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Pankin D, Povolotckaia A, Borisov E, Povolotskiy A, Borzenko S, Gulyaev A, Gerasimenko S, Dorochov A, Khamuev V, Moskovskiy M. Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains. Foods 2023; 12:3426. [PMID: 37761134 PMCID: PMC10528831 DOI: 10.3390/foods12183426] [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: 06/08/2023] [Revised: 08/24/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Wheat has played an important role in human agriculture since ancient times. Increasing rates of processed wheat product fabrication require more and more laboratory studies of product quality. This, in turn, requires the use, in production and in field conditions, of sufficiently accurate, fast and relatively low-cost quality control methods, including the detection of fungal diseases. One of the most widespread fungal diseases of wheat in the world is ergot caused by the fungi genus Claviceps. Optical methods are promising for this disease identification due to the relative ease of implementation and the possibility of performing fast analyses in large volumes. However, for application in practice, it is necessary to identify and substantiate characteristic spectral markers that make it possible to judge the sample contamination. In this regard, within the framework of this study, the methods of IR absorption spectroscopy in the MIR region and reflection spectroscopy in the UV-vis-NIR ranges, as well as luminescence spectroscopy, were used to study ergot-infected grains of winter wheat of the "Moskovskaya 56" cultivar. To justify the choice of the most specific spectral ranges, the methods of chemometric analysis with supervised classification, namely PCA-LDA and PCA-SVM, were applied. The possibility of separating infected grains according to the IR absorption, reflection spectra in the UV-vis-NIR ranges and visible luminescence spectra was tested.
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Affiliation(s)
- Dmitrii Pankin
- Center for Optical and Laser Materials Research, St. Petersburg State University, Ulianovskaya 5, 198504 St. Petersburg, Russia; (D.P.); (E.B.)
| | - Anastasia Povolotckaia
- Center for Optical and Laser Materials Research, St. Petersburg State University, Ulianovskaya 5, 198504 St. Petersburg, Russia; (D.P.); (E.B.)
| | - Eugene Borisov
- Center for Optical and Laser Materials Research, St. Petersburg State University, Ulianovskaya 5, 198504 St. Petersburg, Russia; (D.P.); (E.B.)
| | - Alexey Povolotskiy
- Institute of Chemistry, St. Petersburg State University, Universitetskii pr. 26, 198504 St. Petersburg, Russia;
| | - Sergey Borzenko
- Federal Scientific Agro-Engineering Center VIM, 1st Institutskiy proezd 5, 109428 Moscow, Russia; (S.B.); (A.G.); (S.G.); (A.D.); (V.K.); (M.M.)
| | - Anatoly Gulyaev
- Federal Scientific Agro-Engineering Center VIM, 1st Institutskiy proezd 5, 109428 Moscow, Russia; (S.B.); (A.G.); (S.G.); (A.D.); (V.K.); (M.M.)
| | - Stanislav Gerasimenko
- Federal Scientific Agro-Engineering Center VIM, 1st Institutskiy proezd 5, 109428 Moscow, Russia; (S.B.); (A.G.); (S.G.); (A.D.); (V.K.); (M.M.)
| | - Alexey Dorochov
- Federal Scientific Agro-Engineering Center VIM, 1st Institutskiy proezd 5, 109428 Moscow, Russia; (S.B.); (A.G.); (S.G.); (A.D.); (V.K.); (M.M.)
| | - Viktor Khamuev
- Federal Scientific Agro-Engineering Center VIM, 1st Institutskiy proezd 5, 109428 Moscow, Russia; (S.B.); (A.G.); (S.G.); (A.D.); (V.K.); (M.M.)
| | - Maksim Moskovskiy
- Federal Scientific Agro-Engineering Center VIM, 1st Institutskiy proezd 5, 109428 Moscow, Russia; (S.B.); (A.G.); (S.G.); (A.D.); (V.K.); (M.M.)
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Rapid resolution of types and proportions of broken grains using hyperspectral imaging and optimization algorithm. J Cereal Sci 2022. [DOI: 10.1016/j.jcs.2022.103565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging. Talanta 2020; 216:120993. [PMID: 32456911 DOI: 10.1016/j.talanta.2020.120993] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 03/29/2020] [Accepted: 03/31/2020] [Indexed: 11/24/2022]
Abstract
The detection of adulterations in food powder products represents a high interest especially when it concerns the health of the consumers. The food industry is concerned by peanut adulteration since it is a major food allergen often used in transformed food products. Near-infrared hyperspectral imaging is an emerging technology for food inspection. It was used in this work to detect peanut flour adulteration in wheat flour. The detection of peanut particles was challenging for two reasons: the particle size is smaller than the pixel size leading to impure spectral profiles; peanut and wheat flour exhibit similar spectral signatures and variability. A Matched Subspace Detector (MSD) algorithm was designed to take these difficulties into account and detect peanut adulteration at the pixel scale using the associated spectrum. A set of simulated data was generated to overcome the lack of reference values at the pixel scale and to design appropriate MSD algorithms. The best designs were compared by estimating the detection sensitivity. Defatted peanut flour and wheat flour were mixed in eight different proportions (from 0.02% to 20%) to test the detection performances of the algorithm on real hyperspectral measurements. The number and positions of the detected pixels were investigated to show the relevancy of the results and validate the design of the MSD algorithm. The presented work proved that the use of hyperspectral imaging and a fine-tuned MSD algorithm enables to detect a global adulteration of 0.2% of peanut in wheat flour.
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Calvini R, Luciano A, Ottoboni M, Ulrici A, Tretola M, Pinotti L. Multivariate image analysis for the rapid detection of residues from packaging remnants in former foodstuff products (FFPs) – a feasibility study. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2020; 37:1399-1411. [DOI: 10.1080/19440049.2020.1769195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Rosalba Calvini
- Department of Life Sciences and Interdepartmental Centre BIOGEST-SITEIA, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Alice Luciano
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
| | - Matteo Ottoboni
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
| | - Alessandro Ulrici
- Department of Life Sciences and Interdepartmental Centre BIOGEST-SITEIA, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Marco Tretola
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
- Agroscope, Institute for Livestock Sciences, Posieux, Switzerland
| | - Luciano Pinotti
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
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Jones NS, Comparin JH. Interpol review of controlled substances 2016-2019. Forensic Sci Int Synerg 2020; 2:608-669. [PMID: 33385148 PMCID: PMC7770462 DOI: 10.1016/j.fsisyn.2020.01.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 01/23/2020] [Indexed: 12/14/2022]
Abstract
This review paper covers the forensic-relevant literature in controlled substances from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.
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Affiliation(s)
- Nicole S. Jones
- RTI International, Applied Justice Research Division, Center for Forensic Sciences, 3040 E. Cornwallis Road, Research Triangle Park, NC, 22709-2194, USA
| | - Jeffrey H. Comparin
- United States Drug Enforcement Administration, Special Testing and Research Laboratory, USA
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Shao Y, Xuan G, Hu Z, Wang Y. Detection of adulterants and authenticity discrimination for coarse grain flours using NIR hyperspectral imaging. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13265] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical EngineeringShandong Agricultural University Tai'an China
- Nanjing Research Institute for Agricultural MechanizationMinistry of Agriculture Nanjing China
| | - Guantao Xuan
- College of Mechanical and Electrical EngineeringShandong Agricultural University Tai'an China
- College of Agriculture, Food and Natural ResourcesUniversity of Missouri Columbia Missouri
| | - Zhichao Hu
- Nanjing Research Institute for Agricultural MechanizationMinistry of Agriculture Nanjing China
| | - Yongxian Wang
- College of Mechanical and Electrical EngineeringShandong Agricultural University Tai'an China
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Near Infrared Hyperspectral Imaging for White Maize Classification According to Grading Regulations. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01464-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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8
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Gerssen A, Bovee TH, van Ginkel LA, van Iersel ML, Hoogenboom RL. Food and feed safety: Cases and approaches to identify the responsible toxins and toxicants. Food Control 2019. [DOI: 10.1016/j.foodcont.2018.10.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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9
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A Micro-Damage Detection Method of Litchi Fruit Using Hyperspectral Imaging Technology. SENSORS 2018; 18:s18030700. [PMID: 29495421 PMCID: PMC5876671 DOI: 10.3390/s18030700] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 02/22/2018] [Accepted: 02/23/2018] [Indexed: 11/19/2022]
Abstract
The non-destructive testing of litchi fruit is of great significance to the fresh-keeping, storage and transportation of harvested litchis. To achieve quick and accurate micro-damage detection, a non-destructive grading test method for litchi fruits was studied using 400–1000 nm hyperspectral imaging technology. The Huaizhi litchi was chosen in this study, and the hyperspectral data average for the region of interest (ROI) of litchi fruit was extracted for spectral data analysis. Then the hyperspectral data samples of fresh and micro-damaged litchi fruits were selected, and a partial least squares discriminant analysis (PLS-DA) was used to establish a prediction model for the realization of qualitative analysis for litchis with different qualities. For the external validation set, the mean per-type recall and precision were 94.10% and 93.95%, respectively. Principal component analysis (PCA) was used to determine the sensitive wavelength for recognition of litchi quality characteristics, with the results of wavelengths corresponding to the local extremum for the weight coefficient of PC3, i.e., 694, 725 and 798 nm. Then the single-band images corresponding to each sensitive wavelength were analyzed. Finally, the 7-dimension features of the PC3 image were extracted using the Gray Level Co-occurrence Matrix (GLCM). Through image processing, least squares support vector machine (LS-SVM) modeling was conducted to classify the different qualities of litchis. The model was validated using the experiment data, and the average accuracy of the validation set was 93.75%, while the external validation set was 95%. The results indicate the feasibility of using hyperspectral imaging technology in litchi postpartum non-destructive detection and classification.
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Berthiller F, Cramer B, Iha M, Krska R, Lattanzio V, MacDonald S, Malone R, Maragos C, Solfrizzo M, Stranska-Zachariasova M, Stroka J, Tittlemier S. Developments in mycotoxin analysis: an update for 2016-2017. WORLD MYCOTOXIN J 2018. [DOI: 10.3920/wmj2017.2250] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
This review summarises developments in the determination of mycotoxins over a period between mid-2016 and mid-2017. Analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone are covered in individual sections. Advances in proper sampling strategies are discussed in a dedicated section, as are methods used to analyse botanicals and spices and newly developed LC-MS based multi-mycotoxin methods. This critical review aims to briefly discuss the most important recent developments and trends in mycotoxin determination as well as to address limitations of the presented methodologies.
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Affiliation(s)
- F. Berthiller
- Department of Agrobiotechnology (IFA-Tulln), Christian Doppler Laboratory for Mycotoxin Metabolism and Center for Analytical Chemistry, University of Natural Resources and Life Sciences, Konrad Lorenz Str. 20, 3430 Tulln, Austria
| | - B. Cramer
- Institute of Food Chemistry, University of Münster, Corrensstr. 45, 48149 Münster, Germany
| | - M.H. Iha
- Nucleous of Chemistry and Bromatology Science, Adolfo Lutz Institute of Ribeirão Preto, Rua Minas 866, CEP 14085-410, Ribeirão Preto, SP, Brazil
| | - R. Krska
- Department of Agrobiotechnology (IFA-Tulln), Christian Doppler Laboratory for Mycotoxin Metabolism and Center for Analytical Chemistry, University of Natural Resources and Life Sciences, Konrad Lorenz Str. 20, 3430 Tulln, Austria
| | - V.M.T. Lattanzio
- National Research Council of Italy, Institute of Sciences of Food Production, via amendola 122/O, 70126 Bari, Italy
| | - S. MacDonald
- Department of Contaminants and Authenticity, Fera Science Ltd., Sand Hutton, York YO41 1LZ, United Kingdom
| | - R.J. Malone
- Trilogy Analytical Laboratory, 870 Vossbrink Dr, Washington, MO 63090, USA
| | - C. Maragos
- Mycotoxin Prevention and Applied Microbiology Research Unit, USDA, ARS National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, USA
| | - M. Solfrizzo
- National Research Council of Italy, Institute of Sciences of Food Production, via amendola 122/O, 70126 Bari, Italy
| | - M. Stranska-Zachariasova
- Department of Food Analysis and Nutrition, Faculty of Food and Biochemical Technology, University of Chemistry and Technology, Technická 5, 166 28 Prague 6 – Dejvice, Czech Republic
| | - J. Stroka
- European Commission, Joint Research Centre, Retieseweg 111, 2440 Geel, Belgium
| | - S.A. Tittlemier
- Canadian Grain Commission, Grain Research Laboratory, 1404-303 Main Street, Winnipeg, MB R3C 3G8, Canada
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Zhang C, Liu F, He Y. Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis. Sci Rep 2018; 8:2166. [PMID: 29391427 PMCID: PMC5794930 DOI: 10.1038/s41598-018-20270-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 01/17/2018] [Indexed: 12/22/2022] Open
Abstract
Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.
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Affiliation(s)
- Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
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12
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Su WH, Sun DW. Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review. Compr Rev Food Sci Food Saf 2017; 17:104-122. [DOI: 10.1111/1541-4337.12314] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 09/12/2017] [Accepted: 09/14/2017] [Indexed: 12/13/2022]
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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