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Liu F, Yang R, Chen R, Lamine Guindo M, He Y, Zhou J, Lu X, Chen M, Yang Y, Kong W. Digital techniques and trends for seed phenotyping using optical sensors. J Adv Res 2024; 63:1-16. [PMID: 37956859 PMCID: PMC11380022 DOI: 10.1016/j.jare.2023.11.010] [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: 11/09/2022] [Revised: 10/19/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. AIM OF REVIEW This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.
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Affiliation(s)
- Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jun Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
| | - Xiangyu Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mengyuan Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yinhui Yang
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, 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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3
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Han C, Qu F, Wang X, Zhai X, Li J, Yu K, Zhao Y. Terahertz Spectroscopy and Imaging Techniques for Herbal Medicinal Plants Detection: A Comprehensive Review. Crit Rev Anal Chem 2023; 54:2485-2499. [PMID: 36856792 DOI: 10.1080/10408347.2023.2183077] [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] [Indexed: 03/02/2023]
Abstract
Herbal medicine (HM), derived from various therapeutic plants, has garnered considerable attention for its remarkable effectiveness in treating diseases. However, numerous issues including improved varieties selection, hazardous residue detection, and concoction management affect herb quality throughout the manufacturing process. Therefore, a practical, rapid, nondestructive detection technology is necessary. Terahertz (THz) spectroscopy, with low energy, penetration, and fingerprint features, becomes preferable method for herb quality appraisal. There are three parts in our review. THz techniques, data processing, and modeling methods were introduced in Part I. Three primary applications (authenticity, composition and active ingredients, and origin detection) of THz in medicinal plants quality detection in industrial processing and marketing were detailed in Part II. A thorough investigation and outlook on the well-known applications and advancements of this field were presented in Part III. This review aims to bring new enlightenment to the in-depth THz application research in herbal medicinal plants.
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Affiliation(s)
- Chaoyue Han
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Fangfang Qu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350000, China
| | - Xiaohui Wang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xuedong Zhai
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Junmeng Li
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
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CRUZ JFOBLITAS. Classification of chocolate according to its cocoa percentage by using Terahertz time-domain spectroscopy. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.89222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Zhao Y, Gouda M, Yu G, Zhang C, Lin L, Nie P, Huang W, Ye H, Ye Y, Zhou C, He Y. Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 225:112800. [PMID: 34547661 DOI: 10.1016/j.ecoenv.2021.112800] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/13/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC2), Cd-PC2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC2 chelates Cd2+ in a 2:1 ratio to form Cd(PC2)2 complex; Cd connected to thoils of PC2 and changed β-turn and random coil of PC2 peptide chain to β-Sheet which presented as terahertz vibrations of PC2 around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC2 as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.
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Affiliation(s)
- Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Department of Nutrition & Food Science, National Research Centre, Dokki, Giza, Egypt
| | - Guohong Yu
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Chenghao Zhang
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Lei Lin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Pengcheng Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
| | - Wei Huang
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Hongbao Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Yunxiang Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Chengquan Zhou
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
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Shchepetilnikov AV, Gusikhin PA, Muravev VM, Kaysin BD, Tsydynzhapov GE, Dremin AA, Kukushkin IV. Linear scanning system for THz imaging. APPLIED OPTICS 2021; 60:10448-10452. [PMID: 34807056 DOI: 10.1364/ao.442060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/29/2021] [Indexed: 06/13/2023]
Abstract
A linear scanning system utilizing constant wave 280 GHz radiation has been developed and characterized. The system comprises a linear array of detectors based on a unique plasma wave approach in terahertz sensing, an impact ionization avalanche transit-time-diode signal generator coupled to a frequency multiplier and an optical system. The performed tests allowed us to estimate the resolution of the system reaching the value of 2.3 mm and to determine the dynamic range of the system to be around 200. The imaging capabilities of the scanner were tested in realistic cases of non-destructive testing and security screening.
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Yang S, Li C, Mei Y, Liu W, Liu R, Chen W, Han D, Xu K. Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods. Front Nutr 2021; 8:680627. [PMID: 34222305 PMCID: PMC8247636 DOI: 10.3389/fnut.2021.680627] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.
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Affiliation(s)
- Si Yang
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.,School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Chenxi Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.,School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Yang Mei
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.,School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Wen Liu
- School of Chemical Engineering, Xiangtan University, Xiangtan, China
| | - Rong Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.,School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Wenliang Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.,School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Donghai Han
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Kexin Xu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.,School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices. SENSORS 2021; 21:s21093238. [PMID: 34067111 PMCID: PMC8125531 DOI: 10.3390/s21093238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 02/06/2023]
Abstract
It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (Rp) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10−6, and the limit of detection (LOD) is 2.3610 × 10−5 g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.
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Attenuated Total Reflection for Terahertz Modulation, Sensing, Spectroscopy and Imaging Applications: A Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10144688] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Terahertz (THz) technique has become one of the most promising analytical methods and has been applied in many fields. Attenuated total reflection (ATR) technique applied in THz spectroscopy and imaging has been proven to be superior in functionalities such as modulation, sensing, analyzing, and imaging. Here, we first provide a concise introduction to the principle of ATR, discuss the factors that impact the ATR system, and demonstrate recent advances on THz wave modulation and THz surface plasmon sensing based on the THz-ATR system. Then, applications on THz-ATR spectroscopy and imaging are reviewed. Towards the later part, the advantages and limitations of THz-ATR are summarized, and prospects of modulation, surface plasmon sensing, spectroscopy and imaging are discussed.
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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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Non-destructive inspection of food and technical oils by terahertz spectroscopy. Sci Rep 2018; 8:18025. [PMID: 30575766 PMCID: PMC6303405 DOI: 10.1038/s41598-018-36151-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 11/15/2018] [Indexed: 11/23/2022] Open
Abstract
Quality control and non-destructive monitoring are of notable interest of food and pharmaceutical industries. It relies on the ability of non-invasive inspection which can be employed for manufacturing process control. We hereby apply terahertz (THz) time-domain spectroscopy as non-destructive technique to monitor pure and degraded oils as well as hydrocarbon chemicals. Significant differences in the spectra of refractive index (RI) and absorption coefficient arising from the presence of ester linkages in the edible and technical oils were obtained. Explicit increase from 1.38 to 1.5 of the RI in all THz spectrum range was observed in hydrocarbons and mono-functional esters with the increase of molar mass. This fact is in contrast of RI dependence on molar mass in multi-functional esters, such as Adipate or vegetable oils, where it is around 1.54. Degradation products, Oleic Acid (OA) and water in particular, lead only to some changes in absorption coefficient and RI spectra of vegetable oils. We demonstrate that complex colloidal and supramolecular processes, such as dynamics of inverse micelles and oil hydrolysis, take part during oil degradation and are responsible for non-uniform dependence of optical properties on extent of degradation.
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Long Y, Li B, Liu H. Analysis of fluoroquinolones antibiotic residue in feed matrices using terahertz spectroscopy. APPLIED OPTICS 2018; 57:544-550. [PMID: 29400779 DOI: 10.1364/ao.57.000544] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 11/22/2017] [Indexed: 06/07/2023]
Abstract
As antibiotic residue becomes more and more serious all over the world, a rapid and effective detection method is needed to evaluate the antibiotic residue in feed matrices to ensure food safety for consumers. In this study, three different kinds of fluoroquinolones (norfloxacin, enrofloxacin, and ofloxacin) in feed matrices were analyzed using terahertz (THz) spectroscopy, respectively. Meanwhile, pure fluoroquinolones and pure feed matrices were also measured in the same way. Then, the absorption spectra of all of the samples were extracted in the transmission mode. Pure norfloxacin has two absorption peaks at 0.825 and 1.187 THz, and they could still be observed when mixing norfloxacin with feed matrices. Also, there was an obvious and strong absorption peak for ofloxacin at 1.044 THz. However, no obvious absorption peak for enrofloxacin was observed, and only a weak absorption peak was located at 0.8 THz. Then, the different models were established with different chemometrics to identify the fluoroquinolones in feed matrices and determined the fluoroquinolones content in the feed matrices. The least squares support vector machines, Naive Bayes, Mahalanobis distance, and back propagation neural network (BPNN) were used to build the identification model with a Savitzky-Golay filter and standardized normal variate pretreatments. The results show that the excellent classification model was acquired with the BPNN combined with no pretreatment. The optimal classification accuracy was 80.56% in the testing set. After that, multiple linear regression and stepwise regression were used to establish the quantitative detection model for different kinds of fluoroquinolones in feed matrices. The optimal correlation coefficients for norfloxacin, enrofloxacin, and ofloxacin in the prediction set were obtained with multiple linear regression that combined absorption peaks with wavelengths selected by stepwise regression, which were 0.867, 0.828, and 0.964, respectively. Overall, this research explored the potential of identifying the fluoroquinolones in feed matrices using THz spectroscopy without a complex pretreatment process and then quantitatively detecting the fluoroquinolones content in feed matrices. The results demonstrate that THz spectra could be used to identify fluoroquinolones in feed matrices and also detect their content quantitatively, which has great significance for the food safety industry.
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Emerging non-destructive terahertz spectroscopic imaging technique: Principle and applications in the agri-food industry. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.06.001] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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14
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Zou Y, Li J, Cui Y, Tang P, Du L, Chen T, Meng K, Liu Q, Feng H, Zhao J, Chen M, Zhu LG. Terahertz Spectroscopic Diagnosis of Myelin Deficit Brain in Mice and Rhesus Monkey with Chemometric Techniques. Sci Rep 2017; 7:5176. [PMID: 28701795 PMCID: PMC5507969 DOI: 10.1038/s41598-017-05554-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 05/31/2017] [Indexed: 02/06/2023] Open
Abstract
While myelin deficit of the central nervous system leads to several severe diseases, the definitive diagnostic means are lacking. We proposed and performed terahertz time-domain spectroscopy (THz-TDS) combined with chemometric techniques to discriminate and evaluate the severity of myelin deficit in mouse and rhesus monkey brains. The THz refractive index and absorption coefficient of paraffin-embedded brain tissues from both normal and mutant dysmyelinating mice are shown. Principal component analysis of time-domain THz signal (PCA-tdTHz) and absorption-refractive index relation of THz spectrum identified myelin deficit without exogenous labeling or any pretreatment. Further, with the established PCA-tdTHz, we evaluated the severity of myelin deficit lesions in rhesus monkey brain induced by experimental autoimmune encephalomyelitis, which is the most-studied animal model of multiple sclerosis. The results well matched the pathological analysis, indicating that PCA-tdTHz is a quick, powerful, evolving tool for identification and evaluation myelin deficit in preclinical animals and potentially in para-clinical human biopsy.
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Affiliation(s)
- Yi Zou
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China
| | - Jiang Li
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China.,Microsystem and Terahertz Research Center, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China
| | - Yiyuan Cui
- The State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Peiren Tang
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China
| | - Lianghui Du
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China.,Microsystem and Terahertz Research Center, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China
| | - Tunan Chen
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China
| | - Kun Meng
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China.,School of Electronic and Electrical Engineering University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, United Kingdom
| | - Qiao Liu
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China.,Microsystem and Terahertz Research Center, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China
| | - Hua Feng
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China
| | - Jianheng Zhao
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China.
| | - Mina Chen
- The State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Li-Guo Zhu
- Interdisciplinary Laboratory of Physics and Biomedicine, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China. .,Microsystem and Terahertz Research Center, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, China.
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15
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Mumtaz M, Mahmood A, Khan SD, Zia MA, Ahmed M, Ahmad I. Investigation of Dielectric Properties of Polymers and their Discrimination Using Terahertz Time-Domain Spectroscopy with Principal Component Analysis. APPLIED SPECTROSCOPY 2017; 71:456-462. [PMID: 27798383 DOI: 10.1177/0003702816675361] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Polymers are among the most commonly used materials in our everyday life. They are generally transparent to terahertz (THz) radiation, but are quite difficult to differentiate using optical techniques as few or no characteristic features exist in the spectral range of <2.0 THz for small and portable radiation systems. In this work, we report experimental measurement of refractive indices and absorption coefficients of styrene acrylonitrile (SAN) and Bakelite in the spectral range of 0.2-2.0 THz for the first time. Additionally, we demonstrate that by combining principle component analysis (PCA) with THz time-domain spectroscopy one can differentiate such polymers. In this analysis, the first three principle components PC1, PC2, and PC3 depict >94% variance with a distribution of 72.45%, 11.52%, and 9.38%, respectively.
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Affiliation(s)
- Muhammad Mumtaz
- National Institute of Lasers and Optronics, Nilore, Islamabad, Pakistan
| | - Ahsan Mahmood
- National Institute of Lasers and Optronics, Nilore, Islamabad, Pakistan
| | - Sabih D Khan
- National Institute of Lasers and Optronics, Nilore, Islamabad, Pakistan
| | - M Aslam Zia
- National Institute of Lasers and Optronics, Nilore, Islamabad, Pakistan
| | - Mushtaq Ahmed
- National Institute of Lasers and Optronics, Nilore, Islamabad, Pakistan
| | - Izhar Ahmad
- National Institute of Lasers and Optronics, Nilore, Islamabad, Pakistan
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16
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Application of terahertz spectroscopy imaging for discrimination of transgenic rice seeds with chemometrics. Food Chem 2016; 210:415-21. [DOI: 10.1016/j.foodchem.2016.04.117] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Revised: 04/24/2016] [Accepted: 04/25/2016] [Indexed: 11/20/2022]
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17
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Liu W, Liu C, Chen F, Yang J, Zheng L. Discrimination of transgenic soybean seeds by terahertz spectroscopy. Sci Rep 2016; 6:35799. [PMID: 27782205 PMCID: PMC5080623 DOI: 10.1038/srep35799] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 09/30/2016] [Indexed: 11/09/2022] Open
Abstract
Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.
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Affiliation(s)
- Wei Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Changhong Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Feng Chen
- Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, SC 29634, United States
| | - Jianbo Yang
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Lei Zheng
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
- School of Medical Engineering, Hefei University of Technology, Hefei 230009, China
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18
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Early detection of germinated wheat grains using terahertz image and chemometrics. Sci Rep 2016; 6:21299. [PMID: 26892180 PMCID: PMC4759576 DOI: 10.1038/srep21299] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 01/21/2016] [Indexed: 01/30/2023] Open
Abstract
In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.
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