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Concepcion JS, Noble AD, Thompson AM, Dong Y, Olson EL. Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat. Sci Rep 2024; 14:19340. [PMID: 39164367 PMCID: PMC11336138 DOI: 10.1038/s41598-024-69830-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 08/09/2024] [Indexed: 08/22/2024] Open
Abstract
The quantitative nature of fusarium head blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identified regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397-673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. The first principal component (PC1) of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in a genome wide association study and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB.
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Affiliation(s)
- Jonathan S Concepcion
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Amanda D Noble
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Addie M Thompson
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Eric L Olson
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA.
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Wang Y, Ou X, He HJ, Kamruzzaman M. Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chem X 2024; 21:101235. [PMID: 38420503 PMCID: PMC10900407 DOI: 10.1016/j.fochx.2024.101235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.
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Affiliation(s)
- Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Ren L, Liu W, Liu C, Zheng L. Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3139-3145. [PMID: 36694937 DOI: 10.1002/jsfa.12467] [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: 04/14/2022] [Revised: 12/30/2022] [Accepted: 01/25/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND The characteristics of corn kernels are strongly connected with the content of three statuses of water: bound water, immobilized water, and free water. Monitoring different water contents is very important to optimize the drying process, improve corn quality, and reduce energy consumption. The feasibility of nondestructive detection of water status and its distribution in corn kernels during the hot-air drying process using multispectral imaging was investigated. RESULTS The chemometric methods used to develop prediction models were back propagation neural network, least-squares support vector machine, and partial least squares. The back propagation neural network achieved the best prediction performance for total and free water contents, with correlation coefficient of prediction (Rp ) of 0.9717 and 0.9782 respectively, root-mean-square error of prediction (RMSEP) of 4.48% and 2.54% respectively, and ratio of prediction to deviation (RPD) of 4.87 and 4.29 respectively. And partial least squares was better for the prediction of immobilized and bound water contents, with Rp of 0.9612 and 0.9798 respectively, RMSEP of 0.57% and 0.06% respectively, and RPD of 4.78 and 4.42 respectively. CONCLUSION It could be concluded that multispectral imaging combined with chemometric methods would be a promising technique for rapid and nondestructive detection of water status and its distribution in corn kernels. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Lin Ren
- Engineering Research Center of Bio-Process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
| | - Wei Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei, China
| | - Changhong Liu
- Engineering Research Center of Bio-Process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
| | - Lei Zheng
- Engineering Research Center of Bio-Process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
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Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Freitag S, Sulyok M, Logan N, Elliott CT, Krska R. The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops. Compr Rev Food Sci Food Saf 2022; 21:5199-5224. [PMID: 36215130 DOI: 10.1111/1541-4337.13054] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/18/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
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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Affiliation(s)
- Stephan Freitag
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria
| | - Michael Sulyok
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria
| | - Natasha Logan
- Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Rudolf Krska
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria.,Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
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Liu W, He L, Xia Y, Ren L, Liu C, Zheng L. Monitoring the growth of Fusarium graminearum in wheat kernels using multispectral imaging with chemometric methods. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:106-113. [PMID: 34877944 DOI: 10.1039/d1ay01586a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Wheat is an important agricultural economic crop providing energy and nutrition for human beings. However, wheat kernels are easily contaminated with Fusarium graminearum that is harmful to human health. In this study, a rapid and nondestructive detection method has been developed to identify the degree of contamination and determine the count of Fusarium graminearum in wheat kernels using multispectral imaging technology. Based on genetic algorithm (GA) and principal component analysis (PCA) data preprocessing methods combined with partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN) chemometric methods, identification and quantitative determination models were established. The best result was obtained by GA-BPNN with an accuracy of up to 100% in the identification of the degree of contamination in wheat kernels at different contamination periods. Comparison of the results from different methods revealed that the best prediction of the count of Fusarium graminearum was obtained by GA-SVM with the correlation coefficient (R) in the calibration set and prediction set being 0.9663 and 0.9292, while the root mean square error (RMSE) in the calibration set and prediction set was 0.5992 and 0.6725 CFU g-1, respectively. It can be concluded that the combination of multispectral imaging and chemometric methods was potentially useful for rapid and nondestructive detection of cereal fungi in practice.
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Affiliation(s)
- Wei Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Lin He
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Yiming Xia
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Lin Ren
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Changhong Liu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Lei Zheng
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China.
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