch of naturally contaminated maize kernels within three subsamples showing a total aflatoxin concentration of 0 µg/kg, 0.6 µg/kg and 1647.8 µg/kg. Consequently, our novel sensing methodology presents good sensitivity and high potential for integration along the food chain, paving the way toward improved food safety.
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Affiliation(s)
- Lien Smeesters
- Department of Applied Physics and Photonics, Brussels Photonics (B-PHOT), Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, 1050 Brussels, Belgium
| | - Thomas Kuntzel
- GoyaLab, Institut d'Optique d'Aquitaine, Rue François Mitterrand, 33400 Talence, France
| | - Hugo Thienpont
- Department of Applied Physics and Photonics, Brussels Photonics (B-PHOT), Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, 1050 Brussels, Belgium
| | - Ludovic Guilbert
- GoyaLab, Institut d'Optique d'Aquitaine, Rue François Mitterrand, 33400 Talence, France
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Jin S, Liu X, Wang J, Pan L, Zhang Y, Zhou G, Tang C. Hyperspectral imaging combined with fluorescence for the prediction of microbial growth in chicken breasts under different packaging conditions. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods 2023; 12:foods12061242. [PMID: 36981168 PMCID: PMC10048131 DOI: 10.3390/foods12061242] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.
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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Xu L, Chen Y, Wang X, Chen H, Tang Z, Shi X, Chen X, Wang Y, Kang Z, Zou Z, Huang P, He Y, Yang N, Zhao Y. Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging. FRONTIERS IN PLANT SCIENCE 2023; 13:1075929. [PMID: 36743568 PMCID: PMC9889828 DOI: 10.3389/fpls.2022.1075929] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the R p 2 , R c 2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the R p 2 , R c 2 , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.
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Affiliation(s)
- Lijia Xu
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Yanjun Chen
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Xiaohui Wang
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Heng Chen
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Zuoliang Tang
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Xiaoshi Shi
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Xinyuan Chen
- College of Engineering, Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yuchao Wang
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Zhilang Kang
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Zhiyong Zou
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Peng Huang
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Ning Yang
- School of Electical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Yongpeng Zhao
- College of mechanical and electrical engineering, Sichuan Agriculture University, Ya’an, China
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