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Vicens-Sans A, Pascari X, Molino F, Ramos AJ, Marín S. Near infrared hyperspectral imaging as a sorting tool for deoxynivalenol reduction in wheat batches. Food Res Int 2024; 178:113984. [PMID: 38309885 DOI: 10.1016/j.foodres.2024.113984] [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: 11/24/2023] [Revised: 12/30/2023] [Accepted: 01/05/2024] [Indexed: 02/05/2024]
Abstract
The present study aimed to evaluate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics for classification of individual wheat kernels according to their deoxynivalenol (DON) level. In total, 600 wheat kernels from samples naturally contaminated over the maximum EU level were collected, and the DON content in each individual wheat kernel was analyzed by UHPLC. Linear discriminant analysis (LDA) was employed for building classification models of DON using the EU maximum level as cut off level, and they were tested on balanced and imbalanced test sets. The results showed that the models presented a balanced accuracy of 0.71, that would allow to obtain safe batches from contaminated batches once the unsafe kernels had been rejected, but often more than 30% of the batch would be rejected. The work confirmed that NIR-HSI could be a feasible method for monitoring DON in individual kernels and removing highly contaminated kernels prior to food chain entry.
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Affiliation(s)
- A Vicens-Sans
- Applied Mycology Unit, Food Technology, Engineering and Science Department, University of Lleida, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - X Pascari
- Applied Mycology Unit, Food Technology, Engineering and Science Department, University of Lleida, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - F Molino
- Applied Mycology Unit, Food Technology, Engineering and Science Department, University of Lleida, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - A J Ramos
- Applied Mycology Unit, Food Technology, Engineering and Science Department, University of Lleida, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - S Marín
- Applied Mycology Unit, Food Technology, Engineering and Science Department, University of Lleida, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
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2
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Cozzolino D, Chapman J. Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety. Anal Bioanal Chem 2024; 416:611-620. [PMID: 37542534 DOI: 10.1007/s00216-023-04849-7] [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: 03/26/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 08/07/2023]
Abstract
Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.
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Affiliation(s)
- Daniel Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, St. Lucia, Brisbane, QLD, 4072, Australia.
| | - James Chapman
- School of Science, RMIT University, GPO Box 2476, Melbourne, VIC, 3001, Australia
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3
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Cebi N, Bekiroglu H, Erarslan A. Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening. Molecules 2023; 28:7933. [PMID: 38067662 PMCID: PMC10707828 DOI: 10.3390/molecules28237933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.
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Affiliation(s)
- Nur Cebi
- Food Engineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey;
| | - Hatice Bekiroglu
- Food Engineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey;
- Food Engineering Department, Faculty of Agriculture, Sirnak University, 73300 Sirnak, Turkey
| | - Azime Erarslan
- Bioengineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey;
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4
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Ba W, Jin X, Lu J, Rao Y, Zhang T, Zhang X, Zhou J, Li S. Research on predicting early Fusarium head blight with asymptomatic wheat grains by micro-near infrared spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:122047. [PMID: 36327806 DOI: 10.1016/j.saa.2022.122047] [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: 07/23/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
Fusarium head blight (FHB) is considered one of the most serious fungal diseases of wheat. Fusarium resulted in yield losses and contamination of harvested grains with mycotoxins. Therefore, diagnosing Fusarium head blight in early asymptomatic wheat is vital. To detect early FHB, a micro-near-infrared spectrometer was used to collect the spectrum of wheat grains, and FHB infection of wheat was detected by combining chemometrics in the 900-1700 nm near-infrared spectral region. First, the obtained spectra were analysed accordingly, and the pre-processed data were compared. The modelling analysis was then performed using the support vector machine (SVM), random forest (RF), extreme gradient descent (XGBoost), Autokeras, and Autogluon (with SVM) algorithms. The results showed that SG smoothing with standard normal variate (SG + SNV) was the best pre-treatment method. In addition, after SG + SNV was combined with the Autogluon (with SVM) model, the optimal classification results were obtained, with an accuracy of 73.33 % and an F1 value of 72.86 %. Autogluon (with SVM) could prevent overfitting and optimize generalization. Then, this manuscript discusses the performance of the Autogluon (with SVM) model with different stacking layers. The results show that one stacking layer can obtain a classification model with excellent performance. These results indicated that the near infrared spectrum (NIR) has the potential for early detection of Fusarium head blight with asymptomatic early statements.
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Affiliation(s)
- Wenjing Ba
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China
| | - Xiu Jin
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China.
| | - Jie Lu
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Agriculture, Anhui Agricultural University, Hefei 230001, China
| | - Yuan Rao
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China
| | - Tong Zhang
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China
| | - XiaoDan Zhang
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China
| | - Jun Zhou
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China
| | - Shaowen Li
- Anhui Province Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Anhui Agriculture University, Hefei 230001, China; College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China
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5
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Serva L, Marchesini G, Cullere M, Ricci R, Dalle Zotte A. Testing two NIRs instruments to predict chicken breast meat quality and exploiting machine learning approaches to discriminate among genotypes and presence of myopathies. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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6
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Du Z, Tian W, Tilley M, Wang D, Zhang G, Li Y. Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review. Compr Rev Food Sci Food Saf 2022; 21:2956-3009. [PMID: 35478437 DOI: 10.1111/1541-4337.12958] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 01/15/2023]
Abstract
Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.
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Affiliation(s)
- Zhenjiao Du
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
| | - Wenfei Tian
- National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Michael Tilley
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, Kansas, USA
| | - Donghai Wang
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas, USA
| | - Guorong Zhang
- Agricultural Research Center-Hays, Kansas State University, Hays, Kansas, USA
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
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7
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Li FL, Xie J, Wang S, Wang Y, Xu CH. Direct qualitative and quantitative determination methodology for massive screening of DON in wheat flour based on multi-molecular infrared spectroscopy (MM-IR) with 2T-2DCOS. Talanta 2021; 234:122653. [PMID: 34364462 DOI: 10.1016/j.talanta.2021.122653] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 12/20/2022]
Abstract
Deoxynivalenol (DON) contamination in wheat flour induces a number of adverse health effects to consumers and livestock, even at very low concentrations. Direct detection methods for massive screening of DON in wheat flour is still lacking. A new methodology integrating multi-molecular infrared spectroscopy (MM-IR) with two-trace two-dimensional correlation spectroscopy (2T-2DCOS) was developed for in-situ qualitative and quantitative determination of DON in wheat flour as a whole. Typical spectral variation of wheat flour samples with diverse concentration of DON were stepwise characterized by MM-IR and tiny spectral profile differences resulting from concentration variation of DON were visually disclosed by 2T-2DCOS. Based on the obtained key spectral features of DON, 180 of wheat flour samples with DON higher and lower than 1.00 mg/kg were undoubtedly classified by Principal Component Analysis (PCA) and Support Vector Machines (SVM) with an accuracy rate up to 100% (for Second derivative spectra consisted of selected bands, SD-SS). Furthermore, a robust quantitative prediction model was established based on partial least squares (PLS) of SD-SS (Rc: 0.998, RMSEC: 0.135; Rp: 0.968, RMSEP: 0.421), and its excellent predictive capacity of model was validated by both residual prediction deviation (RPD) value of 3.2 and t-test. It was demonstrated that the developed methodology was applicable for screening and quantitative detection of DON in wheat flour based on the novel correlation analysis methods (SD-2DCOS-IR and 2T-2DCOS-IR) with chemometrics tools, which could be utilized both at laboratory and industrial level for quality control purposes of a large wheat flour sample set.
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Affiliation(s)
- Fei-Li Li
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai, 201306, China
| | - Jun Xie
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai, 201306, China
| | - Song Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai, 201306, China
| | - Yang Wang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300112, China
| | - Chang-Hua Xu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai, 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai, 201306, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Shanghai, 201306, China.
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8
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Gu C, Wang D, Zhang H, Zhang J, Zhang D, Liang D. Fusion of Deep Convolution and Shallow Features to Recognize the Severity of Wheat Fusarium Head Blight. FRONTIERS IN PLANT SCIENCE 2021; 11:599886. [PMID: 33552097 PMCID: PMC7859649 DOI: 10.3389/fpls.2020.599886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 11/30/2020] [Indexed: 05/18/2023]
Abstract
A fast and nondestructive method for recognizing the severity of wheat Fusarium head blight (FHB) can effectively reduce fungicide use and associated costs in wheat production. This study proposed a feature fusion method based on deep convolution and shallow features derived from the high-resolution digital Red-green-blue (RGB) images of wheat FHB at different disease severity levels. To test the robustness of the proposed method, the RGB images were taken under different influence factors including light condition, camera shooting angle, image resolution, and crop growth period. All images were preprocessed to eliminate background noises to improve recognition accuracy. The AlexNet model parameters trained by the ImageNet 2012 dataset were transferred to the test dataset to extract the deep convolution feature of wheat FHB. Next, the color and texture features of wheat ears were extracted as shallow features. Then, the Relief-F algorithm was used to fuse the deep convolution feature and shallow features as the final FHB features. Finally, the random forest was used to classify and identify the features of different FHB severity levels. Results show that the recognition accuracy of the proposed fusion feature model was higher than those of models using other features in all conditions. The highest recognition accuracy of severity levels was obtained when images were taken under indoor conditions, with high resolution (12 MB pixels), at 90° shooting angle during the crop filling period. The Relief-F algorithm assigned different weights to the features under different influence factors; it made the fused feature model more robust and improved the ability to recognize wheat FHB severity levels using RGB images.
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Affiliation(s)
- Chunyan Gu
- Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei, China
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Daoyong Wang
- Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei, China
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Huihui Zhang
- Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, United States
| | - Jian Zhang
- Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
| | - Dongyan Zhang
- Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei, China
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
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Femenias A, Gatius F, Ramos AJ, Sanchis V, Marín S. Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples. Food Chem 2020; 341:128206. [PMID: 33035826 DOI: 10.1016/j.foodchem.2020.128206] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 09/07/2020] [Accepted: 09/23/2020] [Indexed: 10/23/2022]
Abstract
The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, one that was analysed for ergosterol and another that was analysed for DON by HPLC. This method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The prediction models presented a RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the EU legal limit for DON.
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Affiliation(s)
- Antoni Femenias
- Applied Mycology Unit, Food Technology Department, University of Lleida, Agrotecnio Center, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Ferran Gatius
- Department of Chemistry, University of Lleida (UdL), Av. Rovira Roure, 191, Lleida 25198, Spain
| | - Antonio J Ramos
- Applied Mycology Unit, Food Technology Department, University of Lleida, Agrotecnio Center, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Vicente Sanchis
- Applied Mycology Unit, Food Technology Department, University of Lleida, Agrotecnio Center, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Sonia Marín
- Applied Mycology Unit, Food Technology Department, University of Lleida, Agrotecnio Center, Av. Rovira Roure 191, 25198 Lleida, Spain.
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10
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Jia B, Wang W, Ni X, Chu X, Yoon S, Lawrence K. Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review. WORLD MYCOTOXIN J 2020. [DOI: 10.3920/wmj2019.2510] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Nutrition-rich cereal grains and oil seeds are the major sources of food and feed for human and livestock, respectively. Infected by fungi and contaminated with mycotoxins are serious problems worldwide for cereals and oil seeds before and after harvest. The growth and development activities of fungi consume seed nutrients and destroy seed structures, leading to dramatic declines of crop yield and quality. In addition, the toxic secondary metabolites produced by these fungi pose a well-known threat to both human and animals. The existence of fungi and mycotoxins has been a redoubtable problem worldwide for decades but tends to be a severe food safety issue in developing countries and regions, such as China and Africa. Detection of fungal infection at an early stage and of mycotoxin contaminants, even at a small amount, is of great significance to prevent harmful toxins from entering the food supply chains worldwide. This review focuses on the recent advancements in utilising infrared spectroscopy, Raman spectroscopy, and hyperspectral imaging to detect fungal infections and mycotoxin contaminants in cereals and oil seeds worldwide, with an emphasis on recent progress in China. Brief introduction of principles, and corresponding shortcomings, as well as latest advances of each technique, are also being presented herein.
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Affiliation(s)
- B. Jia
- Beijing Key Laboratory of Optimized Design for modern Agricultural Equipment, College of Engineering, China Agriculture University, No. 17 Tsinghua East Road, Beijing, 100083, China P.R
| | - W. Wang
- Beijing Key Laboratory of Optimized Design for modern Agricultural Equipment, College of Engineering, China Agriculture University, No. 17 Tsinghua East Road, Beijing, 100083, China P.R
| | - X.Z. Ni
- Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
| | - X. Chu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China P.R
| | - S.C. Yoon
- Quality and Safety Assessment Research Unit, USDA-ARS, Athens, GA 30605, USA
| | - K.C. Lawrence
- Quality and Safety Assessment Research Unit, USDA-ARS, Athens, GA 30605, USA
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11
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Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field. REMOTE SENSING 2018. [DOI: 10.3390/rs10030395] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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