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Aslam N, Fatima R, Altemimi AB, Ahmad T, Khalid S, Hassan SA, Aadil RM. Overview of industrial food fraud and authentication through chromatography technique and its impact on public health. Food Chem 2024; 460:140542. [PMID: 39079380 DOI: 10.1016/j.foodchem.2024.140542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/09/2024] [Accepted: 07/18/2024] [Indexed: 09/05/2024]
Abstract
Food fraud is widespread nowadays in the food products supply chain, from raw materials processing to the final product and during storage and transport. The most frequent fraud is practiced in staple food commodities like cereals. Their origin, variety, genotype, and bioactive compounds are altered to deceive consumers. Similarly, in various food sectors like beverage, baking, and confectionary, items like melamine, flour improver, and food colors are used in the market to temple consumers. To tackle food fraud and authentication, non-destructive techniques are being used. These techniques have limitations like lack of standardization, interference from multiple absorbing species, ambiguous results, and time-consuming to perform, depending on the type, size, and location of the system proved difficult to quantify the samples of adulteration. Chromatography has been introduced as an effective technique. It serves to safeguard public health due to its detection capabilities. Chromatography proved a crucial tool against fraudulent practices to preserve consumer trust.
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Affiliation(s)
- Nabila Aslam
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Rida Fatima
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Ammar B Altemimi
- Food Science Department, College of Agriculture, University of Basrah, Basrah 61004, Iraq
| | - Talha Ahmad
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Samran Khalid
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Syed Ali Hassan
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
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2
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Li J, Ma Y, Zhang J, Kong D. Rapid detection of fertilizer information based on Raman spectroscopy and machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 324:124985. [PMID: 39173320 DOI: 10.1016/j.saa.2024.124985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/21/2024] [Accepted: 08/13/2024] [Indexed: 08/24/2024]
Abstract
The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.
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Affiliation(s)
- Jianian Li
- Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Yongzheng Ma
- Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Jian Zhang
- Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Dandan Kong
- Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China.
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3
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Zacometti C, Sammarco G, Massaro A, Lefevre S, Frégière-Salomon A, Lafeuille JL, Candalino IF, Piro R, Tata A, Suman M. Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning. Food Res Int 2024; 179:114023. [PMID: 38342542 DOI: 10.1016/j.foodres.2024.114023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/13/2024]
Abstract
Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.
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Affiliation(s)
- Carmela Zacometti
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Giuseppe Sammarco
- Advanced Laboratory Research, Barilla G. e R. Fratelli S.p.A., Via Mantova, 166, 43122 Parma, Italy
| | - Andrea Massaro
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Stephane Lefevre
- Food Integrity Laboratory, Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., 999 avenue des Marchés, 84200 Carpentras, France
| | - Aline Frégière-Salomon
- Food Integrity Laboratory, Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., 999 avenue des Marchés, 84200 Carpentras, France
| | - Jean-Louis Lafeuille
- Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., 999 avenue des Marchés, 84200 Carpentras, France
| | - Ingrid Fiordaliso Candalino
- Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., Viale Iotti Nilde, 50038 San Piero (FI), Italy
| | - Roberto Piro
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Alessandra Tata
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Michele Suman
- Advanced Laboratory Research, Barilla G. e R. Fratelli S.p.A., Via Mantova, 166, 43122 Parma, Italy; Catholic University Sacred Heart, Department for Sustainable Food Process, Piacenza, Italy.
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4
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
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Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
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Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5410-5440. [PMID: 37818969 DOI: 10.1039/d3ay01132a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.
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Affiliation(s)
- Hala Abi-Rizk
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
| | | | - Julien Chamberland
- Department of Food Sciences, STELA Dairy Research Center, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada
| | - Christophe B Y Cordella
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
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Zacometti C, Massaro A, di Gioia T, Lefevre S, Frégière-Salomon A, Lafeuille JL, Fiordaliso Candalino I, Suman M, Piro R, Tata A. Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study. JOURNAL OF MASS SPECTROMETRY : JMS 2023; 58:e4953. [PMID: 37401136 DOI: 10.1002/jms.4953] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/12/2023] [Accepted: 06/01/2023] [Indexed: 07/05/2023]
Abstract
Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.
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Affiliation(s)
- Carmela Zacometti
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico Sperimentale delle Venezie, Vicenza, Italy
| | - Andrea Massaro
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico Sperimentale delle Venezie, Vicenza, Italy
| | - Tommaso di Gioia
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico Sperimentale delle Venezie, Vicenza, Italy
| | - Stephane Lefevre
- Food Integrity Laboratory, Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., Carpentras, France
| | - Aline Frégière-Salomon
- Food Integrity Laboratory, Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., Carpentras, France
| | - Jean-Louis Lafeuille
- Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., Carpentras, France
| | | | - Michele Suman
- Advanced Laboratory Research, Barilla G. e R. Fratelli S.p.A., Parma, Italy
- Department for Sustainable Food Process, Catholic University Sacred Heart, Piacenza, Italy
| | - Roberto Piro
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico Sperimentale delle Venezie, Vicenza, Italy
| | - Alessandra Tata
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico Sperimentale delle Venezie, Vicenza, Italy
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Velázquez R, Rodríguez A, Hernández A, Casquete R, Benito MJ, Martín A. Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art. Foods 2023; 12:3373. [PMID: 37761082 PMCID: PMC10528162 DOI: 10.3390/foods12183373] [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: 07/31/2023] [Revised: 08/25/2023] [Accepted: 08/27/2023] [Indexed: 09/29/2023] Open
Abstract
There is a necessity to protect the quality and authenticity of herbs and spices because of the increase in the fraud and adulteration incidence during the last 30 years. There are several aspects that make herbs and spices quite vulnerable to fraud and adulteration, including their positive and desirable sensorial and health-related properties, the form in which they are sold, which is mostly powdered, and their economic relevance around the world, even in developing countries. For these reasons, sensitive, rapid, and reliable techniques are needed to verify the authenticity of these agri-food products and implement effective adulteration prevention measures. This review highlights why spices and herbs are highly valued ingredients, their economic importance, and the official quality schemes to protect their quality and authenticity. In addition to this, the type of frauds that can take place with spices and herbs have been disclosed, and the fraud incidence and an overview of scientific articles related to fraud and adulteration based on the Rapid Alert System Feed and Food (RASFF) and the Web of Science databases, respectively, during the last 30 years, is carried out here. Next, the methods used to detect adulterants in spices and herbs are reviewed, with DNA-based techniques and mainly spectroscopy and image analysis methods being the most recommended. Finally, the available adulteration prevention measurements for spices and herbs are presented, and future perspectives are also discussed.
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Affiliation(s)
- Rocío Velázquez
- Departamento de Ingeniería, Medio Agronómico y Forestal, Investigación Aplicada en Hortofruticultura y Jardinería, Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avda. Adolfo Suárez s/n, 06007 Badajoz, Spain;
- Instituto Universitario de Investigación de Recursos Agrarios (INURA), Universidad de Extremadura, Avda. de la Investigación s/n, Campus Universitario, 06006 Badajoz, Spain; (A.H.); (R.C.); (M.J.B.); (A.M.)
| | - Alicia Rodríguez
- Instituto Universitario de Investigación de Recursos Agrarios (INURA), Universidad de Extremadura, Avda. de la Investigación s/n, Campus Universitario, 06006 Badajoz, Spain; (A.H.); (R.C.); (M.J.B.); (A.M.)
- Departamento de Producción Animal y Ciencia de los Alimentos, Nutrición y Bromatología, Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avda. Adolfo Suárez s/n, 06007 Badajoz, Spain
| | - Alejandro Hernández
- Instituto Universitario de Investigación de Recursos Agrarios (INURA), Universidad de Extremadura, Avda. de la Investigación s/n, Campus Universitario, 06006 Badajoz, Spain; (A.H.); (R.C.); (M.J.B.); (A.M.)
- Departamento de Producción Animal y Ciencia de los Alimentos, Nutrición y Bromatología, Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avda. Adolfo Suárez s/n, 06007 Badajoz, Spain
| | - Rocío Casquete
- Instituto Universitario de Investigación de Recursos Agrarios (INURA), Universidad de Extremadura, Avda. de la Investigación s/n, Campus Universitario, 06006 Badajoz, Spain; (A.H.); (R.C.); (M.J.B.); (A.M.)
- Departamento de Producción Animal y Ciencia de los Alimentos, Nutrición y Bromatología, Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avda. Adolfo Suárez s/n, 06007 Badajoz, Spain
| | - María J. Benito
- Instituto Universitario de Investigación de Recursos Agrarios (INURA), Universidad de Extremadura, Avda. de la Investigación s/n, Campus Universitario, 06006 Badajoz, Spain; (A.H.); (R.C.); (M.J.B.); (A.M.)
- Departamento de Producción Animal y Ciencia de los Alimentos, Nutrición y Bromatología, Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avda. Adolfo Suárez s/n, 06007 Badajoz, Spain
| | - Alberto Martín
- Instituto Universitario de Investigación de Recursos Agrarios (INURA), Universidad de Extremadura, Avda. de la Investigación s/n, Campus Universitario, 06006 Badajoz, Spain; (A.H.); (R.C.); (M.J.B.); (A.M.)
- Departamento de Producción Animal y Ciencia de los Alimentos, Nutrición y Bromatología, Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avda. Adolfo Suárez s/n, 06007 Badajoz, Spain
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Han Q, Peller J, Erasmus SW, Elliott CT, van Ruth SM. Interpreting the variation in particle size of ground spice by high-resolution visual and spectral imaging: A ginger case study. Food Res Int 2023; 170:113023. [PMID: 37316086 DOI: 10.1016/j.foodres.2023.113023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/16/2023]
Abstract
High-resolution (HR) visual imaging and spectral imaging are common computer vision-based techniques used for food quality analysis and/or authentication based on the interaction of light and material surface and/or composition. The particle size of ground spices is an important morphological feature that affects the physico-chemical properties of food products containing such particles. This study aimed to interpret the impact of particle size of ground spice on its HR visual profile and spectral imaging profile using ginger powder as a representative spice powder model. The results revealed an increase in the light reflection with the decrease of particle size of ginger powder, which was manifested by the lighter colour (higher percentage of the colour code with lighter yellow colour) of the HR visual image and stronger reflection with spectral imaging. The study also revealed that, in spectral imaging, the influence of the particle size of ginger powder increased with rising wavelengths. Finally, the results indicated a relationship between spectral wavelengths, ginger particle size, and other natural variables of the products which might be generated from cultivation to processing. Ultimately, the impact of natural variables arising during the food production process on the physico-chemical properties of the product should be fully considered or even additionally evaluated prior to the application of specific food quality and/or authentication analytical techniques.
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Affiliation(s)
- Qing Han
- Food Quality and Design Group, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands; Institute for Global Food Security, Biological Sciences, 19 Chlorine Gardens, Queen's University Belfast, BT9 5DL, Belfast, Northern Ireland, United Kingdom
| | - Joseph Peller
- Plant Sciences Group, Wageningen University & Research, 6708 PB Wageningen, the Netherlands
| | - Sara W Erasmus
- Food Quality and Design Group, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands
| | - Christopher T Elliott
- Institute for Global Food Security, Biological Sciences, 19 Chlorine Gardens, Queen's University Belfast, BT9 5DL, Belfast, Northern Ireland, United Kingdom; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Phahonyothin Road, Khong Luang, Pathum Thani 12120, Thailand
| | - Saskia M van Ruth
- Food Quality and Design Group, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands; Institute for Global Food Security, Biological Sciences, 19 Chlorine Gardens, Queen's University Belfast, BT9 5DL, Belfast, Northern Ireland, United Kingdom.
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Saha D, Senthilkumar T, Singh CB, Manickavasagan A. Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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11
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Medeiros MLDS, Freitas Lima A, Correia Gonçalves M, Teixeira Godoy H, Fernandes Barbin D. Portable near-infrared (NIR) spectrometer and chemometrics for rapid identification of butter cheese adulteration. Food Chem 2023; 425:136461. [PMID: 37285626 DOI: 10.1016/j.foodchem.2023.136461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/22/2023] [Accepted: 05/16/2023] [Indexed: 06/09/2023]
Abstract
Artisanal cheeses are highly valued around the world for their distinct sensory characteristics, thus being prone to adulteration by substituting authentic material for cheaper products, such as vegetable oil. In this work, we developed a method based on a portable NIR spectrometer as a non-destructive and low-cost alternative to identify adulteration in butter cheese. Dataset consisted of authentic and intentionally adulterated cheeses in the laboratory and commercial cheeses, which were identified as authentic and adulterated with vegetable oil after analysis of the fatty acid profile. PLS-DA classification models identified adulterated samples with an accuracy of 94.44%. PLS prediction models showed excellent performance (RPD > 3.0) to predict the adulterant level. These results demonstrate that NIR spectra can be used to identify the replacement of authentic fat by soybean oil in butter cheese and that the developed models can be used to identify adulteration in external samples with good performance.
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Affiliation(s)
| | - Adriano Freitas Lima
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Mônica Correia Gonçalves
- Agrifood Science and Technology Center, Federal University of Campina Grande, Pombal, PB, Brazil
| | - Helena Teixeira Godoy
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
| | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
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12
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Liu ZX, Xiong SR, Tang SH, Wang Y, Tan J. A practical application of front-face synchronous fluorescence spectroscopy to rapid, simultaneous and non-destructive determination of piperine and multiple adulterants in ground black and white pepper (Piper nigrum L.). Food Res Int 2023; 167:112654. [PMID: 37087244 DOI: 10.1016/j.foodres.2023.112654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023]
Abstract
Based on the distinct fluorescence of piperine and tryptophan, and their different profiles in pepper and several possible adulterants, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of ground black pepper adulterated with papaya seed powder and buckwheat flour, and ground white pepper adulterated with whole wheat and maize flours. For either single adulterant or dual adulterants in the range of 10-40% w/w, prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The built PLS2 models produced suitable results, with most of the determination coefficients of prediction (Rp2) greater than 0.8, the root mean square error of prediction (RMSEP) < 5% and residual predictive deviation (RPD) greater than 2. The limits of detection (LODs) were 11.1, 5.5, 10.6 and 12.0% for papaya seed powder, buckwheat, whole wheat and maize flours, respectively. Most relative prediction errors for simulated blind samples were within ± 30%. Besides, piperine in ground black and white pepper was also determined with acceptable PLS results.
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13
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Van Haute S, Nikkhah A, Malavi D, Kiani S. Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics. Sci Rep 2023; 13:4261. [PMID: 36918607 PMCID: PMC10014940 DOI: 10.1038/s41598-023-31517-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/13/2023] [Indexed: 03/16/2023] Open
Abstract
Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.
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Affiliation(s)
- Sam Van Haute
- Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Ghent, Belgium. .,Department of Molecular Biotechnology, Environmental Technology, and Food Technology, Ghent University Global Campus, 119, Songdomunhwa-Ro, Yeonsu-Gu, Incheon, 21985, South Korea.
| | - Amin Nikkhah
- Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.,Department of Molecular Biotechnology, Environmental Technology, and Food Technology, Ghent University Global Campus, 119, Songdomunhwa-Ro, Yeonsu-Gu, Incheon, 21985, South Korea.,Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Derick Malavi
- Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.,Department of Molecular Biotechnology, Environmental Technology, and Food Technology, Ghent University Global Campus, 119, Songdomunhwa-Ro, Yeonsu-Gu, Incheon, 21985, South Korea
| | - Sajad Kiani
- Biosystems Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
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14
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Wu Q, Mousa MA, Al-qurashi AD, Ibrahim OH, Abo-Elyousr KA, Rausch K, Abdel Aal AM, Kamruzzaman M. Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Curr Res Food Sci 2023; 6:100483. [PMID: 37033735 PMCID: PMC10073987 DOI: 10.1016/j.crfs.2023.100483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.
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15
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Detection of small yellow croaker freshness by hyperspectral imaging. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Xu Y, Zhang J, Wang Y. Recent trends of multi-source and non-destructive information for quality authentication of herbs and spices. Food Chem 2023; 398:133939. [DOI: 10.1016/j.foodchem.2022.133939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/19/2022] [Accepted: 08/10/2022] [Indexed: 11/15/2022]
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17
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Feasibility study on prediction of the grain mixtures for black sesame paste recipe with different chemometric methods. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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18
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Non-targeted authentication of black pepper using a local web platform: Development, validation and post-analytical challenges of a combined NIR spectroscopy and LASSO method. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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19
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Alewijn M, Akridopoulou V, Venderink T, Müller-Maatsch J, Silletti E. Fusing one-class and two-class classification – A case study on the detection of pepper fraud. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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20
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Hashemi-Nasab FS, Talebian S, Parastar H. Multiple adulterants detection in turmeric powder using VIS-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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21
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Sahachairungrueng W, Meechan C, Veerachat N, Thompson AK, Teerachaichayut S. Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy. Foods 2022; 11:3122. [PMID: 36230198 PMCID: PMC9562924 DOI: 10.3390/foods11193122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 11/28/2022] Open
Abstract
It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared spectroscopy (FTIRs) could be used to test whether samples of coffee were pure Arabica or whether they contained Robusta, and if so, what were the levels of Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs techniques were established with support vector machine classification (SVMC). Results showed that the highest levels of accuracy in the prediction set were 98.04 and 97.06%, respectively. Quantitative models of both techniques for predicting the concentration of Robusta in the samples of Arabica with Robusta were established using support vector machine regression (SVMR), which gave the highest levels of accuracy in the prediction set with a coefficient of determination for prediction (Rp2) of 0.964 and 0.956 and root mean square error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to classify and determine the respective levels of Arabica and Robusta within the mixture.
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Affiliation(s)
- Woranitta Sahachairungrueng
- Department of Food Science, School of Food-Industry, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand
| | - Chanyanuch Meechan
- Department of Food Process Engineering, School of Food-Industry, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand
| | - Nutchaya Veerachat
- Department of Food Process Engineering, School of Food-Industry, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Bedford MK43 0AL, UK
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand
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22
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Chen R, Mei J, Du G, Shi Y, Huang Y. Convenient detection of white pepper adulteration by portable NIRS and spectral imaging with chemometrics. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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23
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Faith Ndlovu P, Samukelo Magwaza L, Zeray Tesfay S, Ramaesele Mphahlele R. Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review. Food Res Int 2022; 157:111198. [DOI: 10.1016/j.foodres.2022.111198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 03/25/2022] [Accepted: 03/27/2022] [Indexed: 01/17/2023]
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24
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Kong D, Sun D, Qiu R, Zhang W, Liu Y, He Y. Rapid and nondestructive detection of marine fishmeal adulteration by hyperspectral imaging and machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:120990. [PMID: 35183858 DOI: 10.1016/j.saa.2022.120990] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Pure fishmeal (PFM) from whole marine-origin fish is an expensive and indispensable protein ingredient in most aquaculture feeds. In China, the supply shortage of domestically produced PFM has caused frequent PFM adulteration with low-cost protein sources such as feather meal (FTM) and fishmeal from by-products (FBP). The aim of this study was to develop a rapid and nondestructive detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms for the identification of PFM adulterated with FTM, FBP, and the binary adulterant (composed of FTM and FBP). A hierarchical modelling strategy was adopted to acquire a better classification accuracy. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) coupled with four spectral preprocessing methods were employed to construct classification models. The SVM with baseline offset (BO-SVM) model using 20 effective wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) achieved classification accuracy of 100% and 99.43% for discriminating PFM from the adulterants (FTM, FBP) and adulterated fishmeal (AFM), respectively. This study confirmed that NIR-HSI offered a promising technique for feed mills to identify AFM containing FTM, FBP, or binary adulterants.
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Affiliation(s)
- Dandan Kong
- 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
| | - Dawei Sun
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Ruicheng Qiu
- 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
| | - Wenkai 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
| | - Yufei 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.
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25
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Detection of nutshells in cumin powder using NIR hyperspectral imaging and chemometrics tools. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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26
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Kong D, Shi Y, Sun D, Zhou L, Zhang W, Qiu R, He Y. Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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27
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da Silva Medeiros ML, Cruz-Tirado J, Lima AF, de Souza Netto JM, Ribeiro APB, Bassegio D, Godoy HT, Barbin DF. Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104403] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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28
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Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104343] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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29
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Bai Z, Tian J, Hu X, Sun T, Luo H, Huang D. A
back‐propagation neural network
model using hyperspectral imaging applied to variety nondestructive detection of cereal. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.13973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhizhen Bai
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Jianping Tian
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Xinjun Hu
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Ting Sun
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Huibo Luo
- College of Bioengineering Sichuan University of Science and Engineering Zigong China
| | - Dan Huang
- College of Bioengineering Sichuan University of Science and Engineering Zigong China
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30
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Screening Method for the Detection of Other Allergenic Nuts in Cashew Nuts Using Chemometrics and a Portable Near-Infrared Spectrophotometer. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-021-02184-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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31
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Sabanci K, Aslan MF, Ropelewska E, Unlersen MF. A convolutional neural network
‐based
comparative study for pepper seed classification: Analysis of selected deep features with
support vector machine. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13955] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Kadir Sabanci
- Department of Electrical‐Electronic Engineering Karamanoglu Mehmetbey University Karaman Turkey
| | - Muhammet Fatih Aslan
- Department of Electrical‐Electronic Engineering Karamanoglu Mehmetbey University Karaman Turkey
| | - Ewa Ropelewska
- Fruit and Vegetable Storage and Processing Department The National Institute of Horticultural Research Skierniewice Poland
| | - Muhammed Fahri Unlersen
- Department of Electrical and Electronics Engineering Necmettin Erbakan University Konya Turkey
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32
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Thangaraju S, Modupalli N, Natarajan V. Food Adulteration and Its Impacts on Our Health/Balanced Nutrition. Food Chem 2021. [DOI: 10.1002/9781119792130.ch7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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33
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Bonifazi G, Capobianco G, Gasbarrone R, Serranti S. Contaminant detection in pistachio nuts by different classification methods applied to short-wave infrared hyperspectral images. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108202] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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34
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Rocha PD, Medeiros EP, Silva CS, da Silva Simões S. Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:5065-5074. [PMID: 34651617 DOI: 10.1039/d1ay01076j] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based on HSI in the near infrared range (HSI-NIR) to distinguish conventional from transgenic cotton seeds. Three different chemometric approaches, one pixel-based and two object-based, using partial least squares discriminant analysis (PLS-DA) were built and their performances were compared considering the pros and cons of each approach. Specificity and sensitivity values ranged from 0.78-0.92 and 0.62-0.93, respectively, for the different approaches.
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Affiliation(s)
- Priscilla Dantas Rocha
- State University of Paraiba, Bairro Universitário, Rua Baraúnas, 351 Campina Grande, Paraiba, 58429-500, Brazil.
| | - Everaldo Paulo Medeiros
- Brazilian Agricultural Research Corporation, Embrapa Cotton, Rua Osvaldo Cruz, 1143, Bairro Centenário, Campina Grande, Paraiba, 58428-095, Brazil
| | - Carolina Santos Silva
- Department of Chemistry Engineering, Federal University of Pernambuco, Av. da Arquitetura, Cidade Universitária, Recife, Pernambuco, 50740-540, Brazil.
- Department of Food Sciences and Nutrition, Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Simone da Silva Simões
- State University of Paraiba, Bairro Universitário, Rua Baraúnas, 351 Campina Grande, Paraiba, 58429-500, Brazil.
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35
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Fatima N, Areeb QM, Khan IM, Khan MM. Siamese network‐based computer vision approach to detect papaya seed adulteration in black peppercorns. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.16043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Noor Fatima
- Department of Computer Science Aligarh Muslim University Aligarh Uttar Pradesh202002India
| | - Qazi Mohammad Areeb
- Department of Computer Science Aligarh Muslim University Aligarh Uttar Pradesh202002India
| | - Irfan Mabood Khan
- Zakir Husain College of Engineering and Technology Aligarh Muslim University Aligarh Uttar Pradesh202002India
| | - Mohd. Maaz Khan
- Zakir Husain College of Engineering and Technology Aligarh Muslim University Aligarh Uttar Pradesh202002India
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36
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Manzoor MF, Hussain A, Sameen A, Sahar A, Khan S, Siddique R, Aadil RM, Xu B. Novel extraction, rapid assessment and bioavailability improvement of quercetin: A review. ULTRASONICS SONOCHEMISTRY 2021; 78:105686. [PMID: 34358980 PMCID: PMC8350193 DOI: 10.1016/j.ultsonch.2021.105686] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 05/12/2023]
Abstract
Quercetin (QUR) have got the attention of scientific society frequently due to their wide range of potential applications. QUR has been the focal point for research in various fields, especially in food development. But, the QUR is highly unstable and can be interrupted by using conventional assessment methods. Therefore, researchers are focusing on novel extraction and non-invasive tools for the non-destructive assessment of QUR. The current review elaborates the different novel extraction (ultrasound-assisted extraction, microwave-assisted extraction, supercritical fluid extraction, and enzyme-assisted extraction) and non-destructive assessment techniques (fluorescence spectroscopy, terahertz spectroscopy, near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, and surface-enhanced Raman spectroscopy) for the extraction and identification of QUR in agricultural products. The novel extraction approaches facilitate shorter extraction time, involve less organic solvent, and are environmentally friendly. While the non-destructive techniques are non-interruptive, label-free, reliable, accurate, and environmental friendly. The non-invasive spectroscopic and imaging methods are suitable for the sensitive detection of bioactive compounds than conventional techniques. QUR has potential therapeutic properties such as anti-obesity, anti-diabetes, antiallergic, antineoplastic agent, neuroprotector, antimicrobial, and antioxidant activities. Besides, due to the low bioavailability of QUR innovative drug delivery strategies (QUR loaded gel, QUR polymeric micelle, QUR nanoparticles, glucan-QUR conjugate, and QUR loaded mucoadhesive nanoemulsions) have been proposed to improve its bioavailability and providing novel therapeutic approaches.
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Affiliation(s)
- Muhammad Faisal Manzoor
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu Province 212013, China; Riphah College of Rehabilitation and Allied Health Sciences, Riphah International University, Faisalabad 38000, Pakistan
| | - Abid Hussain
- Department of Agriculture and Food Technology, Karakoram International University Gilgit, Pakistan
| | - Aysha Sameen
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Amna Sahar
- Department of Food Engineering, University of Agriculture, Faisalabad 38000, Pakistan
| | - Sipper Khan
- University of Hohenheim, Institute of Agricultural Engineering, Tropics and Subtropics Group, Garbenstrasse 9, 70593 Stuttgart, Germany
| | - Rabia Siddique
- Department of Chemistry, Government College University Faisalabad, 38000, Pakistan
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu Province 212013, China.
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Yang J, Sun L, Xing W, Feng G, Bai H, Wang J. Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 253:119585. [PMID: 33662700 DOI: 10.1016/j.saa.2021.119585] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/13/2021] [Accepted: 02/01/2021] [Indexed: 06/12/2023]
Abstract
How to quickly and accurately select sugarbeet seeds with reliable germination is very important to sugarbeet planting. In this study, the hyperspectral images of 3072 sugarbeet seeds of the same variety were collected, and were successively processed by binarization, morphology, contour extraction and so on. The average spectrum of the single seed image was obtained by image segmentation. Comprehensive analysis of the evaluation parameters of the five spectral preprocessing methods revealed that the second derivative (2D) processing was optimal. Successive projections algorithm (SPA) was used to extract 16 characteristic wavelengths. Support vector machine radial basis function (SVM-RBF), k-nearest neighbor (KNN) and random forest (RF) models were established at the full wavelength and characteristic wavelength respectively to predict the germination of sugarbeet seeds. By analyzing the prediction accuracy of the three models, it was found that the SVM-RBF model provided the highest prediction accuracy in the test set (the prediction accuracy of the full wavelength was 95.5%, and the prediction accuracy of the characteristic wavelength was 92.32%). The research results showed that the hyperspectral image processing technology could accurately predict the germination rate of sugarbeet seeds, and realize the rapid and non-destructive prediction of the germination status of sugarbeet seeds.
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Affiliation(s)
- Jun Yang
- Key Laboratory of Electronic Engineering of Heilongjiang Province, Heilongjiang University, Harbin 150080, China
| | - Laijun Sun
- Key Laboratory of Electronic Engineering of Heilongjiang Province, Heilongjiang University, Harbin 150080, China.
| | - Wang Xing
- Key Laboratory of Sugarbeet Genetics and Breeding, Heilongjiang University, Harbin 150080, China.
| | - Guojun Feng
- Key Laboratory of Sugarbeet Genetics and Breeding, Heilongjiang University, Harbin 150080, China.
| | - Hongyi Bai
- Key Laboratory of Electronic Engineering of Heilongjiang Province, Heilongjiang University, Harbin 150080, China.
| | - Jiaying Wang
- Key Laboratory of Electronic Engineering of Heilongjiang Province, Heilongjiang University, Harbin 150080, China.
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38
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Li H, Wu X, Li X, Cao X, Li Y, Cao H, Men Y. Multistage Extraction of Star Anise and Black Pepper Derivatives for Antibacterial, Antioxidant, and Anticancer Activity. Front Chem 2021; 9:660138. [PMID: 34055736 PMCID: PMC8160366 DOI: 10.3389/fchem.2021.660138] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/06/2021] [Indexed: 12/15/2022] Open
Abstract
Recently, natural resources have attracted considerable interest for their applications in food security and human health problems. Traditional natural spices, such as star anise and black pepper, played important roles in the pharmaceutical and food industries due to their strong pharmacological activity, antioxidant potential and rare complications. In order to achieve biomasses from the natural product with multiple bioactivities, we developed the multistage extraction method to extract and separate various bioactive compounds from these natural plants. Our work demonstrated that various bioactive-rich extractives were achieved using steam distilled- or oxidative-extraction methods with high extraction yields and purity. Furthermore, the extractives in each step can be used not only as bioactive compounds, but also as a resource to further prepare different derivatives during the next extractive step, providing biomass-saving to a great extent. The extractives obtained with high yields and purities (>82%) were identified by 1H NMR, 13C NMR, FTIR, UV-vis, fluorescence spectroscopy, and high-performance liquid chromatography (HPLC). Moreover, these biomasses display potent antibacterial activities against some types of microorganisms such as S.aureus, S.pyogenes, E.coli, and S.typhi with a lowest MIC of 400 μg/ml for the development of antibacterial agents, significant antioxidant activity as the natural antioxidant for enhancing food shelf-life, and excellent anticancer activity that induces significant cancer cell apoptosis. This work showed the different multistage extracts from natural products, which enable them to be applied in the fields of the pharmaceutical industry and the food industry.
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Affiliation(s)
- Helin Li
- Zhejiang Provincial Key Laboratory of Chemical Utilization of Forestry Biomass, Zhejiang A & F University, Hangzhou, China.,Institute of Technical and Macromolecular Chemistry, RWTH Aachen University, Aachen, Germany
| | - Xiaoyu Wu
- Department of Surgical Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xin Li
- Institute of Technical and Macromolecular Chemistry, RWTH Aachen University, Aachen, Germany
| | - Xiaobing Cao
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, China
| | - Yanjun Li
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, China
| | - Huaru Cao
- Zhejiang Provincial Key Laboratory of Chemical Utilization of Forestry Biomass, Zhejiang A & F University, Hangzhou, China
| | - Yongzhi Men
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Huang Y, Li J, Yang R, Wang F, Li Y, Zhang S, Wan F, Qiao X, Qian W. Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth. FRONTIERS IN PLANT SCIENCE 2021; 12:626516. [PMID: 33995432 PMCID: PMC8119880 DOI: 10.3389/fpls.2021.626516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 04/08/2021] [Indexed: 06/12/2023]
Abstract
Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450-998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild.
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Affiliation(s)
- Yiqi Huang
- College of Mechanical Engineering, Guangxi University, Nanning, China
| | - Jie Li
- College of Mechanical Engineering, Guangxi University, Nanning, China
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Rui Yang
- College of Mechanical Engineering, Guangxi University, Nanning, China
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Fukuan Wang
- College of Mechanical Engineering, Guangxi University, Nanning, China
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yanzhou Li
- College of Mechanical Engineering, Guangxi University, Nanning, China
| | - Shuo Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
| | - Fanghao Wan
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Xi Qiao
- College of Mechanical Engineering, Guangxi University, Nanning, China
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Wanqiang Qian
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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Sabzi S, Pourdarbani R, Rohban MH, Fuentes-Penna A, Hernández-Hernández JL, Hernández-Hernández M. Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10050898. [PMID: 33946780 PMCID: PMC8146435 DOI: 10.3390/plants10050898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/10/2021] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network-imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network-harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network-biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.
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Affiliation(s)
- Sajad Sabzi
- Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
| | - Razieh Pourdarbani
- Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
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41
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Ren G, Wang Y, Ning J, Zhang Z. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2135-2142. [PMID: 32981110 DOI: 10.1002/jsfa.10836] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/28/2020] [Accepted: 09/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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42
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Mei J, Zhao F, Xu R, Huang Y. A review on the application of spectroscopy to the condiments detection: from safety to authenticity. Crit Rev Food Sci Nutr 2021; 62:6374-6389. [PMID: 33739226 DOI: 10.1080/10408398.2021.1901257] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Condiments are the magical ingredients that make the food present a richer taste. In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food condiments safety, authentication, and traceability. The potential of spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), Raman, fluorescence, inductively coupled plasma (ICP), and hyperspectral imaging techniques, has been widely enhanced by numerous applications in this field because of their advantages over other analytical techniques. Following a brief introduction of condiment and safety basics, this review mainly focuses on recent vibrational and atomic spectral applications for condiment nondestructive analysis and evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; and (3) authenticity concerns. The review shows current spectroscopies to be effective tools that will play indispensable roles for food condiment evaluation. In addition, online/real-time applications of these techniques promise to be a huge growth field in the near future.
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Affiliation(s)
- Jianhua Mei
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P. R. China.,Health Food Industry Research Institute (Xinghua), China Agricultural University, Xinghua, Jiangsu, 225700, P. R. China
| | - Fangyuan Zhao
- College of Food Science and Engineering, Qingdao Agricultural University, Qingdao, Shandong, 266109, P. R. China
| | - Runqi Xu
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P. R. China.,Health Food Industry Research Institute (Xinghua), China Agricultural University, Xinghua, Jiangsu, 225700, P. R. China
| | - Yue Huang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P. R. China.,Health Food Industry Research Institute (Xinghua), China Agricultural University, Xinghua, Jiangsu, 225700, P. R. China
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43
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Modupalli N, Naik M, Sunil C, Natarajan V. Emerging non-destructive methods for quality and safety monitoring of spices. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2020.12.021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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44
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Shao Y, Liu Y, Xuan G, Wang Y, Gao Z, Hu Z, Han X, Gao C, Wang K. Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato. RSC Adv 2020; 10:33148-33154. [PMID: 35515022 PMCID: PMC9056662 DOI: 10.1039/c9ra10630h] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/13/2020] [Indexed: 12/02/2022] Open
Abstract
Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in 'Beijing 553' and 'Red Banana' sweet potatoes. Hyperspectral images were acquired from 420 ROIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from 'Beijing 553' and 'Red Banana' sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA-SVR model with R p 2 of 0.8581, RMSEP of 0.2951 and RPDp of 2.56 for 'Beijing 553' sweet potato, and the CARS-MLR model with R p 2 of 0.8153, RMSEP of 0.2744 and RPDp of 2.09 for 'Red Banana' sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA-SVR and CARS-MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.
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Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
- Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs Nanjing China
| | - Yi Liu
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
| | - Guantao Xuan
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
| | - Yongxian Wang
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
| | - Zongmei Gao
- Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, Washington State University Prosser USA
| | - Zhichao Hu
- Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs Nanjing China
| | - Xiang Han
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
| | - Chong Gao
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
| | - Kaili Wang
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University Tai'an China
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Park JR, Kang HH, Cho JK, Moon KD, Kim YJ. Feasibility of rapid piperine quantification in whole and black pepper using near infrared spectroscopy and chemometrics. J Food Sci 2020; 85:3094-3101. [PMID: 32888358 DOI: 10.1111/1750-3841.15428] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/14/2020] [Accepted: 08/10/2020] [Indexed: 12/21/2022]
Abstract
Piperine is a bioactive alkaloid that possesses various health benefits and is responsible for the pungent aroma of pepper. Piperine content in whole and ground black pepper (n = 132) was analyzed by near-infrared spectroscopy (NIRS) in the 950 to 1650 nm wavelength window. Chemometric modeling using partial least square regression was performed, and outliers were checked and removed during the preparation of the calibration curve by considering sample residual variance and sample leverage. Model accuracy was evaluated with a low root-mean-square error of cross-validation (RMSECV) and a high ratio performance to deviation (RPD). The optimal model had a coefficient of determination (R2 ) of 0.726, RMSECV of 0.289 g/100 g, and RPD of 1.744 for the whole black pepper. The results of R2 , RMSECV, and RPD for the ground black pepper were 0.850, 0.231 g/100 g, and 2.424, respectively. Therefore, based on the perspective of onsite process, the proposed NIRS method can be employed for selecting abnormal samples during the inspection of black pepper raw material and for quantifying piperine contents of final black pepper product. PRACTICAL APPLICATION: Generally, the quality indicators of black pepper are graded solely based on their external appearance, quality, and size. This study discloses the development of a near-infrared spectroscopy-based fast and accurate nondestructive analytical method for the detection of piperine, a bioactive constituent of pepper, as a tool for the quality control of whole and ground black pepper.
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Affiliation(s)
- Jong-Rak Park
- School of Food Science and Biotechnology, Kyungpook National University, Daegu, 41566, Korea
| | - Hyun-Hee Kang
- Department of Food Science and Technology, Seoul National University of Science and Technology, Seoul, 01811, Korea
| | - Jong-Ku Cho
- Nanomarkers Co. Ltd., Seongnam, 13595, Korea
| | - Kwang-Deog Moon
- School of Food Science and Biotechnology, Kyungpook National University, Daegu, 41566, Korea
| | - Young-Jun Kim
- Department of Food Science and Technology, Seoul National University of Science and Technology, Seoul, 01811, Korea
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46
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Esquerre CA, Achata EM, García-Vaquero M, Zhang Z, Tiwari BK, O'Donnell CP. Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109761] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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47
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Ren G, Wang Y, Ning J, Zhang Z. Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 237:118407. [PMID: 32361218 DOI: 10.1016/j.saa.2020.118407] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/19/2020] [Accepted: 04/21/2020] [Indexed: 06/11/2023]
Abstract
The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China.
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Arendse E, Nieuwoudt H, Magwaza LS, Nturambirwe JFI, Fawole OA, Opara UL. Recent Advancements on Vibrational Spectroscopic Techniques for the Detection of Authenticity and Adulteration in Horticultural Products with a Specific Focus on Oils, Juices and Powders. FOOD BIOPROCESS TECH 2020. [DOI: 10.1007/s11947-020-02505-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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49
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Zhang J, Yang Y, Feng X, Xu H, Chen J, He Y. Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network. FRONTIERS IN PLANT SCIENCE 2020; 11:821. [PMID: 32670316 PMCID: PMC7326944 DOI: 10.3389/fpls.2020.00821] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 05/22/2020] [Indexed: 06/02/2023]
Abstract
Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.
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Affiliation(s)
- Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, China
| | - Yong Yang
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, China
| | - Hongxia Xu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jianping Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Plant Virology, Ningbo University, Ningbo, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, China
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Bai Z, Hu X, Tian J, Chen P, Luo H, Huang D. Rapid and nondestructive detection of sorghum adulteration using optimization algorithms and hyperspectral imaging. Food Chem 2020; 331:127290. [PMID: 32544654 DOI: 10.1016/j.foodchem.2020.127290] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 05/26/2020] [Accepted: 06/07/2020] [Indexed: 12/31/2022]
Abstract
This paper proposes a sorghum adulteration detection model using hyperspectral imaging technology (HSI), image processing technology, and multivariate analysis technology. The model used a watershed algorithm to extract hyperspectral data from sorghum grains. Principal component analysis (PCA) and clustering analysis (CA) were used to remove abnormal samples of sorghum. Partial least squares discriminant analysis (PLS-DA) was used to identify the variety of sample, and a sorghum distribution map and adulteration ratios were obtained by marking varieties with different colors. This paper presents, for the first time, HSI use for identification of adulteration in sorghum using PCA and CA. Accuracy of the model identification for the validation set reached 96%, and for the adulterated samples reached 91%, and comprehensive accuracy of the model could reach more than 90%. These results show that the model can rapidly and nondestructively detect sorghum adulteration.
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Affiliation(s)
- Zhizhen Bai
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China.
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China.
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China.
| | - Ping Chen
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China
| | - Huibo Luo
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China
| | - Dan Huang
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China
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