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Shao Y, Ji S, Shi Y, Xuan G, Jia H, Guan X, Chen L. Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 319:124538. [PMID: 38833885 DOI: 10.1016/j.saa.2024.124538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 05/14/2024] [Accepted: 05/25/2024] [Indexed: 06/06/2024]
Abstract
Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes' growth stage and color coordinates, providing great significance for designing a tomato quality grading system.
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Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Shengheng Ji
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Yukang Shi
- \Shandong Industrial Technician College, Weifang 261000, China
| | - Guantao Xuan
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
| | - Huijie Jia
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Xianlu Guan
- College of Engineering, South China Agricultural University and Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
| | - Long Chen
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
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Qi H, Luo J, Wu X, Zhang C. Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review. J Food Sci 2024. [PMID: 39366769 DOI: 10.1111/1750-3841.17388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 08/26/2024] [Accepted: 08/27/2024] [Indexed: 10/06/2024]
Abstract
Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.
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Affiliation(s)
- Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Jiahao Luo
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Xiaoping Wu
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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Chen X, He W, Ye Z, Gai J, Lu W, Xing G. Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM. PLANT METHODS 2024; 20:130. [PMID: 39164761 PMCID: PMC11337654 DOI: 10.1186/s13007-024-01257-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 08/02/2024] [Indexed: 08/22/2024]
Abstract
Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficientμ ' s and absorption coefficient μ a of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and μ a at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for μ a and less than 10% forμ ' s . After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.
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Affiliation(s)
- Xuanyu Chen
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China
| | - Wei He
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Zhihao Ye
- Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Junyi Gai
- Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wei Lu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China.
| | - Guangnan Xing
- Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
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Xu S, Guo Y, Liang X, Lu H. Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review. Foods 2024; 13:1116. [PMID: 38611420 PMCID: PMC11012010 DOI: 10.3390/foods13071116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.
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Affiliation(s)
- Sai Xu
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;
| | - Yinghua Guo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China;
| | - Xin Liang
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;
- College of Engineering, South China Agricultural University, Guangzhou 510642, China;
| | - Huazhong Lu
- Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
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Zhang Y, Wang Y. Recent trends of machine learning applied to multi-source data of medicinal plants. J Pharm Anal 2023; 13:1388-1407. [PMID: 38223450 PMCID: PMC10785154 DOI: 10.1016/j.jpha.2023.07.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 01/16/2024] Open
Abstract
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
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Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
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Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5410-5440. [PMID: 37818969 DOI: 10.1039/d3ay01132a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.
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Affiliation(s)
- Hala Abi-Rizk
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
| | | | - Julien Chamberland
- Department of Food Sciences, STELA Dairy Research Center, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada
| | - Christophe B Y Cordella
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
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Xu Y, Mao Y, Li H, Sun L, Wang S, Li X, Shen J, Yin X, Fan K, Ding Z, Wang Y. A deep learning model for rapid classification of tea coal disease. PLANT METHODS 2023; 19:98. [PMID: 37689676 PMCID: PMC10492339 DOI: 10.1186/s13007-023-01074-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/29/2023] [Indexed: 09/11/2023]
Abstract
BACKGROUND The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification. RESULTS Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging. CONCLUSIONS This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.
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Affiliation(s)
- Yang Xu
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Yilin Mao
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - He Li
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Litao Sun
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Shuangshuang Wang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Xiaojiang Li
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Jiazhi Shen
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Xinyue Yin
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Kai Fan
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Zhaotang Ding
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
| | - Yu Wang
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China.
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Haghbin N, Bakhshipour A, Zareiforoush H, Mousanejad S. Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics. PLANT METHODS 2023; 19:53. [PMID: 37268945 PMCID: PMC10236597 DOI: 10.1186/s13007-023-01032-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1st derivative, and Savitzky-Golay 2nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits' firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky-Golay 1st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R2) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R2 values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.
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Affiliation(s)
- Najmeh Haghbin
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Hemad Zareiforoush
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Sedigheh Mousanejad
- Department of Plant Protection, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
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Chu X, Zhang K, Wei H, Ma Z, Fu H, Miao P, Jiang H, Liu H. A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae. FRONTIERS IN PLANT SCIENCE 2023; 14:1180203. [PMID: 37332705 PMCID: PMC10272841 DOI: 10.3389/fpls.2023.1180203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 05/09/2023] [Indexed: 06/20/2023]
Abstract
Introduction Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. Methods This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. Results The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. Discussion These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.
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Affiliation(s)
- Xuan Chu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Kun Zhang
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongyu Wei
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiyu Ma
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Han Fu
- College of Engineering, South China Agricultural University, Guangzhou, China
| | - Pu Miao
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongzhe Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Hongli Liu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
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Jiang M, You S, Sha H, Bai B, Zhang L, Tu K, Peng J, Song L, Lan W, Pan L. Detection of Alternaria alternata infection in winter jujubes based on optical properties and their correlation with internal quality parameters during storage. Food Chem 2023; 409:135298. [PMID: 36584526 DOI: 10.1016/j.foodchem.2022.135298] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/01/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022]
Abstract
In this work, a single integrating sphere system was applied to characterize the optical absorption (μa) and reduced scattering (μs') properties (550 - 1050 nm) in winter jujube flesh infected by Alternaria alternata during storage at 4 and 20 °C, respectively. Meanwhile, physical (L*, a*, weight loss) and biochemical characteristics (soluble solids content, titratable acids, chlorophyll, total phenolic, and ascorbic acid) of winter jujubes were measured. Among them, chlorophyll, weight loss and ascorbic acid were highly correlated with μa at 680 nm, 690 nm, while chlorophyll and a* had the best correlations with μs' at 700 - 920 nm. These optimal optical properties were proved efficiently contributed to the disease detection of winter jujubes after 12 days at 4 °C and 3 days at 20 °C during storage, with satisfactory discrimination accuracies (acc > 93.75 %). Consequently, optical properties in Vis-NIR region were available to detect the postharvest disease in winter jujubes.
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Affiliation(s)
- Mengwei Jiang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China.
| | - Sicong You
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China.
| | - Hao Sha
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China.
| | - Bingyao Bai
- College of Food Science and Engineering, Tarim University, Alar 843300, China.
| | - Li Zhang
- College of Food Science and Engineering, Tarim University, Alar 843300, China; College of Food and Biological Engineering, Bengbu University, Bengbu 233030, Anhui, China.
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China.
| | - Jing Peng
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China.
| | - Lijun Song
- College of Food Science and Engineering, Tarim University, Alar 843300, China; College of Food and Biological Engineering, Bengbu University, Bengbu 233030, Anhui, China.
| | - Weijie Lan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China.
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing, Jiangsu 210095, China; Sanya Institute of Nanjing Agricultural University, Sanya, Hainan 572024, China.
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Yang Y, Nan R, Mi T, Song Y, Shi F, Liu X, Wang Y, Sun F, Xi Y, Zhang C. Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging. Int J Mol Sci 2023; 24:ijms24065825. [PMID: 36982900 PMCID: PMC10056805 DOI: 10.3390/ijms24065825] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R2, 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.
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Affiliation(s)
- Yucun Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Rui Nan
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Tongxi Mi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Yingxin Song
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Fanghui Shi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Xinran Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Yunqi Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Fengli Sun
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture, Xianyang 712100, China
| | - Yajun Xi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture, Xianyang 712100, China
| | - Chao Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture, Xianyang 712100, China
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12
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Nirere A, Sun J, Kama R, Atindana VA, Nikubwimana FD, Dusabe KD, Zhong Y. Nondestructive detection of adulterated wolfberry (
Lycium Chinense
) fruits based on hyperspectral imaging technology. J FOOD PROCESS ENG 2023. [DOI: 10.1111/jfpe.14293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Adria Nirere
- School of Electrical and Information Engineering Jiangsu University Zhenjiang Jiangsu China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang Jiangsu China
| | - Rakhwe Kama
- Institute of Farmland Irrigation of CAAS Xinxing China
| | | | | | - Keza Dominique Dusabe
- School of Food Science and Biological Engineering Jiangsu University Zhenjiang Jiangsu China
| | - Yuhao Zhong
- School of Electrical and Information Engineering Jiangsu University Zhenjiang Jiangsu China
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13
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Liu X, Li N, Huang Y, Lin X, Ren Z. A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology. FRONTIERS IN PLANT SCIENCE 2023; 13:1084847. [PMID: 36777535 PMCID: PMC9909479 DOI: 10.3389/fpls.2022.1084847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.
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Affiliation(s)
- Xuan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Na Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Yirui Huang
- College of Information Engineering, Hebei GEO University, Shijiazhuang, China
| | - Xiujun Lin
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Zhenhui Ren
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
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14
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Jiang M, Li Y, Song J, Wang Z, Zhang L, Song L, Bai B, Tu K, Lan W, Pan L. Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System. Foods 2023; 12:foods12030435. [PMID: 36765962 PMCID: PMC9914266 DOI: 10.3390/foods12030435] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.
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Affiliation(s)
- Mengwei Jiang
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Yiting Li
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Jin Song
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhenjie Wang
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Li Zhang
- College of Food Science and Technology, Hebei Normal University of Science & Technology, Qinghuangdao 066600, China
- College of Life Sciences, Tarim University, Alaer 843300, China
| | - Lijun Song
- College of Food Science and Technology, Hebei Normal University of Science & Technology, Qinghuangdao 066600, China
- College of Life Sciences, Tarim University, Alaer 843300, China
| | - Bingyao Bai
- College of Life Sciences, Tarim University, Alaer 843300, China
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Weijie Lan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
- Correspondence: (W.L.); (L.P.); Tel.: +86-25-84399016 (L.P.)
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
- Sanya Institute of Nanjing Agricultural University, Sanya 572024, China
- Correspondence: (W.L.); (L.P.); Tel.: +86-25-84399016 (L.P.)
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15
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Sun Y, Wang X, Pan L, Hu Y. Influence of maturity on bruise detection of peach by structured multispectral imaging. Curr Res Food Sci 2023; 6:100476. [PMID: 36941891 PMCID: PMC10023935 DOI: 10.1016/j.crfs.2023.100476] [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: 11/25/2022] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669-955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1-S3) were 91-92%, 90.71-97.43%, and 97.14-99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels.
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Affiliation(s)
- Ye Sun
- College of Food Science and Light Industry, Nanjing Technology University, Nanjing, 211816, China
- College of Engineering, Nanjing Agricultural University, 210031, Nanjing, China
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, 210031, Nanjing, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yonghong Hu
- College of Food Science and Light Industry, Nanjing Technology University, Nanjing, 211816, China
- Corresponding author. 30 Puzhu South Road, College of Food Science and Light Industry, Nanjing Technology University, 211816, Nanjing, China.
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16
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Shen F, Deng H, Yu L, Cai F. Open-source mobile multispectral imaging system and its applications in biological sample sensing. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121504. [PMID: 35717925 DOI: 10.1016/j.saa.2022.121504] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/10/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
Visible-near-infrared spectroscopy data can be utilized as an important quantitative indicator of biomolecular quantitative analysis. When acquiring spectral information, hyperspectral/multispectral imaging systems can obtain the spatial information of the object of interest. This allows the complete spatial-spectral information of the object of interest to be acquired and the spatial distribution of biomolecules to be analyzed. In this study, we present an open-source mobile multispectral imaging system, test the influence of the utilization of LEDs on the multispectral image, and design image-processing algorithms to correct this influence. Todemonstrate the effectivenessofthesystem, the system is applied to meat freshness analysis, small-animal tumor in-vivo imaging, and chlorophyll spatial distribution imaging. The experimental results verify that our system has stable performance and is compatible with a wide range of spectral imaging applications.
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Affiliation(s)
- Fuzhou Shen
- Mechanical and Electrical Engineering College School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haikou 570228, China
| | - Hancheng Deng
- Mechanical and Electrical Engineering College School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haikou 570228, China
| | - Lejun Yu
- Mechanical and Electrical Engineering College School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haikou 570228, China
| | - Fuhong Cai
- Mechanical and Electrical Engineering College School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Haikou 570228, China
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17
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Study on Qualitative Impact Damage of Loquats Using Hyperspectral Technology Coupled with Texture Features. Foods 2022; 11:foods11162444. [PMID: 36010443 PMCID: PMC9407320 DOI: 10.3390/foods11162444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/26/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Bruising is one of the main problems in the post-harvest grading and processing of ‘Zaozhong 6’ loquats, reducing the economic value of loquats, and even food quality and safety problems are caused by it. Therefore, one of the main tasks in the post-harvest processing of loquats is to detect whether loquats are bruised, as well as the degree of bruising of loquats, to reduce the loss by proper treatment. An appropriate dimensionality reduction method can be used to reduce the redundancy of variables and improve the detection speed. The multispectral analysis method (MAM) has the advantage of accurate, rapid, and nondestructive detection, which was proposed to identify the different bruising degrees of loquats in this study. Firstly, the visible and near-infrared region (Vis–NIR, 400–1000 nm), the visible region (Vis, 400–780 nm), and the near-infrared region (NIR, 781–1000 nm) were analyzed using principal component analysis (PCA) to obtain the spectral regions and PC vectors, which could be used to effectively distinguish bruised loquats from normal loquats. Then, based on the selected second PC (PC2) score images, a morphological segmentation method (MSM) was proposed to distinguish bruised loquats from normal loquats. Furthermore, the weight coefficients of corresponding wavelength points of different degrees of bruising of loquats were analyzed, and the local extreme points and both sides of the interval were selected as the characteristic wavelength points for multi-spectral image processing. A gray level co-occurrence matrix (GLCM) was used to extract texture features and gray information from two-band ratio images K782/999. Finally, the MAM was proposed to detect the degree of bruising of loquats, which included the spectral data of three characteristic wavelength points in the NIR region coupled with texture features of the two-band ratio images, and the classification accuracy was 91.3%. This study shows that the MAM can be used as an effective dimensionality reduction method. The method not only improves the effect of prediction but also simplifies the process of prediction and ensures the accuracy of classification. The MSM can be used for rapid detection of normal and bruised fruits, and the MAM can be used to classify the degree of bruising of bruised fruits. Consequently, the processed methods are effective and can be used for the rapid and nondestructive detection of the degree of bruising of fruit.
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18
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Wang S, Sun J, Fu L, Xu M, Tang N, Cao Y, Yao K, Jing J. Identification of red jujube varieties based on hyperspectral imaging technology combined with
CARS‐IRIV
and
SSA‐SVM. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Simin Wang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Lvhui Fu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Ningqiu Tang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Yan Cao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jianpeng Jing
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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19
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Xuan G, Gao C, Shao Y. Spectral and image analysis of hyperspectral data for internal and external quality assessment of peach fruit. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 272:121016. [PMID: 35158140 DOI: 10.1016/j.saa.2022.121016] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/25/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Hyperspectral imaging was attempted to evaluate the internal and external quality of 'Feicheng' peach by providing the spectral and spatial data simultaneously. Mask-image was created from hyperspectral image at 810 nm and used to segment the fruit region where the average spectrum, after area normalization, was obtained for soluble solids content (SSC) and firmness evaluation. Pixel size and area were used for diameter and weight estimation. Then effective wavelengths were selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), and employed to develop multiple linear regression (MLR) models. The more effective prediction performances emerged from CARS-MLR model withRV2 = 0.841, RMSEV = 0.546, RPD = 2.51 for SSC andRV2 = 0.826, RMSEV = 1.008, RPD = 2.401 for firmness, followed by creating pixel-wise and object-wise visualization maps for quantifying SSC and firmness. Furthermore, peach diameter was estimated by calculating the minimum bounding rectangle with an average percentage error of 1.01 %, and the MLR model forweightpredictionachieveda good performance ofRV2 = 0.957, RMSEV = 9.203, and RPD = 4.819. The overall results showed that hyperspectral imaging could be used as an effective and non-destructive tool for evaluating the internal and external quality attributes of 'Feicheng' peach, and provided a holistic approach to develop online grading systems for quality tiers identification.
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Affiliation(s)
- Guantao Xuan
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Chong Gao
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Yuanyuan Shao
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
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20
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Soltani Firouz M, Sardari H. Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing. FOOD ENGINEERING REVIEWS 2022. [DOI: 10.1007/s12393-022-09307-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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Shi J, He H, Hu D, Song B. Defense Mechanism of Capsicum annuum L. Infected with Pepper Mild Mottle Virus Induced by Vanisulfane. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:3618-3632. [PMID: 35297641 DOI: 10.1021/acs.jafc.2c00659] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Pepper mild mottle virus (PMMoV), an RNA virus, is one of the most devastating pathogens in pepper crops and has a significant influence on global crop yields. PMMoV poses a major threat to the global shortage of pepper plants and other Solanaceae crops due to the lack of an effective antiviral agent. In this study, we have developed a plant immune inducer (vanisulfane), as a "plant vaccine" that boosts plant immunity against PMMoV, and studied its resistance mechanism. The protective activity of vanisulfane against PMMoV was 59.4%. Vanisulfane can enhance the activity of defense enzymes and improve the content of chlorophyll, flavonoids, and total phenols for removing harmful free radicals from plants. Furthermore, vanisulfane was found to enhance defense genes. Label-free quantitative proteomics would tackle disease resistance pathways of vanisulfane. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, differentially abundant proteins (DAPs) are mainly involved in starch and sucrose metabolism, photosynthesis, MAPK signaling pathway, and oxidative phosphorylation pathway. These results are crucial for the discovery of new pesticides, understanding the improvement of plant immunity and the antiviral activity of plant immune inducers.
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Affiliation(s)
- Jing Shi
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Hongfu He
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Deyu Hu
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Baoan Song
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
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22
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Liu Q, Zhang W, Zhang B, Du C, Wei N, Liang D, Sun K, Tu K, Peng J, Pan L. Determination of total protein and wet gluten in wheat flour by Fourier transform infrared photoacoustic spectroscopy with multivariate analysis. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104349] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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23
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Zhou X, Sun J, Tian Y, Yao K, Xu M. Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 266:120460. [PMID: 34637985 DOI: 10.1016/j.saa.2021.120460] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/18/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
The feasibility analysis of fluorescence hyperspectral imaging technology was studied for the detection of lead content in lettuce leaves. Further, Monte Carlo optimized wavelet transform stacked auto-encoders (WT-MC-SAE) was proposed for dimensionality reduction and depth feature extraction of fluorescence spectral data. The fluorescence hyperspectral images of 2800 lettuce leaf samples were selected and the whole lettuce leaf was used as the region of interest (ROI) to extract the fluorescence spectrum. Five different pre-processing algorithms were used to pre-process the original ROI spectral data including standard normalized variable (SNV), first derivative (1st Der), second derivative (2ndDer), third derivative (3rd Der) and fourth derivative (4th Der). Moreover, wavelet transform stacked auto-encoders (WT-SAE) and WT-MC-SAE were used for data dimensionality reduction, and support vector machine regression (SVR) was used for modeling analysis. Among them, 4th Der tends to be the most useful fluorescence spectral data for Pb content detection at 0.067 ∼ 1.400 mg/kg in lettuce leaves, with Rc2 of 0.9802, RMSEC of 0.02321 mg/kg, Rp2 of 0.9467, RMSEP of 0.04017 mg/kg and RPD of 3.273, and model scale (the number of nodes in the input layer, hidden layer and output layer) was 407-314-286-121-76 under the fifth level of wavelet decomposition. Further studies showed that WT-MC-SAE realizes the depth feature extraction of the fluorescence spectrum, and it is of great significance to use fluorescence hyperspectral imaging to realize the quantitative detection of lead in lettuce leaves.
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Affiliation(s)
- Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Yan Tian
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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24
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Sun Y, Pessane I, Pan L, Wang X. Hyperspectral characteristics of bruised tomatoes as affected by drop height and fruit size. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.110863] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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25
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He Y, Xiao Q, Bai X, Zhou L, Liu F, Zhang C. Recent progress of nondestructive techniques for fruits damage inspection: a review. Crit Rev Food Sci Nutr 2021; 62:5476-5494. [PMID: 33583246 DOI: 10.1080/10408398.2021.1885342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
In the process of growing, harvesting, and storage, fruits are vulnerable to mechanical damage, microbial infections, and other types of damage, which not only reduce the quality of fruits, increase the risk of fungal infections, in turn greatly affect food safety, but also sharply reduce economic benefits. Hence, it is essential to identify damaged fruits in time. Rapid and nondestructive detection of fruits damage is in great demand. In this paper, the latest research progresses on the detection of fruits damage by nondestructive techniques, including visible/near-infrared spectroscopy, chlorophyll fluorescence techniques, computer vision, multispectral and hyperspectral imaging, structured-illumination reflectance imaging, laser-induced backscattering imaging, optical coherence tomography, nuclear magnetic resonance and imaging, X-ray imaging, electronic nose, thermography, and acoustic methods, are summarized. We briefly introduce the principles of these techniques, summarize their applicability. The challenges and future trends are also proposed to provide beneficial reference for future researches and real-world applications.
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Affiliation(s)
- Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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26
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Sun Y, Huang Y, Pan L, Wang X. Evaluation of the Changes in Optical Properties of Peaches with Different Maturity Levels during Bruising. Foods 2021; 10:foods10020388. [PMID: 33578918 PMCID: PMC7916705 DOI: 10.3390/foods10020388] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 01/30/2021] [Accepted: 02/06/2021] [Indexed: 01/26/2023] Open
Abstract
The main objective was to measure the optical coefficients of peaches after bruising at different maturity levels and detect bruises. A spatially resolved method was used to acquire absorption coefficient (μa) and the reduced scattering coefficient (µs') spectra from 550 to 1000 nm, and a total of 12 groups (3 maturity levels * 4 detection times) were used to assess changes in µa and µs' resulting from bruising. Maturation and bruising both caused a decrease in µs' and an increase in µa, and the optical properties of immature peaches changed more after bruising than the optical properties of ripe peaches. Four hours after bruising, the optical properties of most samples were significantly different from those of intact peaches (p < 0.05), and the optical properties showed damage to tissue earlier than the appearance symptoms observed with the naked eye. The classification results of the Support Vector Machine model for bruised peaches showed that μa had the best classification accuracy compared to μs' and their combinations (µa × µs', µeff). Overall, based on μa, the average detection accuracies for peaches after bruising of 0 h, 4 h, and 24 h were increased.
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Affiliation(s)
- Ye Sun
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China;
| | - Yuping Huang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China;
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China;
- Correspondence: ; Tel.: +86-139-5160-6492
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Huang Y, Wang D, Liu Y, Zhou H, Sun Y. Measurement of Early Disease Blueberries Based on Vis/NIR Hyperspectral Imaging System. SENSORS 2020; 20:s20205783. [PMID: 33066056 PMCID: PMC7600744 DOI: 10.3390/s20205783] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/10/2020] [Accepted: 10/10/2020] [Indexed: 11/16/2022]
Abstract
Blueberries, which are rich in nutrition, are susceptible to fungal infection during postharvest or storage. However, early detection of diseases in blueberry is challenging because of their opaque appearance and the inconspicuousness of spots in the early stage of disease. The goal of this study was to investigate the potential of hyperspectral imaging over the spectral range of 400–1000 nm to discriminate early disease in blueberries. Scanning electron microscope observation verified that fungal damage to the cellular structure takes place during the early stages. A total of 400 hyperspectral images, 200 samples each of healthy and early disease groups, were collected to obtain mean spectra of each blueberry samples. Spectral correlation analysis was performed to select an effective spectral range. Partial least square discrimination analysis (PLSDA) models were developed using two types of spectral range (i.e., full wavelength range of 400–1000 nm and effective spectral range of 685–1000 nm). The results showed that the effective spectral range made it possible to provide better classification results due to the elimination of the influence of irrelevant variables. Moreover, the effective spectral range combined with an autoscale preprocessing method was able to obtain optimal classification accuracies, with recognition rates of 100% and 99% for healthy and early disease blueberries. This study demonstrated that it is feasible to use hyperspectral imaging to measure early disease blueberries.
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Affiliation(s)
- Yuping Huang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (Y.H.); (D.W.); (Y.L.); (H.Z.)
| | - Dezhen Wang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (Y.H.); (D.W.); (Y.L.); (H.Z.)
| | - Ying Liu
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (Y.H.); (D.W.); (Y.L.); (H.Z.)
| | - Haiyan Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (Y.H.); (D.W.); (Y.L.); (H.Z.)
| | - Ye Sun
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
- Correspondence: ; Tel.: +86-159-9630-1891
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Zhu M, Huang D, Hu X, Tong W, Han B, Tian J, Luo H. Application of hyperspectral technology in detection of agricultural products and food: A Review. Food Sci Nutr 2020; 8:5206-5214. [PMID: 33133524 PMCID: PMC7590284 DOI: 10.1002/fsn3.1852] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 11/06/2022] Open
Abstract
Food is the foundation of human survival. With the development and progress of society, people increasingly focus on the problems of food quality and safety, which is closely related to human's health. Thus, the whole industrial chain from farmland to dining table need to be strictly controlled. Traditional detection methods are time-consuming, laborious, and destructive. In recent years, hyperspectral technology has been more and more applied to food safety and quality detection, because the technology can achieve rapid and nondestructive detection of food, and the requirement to experimental condition is low; operability is strong. In this paper, hyperspectral imaging technology was briefly introduced, and its application in agricultural products and food detection in recent years was systematically summarized, and the key points in the research process were deeply discussed. This work lays a solid foundation for the peers to the following in-depth research and application of this technology.
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Affiliation(s)
- Min Zhu
- College of BioengineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
| | - Dan Huang
- College of BioengineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
- Engineering Laboratory for Biological Brewing Technology of Bran Vinegar in the South of SichuanZigongChina
| | - Xin‐Jun Hu
- College of Mechanical EngineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
| | - Wen‐Hua Tong
- College of BioengineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
| | - Bao‐Lin Han
- College of BioengineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
| | - Jian‐Ping Tian
- College of Mechanical EngineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
| | - Hui‐Bo Luo
- College of BioengineeringSichuan University of Science and EngineeringZigong CitySichuan ProvinceChina
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Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing. PLoS One 2020; 15:e0238530. [PMID: 32915830 PMCID: PMC7485794 DOI: 10.1371/journal.pone.0238530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 08/18/2020] [Indexed: 11/19/2022] Open
Abstract
Chlorophyll content is an important indicator of the growth status of japonica rice. The objective of this paper is to develop an inversion model that can predict japonica rice chlorophyll content by using hyperspectral image of rice canopy collected with unmanned aerial vehicle (UAV). UAV-based hyperspectral remote sensing can provide timely and cost-effective monitoring of chlorophyll content over a large region. The study was based on hyperspectral data collected at the Shenyang Agricultural College Academician Japonica Rice Experimental Base in 2018 and 2019. In order to extract the salient information embedded in the high-dimensional hyperspectral data, we first perform dimension reduction by using a successive projection algorithm (SPA). The SPA extracts the characteristic hyperspectral bands that are used as input to the inversion model. The characteristic bands extracted by SPA are 410 nm, 481 nm, 533 nm, 702 nm, and 798 nm, respectively. The inversion model is developed by using an extreme learning machine (ELM), the parameters of which are optimized by using particle swarm optimization (PSO). The PSO-ELM algorithm can accurately model the nonlinear relationship between hyperspectral data and chlorophyll content. The model achieves a coefficient of determination R2 = 0.791 and a root mean square error of RMSE = 8.215 mg/L. The model exhibits good predictive ability and can provide data support and model reference for research on nutrient diagnosis of japonica rice.
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Assessment of the optical properties of peaches with fungal infection using spatially-resolved diffuse reflectance technique and their relationships with tissue structural and biochemical properties. Food Chem 2020; 321:126704. [PMID: 32234637 DOI: 10.1016/j.foodchem.2020.126704] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/26/2020] [Accepted: 03/27/2020] [Indexed: 01/01/2023]
Abstract
This research measured the optical absorption (µa) and reduced scattering (μs') properties in peaches during quality deterioration, and determine the relationships of the optical parameters with select structural and biochemical parameters. Spatially resolved reflectance was measured for healthy and fungal infected peaches, followed by physical (the size and tissue color), structural [membrane permeability and SEM], and biochemical (Vc, soluble sugar, titratable acid, chlorophyll, total phenolic content) measurements. Both µa and µs' were correlated well with the cellulosic structural and biochemical parameters of peaches, and they had the best correlations with those quality parameters at 675 nm. The correlation of μs' with membrane permeability was the highest from -0.962-0.743, while μa had the best correlation with the chlorophyll content at 675 nm which is an indicator of plant maturation and senescence. These findings would be useful for further development of an effective optical technique for early disease detection of peach fruit.
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31
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Zhang L, Sun J, Zhou X, Nirere A, Wu X, Dai R. Classification detection of saccharin jujube based on hyperspectral imaging technology. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.14591] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Lin Zhang
- School of Electrical and Information EngineeringJiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information EngineeringJiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information EngineeringJiangsu University Zhenjiang China
| | - Adria Nirere
- School of Electrical and Information EngineeringJiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information EngineeringJiangsu University Zhenjiang China
| | - Ruimin Dai
- School of Electrical and Information EngineeringJiangsu University Zhenjiang China
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Ni C, Liu H, Liu Q, Sun Y, Pan L, Fisk ID, Liu Y. Rapid and nondestructive monitoring for the quality of Jinhua dry‐cured ham using hyperspectral imaging and chromometer. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13443] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Chendie Ni
- College of Food Science and TechnologyShanghai Ocean University Shanghai China
| | - Hai Liu
- College of Food Science and TechnologyShanghai Ocean University Shanghai China
| | - Qiang Liu
- College of Food Science and TechnologyNanjing Agricultural University Nanjing China
| | - Ye Sun
- College of Food Science and TechnologyNanjing Agricultural University Nanjing China
| | - Leiqing Pan
- College of Food Science and TechnologyNanjing Agricultural University Nanjing China
| | - Ian Denis Fisk
- Division of Food SciencesUniversity of Nottingham Loughborough UK
| | - Yuan Liu
- Department of Food Science & TechnologyShanghai Jiao Tong University Shanghai China
- Shanghai Engineering Research Center of Food Safety Shanghai China
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Jiang J, Gong L, Dong Q, Kang Y, Osako K, Li L. Characterization of PLA-P3,4HB active film incorporated with essential oil: Application in peach preservation. Food Chem 2020; 313:126134. [DOI: 10.1016/j.foodchem.2019.126134] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/03/2019] [Accepted: 12/26/2019] [Indexed: 10/25/2022]
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34
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Liu Q, Zhou D, Tu S, Xiao H, Zhang B, Sun Y, Pan L, Tu K. Quantitative Visualization of Fungal Contamination in Peach Fruit Using Hyperspectral Imaging. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01747-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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35
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Response of nutritional and functional composition, anti-nutritional factors and antioxidant activity in germinated soybean under UV-B radiation. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2019.108709] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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36
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Zhu M, Chen P, Hu X, Mao X, Tian J, Luo H, Huang D. Rapid determination of pit mud moisture content using hyperspectral imaging. Food Sci Nutr 2020; 8:179-189. [PMID: 31993144 PMCID: PMC6977493 DOI: 10.1002/fsn3.1289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/17/2019] [Accepted: 10/22/2019] [Indexed: 11/21/2022] Open
Abstract
Lack of moisture can lead to the aging of pit mud, excessive moisture will make it difficult to maintain its shape or even collapse. Therefore, a rapid and nondestructive detection technology for moisture in pit mud using hyperspectral imaging was firstly investigated. Modeling efficiency of various processing was compared in visible (400-1,000 nm) and near-infrared (900-1,700 nm) regions, and the optimal model was SNV-SPA-SVM in near-infrared spectroscopy; the R pre 2 and RMSEP of model were .9953 and 0.0029, respectively. Furthermore, the distribution map showed that the moisture in the new cellar was generally lower than that of old, and the moisture distribution of the old pit mud was more even. Moreover, the moisture content of different layers in the same cellar increased from top to bottom. This work provides strong technical support for liquor brewing enterprises to effectively implement online monitoring of pit mud changes and open a new era for the application of hyperspectral imaging technology in the field of liquor solid-state fermentation.
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Affiliation(s)
- Min Zhu
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
| | - Ping Chen
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
| | - Xin‐Jun Hu
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
| | - Xiang Mao
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
| | - Jian‐Ping Tian
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
| | - Hui‐Bo Luo
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
| | - Dan Huang
- College of BioengineeringSichuan University of Science & EngineeringZigong CityChina
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37
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Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9183926] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.
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38
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Liu Y, Wang Q, Gao X, Xie A. Total phenolic content prediction in
Flos Lonicerae
using hyperspectral imaging combined with wavelengths selection methods. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13224] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yunhong Liu
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Qingqing Wang
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Xiuwei Gao
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Anguo Xie
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
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39
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Liu Q, Wei K, Xiao H, Tu S, Sun K, Sun Y, Pan L, Tu K. Near-Infrared Hyperspectral Imaging Rapidly Detects the Decay of Postharvest Strawberry Based on Water-Soluble Sugar Analysis. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-018-01430-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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40
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Wang Y, Hu X, Hou Z, Ning J, Zhang Z. Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2018; 98:4659-4664. [PMID: 29607500 DOI: 10.1002/jsfa.8996] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 02/26/2018] [Accepted: 03/01/2018] [Indexed: 05/20/2023]
Abstract
BACKGROUND Nitrogen (N) fertilizer plays an important role in tea plantation management, with significant impacts on the photosynthetic capacity, productivity and nutrition status of tea plants. The present study aimed to establish a method for the discrimination of N fertilizer levels using hyperspectral imaging technique. RESULTS Spectral data were extracted from the region of interest, followed by the first derivative to reduce background noise. Five optimal wavelengths were selected by principal component analysis. Texture features were extracted from the images at optimal wavelengths by gray-level gradient co-occurrence matrix. Support vector machine (SVM) and extreme learning machine were used to build classification models based on spectral data, optimal wavelengths, texture features and data fusion, respectively. The SVM model using fused data gave the best performance with highest correct classification rate of 100% for prediction set. CONCLUSION The overall results indicated that visible and near-infrared hyperspectral imaging combined with SVM were effective in discriminating N fertilizer levels of tea plants. © 2018 Society of Chemical Industry.
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Affiliation(s)
- Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Xin Hu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zhiwei Hou
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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41
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Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis. SENSORS 2018; 18:s18041295. [PMID: 29690625 PMCID: PMC5948498 DOI: 10.3390/s18041295] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 04/08/2018] [Accepted: 04/12/2018] [Indexed: 11/16/2022]
Abstract
Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.
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42
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Munera S, Amigo JM, Aleixos N, Talens P, Cubero S, Blasco J. Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control 2018. [DOI: 10.1016/j.foodcont.2017.10.037] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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43
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Su WH, Sun DW. Multispectral Imaging for Plant Food Quality Analysis and Visualization. Compr Rev Food Sci Food Saf 2018; 17:220-239. [DOI: 10.1111/1541-4337.12317] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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44
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Fan Y, Wang T, Qiu Z, Peng J, Zhang C, He Y. Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System. SENSORS 2017; 17:s17112470. [PMID: 29077040 PMCID: PMC5713110 DOI: 10.3390/s17112470] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 10/22/2017] [Accepted: 10/24/2017] [Indexed: 11/16/2022]
Abstract
Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA). Principal component analysis (PCA) was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM) were extracted from the first two principal component (PC) images. A back-propagation neural network (BPNN) was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation.
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Affiliation(s)
- Yangyang Fan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Tao Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Zhengjun Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Jiyu Peng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
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