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Feng Y, Lv Y, Dong F, Chen Y, Li H, Rodas-González A, Wang S. Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 322:124844. [PMID: 39053116 DOI: 10.1016/j.saa.2024.124844] [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: 12/13/2023] [Revised: 06/07/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R2p) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.
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Affiliation(s)
- Yingjie Feng
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Fujia Dong
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yue Chen
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Hui Li
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | | | - Songlei Wang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
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Sun J, Nirere A, Dusabe KD, Yuhao Z, Adrien G. Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near-infrared hyperspectral imaging technology and machine learning algorithms. J Food Sci 2024; 89:4403-4418. [PMID: 38957090 DOI: 10.1111/1750-3841.17151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 04/28/2024] [Accepted: 05/15/2024] [Indexed: 07/04/2024]
Abstract
The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36 h, whereas the other batch was stored under favorable conditions. Watermelon seeds' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000 nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328 s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.
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Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Adria Nirere
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Keza Dominique Dusabe
- School of Food Science and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Zhong Yuhao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Guverinoma Adrien
- School of Food Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
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Wang X, Ma Z, Xing Y, Peng T, Dun X, He Z, Zhang J, Cheng X. Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240485. [PMID: 39086830 PMCID: PMC11288683 DOI: 10.1098/rsos.240485] [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: 01/16/2024] [Accepted: 06/26/2024] [Indexed: 08/02/2024]
Abstract
Species discrimination of insects is an important aspect of ecology and biodiversity research. The traditional methods based on human visual experience and biochemical analysis cannot strike a balance between accuracy and timeliness. Morphological identification using computer vision and machine learning is expected to solve this problem, but image features have poor accuracy for very similar species and usually require complicated networks that are unfriendly to portable edge devices. In this work, we propose a fast and accurate species discrimination method of similar insects using hyperspectral features and lightweight machine learning algorithm. Feature regions selection, feature spectra selection and model quantification are used for the optimization of discriminating network. The experimental results of six similar butterfly species in the genus of Graphium show that, compared with morphological recognition with machine vision, our work achieves a higher accuracy of 92.36 ± 3.04% and a shorter inference time of 0.6 ms, with the tiny-size convolutional neural network deployed on a neural network chip. This study provides a rapid and high-accuracy species discrimination method for insects with high appearance similarity and paves the way for field discriminations using intelligent micro-spectrometer based on on-chip microstructure and artificial intelligence chip.
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Affiliation(s)
- Xuquan Wang
- MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai200092, People’s Republic of China
- Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai200092, People’s Republic of China
- Frontiers Science Center of Digital Optics, Shanghai200092, People’s Republic of China
| | - Zhiyuan Ma
- MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai200092, People’s Republic of China
- Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai200092, People’s Republic of China
- Frontiers Science Center of Digital Optics, Shanghai200092, People’s Republic of China
| | - Yujie Xing
- MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai200092, People’s Republic of China
- Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai200092, People’s Republic of China
- Frontiers Science Center of Digital Optics, Shanghai200092, People’s Republic of China
| | - Tianfan Peng
- MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai200092, People’s Republic of China
- Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai200092, People’s Republic of China
- Frontiers Science Center of Digital Optics, Shanghai200092, People’s Republic of China
| | - Xiong Dun
- MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai200092, People’s Republic of China
- Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai200092, People’s Republic of China
- Frontiers Science Center of Digital Optics, Shanghai200092, People’s Republic of China
| | - Zhuqing He
- East China Normal University, Shanghai200241, People’s Republic of China
| | - Jian Zhang
- East China Normal University, Shanghai200241, People’s Republic of China
| | - Xinbin Cheng
- MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai200092, People’s Republic of China
- Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai200092, People’s Republic of China
- Frontiers Science Center of Digital Optics, Shanghai200092, People’s Republic of China
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Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
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Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
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Wang Z, An T, Wang W, Fan S, Chen L, Tian X. Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122679. [PMID: 37011441 DOI: 10.1016/j.saa.2023.122679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (Rp) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.
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Affiliation(s)
- Zheli Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ting An
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenchao Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Liping Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xi Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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Dai C, Sun J, Huang X, Zhang X, Tian X, Wang W, Sun J, Luan Y. Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content. Foods 2023; 12:2957. [PMID: 37569225 PMCID: PMC10418690 DOI: 10.3390/foods12152957] [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: 06/08/2023] [Revised: 07/21/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.
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Affiliation(s)
- Chunxia Dai
- School of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China (X.T.)
| | - Xiaorui Zhang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China (X.T.)
| | - Xiaoyu Tian
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China (X.T.)
| | - Wei Wang
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Jingtao Sun
- School of Food Science and Technology, Shihezi University, Shihezi 832000, China
| | - Yu Luan
- Zhenjiang Food and Drug Supervision and Inspection Center, Zhenjiang 212004, China
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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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Fu L, Sun J, Wang S, Xu M, Yao K, Zhou X. Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 281:121641. [PMID: 35870430 DOI: 10.1016/j.saa.2022.121641] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431-962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R2) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves.
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Affiliation(s)
- Lvhui Fu
- 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.
| | - Simin Wang
- 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
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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Yasmin J, Ahmed MR, Wakholi C, Lohumi S, Mukasa P, Kim G, Kim J, Lee H, Cho BK. Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds. FRONTIERS IN PLANT SCIENCE 2022; 13:986754. [PMID: 36420027 PMCID: PMC9676662 DOI: 10.3389/fpls.2022.986754] [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: 07/05/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The viability status of seeds before sowing is important to farmers as it allows them to make yield predictions. Monitoring the seed quality in a rapid and nondestructive manner may create a perfect solution, especially for industrial sorting applications. However, current offline laboratory-based strategies employed for the monitoring of seed viability are time-consuming and thus cannot satisfy industrial needs where there is a substantial number of seeds to be analyzed. In this study, we describe a prototype online near-infrared (NIR) hyperspectral imaging system that can be used for the rapid detection of seed viability. A wavelength range of 900-1700 nm was employed to obtain spectral images of three different varieties of naturally aged watermelon seed samples. The partial least square discriminant analysis (PLS-DA) model was employed for real-time viability prediction for seed samples moving through a conveyor unit at a speed of 49 mm/sec. A suction unit was further incorporated to develop the online system and it was programmatically controlled to separate the detected viable seeds from nonviable ones. For an external validation sample set showed classification accuracy levels of 91.8%, 80.7%, and 77.8% in relation to viability for the three varieties of watermelon seed with healthy seedling growth. The regression coefficients of the classification model distinguished some chemical differences in viable and nonviable seed which was verified by the chromatographic analysis after the detection of the proposed online system. The results demonstrated that the developed online system with the viability prediction model has the potential to be used in the seed industry for the quality monitoring of seeds.
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Affiliation(s)
- Jannat Yasmin
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Mohammed Raju Ahmed
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Collins Wakholi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Santosh Lohumi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Perez Mukasa
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Geonwoo Kim
- Department of Bio-Industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, Jinju-si, Gyeongsangnam-do, South Korea
- Institute of Smart Farm, Gyeongsang National University, Jinju-si, Gyeongsangnam-do, South Korea
| | - Juntae Kim
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, Cheongju, Chungbuk, South Korea
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
- Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman. Foods 2022; 11:foods11182928. [PMID: 36141056 PMCID: PMC9498461 DOI: 10.3390/foods11182928] [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: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.
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11
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Xiang Y, Chen Q, Su Z, Zhang L, Chen Z, Zhou G, Yao Z, Xuan Q, Cheng Y. Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation. FRONTIERS IN PLANT SCIENCE 2022; 13:860656. [PMID: 35586212 PMCID: PMC9108868 DOI: 10.3389/fpls.2022.860656] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.
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Affiliation(s)
- Yun Xiang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Qijun Chen
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Zhongjing Su
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Lu Zhang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Zuohui Chen
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Guozhi Zhou
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Zhuping Yao
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Qi Xuan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Yuan Cheng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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12
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Sun X, Wang Y, Hu W. Estimation of Longitudinal Force, Sideslip Angle and Yaw Rate for Four-Wheel Independent Actuated Autonomous Vehicles Based on PWA Tire Model. SENSORS 2022; 22:s22093403. [PMID: 35591093 PMCID: PMC9104631 DOI: 10.3390/s22093403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/05/2022] [Accepted: 04/21/2022] [Indexed: 11/16/2022]
Abstract
This article introduces an efficient and high-precision estimation framework for four-wheel independently actuated (FWIA) autonomous vehicles based on a novel tire model and adaptive square-root cubature Kalman filter (SCKF) estimation strategy. Firstly, a reliable and concise tire model that considers the tire’s nonlinear mechanics characteristics under combined conditions through the piecewise affine (PWA) identification method is established to improve the accuracy of the lateral dynamics model of FWIA autonomous vehicles. On this basis, the longitudinal relaxation length of each tire is integrated into the lateral dynamics modeling of FWIA autonomous vehicle. A novel nonlinear state function, including the PWA tire model, is proposed in this paper. To reduce the impact of the uncertainty of noise statistics on the estimation accuracy, an adaptive SCKF estimation algorithm based on the maximum a posteriori (MAP) criterion is proposed in the estimation framework. Finally, the estimation accuracy and stability of the adaptive SCKF algorithm are verified by the co-simulation of CarSim and Simulink. The simulation results show that when the statistical characteristics of noise are unknown and the target state changes suddenly under critical maneuvers, the estimation framework proposed in this paper still maintains high accuracy and stability.
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13
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Wang B, Sun J, Xia L, Liu J, Wang Z, Li P, Guo Y, Sun X. The Applications of Hyperspectral Imaging Technology for Agricultural Products Quality Analysis: A Review. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1929297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Bao Wang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Jianfei Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Lianming Xia
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Junjie Liu
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Zhenhe Wang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Pei Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Yemin Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
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14
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Ahmad H, Sun J, Nirere A, Shaheen N, Zhou X, Yao K. Classification of tea varieties based on fluorescence hyperspectral image technology and ABC‐SVM algorithm. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15241] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Hussain Ahmad
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Adria Nirere
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Naila Shaheen
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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15
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Tang N, Sun J, Yao K, Zhou X, Tian Y, Cao Y, Nirere A. Identification of
Lycium barbarum
varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling
‐
whale optimization algorithm
‐
support vector machine. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13603] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Ningqiu Tang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Yan Tian
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Yan Cao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Adria Nirere
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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16
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Hu X, Chen P, Tian J, Huang D, Luo H, Huang D. Predicting the moisture content of Daqu with hyperspectral imaging. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2020. [DOI: 10.1515/ijfe-2019-0243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Abstract
Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R
2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging.
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Affiliation(s)
- Xinjun Hu
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
- Sichuan Provincial Key Laboratory of Process Equipment and Control , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Ping Chen
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Jianping Tian
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Danping Huang
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Huibo Luo
- College of Bioengineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Dan Huang
- College of Bioengineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
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17
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Gao P, Xu W, Yan T, Zhang C, Lv X, He Y. Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster ( Elaeagnus angustifolia) Fruits. Foods 2019; 8:foods8120620. [PMID: 31783592 PMCID: PMC6963922 DOI: 10.3390/foods8120620] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/22/2019] [Accepted: 11/23/2019] [Indexed: 12/15/2022] Open
Abstract
Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.
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Affiliation(s)
- Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China; (P.G.); (T.Y.)
- Key Laboratory of Oasis Ecology Agriculture, Shihezi University, Shihezi 832003, China;
| | - Wei Xu
- College of Agriculture, Shihezi University, Shihezi 832003, China;
- Xinjiang Production and Construction Corps Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization, Shihezi 832003, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China; (P.G.); (T.Y.)
- Key Laboratory of Oasis Ecology Agriculture, Shihezi University, Shihezi 832003, China;
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China;
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Xin Lv
- Key Laboratory of Oasis Ecology Agriculture, Shihezi University, Shihezi 832003, China;
- College of Agriculture, Shihezi University, Shihezi 832003, China;
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China;
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-571-88982143
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18
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Sun J, Shi X, Zhang H, Xia L, Guo Y, Sun X. Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13263] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jianfei Sun
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Xiaojie Shi
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Hui Zhang
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Lianming Xia
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Yemin Guo
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Xia Sun
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
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19
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Li Y, Sun J, Wu X, Chen Q, Lu B, Dai C. Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.14238] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yating Li
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Bing Lu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Chunxia Dai
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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20
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Guo L, Yu Y, Yu H, Tang Y, Li J, Du Y, Chu Y, Ma S, Ma Y, Zeng X. Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:5558-5564. [PMID: 31150114 DOI: 10.1002/jsfa.9824] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 05/26/2019] [Accepted: 05/28/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-1000 nm) was developed in the present study. A Savitsky-Golay first derivative (SG1) transform was utilized to eliminate the constant spectral baseline offset. Then, the adulterated levels of rice samples were quantified by partial least squares regression (PLSR). RESULTS A SG1-PLSR model based on full-wavelength was attained with a coefficient of determination of prediction set (RP ) of 0.9909, root-mean-square error of prediction set (RMSEP ) of 0.0447 g kg-1 and residual predictive deviation (RPDP ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (BW ) and a simplified model (PLSR-15) was established with RP of 0.9769, RMSEP of 0.0708 g kg-1 and RPDP of 3.49. Finally, two visualization maps produced by applying the optimal models (SG1-PLSR and PLSR-15) were used to visualize the adulterated levels of rice. CONCLUSION These results demonstrate that VNIR hyperspectral imaging system is an effective tool for rapidly quantifying and visualizing the adulterated levels of rice. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Lianbo Guo
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Yunxin Yu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Hanyue Yu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Yun Tang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Jun Li
- School of Geography and Planning and Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-sen University, Guangzhou, China
| | - Yu Du
- College of Communication Engineering, Jilin University, Changchun, China
| | - Yanwu Chu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Shixiang Ma
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Yuyang Ma
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyan Zeng
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China
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21
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Feng L, Zhu S, Liu F, He Y, Bao Y, Zhang C. Hyperspectral imaging for seed quality and safety inspection: a review. PLANT METHODS 2019; 15:91. [PMID: 31406499 PMCID: PMC6686453 DOI: 10.1186/s13007-019-0476-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 08/01/2019] [Indexed: 05/22/2023]
Abstract
Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, 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 and Rural Affairs, Hangzhou, 310058 China
| | - Yidan Bao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, 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 and Rural Affairs, Hangzhou, 310058 China
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22
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Dai C, Huang X, Huang D, Lv R, Sun J, Zhang Z, Aheto JH. Real‐time detection of saponin content during the fermentation process of
Tremella aurantialba
using a homemade artificial olfaction system. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Chunxia Dai
- School of Food and Biological EngineeringJiangsu University Zhenjiang Jiangsu China
- School of Electrical and Information EngineeringJiangsu University Zhenjiang Jiangsu China
| | - Xingyi Huang
- School of Food and Biological EngineeringJiangsu University Zhenjiang Jiangsu China
| | - Daming Huang
- School of Food and Biological EngineeringJiangsu University Zhenjiang Jiangsu China
| | - Riqin Lv
- School of Biological Science and Food EngineeringChuzhou University Chuzhou Anhui China
| | - Jun Sun
- School of Electrical and Information EngineeringJiangsu University Zhenjiang Jiangsu China
| | - Zhicai Zhang
- School of Food and Biological EngineeringJiangsu University Zhenjiang Jiangsu China
| | - Joshua H. Aheto
- School of Food and Biological EngineeringJiangsu University Zhenjiang Jiangsu China
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23
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Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9081654] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.
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24
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Fu D, Wang Q, Ma M, Ma Y, Wang B. Nondestructive prediction modeling of S‐ovalbumin content in stored eggs based on hyperspectral fusion information. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Dan‐Dan Fu
- College of EngineeringHuazhong Agricultural University Wuhan China
| | - Qiao‐Hua Wang
- College of EngineeringHuazhong Agricultural University Wuhan China
- National Research and Development Center for Egg ProcessingHuazhong Agricultural University Wuhan China
- Ministry of Agriculture Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River Wuhan China
| | - Mei‐Hu Ma
- National Research and Development Center for Egg ProcessingHuazhong Agricultural University Wuhan China
- College of Food Science and TechnologyHuazhong Agricultural University Wuhan China
| | - Yi‐Xiao Ma
- College of EngineeringHuazhong Agricultural University Wuhan China
| | - Bin Wang
- College of EngineeringHuazhong Agricultural University Wuhan China
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25
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Jun S, Yating L, Xiaohong W, Chunxia D, Yong C. SSC prediction of cherry tomatoes based on IRIV‐CS‐SVR model and near infrared reflectance spectroscopy. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12884] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sun Jun
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang Jiangsu China
| | - Li Yating
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang Jiangsu China
| | - Wu Xiaohong
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang Jiangsu China
| | - Dai Chunxia
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang Jiangsu China
| | - Chen Yong
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang Jiangsu China
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26
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Feng L, Zhu S, Lin F, Su Z, Yuan K, Zhao Y, He Y, Zhang C. Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks. SENSORS 2018; 18:s18061944. [PMID: 29914074 PMCID: PMC6021935 DOI: 10.3390/s18061944] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 06/13/2018] [Accepted: 06/13/2018] [Indexed: 11/16/2022]
Abstract
Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
| | - Fucheng Lin
- State Key Laboratory for Rice Biology, Institute of Biotechnology, Zhejiang University, Hangzhou 310058, China.
| | - Zhenzhu Su
- State Key Laboratory for Rice Biology, Institute of Biotechnology, Zhejiang University, Hangzhou 310058, China.
| | - Kangpei Yuan
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Yiying Zhao
- 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.
| | - 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.
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Sun J, Tang K, Wu X, Dai C, Chen Y, Shen J. Nondestructive identification of green tea varieties based on hyperspectral imaging technology. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12800] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Kai Tang
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Xiaohong Wu
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Chunxia Dai
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Yong Chen
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Jifeng Shen
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
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Sun J, Cong S, Mao H, Wu X, Yang N. Quantitative detection of mixed pesticide residue of lettuce leaves based on hyperspectral technique. J FOOD PROCESS ENG 2017. [DOI: 10.1111/jfpe.12654] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang China
| | - Sunli Cong
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang China
| | - Hanping Mao
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education; Jiangsu University; Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang China
| | - Ning Yang
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang China
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Feng X, Zhao Y, Zhang C, Cheng P, He Y. Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis. SENSORS (BASEL, SWITZERLAND) 2017; 17:E1894. [PMID: 28817075 PMCID: PMC5580036 DOI: 10.3390/s17081894] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 08/08/2017] [Accepted: 08/13/2017] [Indexed: 12/02/2022]
Abstract
There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.
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Affiliation(s)
- Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Peng Cheng
- Institute of Quality and Standard for Agro-Products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
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