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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 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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Cheng J, Xue C, Yang M, Wang X, Xu Z, Li N, Zhang X, Feng X, Liu X, Liu Y, Liu SF, Yang Z. Dense Perovskite Thick Film Enabled by Saturated Solution Filling for Sensitive X-ray Detection and Imaging. ACS APPLIED MATERIALS & INTERFACES 2024; 16:36649-36657. [PMID: 38961051 DOI: 10.1021/acsami.4c08706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Thick polycrystalline perovskite films synthesized by using solution processes show great potential in X-ray detection applications. However, due to the evaporation of the solvent, many pinholes and defects appear in the thick films, which deteriorate their optoelectronic properties and diminish their X-ray detection performance. Therefore, the preparation of large area and dense perovskite thick films is desired. Herein, we propose an effective strategy of filling the pores with a saturated precursor solution. By adding the saturated perovskite solution to the polycrystalline perovskite thick film, the original perovskite film will not be destroyed because of the solution-solute equilibrium relationship. Instead, it promotes in situ crystal growth within the thick film during the annealing process. The loosely packed grains in the original thick perovskite film are connected, and the pores and defects are partially filled and fixed. Finally, a much denser perovskite thick film with improved optoelectronic properties has been obtained. The optimized thick film exhibits an X-ray sensitivity of 1616.01 μC Gyair-1 cm-2 under an electric field of 44.44 V mm-1 and a low detection limit of 28.64 nGyair s-1 under an electric field of 22.22 V mm-1. These values exceed the 323.86 μC Gyair-1 cm-2 and 40.52 nGyair s-1 of the pristine perovskite thick film measured under the same conditions. The optimized thick film also shows promising working stability and X-ray imaging capability.
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Affiliation(s)
- Jiatian Cheng
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Chengzhi Xue
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Min Yang
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Xi Wang
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Ziwei Xu
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Nan Li
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | | | - Xiaolong Feng
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Xinmei Liu
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Yucheng Liu
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Shengzhong Frank Liu
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
- Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of the Chinese Academy of Sciences, Beijing 100039, China
| | - Zhou Yang
- School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China
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Bieberle M, Papapetrou TN, Lecrivain G, Windisch D, Bieberle A, Wagner M, Hampel U. Simplified Beam Hardening Correction for Ultrafast X-ray CT Imaging of Binary Granular Mixtures. SENSORS (BASEL, SWITZERLAND) 2024; 24:2964. [PMID: 38793819 PMCID: PMC11124780 DOI: 10.3390/s24102964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/19/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas-liquid as well as gas-solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an exemplary use case for analyzing industrial particle mixing systems. As glass particles are used as the denser of two granular species to be mixed, beam hardening artefacts occur and hamper the data analysis. In the general case of a distribution of arbitrary materials, the inverse problem of image reconstruction with energy-dependent attenuation is often ill-posed. Consequently, commonly known beam hardening correction algorithms are often quite complex. In our case, however, the number of materials is limited. We therefore propose a correction algorithm simplified by taking advantage of the known material properties, and demonstrate its ability to improve image quality and subsequent analyses significantly.
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Affiliation(s)
- Martina Bieberle
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, Bautzner Landstr. 400, 01328 Dresden, Germany
| | - Theodoros Nestor Papapetrou
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, Bautzner Landstr. 400, 01328 Dresden, Germany
- Institute of Power Engineering, TUD Dresden University of Technology, Nöthnitzer Str. 69, 01062 Dresden, Germany
| | - Gregory Lecrivain
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, Bautzner Landstr. 400, 01328 Dresden, Germany
| | - Dominic Windisch
- Institute of Power Engineering, TUD Dresden University of Technology, Nöthnitzer Str. 69, 01062 Dresden, Germany
| | - André Bieberle
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, Bautzner Landstr. 400, 01328 Dresden, Germany
| | - Michael Wagner
- Institute of Power Engineering, TUD Dresden University of Technology, Nöthnitzer Str. 69, 01062 Dresden, Germany
| | - Uwe Hampel
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, Bautzner Landstr. 400, 01328 Dresden, Germany
- Institute of Power Engineering, TUD Dresden University of Technology, Nöthnitzer Str. 69, 01062 Dresden, Germany
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Nadimi M, Divyanth LG, Chaudhry MMA, Singh T, Loewen G, Paliwal J. Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging. Foods 2023; 13:120. [PMID: 38201149 PMCID: PMC10778999 DOI: 10.3390/foods13010120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.
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Affiliation(s)
- Mohammad Nadimi
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - L. G. Divyanth
- Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA;
| | | | - Taranveer Singh
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Georgia Loewen
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
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Nadimi M, Hawley E, Liu J, Hildebrand K, Sopiwnyk E, Paliwal J. Enhancing traceability of wheat quality through the supply chain. Compr Rev Food Sci Food Saf 2023; 22:2495-2522. [PMID: 37078119 DOI: 10.1111/1541-4337.13150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/02/2023] [Accepted: 03/16/2023] [Indexed: 04/21/2023]
Abstract
With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.
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Affiliation(s)
- Mohammad Nadimi
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | - Jing Liu
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | | | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
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Scherberich J, Windfelder AG, Krombach GA. Analysis of fixation materials in micro-CT: It doesn't always have to be styrofoam. PLoS One 2023; 18:e0286039. [PMID: 37315002 DOI: 10.1371/journal.pone.0286039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/08/2023] [Indexed: 06/16/2023] Open
Abstract
Good fixation of filigree specimens for micro-CT examinations is often a challenge. Movement artefacts, over-radiation or even crushing of the specimen can easily occur. Since different specimens have different requirements, we scanned, analysed and compared 19 possible fixation materials under the same conditions in the micro-CT. We focused on radiodensity, porosity and reversibility of these fixation materials. Furthermore, we have made sure that all materials are cheap and easily available. The scans were performed with a SkyScan 1173 micro-CT. All dry fixation materials tested were punched into 5 mm diameter cylinders and clamped into 0.2 ml reaction vessels. A voxel size of 5.33 μm was achieved in a 180° scan in 0.3° steps. Ideally, fixation materials should not be visible in the reconstructed image, i.e., barely binarised. Besides common micro-CT fixation materials such as styrofoam (-935 Hounsfield Units) or Basotect foam (-943 Hounsfield Units), polyethylene air cushions (-944 Hounsfield Units), Micropor foam (-926 Hounsfield Units) and polyurethane foam, (-960 Hounsfield Units to -470 Hounsfield Units) have proved to be attractive alternatives. Furthermore, more radiopaque materials such as paraffin wax granulate (-640 Hounsfield Units) and epoxy resin (-190 Hounsfield Units) are also suitable as fixation materials. These materials often can be removed in the reconstructed image by segmentation. Sample fixations in the studies of recent years are almost all limited to fixation in Parafilm, Styrofoam, or Basotect foam if the fixation type is mentioned at all. However, these are not always useful, as styrofoam, for example, dissolves in some common media such as methylsalicylate. We show that micro-CT laboratories should be equipped with various fixation materials to achieve high-level image quality.
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Affiliation(s)
- Jan Scherberich
- Department of Diagnostic and Interventional Radiology (Experimental Radiology), University Hospital Giessen, Giessen, Hesse, Germany
| | - Anton G Windfelder
- Department of Diagnostic and Interventional Radiology (Experimental Radiology), University Hospital Giessen, Giessen, Hesse, Germany
- Branch for Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Giessen, Hesse, Germany
| | - Gabriele A Krombach
- Department of Diagnostic and Interventional Radiology (Experimental Radiology), University Hospital Giessen, Giessen, Hesse, Germany
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Domhoefer M, Chakraborty D, Hufnagel E, Claußen J, Wörlein N, Voorhaar M, Anbazhagan K, Choudhary S, Pasupuleti J, Baddam R, Kholova J, Gerth S. X-ray driven peanut trait estimation: computer vision aided agri-system transformation. PLANT METHODS 2022; 18:76. [PMID: 35668530 PMCID: PMC9169268 DOI: 10.1186/s13007-022-00909-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. RESULTS We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). CONCLUSION Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.
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Affiliation(s)
- Martha Domhoefer
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
- Universität Osnabrück, 49069, Osnabrück, Germany
| | - Debarati Chakraborty
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
| | - Eva Hufnagel
- Development Center for X-Ray Technology (Entwicklungszentrum Röntgentechnik, EZRT), Fraunhofer Institute for Integrated Circuits (Institut Für Integrierte Schaltungen, IIS), Flugplatzstraße 75, 90768, Fürth, Germany
| | - Joelle Claußen
- Development Center for X-Ray Technology (Entwicklungszentrum Röntgentechnik, EZRT), Fraunhofer Institute for Integrated Circuits (Institut Für Integrierte Schaltungen, IIS), Flugplatzstraße 75, 90768, Fürth, Germany
| | - Norbert Wörlein
- Development Center for X-Ray Technology (Entwicklungszentrum Röntgentechnik, EZRT), Fraunhofer Institute for Integrated Circuits (Institut Für Integrierte Schaltungen, IIS), Flugplatzstraße 75, 90768, Fürth, Germany
| | - Marijn Voorhaar
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
| | - Krithika Anbazhagan
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
| | - Sunita Choudhary
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
| | - Janila Pasupuleti
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
| | - Rekha Baddam
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
| | - Jana Kholova
- Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamýcká 129, Prague, 165 00, Czech Republic
| | - Stefan Gerth
- Development Center for X-Ray Technology (Entwicklungszentrum Röntgentechnik, EZRT), Fraunhofer Institute for Integrated Circuits (Institut Für Integrierte Schaltungen, IIS), Flugplatzstraße 75, 90768, Fürth, Germany.
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Du Z, Tian W, Tilley M, Wang D, Zhang G, Li Y. Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review. Compr Rev Food Sci Food Saf 2022; 21:2956-3009. [PMID: 35478437 DOI: 10.1111/1541-4337.12958] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 01/15/2023]
Abstract
Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.
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Affiliation(s)
- Zhenjiao Du
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
| | - Wenfei Tian
- National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Michael Tilley
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, Kansas, USA
| | - Donghai Wang
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas, USA
| | - Guorong Zhang
- Agricultural Research Center-Hays, Kansas State University, Hays, Kansas, USA
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
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A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.
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Kim T, Lee J, Sun GM, Park BG, Park HJ, Choi DS, Ye SJ. Comparison of X-ray computed tomography and magnetic resonance imaging to detect pest-infested fruits: A pilot study. NUCLEAR ENGINEERING AND TECHNOLOGY 2022. [DOI: 10.1016/j.net.2021.07.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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11
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Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2021.12.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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12
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Mohammad-Razdari A, Rousseau D, Bakhshipour A, Taylor S, Poveda J, Kiani H. Recent advances in E-monitoring of plant diseases. Biosens Bioelectron 2022; 201:113953. [PMID: 34998118 DOI: 10.1016/j.bios.2021.113953] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 02/09/2023]
Abstract
Infectious plant diseases are caused by pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, phytoplasma, and nematodes. Plant diseases have a significant effect on the plant quality and yield and they can destroy the entire plant if they are not controlled in time. To minimize disease-related losses, it is essential to identify and control pathogens in the early stages. Plant disease control is thus a fundamental challenge both for global food security and sustainable agriculture. Conventional methods for plant diseases control have given place to electronic control (E-monitoring) due to their lack of portability, being time consuming, need for a specialized user, etc. E-monitoring using electronic nose (e-nose), biosensors, wearable sensors, and 'electronic eyes' has attracted increasing attention in recent years. Detection, identification, and quantification of pathogens based on electronic sensors (E-sensors) are both convenient and practical and may be used in combination with conventional methods. This paper discusses recent advances made in E-sensors as component parts in combination with wearable sensors, in electronic sensing systems to control and detect viruses, bacteria, pathogens and fungi. In addition, future challenges using sensors to manage plant diseases are investigated.
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Affiliation(s)
- Ayat Mohammad-Razdari
- Department of Mechanical Engineering of Biosystems, Shahrekord University, 8818634141, Shahrekord, Iran.
| | - David Rousseau
- Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAe IRHS, Université d'Angers, France
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Stephen Taylor
- Mass Spectrometry and Instrumentation Group, Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK.
| | - Jorge Poveda
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra (UPNA), Campus Arrosadía, Pamplona, Spain
| | - Hassan Kiani
- Department of Biosystems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Gopalakrishnan K, Sharma A, Emanuel N, Prabhakar PK, Kumar R. Sensors for Non‐Destructive Quality Evaluation of Food. Food Chem 2021. [DOI: 10.1002/9781119792130.ch13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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14
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Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.
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15
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Andriiashen V, van Liere R, van Leeuwen T, Batenburg KJ. Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry. J Imaging 2021; 7:104. [PMID: 39080892 PMCID: PMC8321356 DOI: 10.3390/jimaging7070104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 11/16/2022] Open
Abstract
X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.
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Affiliation(s)
- Vladyslav Andriiashen
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
| | - Robert van Liere
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
- Faculteit Wiskunde en Informatica, Technical University Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, The Netherlands
| | - Tristan van Leeuwen
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
- Mathematical Institute, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, The Netherlands
| | - Kees Joost Batenburg
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
- Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
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16
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Abstract
This paper presents a literature review on techniques related to the computed tomography procedure that incorporate automation elements in their research investigations or industrial applications. Computed tomography (CT) is a non-destructive testing (NDT) technique in that the imaging and inspection are performed without damaging the sample, allowing for additional or repeated analysis if necessary. The reviewed literature is organized based on the steps associated with a general NDT task in order to define an end-to-end computed tomography automation architecture. The process steps include activities prior to image collection, during the scan, and after the data are collected. It further reviews efforts related to repeating this process based on a previous scan result. By analyzing the multiple existing but disparate efforts found in the literature, we present a framework for fully automating NDT procedures and discuss the remaining technical gaps in the developed framework.
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17
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Zhang Y, Liang Y, Miao S, Chen D, Yan S, Liu J. Broadband near-infrared BaMSi 3O 9:Cr 3+ (M = Zr, Sn, Hf) phosphors for light-emitting diode applications. Inorg Chem Front 2021. [DOI: 10.1039/d1qi01082d] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cr3+-Doped BaMSi3O9 (M = Zr, Sn, Hf) NIR-emitting phosphors have been developed, which exhibit a broad NIR emission band over 650–1200 nm with a tunable band maximum longer than 800 nm and a FWHM of more than 155 nm upon blue light excitation.
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Affiliation(s)
- Yan Zhang
- Key Laboratory for Liquid-Solid Structure Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China
| | - Yanjie Liang
- Key Laboratory for Liquid-Solid Structure Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China
| | - Shihai Miao
- Key Laboratory for Liquid-Solid Structure Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China
| | - Dongxun Chen
- Key Laboratory for Liquid-Solid Structure Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China
| | - Shao Yan
- Key Laboratory for Liquid-Solid Structure Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China
| | - Jingwei Liu
- Key Laboratory for Liquid-Solid Structure Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China
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18
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Influence of solar drying and storage conditions on microstructure, crack propagation and nano-hardness of paddy and wheat. J Cereal Sci 2020. [DOI: 10.1016/j.jcs.2020.103054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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