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Tantinantrakun A, Thompson AK, Terdwongworakul A, Teerachaichayut S. Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging. Foods 2023; 12:2793. [PMID: 37509885 PMCID: PMC10379852 DOI: 10.3390/foods12142793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (Rp) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.
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Affiliation(s)
- Achiraya Tantinantrakun
- Department of Food Science, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Cranfield, Bedford MK430AL, UK
| | - Anupun Terdwongworakul
- Department of Agricultural Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
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Yang H, Wang C, Zhang H, Zhou Y, Luo B. Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms. PeerJ Comput Sci 2023; 9:e1354. [PMID: 37346683 PMCID: PMC10280578 DOI: 10.7717/peerj-cs.1354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/30/2023] [Indexed: 06/23/2023]
Abstract
Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky-Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition.
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Affiliation(s)
- Huan Yang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Agricultural Intelligent Equipment Engineering Technology Research Center, Beijing, China
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, China
| | - Cheng Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Agricultural Intelligent Equipment Engineering Technology Research Center, Beijing, China
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, China
| | - Han Zhang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Agricultural Intelligent Equipment Engineering Technology Research Center, Beijing, China
| | - Ya’nan Zhou
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Agricultural Intelligent Equipment Engineering Technology Research Center, Beijing, China
| | - Bin Luo
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Agricultural Intelligent Equipment Engineering Technology Research Center, Beijing, China
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Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method. J FOOD QUALITY 2023. [DOI: 10.1155/2023/6104038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food.
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Bai X, Fang H, He Y, Zhang J, Tao M, Wu Q, Yang G, Wei Y, Tang Y, Tang L, Lou B, Deng S, Yang Y, Feng X. Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0019. [PMID: 37040287 PMCID: PMC10076055 DOI: 10.34133/plantphenomics.0019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/09/2022] [Indexed: 05/27/2023]
Abstract
Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an R p 2 of 0.86 with an RMSEp of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Hui Fang
- Huzhou Institute of Zhejiang University, Huzhou 313000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Qingguan Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yuzhen Wei
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yu Tang
- Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Lie Tang
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011-3270, USA
| | - Binggan Lou
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Shuiguang Deng
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
| | - Yong Yang
- State Key Laboratory for Managing Biotic and Chemical Treats 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 Science, Hangzhou 31002, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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Bai X, Zhou Y, Feng X, Tao M, Zhang J, Deng S, Lou B, Yang G, Wu Q, Yu L, Yang Y, He Y. Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique. FRONTIERS IN PLANT SCIENCE 2022; 13:1037774. [PMID: 36340356 PMCID: PMC9627309 DOI: 10.3389/fpls.2022.1037774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Hyperspectral imaging technique combined with machine learning is a powerful tool for the evaluation of disease phenotype in rice disease-resistant breeding. However, the current studies are almost carried out in the lab environment, which is difficult to apply to the field environment. In this paper, we used visible/near-infrared hyperspectral images to analysis the severity of rice bacterial blight (BB) and proposed a novel disease index construction strategy (NDSCI) for field application. A designed long short-term memory network with attention mechanism could evaluate the BB severity robustly, and the attention block could filter important wavelengths. Best results were obtained based on the fusion of important wavelengths and color features with an accuracy of 0.94. Then, NSDCI was constructed based on the important wavelength and color feature related to BB severity. The correlation coefficient of NDSCI extended to the field data reached -0.84, showing good scalability. This work overcomes the limitations of environmental conditions and sheds new light on the rapid measurement of phenotype in disease-resistant breeding.
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yujie Zhou
- Zhuji Agricultural Technology Extension Center, Zhuji, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Shuiguang Deng
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Binggan Lou
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Qingguan Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Li Yu
- Agricultural Experiment Station & Agricultural Sci-Tech Park Management Committee, Zhejiang University, Hangzhou, China
| | - Yong Yang
- State Key Laboratory for Managing Biotic and Chemical Treats 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 Science, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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Kang Z, Huang T, Zeng S, Li H, Dong L, Zhang C. A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology. SENSORS (BASEL, SWITZERLAND) 2022; 22:5333. [PMID: 35891015 PMCID: PMC9315692 DOI: 10.3390/s22145333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.
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Affiliation(s)
| | | | - Shan Zeng
- Correspondence: ; Tel.: +86-153-2728-6970
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Tu K, Wen S, Cheng Y, Xu Y, Pan T, Hou H, Gu R, Wang J, Wang F, Sun Q. A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning. PLANT METHODS 2022; 18:81. [PMID: 35690826 PMCID: PMC9188178 DOI: 10.1186/s13007-022-00918-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/31/2022] [Indexed: 05/24/2023]
Abstract
BACKGROUND Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. RESULTS Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. CONCLUSIONS This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.
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Affiliation(s)
- Keling Tu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Shaozhe Wen
- Beijing Key Laboratory of Vegetable Germplasm Improvement, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Beijing, 100097, People's Republic of China
| | - Ying Cheng
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Yanan Xu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Tong Pan
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Haonan Hou
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Riliang Gu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Jianhua Wang
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Fengge Wang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Beijing, 100097, People's Republic of China.
| | - Qun Sun
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China.
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Liu Q, Wang Z, Long Y, Zhang C, Fan S, Huang W. Variety classification of coated maize seeds based on Raman hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120772. [PMID: 34973616 DOI: 10.1016/j.saa.2021.120772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/18/2021] [Accepted: 12/13/2021] [Indexed: 05/27/2023]
Abstract
As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm-1 were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield.
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Affiliation(s)
- Qingyun Liu
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Zuchao Wang
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China
| | - Yuan Long
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Chi Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
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Feng L, Wu B, Zhu S, He Y, Zhang C. Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins. Front Nutr 2021; 8:680357. [PMID: 34222304 PMCID: PMC8247466 DOI: 10.3389/fnut.2021.680357] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/25/2021] [Indexed: 01/25/2023] Open
Abstract
Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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10
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Ali A, Mashwani WK, Tahir MH, Belhaouari SB, Alrabaiah H, Naeem S, Nasir JA, Jamal F, Chesneau C. Statistical features analysis and discrimination of maize seeds utilizing machine vision approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-200635] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The purpose of this study is the statistical analysis and discrimination of maize seed using a machine vision (MV) approach. The foundation of the digital image dataset holds six maize seed varieties named as Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88. The digital image dataset acquired via a digital imaging laboratory. For preprocessing, we crop the image into a size of 600×600 pixels, and convert it into a gray level image format. After that, line and edge detection are performed by using a Prewitt filter, and five non-overlapping areas of interest (AOIs) size of (200×200), and (250×250) are drawn. A total of 56 statistical features, containing texture features, histogram features, and spectral features, is extracted from each AOI. The 11 optimized statistical features have been selected by deploying “Correlation-based Feature Selection” (CFS) with the Greedy algorithm. For the discrimination analysis, four MV classifiers named as “Support Vector Machine” (SVM), “Logistic” (Lg), “Bagging” (B), and “LogitBoost” (LB) have been deployed on optimized statistical features dataset. After analysis, the SVM classifier has shown a promising accuracy of 99.93% on AOIs size (250×250). The obtained accuracy by SVM classifier on six maize seed varieties, namely Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88, were 99.9%, 99.8%, 100%, 100%, 99.9%, and 99.8%, respectively.
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Affiliation(s)
- Aqib Ali
- Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan
| | - Wali Khan Mashwani
- Institute of Numerical Sciences, Kohat University of Sciences & Technology, Kohat, Pakistan
| | - Muhammad H. Tahir
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Samir Brahim Belhaouari
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Qatar Foundation, Doha, Qatar
| | - Hussam Alrabaiah
- College of Engineering al Ain University, al Ain, UAE & Department of Mathematics, Tafila Technical University Tafila, Jordan
| | - Samreen Naeem
- Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan
| | | | - Farrukh Jamal
- Department of Statistics, The Islamia University of Bahawalpur, Pakistan
| | - Christophe Chesneau
- Department of Mathematics, Université de Caen, LMNO, Campus II, Science 3, Caen, France
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Sricharoonratana M, Thompson AK, Teerachaichayut S. Use of near infrared hyperspectral imaging as a nondestructive method of determining and classifying shelf life of cakes. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2020.110369] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Ribeiro JPO, Medeiros ADD, Caliari IP, Trancoso ACR, Miranda RMD, Freitas FCLD, Silva LJD, Dias DCFDS. FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals. Food Chem 2020; 342:128324. [PMID: 33069535 DOI: 10.1016/j.foodchem.2020.128324] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/03/2020] [Accepted: 10/05/2020] [Indexed: 11/15/2022]
Abstract
Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.
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