Research on nondestructive detection of pine nut quality based on terahertz imaging
[Display omitted] •In this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out, and four preprocessing algorithms of AirPLS, ALS,BEADS and SNV + Detrending are used to remove the redundant information an...
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| Published in: | Infrared physics & technology Vol. 134; p. 104798 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.11.2023
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| ISSN: | 1350-4495, 1879-0275 |
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| Abstract | [Display omitted]
•In this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out, and four preprocessing algorithms of AirPLS, ALS,BEADS and SNV + Detrending are used to remove the redundant information and noise.•The LDA, SVM and XGBoost integration learning discriminant models were established respectively to explore the optimal qualitative discriminant model for the detection of pine nut quality, and the research shows that the BEADS + XGBoost qualitative discriminant model has a recognition rate of up to 98.61%.•The main feature gamut extraction and channel separation preprocessing were carried out for redundant color information of the 0.1–2.0 terahertz pine seed images, and the appropriate threshold is calculated by the one-dimensional maximum entropy threshold segmentation algorithm with the error less than 7%.•This study provides a new rapid and nondestructive effective method for pine nut quality detection, which can provide technical reference for other shelled nut quality detection and has significant practicalvalue.
Pine nuts are of great nutritional and medicinal value, but they cannot avoid such defects as mildew and insect-eating during storage. Because of their hard shells, the internal quality detection of pine nuts is a major problem for industrial sorting. For this reason, it is of great significance to carry out rapid nondestructive detection of the internal quality of pine nuts.
In this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out. Firstly, the terahertz transmission images of pine nut samples were acquired and the terahertz spectral signals of four different regions of interest were extracted for analysis. Secondly, in order to reduce the interference of external environment on the acquisition of terahertz spectra, the terahertz spectra were pre-processedby several methods, such as ALS, AirPLS, BEADS and SNV + Detrending, and then three qualitative discriminant models, namely, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and XGBoost integration learning, were established respectively to explore the optimal qualitative discriminant model for the detection of pine nut quality. Finally, the terahertz transmission image of pine nuts was subjected to image processing. The main feature gamut extraction combined with channel separation strategy were adopted. and then the automatic threshold segmentation algorithm was applied to perform binary threshold segmentation on the separated image, thus the plumpness of the pine nuts was calculated by calculating the ratio of the pixel points of the shell and the kernel.
The prediction set of BEADS + XGBoost model was established after data preprocessing with the optimal effect and the accuracy of 98.61%. The acquired terahertz images of pine nuts were extracted by the main feature gamut and the images of channel B were extracted by using channel separation. Finally, the automatic threshold segmentation of channel B was performed by using the maximum one-dimensional entropy, which can well realize the visual detection of the inner shell kernel of pine nuts.
Terahertz imaging technology can achieve rapid and nondestructive detection of pine mildew as well as pine nut plumpness. This study provides a new rapid and nondestructive effective method for pine nut quality detection, which can provide technical reference for other shelled nut quality detection and has significant practical value. |
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| AbstractList | [Display omitted]
•In this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out, and four preprocessing algorithms of AirPLS, ALS,BEADS and SNV + Detrending are used to remove the redundant information and noise.•The LDA, SVM and XGBoost integration learning discriminant models were established respectively to explore the optimal qualitative discriminant model for the detection of pine nut quality, and the research shows that the BEADS + XGBoost qualitative discriminant model has a recognition rate of up to 98.61%.•The main feature gamut extraction and channel separation preprocessing were carried out for redundant color information of the 0.1–2.0 terahertz pine seed images, and the appropriate threshold is calculated by the one-dimensional maximum entropy threshold segmentation algorithm with the error less than 7%.•This study provides a new rapid and nondestructive effective method for pine nut quality detection, which can provide technical reference for other shelled nut quality detection and has significant practicalvalue.
Pine nuts are of great nutritional and medicinal value, but they cannot avoid such defects as mildew and insect-eating during storage. Because of their hard shells, the internal quality detection of pine nuts is a major problem for industrial sorting. For this reason, it is of great significance to carry out rapid nondestructive detection of the internal quality of pine nuts.
In this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out. Firstly, the terahertz transmission images of pine nut samples were acquired and the terahertz spectral signals of four different regions of interest were extracted for analysis. Secondly, in order to reduce the interference of external environment on the acquisition of terahertz spectra, the terahertz spectra were pre-processedby several methods, such as ALS, AirPLS, BEADS and SNV + Detrending, and then three qualitative discriminant models, namely, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and XGBoost integration learning, were established respectively to explore the optimal qualitative discriminant model for the detection of pine nut quality. Finally, the terahertz transmission image of pine nuts was subjected to image processing. The main feature gamut extraction combined with channel separation strategy were adopted. and then the automatic threshold segmentation algorithm was applied to perform binary threshold segmentation on the separated image, thus the plumpness of the pine nuts was calculated by calculating the ratio of the pixel points of the shell and the kernel.
The prediction set of BEADS + XGBoost model was established after data preprocessing with the optimal effect and the accuracy of 98.61%. The acquired terahertz images of pine nuts were extracted by the main feature gamut and the images of channel B were extracted by using channel separation. Finally, the automatic threshold segmentation of channel B was performed by using the maximum one-dimensional entropy, which can well realize the visual detection of the inner shell kernel of pine nuts.
Terahertz imaging technology can achieve rapid and nondestructive detection of pine mildew as well as pine nut plumpness. This study provides a new rapid and nondestructive effective method for pine nut quality detection, which can provide technical reference for other shelled nut quality detection and has significant practical value. |
| ArticleNumber | 104798 |
| Author | Hu, Jun Liu, Yande Lv, Haohao Qiao, Peng Shi, Hongyang Yang, Liang He, Yong |
| Author_xml | – sequence: 1 givenname: Jun orcidid: 0000-0003-0027-7993 surname: Hu fullname: Hu, Jun email: hujun_ecjtu@163.com organization: School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang, Jiangxi, 330013, PR China – sequence: 2 givenname: Peng surname: Qiao fullname: Qiao, Peng organization: School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang, Jiangxi, 330013, PR China – sequence: 3 givenname: Liang surname: Yang fullname: Yang, Liang organization: School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang, Jiangxi, 330013, PR China – sequence: 4 givenname: Haohao surname: Lv fullname: Lv, Haohao organization: School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang, Jiangxi, 330013, PR China – sequence: 5 givenname: Hongyang surname: Shi fullname: Shi, Hongyang organization: School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang, Jiangxi, 330013, PR China – sequence: 6 givenname: Yong surname: He fullname: He, Yong organization: School of Mechanical Engineering, Zhejiang University, Hangzhou, ZheJiang, 310027, PR China – sequence: 7 givenname: Yande surname: Liu fullname: Liu, Yande email: jxliuyd@163.com organization: School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang, Jiangxi, 330013, PR China |
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| Cites_doi | 10.1016/j.foodcont.2021.108365 10.3390/foods11213498 10.1364/JOSAA.18.001562 10.1016/j.infrared.2017.08.020 10.1016/j.eswa.2021.114925 10.1016/j.jff.2016.03.003 10.1016/j.ijleo.2018.12.066 10.1016/j.chemolab.2014.09.014 10.3390/f12121809 10.1111/jfpe.13562 10.1109/TNN.2003.813835 10.1016/j.foodcont.2020.107265 10.1016/j.foodchem.2021.131246 10.1039/b922045c 10.1145/2939672.2939785 10.1007/s10762-019-00668-z 10.1364/AO.38.000409 10.1016/j.aca.2010.08.033 10.1016/j.jfoodeng.2020.110357 |
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| Keywords | XGBoost integrated learning Plumpness Terahertz imaging technology Automatic threshold segmentation |
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| References | Shi, Zhang, Chen (b0110) 2018; 47 Zhang, Li, Sun (b0115) 2017; 86 Izenman (b0100) 2013 Zhai, Jin, Li (b0090) 2020; 43 Peng, Heisterkamp, Dai (b0095) 2003; 14 Y. Long, W.Q. Huang, Q.Y. Wang (b0025) 2022; 372 Li, Zhao, Li (b0085) 2021; 12 Shen, Y.Q. Wei, B. Zhang (b0030) 2018; 38 Dorney, Baraniuk, Mittleman (b0060) 2001; 18 Peng, Peng, Jiang (b0075) 2010; 683 Zhang, Chen, Liang (b0070) 2010; 135 Canthadai, Muthuraju, Pachava (b0020) 2015; 9488 K.Y Xie, Miles, Calder (b0005) 2016; 23 Campmajó, Saez-Vigo, Saurina (b0010) 2020; 114 Moscetti, Berhe, Agrimi (b0035) 2021; 292 X.R Ning, Selesnick, Duval (b0080) 2014; 139 Jiang, Ge, Zhang (b0050) 2019; 181 Duvillaret, Garet, Coutaz (b0065) 1999; 38 Van de Looverbosch, Raeymaekers, Verboven (b0015) 2021; 176 Ríos-Reina, Callejón, Amigo (b0040) 2021; 130 Hu, Shi, Zhan (b0055) 2022; 11 T.Q. Chen, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016: 785-794. Sun, Liu (b0045) 2020; 41 Shen (10.1016/j.infrared.2023.104798_b0030) 2018; 38 Zhai (10.1016/j.infrared.2023.104798_b0090) 2020; 43 Hu (10.1016/j.infrared.2023.104798_b0055) 2022; 11 Sun (10.1016/j.infrared.2023.104798_b0045) 2020; 41 Peng (10.1016/j.infrared.2023.104798_b0095) 2003; 14 K.Y Xie (10.1016/j.infrared.2023.104798_b0005) 2016; 23 Jiang (10.1016/j.infrared.2023.104798_b0050) 2019; 181 Izenman (10.1016/j.infrared.2023.104798_b0100) 2013 Van de Looverbosch (10.1016/j.infrared.2023.104798_b0015) 2021; 176 Canthadai (10.1016/j.infrared.2023.104798_b0020) 2015; 9488 Moscetti (10.1016/j.infrared.2023.104798_b0035) 2021; 292 10.1016/j.infrared.2023.104798_b0105 Li (10.1016/j.infrared.2023.104798_b0085) 2021; 12 Campmajó (10.1016/j.infrared.2023.104798_b0010) 2020; 114 Dorney (10.1016/j.infrared.2023.104798_b0060) 2001; 18 Duvillaret (10.1016/j.infrared.2023.104798_b0065) 1999; 38 Zhang (10.1016/j.infrared.2023.104798_b0070) 2010; 135 Y. Long (10.1016/j.infrared.2023.104798_b0025) 2022; 372 Ríos-Reina (10.1016/j.infrared.2023.104798_b0040) 2021; 130 Zhang (10.1016/j.infrared.2023.104798_b0115) 2017; 86 X.R Ning (10.1016/j.infrared.2023.104798_b0080) 2014; 139 Peng (10.1016/j.infrared.2023.104798_b0075) 2010; 683 Shi (10.1016/j.infrared.2023.104798_b0110) 2018; 47 |
| References_xml | – volume: 130 year: 2021 ident: b0040 article-title: Feasibility of a rapid and non-destructive methodology for the study and discrimination of pine nuts using near-infrared hyperspectral analysis and chemometrics[J] publication-title: Food Control – volume: 38 start-page: 3748 year: 2018 end-page: 3752 ident: b0030 article-title: Rapid detection of harmful mold infection in rice by near infrared spectroscopy[J] publication-title: Spectrosc. Spectr. Anal. – volume: 11 start-page: 3498 year: 2022 ident: b0055 article-title: Study on the Identification and Detection of Walnut Quality Based on Terahertz Imaging[J] publication-title: Foods – reference: T.Q. Chen, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016: 785-794. – volume: 23 start-page: 464 year: 2016 end-page: 473 ident: b0005 article-title: A review of the potential health benefits of pine nut oil and its characteristic fatty acid pinolenic acid[J] publication-title: J. Funct. Foods – volume: 9488 start-page: 75 year: 2015 end-page: 80 ident: b0020 article-title: Pest damage assessment in fruits and vegetables using thermal imaging[C]//Sensing for Agriculture and Food Quality and Safety VII publication-title: SPIE – volume: 12 start-page: 1809 year: 2021 ident: b0085 article-title: , Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy[J] publication-title: Forests – volume: 683 start-page: 63 year: 2010 end-page: 68 ident: b0075 article-title: Asymmetric least squares for multiple spectra baseline correction [J] publication-title: Anal. Chim. Acta – volume: 114 year: 2020 ident: b0010 article-title: High-performance liquid chromatography with fluorescence detection fingerprinting combined with chemometrics for nut classification and the detection and quantitation of almond-based product adulterations[J] publication-title: Food Control – volume: 372 year: 2022 ident: b0025 article-title: Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics[J] publication-title: Food Chem. – volume: 176 year: 2021 ident: b0015 article-title: Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning[J] publication-title: Expert Syst. Appl. – volume: 41 start-page: 307 year: 2020 end-page: 321 ident: b0045 article-title: Measurement of plumpness for intact sunflower seed using terahertz transmittance imaging[J] publication-title: Journal of Infrared, Millimeter, and Terahertz Waves – volume: 18 start-page: 1562 year: 2001 end-page: 1571 ident: b0060 article-title: Material parameter estimation with terahertz time-domain spectroscopy[J] publication-title: JOSA A – volume: 47 start-page: 446 year: 2018 ident: b0110 article-title: One-dimensional maximum entropy image segmentation algorithm based on the small field of view of measuring robot star map[J] publication-title: Acta Geodaetica et Cartographica Sinica – volume: 86 start-page: 116 year: 2017 end-page: 119 ident: b0115 article-title: Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species[J] publication-title: Infrared Phys. Technol. – volume: 292 year: 2021 ident: b0035 article-title: Pine nut species recognition using NIR spectroscopy and image analysis[J] publication-title: J. Food Eng. – volume: 181 start-page: 1130 year: 2019 end-page: 1138 ident: b0050 article-title: Detection of foreign bodies in grain with terahertz reflection imaging[J] publication-title: Optik – volume: 135 start-page: 1138 year: 2010 end-page: 1146 ident: b0070 article-title: Baseline correction using adaptive iteratively reweighted penalized least squares [J] publication-title: Analyst – volume: 14 start-page: 940 year: 2003 end-page: 942 ident: b0095 article-title: K.LDA/SVM driven nearest neighbor classification[J] publication-title: IEEE Trans. Neural Netw. – start-page: 237 year: 2013 end-page: 280 ident: b0100 article-title: Linear discriminant analysis[M]//Modern multivariate statistical techniques – volume: 43 start-page: e13562 year: 2020 ident: b0090 article-title: Machine learning for detection of walnuts with shriveled kernels by fusing weight and image information[J] publication-title: J. Food Process Eng – volume: 139 start-page: 156 year: 2014 end-page: 167 ident: b0080 article-title: Chromatogram baseline estimation and denoising using sparsity (BEADS) [J] publication-title: Chemom. Intel. Lab. Syst. – volume: 38 start-page: 409 year: 1999 end-page: 415 ident: b0065 article-title: Highly precise determination of optical constants and sample thickness in terahertz time-domain spectroscopy[J] publication-title: Appl. Opt. – volume: 130 year: 2021 ident: 10.1016/j.infrared.2023.104798_b0040 article-title: Feasibility of a rapid and non-destructive methodology for the study and discrimination of pine nuts using near-infrared hyperspectral analysis and chemometrics[J] publication-title: Food Control doi: 10.1016/j.foodcont.2021.108365 – volume: 11 start-page: 3498 issue: 21 year: 2022 ident: 10.1016/j.infrared.2023.104798_b0055 article-title: Study on the Identification and Detection of Walnut Quality Based on Terahertz Imaging[J] publication-title: Foods doi: 10.3390/foods11213498 – volume: 18 start-page: 1562 issue: 7 year: 2001 ident: 10.1016/j.infrared.2023.104798_b0060 article-title: Material parameter estimation with terahertz time-domain spectroscopy[J] publication-title: JOSA A doi: 10.1364/JOSAA.18.001562 – volume: 47 start-page: 446 issue: 4 year: 2018 ident: 10.1016/j.infrared.2023.104798_b0110 article-title: One-dimensional maximum entropy image segmentation algorithm based on the small field of view of measuring robot star map[J] publication-title: Acta Geodaetica et Cartographica Sinica – volume: 86 start-page: 116 year: 2017 ident: 10.1016/j.infrared.2023.104798_b0115 article-title: Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species[J] publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2017.08.020 – volume: 176 year: 2021 ident: 10.1016/j.infrared.2023.104798_b0015 article-title: Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning[J] publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114925 – volume: 23 start-page: 464 year: 2016 ident: 10.1016/j.infrared.2023.104798_b0005 article-title: A review of the potential health benefits of pine nut oil and its characteristic fatty acid pinolenic acid[J] publication-title: J. Funct. Foods doi: 10.1016/j.jff.2016.03.003 – volume: 181 start-page: 1130 year: 2019 ident: 10.1016/j.infrared.2023.104798_b0050 article-title: Detection of foreign bodies in grain with terahertz reflection imaging[J] publication-title: Optik doi: 10.1016/j.ijleo.2018.12.066 – volume: 139 start-page: 156 year: 2014 ident: 10.1016/j.infrared.2023.104798_b0080 article-title: Chromatogram baseline estimation and denoising using sparsity (BEADS) [J] publication-title: Chemom. Intel. Lab. Syst. doi: 10.1016/j.chemolab.2014.09.014 – start-page: 237 year: 2013 ident: 10.1016/j.infrared.2023.104798_b0100 – volume: 12 start-page: 1809 issue: 12 year: 2021 ident: 10.1016/j.infrared.2023.104798_b0085 article-title: , Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy[J] publication-title: Forests doi: 10.3390/f12121809 – volume: 43 start-page: e13562 issue: 12 year: 2020 ident: 10.1016/j.infrared.2023.104798_b0090 article-title: Machine learning for detection of walnuts with shriveled kernels by fusing weight and image information[J] publication-title: J. Food Process Eng doi: 10.1111/jfpe.13562 – volume: 14 start-page: 940 issue: 4 year: 2003 ident: 10.1016/j.infrared.2023.104798_b0095 article-title: K.LDA/SVM driven nearest neighbor classification[J] publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2003.813835 – volume: 114 year: 2020 ident: 10.1016/j.infrared.2023.104798_b0010 article-title: High-performance liquid chromatography with fluorescence detection fingerprinting combined with chemometrics for nut classification and the detection and quantitation of almond-based product adulterations[J] publication-title: Food Control doi: 10.1016/j.foodcont.2020.107265 – volume: 372 year: 2022 ident: 10.1016/j.infrared.2023.104798_b0025 article-title: Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics[J] publication-title: Food Chem. doi: 10.1016/j.foodchem.2021.131246 – volume: 135 start-page: 1138 issue: 5 year: 2010 ident: 10.1016/j.infrared.2023.104798_b0070 article-title: Baseline correction using adaptive iteratively reweighted penalized least squares [J] publication-title: Analyst doi: 10.1039/b922045c – ident: 10.1016/j.infrared.2023.104798_b0105 doi: 10.1145/2939672.2939785 – volume: 9488 start-page: 75 year: 2015 ident: 10.1016/j.infrared.2023.104798_b0020 article-title: Pest damage assessment in fruits and vegetables using thermal imaging[C]//Sensing for Agriculture and Food Quality and Safety VII publication-title: SPIE – volume: 38 start-page: 3748 issue: 12 year: 2018 ident: 10.1016/j.infrared.2023.104798_b0030 article-title: Rapid detection of harmful mold infection in rice by near infrared spectroscopy[J] publication-title: Spectrosc. Spectr. Anal. – volume: 41 start-page: 307 issue: 3 year: 2020 ident: 10.1016/j.infrared.2023.104798_b0045 article-title: Measurement of plumpness for intact sunflower seed using terahertz transmittance imaging[J] publication-title: Journal of Infrared, Millimeter, and Terahertz Waves doi: 10.1007/s10762-019-00668-z – volume: 38 start-page: 409 issue: 2 year: 1999 ident: 10.1016/j.infrared.2023.104798_b0065 article-title: Highly precise determination of optical constants and sample thickness in terahertz time-domain spectroscopy[J] publication-title: Appl. Opt. doi: 10.1364/AO.38.000409 – volume: 683 start-page: 63 issue: 1 year: 2010 ident: 10.1016/j.infrared.2023.104798_b0075 article-title: Asymmetric least squares for multiple spectra baseline correction [J] publication-title: Anal. Chim. Acta doi: 10.1016/j.aca.2010.08.033 – volume: 292 year: 2021 ident: 10.1016/j.infrared.2023.104798_b0035 article-title: Pine nut species recognition using NIR spectroscopy and image analysis[J] publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2020.110357 |
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•In this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology... |
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| StartPage | 104798 |
| SubjectTerms | Automatic threshold segmentation Plumpness Terahertz imaging technology XGBoost integrated learning |
| Title | Research on nondestructive detection of pine nut quality based on terahertz imaging |
| URI | https://dx.doi.org/10.1016/j.infrared.2023.104798 |
| Volume | 134 |
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