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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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Sha W, Hu K, Weng S. Statistic and Network Features of RGB and Hyperspectral Imaging for Determination of Black Root Mold Infection in Apples. Foods 2023; 12:foods12081608. [PMID: 37107403 PMCID: PMC10137991 DOI: 10.3390/foods12081608] [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: 02/20/2023] [Revised: 03/24/2023] [Accepted: 03/31/2023] [Indexed: 04/29/2023] Open
Abstract
Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging and hyperspectral imaging (HSI) are combined to detect the infection degree of BRM in apple fruits. First, RGB and HSI images of healthy, mildly, moderately, and severely infected fruits are measured, and those with effective wavelengths (EWs) are screened from HSI by random frog. Second, the statistic and network features of images are extracted by using color moment and convolutional neural network. Meanwhile, random forest (RF), K-nearest neighbor, and support vector machine are used to construct classification models with the above two features of RGB and HSI images of EWs. Optimal results with the 100% accuracy of training set and 96% accuracy of prediction set are obtained by RF with the statistic and network features of the two images, outperforming the other cases. The proposed method furnishes an accurate and effective solution for determining the BRM infection degree in apples.
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Affiliation(s)
- Wen Sha
- School of Electrical Engineering and Automation, Anhui University, 111 Jiulong Road Hefei, Hefei 230601, China
- Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui University, 111 Jiulong Road Hefei, Hefei 230601, China
| | - Kang Hu
- School of Electrical Engineering and Automation, Anhui University, 111 Jiulong Road Hefei, Hefei 230601, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road Hefei, Hefei 230601, China
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4
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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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5
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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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6
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Moraes WB, Madden LV, Paul PA. Efficacy of Genetic Resistance and Fungicide Application Against Fusarium Head Blight and Mycotoxins in Wheat Under Persistent Pre- and Postanthesis Moisture. PLANT DISEASE 2022; 106:2839-2855. [PMID: 35471074 DOI: 10.1094/pdis-02-22-0263-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Field experiments were conducted to investigate the efficacy of fungicide treatments in combination with genetic resistance against Fusarium head blight (FHB) and its associated mycotoxins under persistently wet pre- and postanthesis conditions in plots inoculated with Fusarium graminearum-colonized corn spawn. Treatments consisted of a single application of prothioconazole + tebuconazole at early anthesis (PA), or at 3 (P3), 6 (P6), or 9 (P9) days after early anthesis, or PA followed by a single application of metconazole at 3 (PA+C3), 6 (PA+C6), or 9 (PA+C9) days after early anthesis. PA and P3 were the most efficacious of the single-application treatments in terms of mean percentage control of FHB index (IND), deoxynivalenol (DON), zearalenone (ZEA), and mean increase in grain yield and test weight (TW) relative to the nontreated susceptible check (S_CK). The double-application treatments (PA+C3, PA+C6, and PA+C9) were the most effective of all tested fungicide programs. However, relative to S_CK, the highest overall mean percentage reduction in IND, DON, and ZEA and increase in grain yield and TW were observed when the double-application fungicide programs were integrated with genetic resistance. The estimated net cash income (NCI) of the integrated management (IM) programs was consistently higher than the NCI of other tested programs across different grain prices and fungicide application costs. Thus, the benefits of the two-treatment IM programs under highly favorable conditions for FHB development were enough to offset the cost of two applications, making these programs profitable.
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Affiliation(s)
- Wanderson Bucker Moraes
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research and Development Center, Wooster, OH 44691
| | - Laurence V Madden
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research and Development Center, Wooster, OH 44691
| | - Pierce A Paul
- Department of Plant Pathology, The Ohio State University, Ohio Agricultural Research and Development Center, Wooster, OH 44691
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Tyska D, Mallmann AO, Simões CT, da Silva CR, Gressler LT, Mallmann CA. Prediction of fumonisins B 1 and B 2 in corn distiller's dried grains with solubles through near-infrared reflectance spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:4514-4521. [PMID: 35122263 DOI: 10.1002/jsfa.11806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/29/2021] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Distiller's dried grains with solubles (DDGS) are coproducts of the biofuel industries that use corn as raw material. This cereal is commonly contaminated by mycotoxins, including fumonisins (FBs), which can pose a serious health threat to humans and animals. Corn DDGS are typically used as a protein-rich animal feed. As mycotoxins from the original cereal grains become concentrated in DDGS, mycotoxicological monitoring is highly required before their use as ingredient in the industry. RESULTS This work aimed to develop a methodology for predicting fumonisins B1 (FB1 ) and B2 (FB2 ) in corn DDGS using near-infrared reflectance spectroscopy (NIRS) technology associated with chemometric methods. One hundred and ninety corn DDGS samples originating from Brazilian ethanol plants and feed mills were included in this assessment. Two datasets were created: one for calibration (132 samples) and another for external validation (58 samples). Partial least squares regression and a cross-validation approach were applied to build the models. Liquid chromatography coupled to tandem mass spectrometry was used as the reference methodology. Calibration results of correlation coefficient and residual prediction deviation for FB1 and FB2 were, respectively, 0.90 and 0.88; and 2.16 and 2.06. CONCLUSION Values of the external validation dataset were compared and no statistical difference was found between groups, indicating a satisfactory predictive ability and confirming the potential of NIRS to predict fumonisins in corn DDGS. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Denize Tyska
- Federal University of Santa Maria (UFSM), Laboratory of Mycotoxicological Analyses (LAMIC), Department of Preventive Veterinary Medicine, Santa Maria, Rio Grande do Sul, Brazil
| | | | - Cristina Tonial Simões
- Federal University of Santa Maria (UFSM), Laboratory of Mycotoxicological Analyses (LAMIC), Department of Preventive Veterinary Medicine, Santa Maria, Rio Grande do Sul, Brazil
| | - Cristiane Rosa da Silva
- Federal University of Santa Maria (UFSM), Laboratory of Mycotoxicological Analyses (LAMIC), Department of Preventive Veterinary Medicine, Santa Maria, Rio Grande do Sul, Brazil
| | | | - Carlos Augusto Mallmann
- Federal University of Santa Maria (UFSM), Laboratory of Mycotoxicological Analyses (LAMIC), Department of Preventive Veterinary Medicine, Santa Maria, Rio Grande do Sul, Brazil
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8
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Taylor M, Newkirk R. Sorting capability and grain recovery of deoxynivalenol contaminated wheat is affected by calibration and vitreous kernel settings from near-infrared transmittance technology. WORLD MYCOTOXIN J 2022. [DOI: 10.3920/wmj2021.2751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Deoxynivalenol (DON) is a toxic secondary metabolite in wheat which affects animal performance. Limited post-harvest sorting technologies are available to remove infected kernels thereby allowing safe use in livestock. A technology was developed which uses near-infrared spectrometry combined with a seed singulation sorter by BoMill AB (Sweden) which is purported by the manufacturer to remove Fusarium infected grain. The objective of this study was to determine if Fusarium infected grain could be removed using the BoMill equipped with the Fusarium calibration resulting in grain with less than 5,000 μg/kg DON, and therefore legal to feed to poultry and beef in Canada. The secondary objective was to determine the optimal HVK settings within the two calibrations to determine if sorting based on Fusarium damage is more effective than sorting based on protein content. The settings tested were HVK, HHVK, and HHHVK. The HVK settings are reported by the manufacturer to be related to the relative opacity from the starch granules. Using the HHVK setting in the Fusarium calibration resulted in highest recovery (50.3% vs HVK 40.8% and HHHVK 45.1%) and intermediate levels of DON (1,800 μg/kg vs HVK 1,600 μg/kg and HHHVK 2,400 μg/kg), and intermediate rejection rates (29.0% vs HVK 38.7% and HHHVK 22.7%). When using the protein calibration with HHVK setting, the recoveries were similar to the Fusarium calibration (51%), the rejection rates were lower (17.5%), but DON concentration was higher (2,900 μg/kg). Sorting of pooled samples was effective, however additional sieving was required to separate grain of like sizes for optimal function. BoMill sorting using the Fusarium calibration and HHVK setting will effectively sort high DON wheat. The Fusarium calibration was superior to the protein calibration as it resulted in similar recovery but lower DON concentrations.
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Affiliation(s)
- M.E. Taylor
- Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - R.W. Newkirk
- Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
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9
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A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy. Toxins (Basel) 2022; 14:toxins14050323. [PMID: 35622570 PMCID: PMC9146547 DOI: 10.3390/toxins14050323] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/21/2022] [Accepted: 04/29/2022] [Indexed: 12/30/2022] Open
Abstract
Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., μg/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean ± standard deviation) was obtained by applying cut-offs of 10,000 µg/kg DM (i.e., 96.0 ± 2.7%) or 34 (i.e., 97.1 ± 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 ± 2.8% was obtained by using a cut-off limit of 350 µg/kg DM as a total sum or 98.6 ± 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.
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10
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Shen G, Cao Y, Yin X, Dong F, Xu J, Shi J, Lee YW. Rapid and nondestructive quantification of deoxynivalenol in individual wheat kernels using near-infrared hyperspectral imaging and chemometrics. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108420] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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11
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Compr Rev Food Sci Food Saf 2021; 20:4612-4651. [PMID: 34338431 DOI: 10.1111/1541-4337.12801] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/07/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022]
Abstract
Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.
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Affiliation(s)
- Gayatri Mishra
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Brajesh Kumar Panda
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Wilmer Ariza Ramirez
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Hyewon Jung
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Chandra B Singh
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia.,Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Lethbridge, Alberta, Canada
| | - Sang-Heon Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Ivan Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
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12
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Tyska D, Mallmann A, Gressler LT, Mallmann CA. Near-infrared spectroscopy as a tool for rapid screening of deoxynivalenol in wheat flour and its applicability in the industry. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2021; 38:1958-1968. [PMID: 34334116 DOI: 10.1080/19440049.2021.1954699] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
This study aimed to evaluate the applicability and efficiency of Near-Infrared Spectroscopy (NIR) by using dispersive NIR and Fourier Transform NIR to analyse 267 samples of Brazilian wheat flour contaminated with deoxynivalenol (DON). For this, Partial Least-squares Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PC-LDA) were used as discriminatory methods. Next, the samples were classified according to the maximum tolerated limits (MTL) for DON in Brazil, 750 μg kg-1, and two groups were established for the calibration set: category A (≤450 μg kg-1), non-contaminated or below the MTL; and category B (>450 μg kg-1), contaminated or above the MTL. Validation samples through PLS-DA showed correct classification rates in the range of 85-87.5% and presented a 10-15% error; for PC-LDA, the hit rate was over 85% with an error of 10-15%. The present findings demonstrate that NIR is an excellent alternative method to classify wheat flour samples according to DON content.
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Affiliation(s)
- Denize Tyska
- Department of Preventive Veterinary Medicine, Federal University of Santa Maria (UFSM), Laboratory of Mycotoxicological Analyses (LAMIC), Santa Maria, Rio Grande Do Sul, Brazil
| | | | | | - Carlos Augusto Mallmann
- Department of Preventive Veterinary Medicine, Federal University of Santa Maria (UFSM), Laboratory of Mycotoxicological Analyses (LAMIC), Santa Maria, Rio Grande Do Sul, Brazil
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13
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Femenias A, Bainotti MB, Gatius F, Ramos AJ, Marín S. Standardization of near infrared hyperspectral imaging for wheat single kernel sorting according to deoxynivalenol level. Food Res Int 2020; 139:109925. [PMID: 33509492 DOI: 10.1016/j.foodres.2020.109925] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 10/22/2022]
Abstract
The spatial recognition feature of near infrared hyperspectral imaging (HSI-NIR) makes it potentially suitable for Fusarium and deoxynivalenol (DON) management in single kernels to break with heterogeneity of contamination in wheat batches to move towards individual kernel sorting and provide more quick, environmental-friendly and non-destructive analysis than wet-chemistry techniques. The aim of this study was to standardize HSI-NIR for individual kernel analysis of Fusarium damage and DON presence, to predict the level of contamination and classify grains according to the EU maximum limit (1250 µg/kg). Visual inspection on Fusarium infection symptoms and HPLC analysis for DON determination were used as reference methods. The kernels were scanned in both crease-up and crease-down position and for different image captures. The spectra were pretreated by Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), 1st and 2nd derivatives and normalisation, and they were evaluated also by removing spectral tails. The best fitted predictive model was on SNV pretreated data (R2 0.88 and RMSECV 4.8 mg/kg) in which 7 characteristic wavelengths were used. Linear Discriminant Analysis (LDA), Naïve Bayes and K-nearest Neighbours models classified with 100% of accuracy 1st derivative and SNV pretreated spectra according to symptomatology and with 98.9 and 98.4% of correctness 1st derivative and SNV spectra, respectively. The starting point results are encouraging for future investigations on HSI-NIR technique application to Fusarium and DON management in single wheat kernels to overcome their contamination heterogeneity.
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Affiliation(s)
- Antoni Femenias
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Maria Belén Bainotti
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Ferran Gatius
- Departament de Química, Universitat de Lleida (UdL), Av. Rovira Roure, 191, Lleida 25198, Spain
| | - Antonio J Ramos
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Sonia Marín
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain.
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14
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Zhang D, Chen G, Zhang H, Jin N, Gu C, Weng S, Wang Q, Chen Y. Integration of spectroscopy and image for identifying fusarium damage in wheat kernels. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 236:118344. [PMID: 32330824 DOI: 10.1016/j.saa.2020.118344] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/26/2020] [Accepted: 04/05/2020] [Indexed: 05/20/2023]
Abstract
Hyperspectral imaging (HSI) was studied for the detection of varying degrees of damage in wheat kernels caused by Fusarium head blight (Gibberella zeae), a major disease in wheat worldwide. A total of 810 wheat kernel samples were collected from a field trial with the three levels of Fusarium infection, healthy, moderate, and severe. Hyperspectral image of the wheat kernels was acquired over a wavelength range of 400-1000 nm. The raw spectral data were pre-processed, and then the optimal wavelengths were selected using principal component analysis (PCA), successive projection algorithm (SPA) and random forest (RF). The image features were extracted based on the optimal wavelengths, and then the spectral features and image features were combined as fusion features. Support vector machine (SVM), random forest (RF) and naive Bayes (NB) were employed to build the classification models to identify the degrees of Fuasrium damage based on spectral and fusion features. The best performance was obtained by using the SPA-RF method to select the optimal wavelengths and corresponding image features, with a classification accuracy of 96.44%. The method developed from this study can provide a more effective way to identify the degrees of Fusarium damage in wheat kernels.
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Affiliation(s)
- Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Gao Chen
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Huihui Zhang
- Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, 80526, USA
| | - Ning Jin
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; Department of Resources and Environment, Shanxi Institute of Energy, Jinzhong 030600, China
| | - Chunyan Gu
- Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Qian Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Yu Chen
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
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15
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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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16
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Gaoqiang L, Changwen D, Fei M, Yazhen S, Jianmin Z. Responses of Leaf Cuticles to Rice Blast: Detection and Identification Using Depth-Profiling Fourier Transform Mid-Infrared Photoacoustic Spectroscopy. PLANT DISEASE 2020; 104:847-852. [PMID: 31940445 DOI: 10.1094/pdis-05-19-1004-re] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Cuticle is the first barrier for rice to resist blast fungus on the surface of the leaf. Studies on how the rice leaf cuticle responds to rice blast and attempts to perform early detection of rice blast are limited, and these two issues were explored in this study via depth-profiling Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS). Rice leaves with four different scales of injury (healthy leaves as CK, asymptomatic leaves from mildly diseased seedlings as S1, infected leaves with fewer than five lesions as S2, and infected leaves with more than 10 lesions as S3) were scanned by three moving mirror velocities 0.32, 0.47, and 0.63 cm/s for the depth profiling of the rice leaf surface. The response patterns were acquired via chemometrics to analyze the variations of the chemical group absorptions in the different layers of a sample and in the same layer between different samples. Results showed that the leaf cuticle tended to be thicker and the relative content of fatty alcohols and cutin, unsaturated compounds, and aromatics in the cuticle increased when rice seedlings were infected by blast fungus. Together with the principal component analysis, the probabilistic neural network was applied to identify the samples in early stages (CK and S1), which reached an accuracy of 90% for the samples in the greenhouse and 82% for the samples in the field. Thus, depth-profiling FTIR-PAS was good at analyzing the variation in cuticle layers and showed great potential in the early detection of rice blast or other diseases in different species.
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Affiliation(s)
- Lv Gaoqiang
- The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- College of Modern Advanced Agricultural Science University of Chinese Academy of Sciences, Beijing 100049, China
| | - Du Changwen
- The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- College of Modern Advanced Agricultural Science University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ma Fei
- The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Shen Yazhen
- The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Zhou Jianmin
- The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
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17
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Femenias A, Gatius F, Ramos AJ, Sanchis V, Marín S. Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review. Food Control 2020. [DOI: 10.1016/j.foodcont.2019.106819] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Alisaac E, Behmann J, Rathgeb A, Karlovsky P, Dehne HW, Mahlein AK. Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging. Toxins (Basel) 2019; 11:toxins11100556. [PMID: 31546581 PMCID: PMC6832122 DOI: 10.3390/toxins11100556] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 09/18/2019] [Accepted: 09/19/2019] [Indexed: 11/16/2022] Open
Abstract
Fusarium head blight (FHB) epidemics in wheat and contamination with Fusarium mycotoxins has become an increasing problem over the last decades. This prompted the need for non-invasive and non-destructive techniques to screen cereal grains for Fusarium infection, which is usually accompanied by mycotoxin contamination. This study tested the potential of hyperspectral imaging to monitor the infection of wheat kernels and flour with three Fusarium species. Kernels of two wheat varieties inoculated at anthesis with F. graminearum, F. culmorum, and F. poae were investigated. Hyperspectral images of kernels and flour were taken in the visible-near infrared (VIS-NIR) (400–1000 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges. The fungal DNA and mycotoxin contents were quantified. Spectral reflectance of Fusarium-damaged kernels (FDK) was significantly higher than non-inoculated ones. In contrast, spectral reflectance of flour from non-inoculated kernels was higher than that of FDK in the VIS and lower in the NIR and SWIR ranges. Spectral reflectance of kernels was positively correlated with fungal DNA and deoxynivalenol (DON) contents. In the case of the flour, this correlation exceeded r = −0.80 in the VIS range. Remarkable peaks of correlation appeared at 1193, 1231, 1446 to 1465, and 1742 to 2500 nm in the SWIR range.
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Affiliation(s)
- Elias Alisaac
- Institute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, University of Bonn, Nussallee 9, 53115 Bonn, Germany.
| | - Jan Behmann
- Institute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, University of Bonn, Nussallee 9, 53115 Bonn, Germany.
| | - Anna Rathgeb
- Molecular Phytopathology and Mycotoxin Research, University of Goettingen, Grisebachstraße 6, 37077 Goettingen, Germany.
| | - Petr Karlovsky
- Molecular Phytopathology and Mycotoxin Research, University of Goettingen, Grisebachstraße 6, 37077 Goettingen, Germany.
| | - Heinz-Wilhelm Dehne
- Institute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, University of Bonn, Nussallee 9, 53115 Bonn, Germany.
| | - Anne-Katrin Mahlein
- Institute of Sugar Beet Research (IfZ), Holtenser Landstraße 77, 37079 Goettingen, Germany.
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19
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Shen F, Zhao T, Jiang X, Liu X, Fang Y, Liu Q, Hu Q, Liu X. On-line detection of toxigenic fungal infection in wheat by visible/near infrared spectroscopy. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.04.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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20
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Delwiche S, Rodriguez IT, Rausch S, Graybosch R. Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging. J Cereal Sci 2019. [DOI: 10.1016/j.jcs.2019.02.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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21
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Xu Y, Yang H, Huang Z, Li Y, He Q, Tu Z, Ji Y, Ren W. A peptide/maltose-binding protein fusion protein used to replace the traditional antigen for immunological detection of deoxynivalenol in food and feed. Food Chem 2018; 268:242-248. [DOI: 10.1016/j.foodchem.2018.06.096] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 11/13/2017] [Accepted: 06/19/2018] [Indexed: 10/28/2022]
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22
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Zhu Z, Chen S, Wu X, Xing C, Yuan J. Determination of soybean routine quality parameters using near-infrared spectroscopy. Food Sci Nutr 2018; 6:1109-1118. [PMID: 29983975 PMCID: PMC6021721 DOI: 10.1002/fsn3.652] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 03/17/2018] [Accepted: 03/25/2018] [Indexed: 11/12/2022] Open
Abstract
Large differences in quality existed between soybean samples. In order to rapidly detect soybean quality between samples from different areas, we have developed near-infrared spectroscopy (NIRS) models for the moisture, crude fat, and protein content of soybeans, based on 360 soybean samples collected from different areas. Compared with whole kernels, soybean powder with particle sizes of 60 mesh was more suitable for modeling of moisture, crude fat, and protein content. To increase the reproducibility of the prediction model, uniform particle sizes of soybeans were prepared by grinding and sieving soybeans with different sizes and colors. Modeling analysis showed that the internal cross-validation correlation coefficients (Rcv) for the moisture, crude fat, and protein content of soybeans were .965, .941, and .949, respectively, and the determination coefficients (R2) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters and provided reference data for the analysis of soybean quality using FT-NIRS.
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Affiliation(s)
- Zhenying Zhu
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
| | - Shangbing Chen
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
| | - Xueyou Wu
- School of Food Science and TechnologyJiangnan UniversityWuxiChina
| | - Changrui Xing
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
| | - Jian Yuan
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
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23
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Holtz C, Krause D, Hussein M, Gastl M, Becker T. Lautering Performance Prediction from Malt by Combining Whole Near-Infrared Spectral Information with Lautering Process Evaluation as Reference Values. JOURNAL OF THE AMERICAN SOCIETY OF BREWING CHEMISTS 2018. [DOI: 10.1094/asbcj-2014-0717-01] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- C. Holtz
- Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephan, Weihenstephaner Steig 20, 85354 Freising, Germany
| | - D. Krause
- Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephan, Weihenstephaner Steig 20, 85354 Freising, Germany
| | - M. Hussein
- Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephan, Weihenstephaner Steig 20, 85354 Freising, Germany
| | - M. Gastl
- Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephan, Weihenstephaner Steig 20, 85354 Freising, Germany
| | - T. Becker
- Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephan, Weihenstephaner Steig 20, 85354 Freising, Germany
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24
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Levasseur-Garcia C. Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley). Toxins (Basel) 2018; 10:E38. [PMID: 29320435 PMCID: PMC5793125 DOI: 10.3390/toxins10010038] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 12/21/2017] [Accepted: 01/03/2018] [Indexed: 12/03/2022] Open
Abstract
Each year, mycotoxins cause economic losses of several billion US dollars worldwide. Consequently, methods must be developed, for producers and cereal manufacturers, to detect these toxins and to comply with regulations. Chromatographic reference methods are time consuming and costly. Thus, alternative methods such as infrared spectroscopy are being increasingly developed to provide simple, rapid, and nondestructive methods to detect mycotoxins. This article reviews research conducted over the last eight years into the use of near-infrared and mid-infrared spectroscopy to monitor mycotoxins in corn, wheat, and barley. More specifically, we focus on the Fusarium species and on the main fusariotoxins of deoxynivalenol, zearalenone, and fumonisin B1 and B2. Quantification models are insufficiently precise to satisfy the legal requirements. Sorting models with cutoff levels are the most promising applications.
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25
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Orina I, Manley M, Williams PJ. Non-destructive techniques for the detection of fungal infection in cereal grains. Food Res Int 2017; 100:74-86. [PMID: 28873744 DOI: 10.1016/j.foodres.2017.07.069] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 07/31/2017] [Accepted: 07/31/2017] [Indexed: 10/19/2022]
Abstract
Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.
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Affiliation(s)
- Irene Orina
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; Department of Food Science and Technology, Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000, Nairobi, Kenya
| | - Marena Manley
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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26
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Peiris KHS, Dong Y, Davis MA, Bockus WW, Dowell FE. Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT-NIR Spectroscopy. Cereal Chem 2017. [DOI: 10.1094/cchem-11-16-0271-r] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Kamaranga H. S. Peiris
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, U.S.A
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, U.S.A
| | - Mark A. Davis
- Department of Plant Pathology, Kansas State University, Manhattan, KS, U.S.A
| | - William W. Bockus
- Department of Plant Pathology, Kansas State University, Manhattan, KS, U.S.A
| | - Floyd E. Dowell
- USDA-ARS, CGAHR, Stored Product Insect and Engineering Research Unit, Manhattan, KS, U.S.A. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer
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27
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An infrared diagnostic system to detect causal agents of grapevine trunk diseases. J Microbiol Methods 2016; 131:1-6. [DOI: 10.1016/j.mimet.2016.09.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 09/27/2016] [Accepted: 09/27/2016] [Indexed: 11/23/2022]
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28
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Peiris KHS, Dong Y, Bockus WW, Dowell FE. Moisture Effects on the Prediction Performance of a Single-Kernel Near-Infrared Deoxynivalenol Calibration. Cereal Chem 2016. [DOI: 10.1094/cchem-04-16-0120-r] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Kamaranga H. S. Peiris
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, U.S.A
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, U.S.A
| | - William W. Bockus
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, U.S.A
| | - Floyd E. Dowell
- USDA-ARS, CGAHR, Stored Product Insect and Engineering Research Unit, Manhattan, KS 66502, U.S.A. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer
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29
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Haas J, Mizaikoff B. Advances in Mid-Infrared Spectroscopy for Chemical Analysis. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:45-68. [PMID: 27070183 DOI: 10.1146/annurev-anchem-071015-041507] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Infrared spectroscopy in the 3-20 μm spectral window has evolved from a routine laboratory technique into a state-of-the-art spectroscopy and sensing tool by benefitting from recent progress in increasingly sophisticated spectra acquisition techniques and advanced materials for generating, guiding, and detecting mid-infrared (MIR) radiation. Today, MIR spectroscopy provides molecular information with trace to ultratrace sensitivity, fast data acquisition rates, and high spectral resolution catering to demanding applications in bioanalytics, for example, and to improved routine analysis. In addition to advances in miniaturized device technology without sacrificing analytical performance, selected innovative applications for MIR spectroscopy ranging from process analysis to biotechnology and medical diagnostics are highlighted in this review.
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Affiliation(s)
- Julian Haas
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89069 Ulm, Germany;
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89069 Ulm, Germany;
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30
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Vrešak M, Halkjaer Olesen M, Gislum R, Bavec F, Ravn Jørgensen J. The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification. PLoS One 2016; 11:e0152011. [PMID: 27010656 PMCID: PMC4807013 DOI: 10.1371/journal.pone.0152011] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 03/08/2016] [Indexed: 11/18/2022] Open
Abstract
Application of rapid and time-efficient health diagnostic and identification technology in the seed industry chain could accelerate required analysis, characteristic description and also ultimately availability of new desired varieties. The aim of the study was to evaluate the potential of multispectral imaging and single kernel near-infrared spectroscopy (SKNIR) for determination of seed health and variety separation of winter wheat (Triticum aestivum L.) and winter triticale (Triticosecale Wittm. & Camus). The analysis, carried out in autumn 2013 at AU-Flakkebjerg, Denmark, included nine winter triticale varieties and 27 wheat varieties provided by the Faculty of Agriculture and Life Sciences Maribor, Slovenia. Fusarium sp. and black point disease-infected parts of the seed surface could successfully be distinguished from uninfected parts with use of a multispectral imaging device (405-970 nm wavelengths). SKNIR was applied in this research to differentiate all 36 involved varieties based on spectral differences due to variation in the chemical composition. The study produced an interesting result of successful distinguishing between the infected and uninfected parts of the seed surface. Furthermore, the study was able to distinguish between varieties. Together these components could be used in further studies for the development of a sorting model by combining data from multispectral imaging and SKNIR for identifying disease(s) and varieties.
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Affiliation(s)
- Martina Vrešak
- Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Pivola, Hoce, Slovenia
- * E-mail:
| | - Merete Halkjaer Olesen
- Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark
| | - René Gislum
- Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark
| | - Franc Bavec
- Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Pivola, Hoce, Slovenia
| | - Johannes Ravn Jørgensen
- Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark
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31
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Peiris KHS, Bockus WW, Dowell FE. Near-Infrared Spectroscopic Evaluation of Single-Kernel Deoxynivalenol Accumulation and Fusarium Head Blight Resistance Components in Wheat. Cereal Chem 2016. [DOI: 10.1094/cchem-03-15-0057-r] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Kamaranga H. S. Peiris
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, 66506, U.S.A
| | - William W. Bockus
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, U.S.A
| | - Floyd E. Dowell
- U.S. Department of Agriculture, Agricultural Research Service, Center for Grain and Animal Health Research, Stored Product Insect and Engineering Research Unit, 1515 College Avenue, Manhattan, KS, 66502, U.S.A. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer
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32
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Kautzman ME, Wickstrom ML, Scott TA. The use of near infrared transmittance kernel sorting technology to salvage high quality grain from grain downgraded due to Fusarium damage. ACTA ACUST UNITED AC 2015; 1:41-46. [PMID: 29767017 PMCID: PMC5884475 DOI: 10.1016/j.aninu.2015.02.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 02/06/2015] [Indexed: 11/30/2022]
Abstract
The mycotoxins associated with specific Fusarium fungal infections of grains are a threat to global food and feed security. These fungal infestations are referred to as Fusarium Head Blight (FHB) and lead to Fusarium Damaged Kernels (FDK). Incidence of FDK >0.25% will lower the grade, with a tolerance of 5% FDK for export feed grain. During infestation, the fungi can produce a variety of mycotoxins, the most common being deoxynivalenol (DON). Fusarium Damaged Kernels have been associated with reduced crude protein (CP), lowering nutritional, functional and grade value. New technology has been developed using Near Infrared Transmittance (NIT) spectra that estimate CP of individual kernels of wheat, barley and durum. Our objective is to evaluate the technology's capability to reduce FDK and DON of downgraded wheat and ability to salvage high quality safe kernels. In five FDK downgraded sources of wheat, the lowest 20% CP kernels had significantly increased FDK and DON with the high CP fractions having decreased FDK and DON, thousand kernel weights (TKW) and bushel weight (Bu). Strong positive correlations were observed between FDK and DON (r = 0.90); FDK and grade (r = 0.62) and DON and grade (r = 0.62). Negative correlations were observed between FDK and DON with CP (r = -0.27 and -0.32); TKW (r = -0.45 and -0.54) and Bu (r = -0.79 and -0.74). Results show improved quality and value of Fusarium downgraded grain using this technology.
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Affiliation(s)
- Michael E Kautzman
- Toxicology Centre, University of Saskatchewan, 44 Campus Drive, Saskatoon S7M 5B3, Canada
| | - Mark L Wickstrom
- Toxicology Centre, University of Saskatchewan, 44 Campus Drive, Saskatoon S7M 5B3, Canada.,Department of Veterinary Biomedical Sciences, College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon S7N 5B4, Canada
| | - Tom A Scott
- Department of Animal and Poultry Science, College of Agriculture, University of Saskatchewan, 51 Campus Drive, Saskatoon S7N 5A8, Canada
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McMullin D, Mizaikoff B, Krska R. Advancements in IR spectroscopic approaches for the determination of fungal derived contaminations in food crops. Anal Bioanal Chem 2015; 407:653-60. [PMID: 25258282 PMCID: PMC4305099 DOI: 10.1007/s00216-014-8145-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 08/25/2014] [Accepted: 08/28/2014] [Indexed: 11/19/2022]
Abstract
Infrared spectroscopy is a rapid, nondestructive analytical technique that can be applied to the authentication and characterization of food samples in high throughput. In particular, near infrared spectroscopy is commonly utilized in the food quality control industry to monitor the physical attributes of numerous cereal grains for protein, carbohydrate, and lipid content. IR-based methods require little sample preparation, labor, or technical competence if multivariate data mining techniques are implemented; however, they do require extensive calibration. Economically important crops are infected by fungi that can severely reduce crop yields and quality and, in addition, produce mycotoxins. Owing to the health risks associated with mycotoxins in the food chain, regulatory limits have been set by both national and international institutions for specific mycotoxins and mycotoxin classes. This article discusses the progress and potential of IR-based methods as an alternative to existing chemical methods for the determination of fungal contamination in crops, as well as emerging spectroscopic methods.
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Affiliation(s)
- David McMullin
- Center for Analytical Chemistry, Department for Agrobiotechnology, University of Natural Resources and Applied Life Sciences Vienna, Konrad-Lorenz-Straße 20, 3430 Tulln, Austria
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89075 Ulm, Germany
| | - Rudolf Krska
- Center for Analytical Chemistry, Department for Agrobiotechnology, University of Natural Resources and Applied Life Sciences Vienna, Konrad-Lorenz-Straße 20, 3430 Tulln, Austria
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Complex assessment of grain quality using image and spectra analyses. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2014. [DOI: 10.1007/s11694-014-9179-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Jin F, Bai G, Zhang D, Dong Y, Ma L, Bockus W, Dowell F. Fusarium-damaged kernels and deoxynivalenol in Fusarium-infected U.S. winter wheat. PHYTOPATHOLOGY 2014; 104:472-478. [PMID: 24400658 DOI: 10.1094/phyto-07-13-0187-r] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Fusarium head blight (FHB) is a devastating disease that threatens wheat (Triticum aestivum) production in many areas worldwide. FHB infection results in Fusarium-damaged kernels (FDK) and deoxynivalenol (DON) that dramatically reduce grain yield and quality. More effective and accurate disease evaluation methods are imperative for successful identification of FHB-resistant sources and selection of resistant cultivars. To determine the relationships among different types of resistance, 363 (74 soft and 289 hard) U.S. winter wheat accessions were repeatedly evaluated for FDK and DON concentration in greenhouse and field experiments. Single-kernel near-infrared (SKNIR)-estimated FDK and DON were compared with visually estimated FDK and gas chromatography-mass spectroscopy-estimated DON. Significant correlations were detected between percentage of symptomatic spikelets and visual FDK in the greenhouse and field, although correlations were slightly lower in the field. High correlation coefficients also were observed between visually scored FDK and SKNIR-estimated FDK (0.72, P < 0.001) and SKNIR-estimated DON (0.68, P < 0.001); therefore, both visual scoring and SKNIR methods are useful for estimating FDK and DON in breeding programs.
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Fox G, Manley M. Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2014; 94:174-9. [PMID: 24038031 DOI: 10.1002/jsfa.6367] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2013] [Revised: 08/14/2013] [Accepted: 08/22/2013] [Indexed: 05/08/2023]
Abstract
Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain.
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Affiliation(s)
- Glen Fox
- Queensland Alliance for Agriculture & Food Innovation, Centre for Nutrition & Food Science, The University of Queensland, P.O. Box 2282, Toowoomba, Qld, 4350, Australia; Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, (Stellenbosch), 7602, South Africa
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Esteve Agelet L, Hurburgh CR. Limitations and current applications of Near Infrared Spectroscopy for single seed analysis. Talanta 2014; 121:288-99. [PMID: 24607140 DOI: 10.1016/j.talanta.2013.12.038] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Revised: 12/15/2013] [Accepted: 12/16/2013] [Indexed: 11/28/2022]
Abstract
Near Infrared Spectroscopy (NIRS) analysis at the single seed level is a useful tool for breeders, farmers, feeding facilities, and food companies according to current researches. As a non-destructive technique, NIRS allows for the selection and classification of seeds according to specific traits and attributes without alteration of their properties. Critical aspects in using NIRS for single seed analysis such as reference method, sample morphology, and spectrometer suitability are discussed in this review. A summary of current applications of NIRS technologies at single seed level is also presented.
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Affiliation(s)
- Lidia Esteve Agelet
- Department of Agriculture and Biosystems Engineering, Iowa State University, USA.
| | - Charles R Hurburgh
- Department of Agriculture and Biosystems Engineering, Iowa State University, USA
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Hossain M, Goto T. Near- and mid-infrared spectroscopy as efficient tools for detection of fungal and mycotoxin contamination in agricultural commodities. WORLD MYCOTOXIN J 2014. [DOI: 10.3920/wmj2013.1679] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
In the last decade, near-infrared (NIR) and mid-infrared (MIR) spectroscopy have proven to be most promising tools for the detection of fungal contamination and estimation of mycotoxins in agricultural commodities, particularly of cereals. Owing to significant economic losses incurred from fungal contamination of foodstuffs, producers and processors are looking for fast, reliable, and less-expensive methods for the detection of fungal damage. In this context, NIR and MIR spectroscopy offer a fast, less-expensive, non-destructive, and relatively simple analytical method. Results from published studies indicate that NIR and MIR spectroscopy can be successfully applied to identifying fungal contamination and estimating specific mycotoxins. This review will focus on the applications of NIR and MIR spectroscopy to the classification of fungal contamination and the determination of specific mycotoxin contamination levels, and to compare this technology with traditional analytical methods.
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Affiliation(s)
- M.Z. Hossain
- Faculty of Agriculture, Shinshu University, 8304 Minami-minowa Mura, Kamiina, Nagano 399-4598, Japan
| | - T. Goto
- Faculty of Agriculture, Shinshu University, 8304 Minami-minowa Mura, Kamiina, Nagano 399-4598, Japan
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Peiris KHS, Dong Y, Bockus WW, Dowell FE. Single-Kernel Near-Infrared Analysis for Evaluating Wheat Samples for Fusarium Head Blight Resistance. Cereal Chem 2014. [DOI: 10.1094/cchem-11-12-0157-r] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Kamaranga H. S. Peiris
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, U.S.A
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, U.S.A
| | - William W. Bockus
- Department of Plant Pathology, Kansas State University, Manhattan, KS, U.S.A
| | - Floyd E. Dowell
- USDA, ARS, CGAHR, Engineering and Wind Erosion Research Unit, Manhattan, KS, U.S.A. The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer
- Corresponding author. Phone: (785) 776-2753. E-mail:
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Tekle S, Bjørnstad Å, Skinnes H, Dong Y, Segtnan VH. Estimating Deoxynivalenol Content of Ground Oats Using VIS-NIR Spectroscopy. Cereal Chem 2013. [DOI: 10.1094/cchem-07-12-0084-r] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Selamawit Tekle
- Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, Ås, Norway
- Corresponding author. Phone: +47 45521244. Fax: +47 64965001. E-mail:
| | - Åsmund Bjørnstad
- Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, Ås, Norway
| | - Helge Skinnes
- Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, Ås, Norway
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN, U.S.A
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41
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Near Infrared Spectroscopy—Advanced Analytical Tool in Wheat Breeding, Trade, and Processing. FOOD BIOPROCESS TECH 2012. [DOI: 10.1007/s11947-012-0917-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Peiris KHS, Pumphrey MO, Dong Y, Dowell FE. Fusarium Head Blight Symptoms and Mycotoxin Levels in Single Kernels of Infected Wheat Spikes. Cereal Chem 2011. [DOI: 10.1094/cchem-08-10-0112] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- K. H. S. Peiris
- Department of Agricultural and Biological Engineering, Kansas State University, Manhattan, KS 66506
| | - M. O. Pumphrey
- USDA-ARS, CGAHR Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply the recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer
| | - Y. Dong
- Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108
| | - F. E. Dowell
- USDA-ARS, CGAHR Engineering & Wind Erosion Research Unit, Manhattan, KS 66506
- Corresponding author. E-mail:
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