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Li Q, Lei T, Cheng Y, Wei X, Sun DW. Predicting wheat gluten concentrations in potato starch using GPR and SVM models built by terahertz time-domain spectroscopy. Food Chem 2024; 432:137235. [PMID: 37688814 DOI: 10.1016/j.foodchem.2023.137235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 08/10/2023] [Accepted: 08/20/2023] [Indexed: 09/11/2023]
Abstract
The purpose of this study was for the first time to explore the feasibility of terahertz (THz) spectral imaging for the detection of gluten contents in food samples. Based on the obtained 80 THz spectrum data, Gaussian process regression (GPR) and support vector machine (SVM) models were established to predict wheat gluten concentrations in 40 potato starch mixture samples. The prediction performances of GPR and SVM obtained were R2 = 0.859 and RMSE = 0.070, and R2 = 0.715 and RMSE = 0.101 in the gluten concentration range of 1.3%-100%, respectively, showing that the linear SVM algorithm had better prediction performance. The results indicated that THz spectral imaging combined with GPR could be used to predict the gluten content in food samples. It is thus hoped that this research should provide a novel technique for gluten content detection to ensure gluten-free food samples.
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Affiliation(s)
- Qingxia Li
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
| | - Yunlong Cheng
- School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Xin Wei
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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Pu H, Yu J, Sun DW, Wei Q, Li Q. Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122771. [PMID: 37244024 DOI: 10.1016/j.saa.2023.122771] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 05/29/2023]
Abstract
The geographical indication of pericarpium citri reticulatae (PCR) is very important in grading the quality and price of PCRs. Therefore, terahertz time-domain spectroscopy (THz-TDS) technology combined with convolutional neural networks (CNN) was proposed to distinguish PCRs of different origins without damage in this study. The one-dimensional CNN (1D-CNN) model with an accuracy of 82.99% based on spectral data processed with SNV was established. The two-dimensional image features were transformed from unprocessed spectral data using the gramian angular field (GAF), the Markov transition field (MTF) and the recurrence plot (RP), which were used to build a two-dimensional CNN (2D-CNN) model with an accuracy of 78.33%. Further, the CNN models with different fusion methods were developed for fusing spectra data and image data. In addition, the adding spectra and images based on the CNN (Add-CNN) model with an accuracy of 86.17% performed better. Eventually, the Add-CNN model based on ten frequencies extracted using permutation importance (PI) achieved the identification of PCRs from different origins. Overall, the current study would provide a new method for identifying PCRs of different origins, which was expected to be used for the traceability of PCRs products.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jingxiao Yu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qian Li
- Shenzhen Institute of Terahertz Technology and Innovation, Shenzhen, Guangdong 518102, China
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Lee MJ, Lee HN, Lee GE, Han ST, Kang DW, Yang JR. CMOS Detector Staggered Array Module for Sub-Terahertz Imaging on Conveyor Belt System. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031232. [PMID: 36772271 PMCID: PMC9920622 DOI: 10.3390/s23031232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 05/27/2023]
Abstract
A complementary metal-oxide-semiconductor (CMOS) detector array is proposed to improve the sub-terahertz imaging resolution for objects in the conveyor belt system. The image resolution is limited to the implemented configuration, such as the wide spacing in the detector array, the high conveyor belt speed, and the slow response of the signal conditioning block. The proposed array can improve the image resolution in the direction perpendicular to the movement of the belt, which is determined by the size and interval of the detector pixel, by configuring the array into two replaceable columns located at the misaligned horizontal positions. Replaceable detector unit pixels are individually attached to the motherboard after measuring and evaluating the detection performance to construct the proposed array. The intensities of 32 detector pixels placed under the conveyor belt with a width of 160 mm were initially calibrated in every image, including the beam pattern of 0.2 THz signals generated from the gyrotron. The image resolution of the perpendicular direction obtained from the proposed array was measured to be approximately 5 mm at a conveyor belt speed of 16 mm/s, demonstrating a 200% improvement in resolution compared to the conventional linear array under the same conditions.
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Affiliation(s)
- Moon-Jeong Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Ha-Neul Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Ga-Eun Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Seong-Tae Han
- Electrophysics Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Republic of Korea
| | - Dong-Woo Kang
- AI Compact SoC Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea
| | - Jong-Ryul Yang
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Abstract
Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.
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