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Huang Y, Tian J, Yang H, Hu X, Han L, Fei X, He K, Liang Y, Xie L, Huang D, Zhang H. Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:4145-4156. [PMID: 38294322 DOI: 10.1002/jsfa.13296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/27/2023] [Accepted: 12/29/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND Wheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non-destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL). RESULTS This study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full-band spectral data and the characteristic wavelengths. The findings indicate that the MSC-competitive adaptive reweighted sampling-SEL model demonstrated the highest prediction accuracy, with Rp 2 (test set-determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg-1, respectively. CONCLUSION The predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non-destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Yuexiang Huang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Haili Yang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - Lipeng Han
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Xue Fei
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Kangling He
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Yan Liang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Liangliang Xie
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Dan Huang
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - HengJing Zhang
- Sichuan Machinery Research and Design Institute (Group) Co. Ltd, Chengdu, China
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Romaniello R, Barrasso AE, Perone C, Tamborrino A, Berardi A, Leone A. Optimisation of an Industrial Optical Sorter of Legumes for Gluten-Free Production Using Hyperspectral Imaging Techniques. Foods 2024; 13:404. [PMID: 38338540 PMCID: PMC10855930 DOI: 10.3390/foods13030404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
The market demand for gluten-free food is increasing due to the growing gluten sensitivity and coeliac disease (CD) in the population. The market requires grass-free cereals to produce gluten-free food. This requires sorting methods that guarantee the perfect separation of gluten contaminants from the legumes. The objective of the research was the development of an optical sorting system based on hyperspectral image processing, capable of identifying the spectral characteristics of the products under investigation to obtain a statistical classifier capable of enabling the total elimination of contaminants. The construction of the statistical classifier yielded excellent results, with a 100% correct classification rate of the contaminants. Tests conducted subsequently on an industrial optical sorter validated the result of the preliminary tests. In fact, the application of the developed classifier was able to correctly select the contaminants from the mass of legumes with a correct classification percentage of 100%. A small proportion of legumes was misclassified as contaminants, but this did not affect the scope of the work. Further studies will aim to reduce even this small share of waste with investigations into optimising the seed transport systems of the optical sorter.
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Affiliation(s)
- Roberto Romaniello
- Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy; (R.R.); (A.E.B.); (C.P.)
| | - Antonietta Eliana Barrasso
- Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy; (R.R.); (A.E.B.); (C.P.)
| | - Claudio Perone
- Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy; (R.R.); (A.E.B.); (C.P.)
| | - Antonia Tamborrino
- Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, Via Amendola 165/a, 70126 Bari, Italy; (A.T.); (A.L.)
| | - Antonio Berardi
- Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, Via Amendola 165/a, 70126 Bari, Italy; (A.T.); (A.L.)
| | - Alessandro Leone
- Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, Via Amendola 165/a, 70126 Bari, Italy; (A.T.); (A.L.)
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Jaćević V, Dumanović J, Alomar SY, Resanović R, Milovanović Z, Nepovimova E, Wu Q, Franca TCC, Wu W, Kuča K. Research update on aflatoxins toxicity, metabolism, distribution, and detection: A concise overview. Toxicology 2023; 492:153549. [PMID: 37209941 DOI: 10.1016/j.tox.2023.153549] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/07/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023]
Abstract
Serious health risks associated with the consumption of food products contaminated with aflatoxins (AFs) are worldwide recognized and depend predominantly on consumed AF concentration by diet. A low concentration of aflatoxins in cereals and related food commodities is unavoidable, especially in subtropic and tropic regions. Accordingly, risk assessment guidelines established by regulatory bodies in different countries help in the prevention of aflatoxin intoxication and the protection of public health. By assessing the maximal levels of aflatoxins in food products which are a potential risk to human health, it's possible to establish appropriate risk management strategies. Regarding, a few factors are crucial for making a rational risk management decision, such as toxicological profile, adequate information concerning the exposure duration, availability of routine and some novel analytical techniques, socioeconomic factors, food intake patterns, and maximal allowed levels of each aflatoxin in different food products which may be varied between countries.
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Affiliation(s)
- Vesna Jaćević
- Department for Experimental Pharmacology and Toxicology, National Poison Control Centre, Military Medical Academy, Crnotravska 17, 11000 Belgrade, Serbia; Medical Faculty of the Military Medical Academy, University of Defence, Crnotravska 17, 11000 Belgrade, Serbia; Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanského 62, 500 03 Hradec Králové, Czech Republic.
| | - Jelena Dumanović
- Medical Faculty of the Military Medical Academy, University of Defence, Crnotravska 17, 11000 Belgrade, Serbia; Department of Analytical Chemistry, Faculty of Chemistry, University of Belgrade, 11158 Belgrade, Serbia
| | - Suliman Y Alomar
- King Saud University, College of Science, Zoology Department, Riyadh, 11451, Saudi Arabia
| | - Radmila Resanović
- Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobođenja 18, 11000 Belgrade, Serbia
| | - Zoran Milovanović
- Special Police Unit, Ministry of Interior, Trebevićka 12/A, 11 030 Belgrade, Serbia
| | - Eugenie Nepovimova
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanského 62, 500 03 Hradec Králové, Czech Republic
| | - Qinghua Wu
- College of Life Science, Yangtze University, 1 Nanhuan Road, 434023 Jingzhou, Hubei, China; Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanského 62, 500 03 Hradec Králové, Czech Republic
| | - Tanos Celmar Costa Franca
- Laboratory of Molecular Modeling Applied to the Chemical and Biological Defense, Military Institute of Engineering, Praça General Tibúrcio 80, Rio de Janeiro, RJ 22290-270, Brazil; Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanského 62, 500 03 Hradec Králové, Czech Republic
| | - Wenda Wu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China; MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanského 62, 500 03 Hradec Králové, Czech Republic
| | - Kamil Kuča
- Biomedical Research Center, University Hospital Hradec Kralove, 50005, Hradec Kralove, Czech Republic; Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanského 62, 500 03 Hradec Králové, Czech Republic
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4
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Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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5
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Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning. Processes (Basel) 2023. [DOI: 10.3390/pr11020486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.
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6
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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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7
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Lavrinenko IA, Donskikh AO, Minakov DA, Sirota AA. Analysis and classification of peanuts with fungal diseases based on real-time spectral processing. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2022; 39:990-1000. [PMID: 35044871 DOI: 10.1080/19440049.2021.2017001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The study presents an approach to the analysis and classification of peanuts performed in order to detect kernels with fungi diseases, i.e. kernels prone to contamination with mycotoxigenic Aspergillus flavus (Aspergillus parasiticus). The aim of this study was to evaluate the effectiveness of luminescent spectroscopy with a violet laser (405 nm wavelength) as the excitation source of the fluorescence when applied for real-time detection of mould in peanuts performed by means of multispectral processing based on machine learning methods. We suggest a laboratory unit used to form, register, and process the luminescence spectra of peanuts in visible and near-infrared wavelength ranges in the real-time mode. The study demonstrated that contaminated peanuts have increased luminous intensity and show a redshift in the fluorescence peaks of the contaminated samples as compared to the pure ones. The difference in the fluorescence spectra of pure and contaminated kernels is compatible with the results obtained when traditional UV-light sources are used (365 nm). To classify peanuts by their spectral characteristics, neural network algorithms were used combined with dimensionality reduction methods. The paper presents the probabilities of incorrect recognition of the peanuts' type depending on the number of relevant secondary features determined when reducing the dimensionality of the initial data. When 10 spectral components were used, the error ratios were 0.7% or 0.3% depending on the method of reducing the dimensionality of the initial data.
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Affiliation(s)
- Igor A Lavrinenko
- Department of Human and Animal Physiology, Voronezh State University, Voronezh, Russia
| | - Artem O Donskikh
- Department of Information Security and Processing Technologies, Voronezh State University, Voronezh, Russia
| | - Dmitriy A Minakov
- Department of Information Security and Processing Technologies, Voronezh State University, Voronezh, Russia
| | - Alexander A Sirota
- Department of Information Security and Processing Technologies, Voronezh State University, Voronezh, Russia
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8
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Zheng L, Bao Q, Weng S, Tao J, Zhang D, Huang L, Zhao J. Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120813. [PMID: 34998050 DOI: 10.1016/j.saa.2021.120813] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/02/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Wheat flour (WF) is a common ingredient in staple foods. However, the presence of intentional or unintentional adulterants makes it difficult to guarantee WF quality. Multi-grained cascade forest (gcForest) model, a non-neural network deep learning structure, fused with image-spectra features from hyperspectral imaging (HSI) was employed for detecting adulterant type (peanut, walnut, or benzoyl peroxide) and the corresponding concentration (0.03%, 0.05%, 0.1%, 0.5%, 1%, and 2%). Based on the spectra of full wavelength and effective wavelength (EW) from hyperspectral images of WF samples, the gcForest-related models exhibited high performance (lowest ACCP = 92.45%) and stability (lowest area under the curve = 0.9986). Furthermore, the fusion of the EW and the image features extracted by the symmetric all convolutional neural network (SACNN) was used to establish the gcForest-related models. The maximum accuracy improvement of the fusion feature model relative to the single spectral model and the image model was 2.45% and 44.37%, respectively. The results indicate that the gcForest-related model, combined with the image-spectra fusion feature of HSI, provides an effective tool for detection in food and agriculture.
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Affiliation(s)
- Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.
| | - Qian Bao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Jianpeng Tao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
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9
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Zhu H, Yang L, Gao J, Gao M, Han Z. Quantitative detection of Aflatoxin B1 by subpixel CNN regression. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 268:120633. [PMID: 34862137 DOI: 10.1016/j.saa.2021.120633] [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: 04/19/2021] [Revised: 07/23/2021] [Accepted: 11/12/2021] [Indexed: 06/13/2023]
Abstract
Aflatoxin is a highly toxic substance dispersed in peanuts, which seriously harms the health of humans and animals. In this paper, we propose a new method for aflatoxin B1(AFB1) detection inspired by quantitative remote sensing. Firstly, we obtained the relative content of AFB1 at the sub-pixel level by subpixel decomposition (endmember extraction, nonnegative matrix decomposition). Then we modified the transfer learning models (LeNet5, AlexNet, VGG16, and ResNet18) to construct a deep learning regression network for quantitative detection of AFB1. There are 67,178 pixels used for training and 67,164 pixels used for testing. After subpixel decomposition, each aflatoxin pixel was determined to contain content, and each pixel had 400 hyperspectral values (415-799 nm). The experimental results showed that, among the four models, the modified ResNet18 model achieved the best effect, with R2 of 0.8898, RMSE of 0.0138, and RPD of 2.8851. Here, we implemented a sub-pixel model for quantitative AFB1 detection and proposed a regression method based on deep learning. Meanwhile, the modified convolution classification model has high predictive ability and robustness. This method provides a new scheme in designing the sorting machine and has practical value.
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Affiliation(s)
- Hongfei Zhu
- School of Computer Science and Technology, Tiangong University, Tianjin 300387, China.
| | - Lianhe Yang
- School of Computer Science and Technology, Tiangong University, Tianjin 300387, China
| | - Jiyue Gao
- College of Science and Information, Qingdao Agricultural University, Qingdao 266109, China
| | - Mei Gao
- School of Humanities, Tiangong University, Tianjin 300387, China
| | - Zhongzhi Han
- College of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.
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10
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112954] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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11
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Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott). Processes (Basel) 2021. [DOI: 10.3390/pr9101804] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.
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12
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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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Rathna Priya TS, Manickavasagan A. Characterising corn grain using infrared imaging and spectroscopic techniques: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00898-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection. SENSORS 2021; 21:s21134257. [PMID: 34206281 PMCID: PMC8271414 DOI: 10.3390/s21134257] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/14/2021] [Accepted: 06/17/2021] [Indexed: 11/23/2022]
Abstract
A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100–2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky–Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels.
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15
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Farrugia J, Griffin S, Valdramidis VP, Camilleri K, Falzon O. Principal component analysis of hyperspectral data for early detection of mould in cheeselets. Curr Res Food Sci 2021; 4:18-27. [PMID: 33554131 PMCID: PMC7859297 DOI: 10.1016/j.crfs.2020.12.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/27/2020] [Accepted: 12/31/2020] [Indexed: 11/27/2022] Open
Abstract
The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples' surface. It is also shown that the PCA loadings obtained from a set of training samples can be applied to hyperspectral data from new test samples to detect the presence of mould on these. For both the agar and cheeselets, the first three principal components contained more than 99 % of the total variance. The spatial projection of the second principal component highlights the presence of mould on cheeselets. The proposed analysis methods can be adopted in industry to detect mould on cheeselets at an early stage and with further testing this application may also be extended to other food products.
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Affiliation(s)
- Jessica Farrugia
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
| | - Sholeem Griffin
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta.,Department of Food Sciences and Nutrition, University of Malta, Msida, Malta.,Centre of Molecular Medicine and Biobanking, University of Malta, Msida, Malta
| | - Vasilis P Valdramidis
- Department of Food Sciences and Nutrition, University of Malta, Msida, Malta.,Centre of Molecular Medicine and Biobanking, University of Malta, Msida, Malta
| | - Kenneth Camilleri
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta.,Department of Systems & Control Engineering, Faculty of Engineering, University of Malta, Msida, Malta
| | - Owen Falzon
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
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16
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Chakraborty SK, Mahanti NK, Mansuri SM, Tripathi MK, Kotwaliwale N, Jayas DS. Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis-NIR (400-1000 nm) hyperspectral imaging. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2021; 58:437-450. [PMID: 33568838 PMCID: PMC7847924 DOI: 10.1007/s13197-020-04552-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 11/26/2022]
Abstract
Aflatoxin-B1 contamination in maize is a major food safety issue across the world. Conventional detection technique of toxins requires highly skilled technicians and is time-consuming. Application of appropriate chemometrics along with hyperspectral imaging (HSI) can identify aflatoxin-B1 infected maize kernels. Present study was undertaken to classify 240 maize kernels inoculated with six different concentrations (25, 40, 70, 200, 300 and 500 ppb) of aflatoxin-B1 by using Vis-NIR HSI. The reflectance spectral data were pre-processed (multiplicative scatter correction (MSC), standard normal variate (SNV), Savitsky-Golay smoothing and their combinations) and classified using partial least square discriminant analysis (PLS-DA) and k-nearest neighbour (k-NN). PLS model was also developed to predict the concentration of aflatoxin-B1in naturally contaminated maize kernels inoculated with Aspergillus flavus. The potential wavelength (508 nm) was selected based on principal component analysis (PCA) loadings to distinguish between sterile and infected maize kernels. PCA score plots revealed a distinct separation of low contaminated samples (25, 40 and 70 ppb) from highly contaminated samples (200, 300 and 500 ppb) without any overlapping of data. The maximum classification accuracy of 94.7% was obtained using PLS-DA with SNV pre-processed data. Across all the combinations of pre-processing and classification models, the best efficiency (98.2%) was exhibited by k-NN model with raw data. The developed PLS model depicted good prediction accuracy ( R CV 2 = 0.820, SECV = 79.425, RPDCV = 2.382) during Venetian-blinds cross-validation. The results of pixel-wise classification (k-NN) and concentration distribution maps (PLS with raw spectra) were quite close to the result obtained by reference method (HPLC analysis) of aflatoxin-B1 detection.
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Affiliation(s)
- Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Naveen Kumar Mahanti
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Shekh Mukhtar Mansuri
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Manoj Kumar Tripathi
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Nachiket Kotwaliwale
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Digvir Singh Jayas
- Department of Bio Systems Engineering, University of Manitoba, Winnipeg, Canada
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17
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Zhang S, Sun L, Ju H, Bao Z, Zeng XA, Lin S. Research advances and application of pulsed electric field on proteins and peptides in food. Food Res Int 2021; 139:109914. [DOI: 10.1016/j.foodres.2020.109914] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/14/2020] [Accepted: 10/26/2020] [Indexed: 12/31/2022]
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18
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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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19
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Bingtong L, Yongliang Z, Liping S. Identification and characterization of the peptides with calcium-binding capacity from tilapia (Oreochromis niloticus) skin gelatin enzymatic hydrolysates. J Food Sci 2019; 85:114-122. [PMID: 31869867 DOI: 10.1111/1750-3841.14975] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 10/15/2019] [Accepted: 10/24/2019] [Indexed: 11/28/2022]
Abstract
The aim of this study was to isolate and identify the peptides with calcium-binding capacity from the different tilapia skin gelatin enzymatic hydrolysates. The complex protease was selected and its hydrolysates were further separated using gel filtration chromatography (Sephadex G-25) and reverse phase high-performance liquid chromatography. Two purified peptides with strong calcium-binding capacity were identified as Tyr-Gly-Thr-Gly-Leu (YGTGL, 509.25 Da) and Leu-Val-Phe-Leu (LVFL, 490.32 Da). The calcium-binding capacities of YGTGL and LVFL reached 76.03 and 79.50 µg/mg, respectively. The structures of the complex of purified peptides and calcium (YGTGL-Ca and LVFL-Ca) were characterized by ultraviolet-visible spectroscopy (UV-VIS), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and mass spectrometry (LC-MS/MS). The results of UV-VIS, SEM, and XRD indicated that YGTGL-Ca and LVFL-Ca were formed as new compounds. The results of FTIR and LC-MS/MS indicated the nitrogen atom of the amino group and the oxygen atom of the carboxyl group in terminates of the peptides provided primary binding sites. Moreover, the hydrophobic amino acids in purified peptides could provide more chelating spaces. This study was of great significance for the development of calcium supplement foods. PRACTICAL APPLICATION: Compared with inorganic calcium and organic calcium, the bioactive gelatin peptide chelated calcium has the characteristics of high utilization rate, high solubility, and high absorption rate. The raw materials are extracted from the tilapia processed waste, which reduce the cost, make full use of resources, and improve the bioavailability. The tilapia skin gelatin peptide calcium chelate can be directly absorbed by the human body, and the absorption efficiency is high, further improving the resource utilization rate and having high economic benefits, which is a comprehensive supplement that can also be used as a functional food.
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Affiliation(s)
- Liu Bingtong
- Yunnan Inst. of Food Safety, Kunming Univ. of Science and Technology, No. 727 South Jingming Road, Kunming, Yunnan, 650500, China
| | - Zhuang Yongliang
- Yunnan Inst. of Food Safety, Kunming Univ. of Science and Technology, No. 727 South Jingming Road, Kunming, Yunnan, 650500, China
| | - Sun Liping
- Yunnan Inst. of Food Safety, Kunming Univ. of Science and Technology, No. 727 South Jingming Road, Kunming, Yunnan, 650500, China
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20
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Mahato DK, Lee KE, Kamle M, Devi S, Dewangan KN, Kumar P, Kang SG. Aflatoxins in Food and Feed: An Overview on Prevalence, Detection and Control Strategies. Front Microbiol 2019; 10:2266. [PMID: 31636616 PMCID: PMC6787635 DOI: 10.3389/fmicb.2019.02266] [Citation(s) in RCA: 146] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 09/17/2019] [Indexed: 12/12/2022] Open
Abstract
Aflatoxins produced by the Aspergillus species are highly toxic, carcinogenic, and cause severe contamination to food sources, leading to serious health consequences. Contaminations by aflatoxins have been reported in food and feed, such as groundnuts, millet, sesame seeds, maize, wheat, rice, fig, spices and cocoa due to fungal infection during pre- and post-harvest conditions. Besides these food products, commercial products like peanut butter, cooking oil and cosmetics have also been reported to be contaminated by aflatoxins. Even a low concentration of aflatoxins is hazardous for human and livestock. The identification and quantification of aflatoxins in food and feed is a major challenge to guarantee food safety. Therefore, developing feasible, sensitive and robust analytical methods is paramount for the identification and quantification of aflatoxins present in low concentrations in food and feed. There are various chromatographic and sensor-based methods used for the detection of aflatoxins. The current review provides insight into the sources of contamination, occurrence, detection techniques, and masked mycotoxin, in addition to management strategies of aflatoxins to ensure food safety and security.
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Affiliation(s)
- Dipendra K. Mahato
- School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC, Australia
| | - Kyung Eun Lee
- Molecular Genetics Laboratory, Department of Biotechnology, Yeungnam University, Gyeongsan, South Korea
| | - Madhu Kamle
- Department of Forestry, North Eastern Regional Institute of Science and Technology, Nirjuli, India
| | | | - Krishna N. Dewangan
- Department of Agricultural Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, India
| | - Pradeep Kumar
- Department of Forestry, North Eastern Regional Institute of Science and Technology, Nirjuli, India
| | - Sang G. Kang
- Molecular Genetics Laboratory, Department of Biotechnology, Yeungnam University, Gyeongsan, South Korea
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21
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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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22
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Rahman HU, Yue X, Yu Q, Xie H, Zhang W, Zhang Q, Li P. Specific antigen-based and emerging detection technologies of mycotoxins. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:4869-4877. [PMID: 30868594 DOI: 10.1002/jsfa.9686] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 03/05/2019] [Accepted: 03/08/2019] [Indexed: 06/09/2023]
Abstract
Mycotoxins are secondary fungal metabolites produced by certain types of filamentous fungi or molds, such as Aspergillus, Fusarium, Penicillium, and Alternaria spp. Mycotoxins are natural contaminants of agricultural commodities, and their prevalence may increase due to global warming. According to the Food and Agriculture Organization of the United Nations, approximately 25% of the world's food crops are annually contaminated with mycotoxins. Mycotoxin-contaminated food and feed pose a high risk to both human and animal health. For instance, they possess carcinogenic, immunosuppressive, hepatotoxic, nephrotoxic, and neurotoxic effects. Hence, various approaches have been used to assess and control mycotoxin contamination. Significant challenges still exist because of the complex heterogeneous nature of food and feed composition. The potential of antigen-based approaches, such as enzyme-linked immunosorbent assay, flow injection immunoassay, chemiluminescence immunoassay, lateral flow immunoassay, and flow-through immunoassay, would contribute to our understanding about mycotoxins' rapid identification, their isolation, and the basic principles of the detection technologies. Additionally, we address other emerging technologies of potential application in the detection of mycotoxins. The data included in this review focus on basic principles and results of the detection technologies and would be useful as benchmark information for future research. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Hamid Ur Rahman
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, PR China
| | - Xiaofeng Yue
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- Laboratory of Quality & Safety Risk Assessment for Oilseeds Products, Wuhan, Ministry of Agriculture, Wuhan, PR China
| | - Qiuyu Yu
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, PR China
- Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, PR China
| | - Huali Xie
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, PR China
| | - Wen Zhang
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- National Reference Laboratory for Agricultural Testing (Biotoxin), Wuhan, PR China
| | - Qi Zhang
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- Laboratory of Quality & Safety Risk Assessment for Oilseeds Products, Wuhan, Ministry of Agriculture, Wuhan, PR China
| | - Peiwu Li
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, PR China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, PR China
- Laboratory of Quality & Safety Risk Assessment for Oilseeds Products, Wuhan, Ministry of Agriculture, Wuhan, PR China
- Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, PR China
- National Reference Laboratory for Agricultural Testing (Biotoxin), Wuhan, PR China
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23
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Feng L, Zhu S, Liu F, He Y, Bao Y, Zhang C. Hyperspectral imaging for seed quality and safety inspection: a review. PLANT METHODS 2019; 15:91. [PMID: 31406499 PMCID: PMC6686453 DOI: 10.1186/s13007-019-0476-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 08/01/2019] [Indexed: 05/22/2023]
Abstract
Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Yidan Bao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
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24
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Tao F, Yao H, Hruska Z, Liu Y, Rajasekaran K, Bhatnagar D. Use of Visible-Near-Infrared (Vis-NIR) Spectroscopy to Detect Aflatoxin B 1 on Peanut Kernels. APPLIED SPECTROSCOPY 2019; 73:415-423. [PMID: 30700102 DOI: 10.1177/0003702819829725] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible-near-infrared (Vis-NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.
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Affiliation(s)
- Feifei Tao
- 1 Geosystems Research Institute, Mississippi State University, Stennis Space Center, MS, USA
| | - Haibo Yao
- 1 Geosystems Research Institute, Mississippi State University, Stennis Space Center, MS, USA
| | - Zuzana Hruska
- 1 Geosystems Research Institute, Mississippi State University, Stennis Space Center, MS, USA
| | - Yongliang Liu
- 2 USDA-ARS, Southern Regional Research Center, New Orleans, LA, USA
| | | | - Deepak Bhatnagar
- 2 USDA-ARS, Southern Regional Research Center, New Orleans, LA, USA
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25
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Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging. Molecules 2018; 23:molecules23123078. [PMID: 30477266 PMCID: PMC6321087 DOI: 10.3390/molecules23123078] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 11/21/2018] [Accepted: 11/23/2018] [Indexed: 11/17/2022] Open
Abstract
Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874⁻1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine-SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.
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Jiang H, Wang W, Ni X, Zhuang H, Yoon SC, Lawrence KC. Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University. ACTA ACUST UNITED AC 2018. [DOI: 10.1177/0960336018804755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Near infrared spectroscopy and hyperspectral imaging are fast-growing, rapid, powerful, and non-destructive optical technologies that can be used especially in quality and safety control of agro-food products. The Non-destructive Detecting Laboratory for Agricultural and Food Products in the College of Engineering, China Agricultural University in Beijing, China, has engaged in research on sensing and characterizing agro-food quality and safety attributes with the latest optical methods including near infrared spectroscopy and hyperspectral imaging for over five years. In this report, some of our latest research and developments through multidisciplinary international collaborations will be highlighted to demonstrate our contributions to this near infrared spectroscopy and hyperspectral imaging sensing area to improve non-destructive diagnosis and quality control of agricultural and food products.
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Affiliation(s)
- Hongzhe Jiang
- College of Engineering, China Agricultural University, Beijing, China
| | - Wei Wang
- College of Engineering, China Agricultural University, Beijing, China
| | - Xinzhi Ni
- Crop Genetics and Breeding Research Unit, USDA-ARS, Tifton, GA, USA
| | - Hong Zhuang
- Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, Athens, GA, USA
| | - Seung-Chul Yoon
- Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, Athens, GA, USA
| | - Kurt C Lawrence
- Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, Athens, GA, USA
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Tao F, Yao H, Hruska Z, Burger LW, Rajasekaran K, Bhatnagar D. Recent development of optical methods in rapid and non-destructive detection of aflatoxin and fungal contamination in agricultural products. Trends Analyt Chem 2018. [DOI: 10.1016/j.trac.2017.12.017] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Su WH, Sun DW. Multispectral Imaging for Plant Food Quality Analysis and Visualization. Compr Rev Food Sci Food Saf 2018; 17:220-239. [DOI: 10.1111/1541-4337.12317] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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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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Vejarano R, Siche R, Tesfaye W. Evaluation of biological contaminants in foods by hyperspectral imaging: A review. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1338729] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ricardo Vejarano
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo (UNT), Ciudad Universitaria, Trujillo, Peru
- Facultad de Ingeniería, Universidad Privada del Norte (UPN), Trujillo, Peru
| | - Raúl Siche
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo (UNT), Ciudad Universitaria, Trujillo, Peru
| | - Wendu Tesfaye
- Departamento de Química y Tecnología de Alimentos, Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7010090] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Siche R, Vejarano R, Aredo V, Velasquez L, Saldaña E, Quevedo R. Evaluation of Food Quality and Safety with Hyperspectral Imaging (HSI). FOOD ENGINEERING REVIEWS 2015. [DOI: 10.1007/s12393-015-9137-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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