1
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Li W, Wang Q, Wang Y. Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy. Foods 2024; 13:1863. [PMID: 38928805 PMCID: PMC11202870 DOI: 10.3390/foods13121863] [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: 05/18/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024] Open
Abstract
Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.
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Affiliation(s)
- Wei Li
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (W.L.); (Y.W.)
| | - Qiaohua Wang
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (W.L.); (Y.W.)
- Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
| | - Yingli Wang
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (W.L.); (Y.W.)
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2
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Feng S, Shang J, Tan T, Wen Q, Meng Q. Nondestructive quality assessment and maturity classification of loquats based on hyperspectral imaging. Sci Rep 2023; 13:13189. [PMID: 37580378 PMCID: PMC10425455 DOI: 10.1038/s41598-023-40553-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/12/2023] [Indexed: 08/16/2023] Open
Abstract
The traditional method for assessing the quality and maturity of loquats has disadvantages such as destructive sampling and being time-consuming. In this study, hyperspectral imaging technology was used to nondestructively predict and visualise the colour, firmness, and soluble solids content (SSC) of loquats and discriminate maturity. On comparison of the performance of different feature variables selection methods and the calibration models, the results indicated that the multiple linear regression (MLR) models combined with the competitive adaptive reweighting algorithm (CARS) yielded the best prediction performance for loquat quality. Particularly, CARS-MLR models with optimal prediction performance were obtained for the colour (R2P = 0.96, RMSEP = 0.45, RPD = 5.38), firmness (R2P = 0.87, RMSEP = 0.23, RPD = 2.81), and SSC (R2P = 0.84, RMSEP = 0.51, RPD = 2.54). Subsequently, distribution maps of the colour, firmness, and SSC of loquats were obtained based on the optimal CARS-MLR models combined with pseudo-colour technology. Finally, on comparison of different classification models for loquat maturity, the partial least square discrimination analysis model demonstrated the best performance, with classification accuracies of 98.19% and 97.99% for calibration and prediction sets, respectively. This study demonstrated that the hyperspectral imaging technique is promising for loquat quality assessment and maturity classification.
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Affiliation(s)
- Shunan Feng
- Food and Pharmaceutical Engineering Institute, Guiyang University, Guiyang, 550005, China
| | - Jing Shang
- Food and Pharmaceutical Engineering Institute, Guiyang University, Guiyang, 550005, China.
- Research Center of Nondestructive Testing for Agricultural Products of Guizhou Province, Guiyang, 550005, China.
| | - Tao Tan
- Food and Pharmaceutical Engineering Institute, Guiyang University, Guiyang, 550005, China
| | - Qingchun Wen
- Food and Pharmaceutical Engineering Institute, Guiyang University, Guiyang, 550005, China
| | - Qinglong Meng
- Food and Pharmaceutical Engineering Institute, Guiyang University, Guiyang, 550005, China
- Research Center of Nondestructive Testing for Agricultural Products of Guizhou Province, Guiyang, 550005, China
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3
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Saha D, Senthilkumar T, Singh CB, Manickavasagan A. Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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4
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Hu Y, Ma B, Wang H, Zhang Y, Li Y, Yu G. Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion. FRONTIERS IN PLANT SCIENCE 2023; 14:1105601. [PMID: 37223822 PMCID: PMC10200917 DOI: 10.3389/fpls.2023.1105601] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/31/2023] [Indexed: 05/25/2023]
Abstract
Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.
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Affiliation(s)
- Yating Hu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Benxue Ma
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Huting Wang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Yuanjia Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Yujie Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Guowei Yu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
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5
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Hu Y, Ma B, Wang H, Li Y, Zhang Y, Yu G. Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging. Foods 2023; 12:foods12091773. [PMID: 37174311 PMCID: PMC10178042 DOI: 10.3390/foods12091773] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.
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Affiliation(s)
- Yating Hu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Benxue Ma
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832003, China
| | - Huting Wang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832003, China
| | - Yujie Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yuanjia Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Guowei Yu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
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6
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Saha D, Senthilkumar T, Sharma S, Singh CB, Manickavasagan A. Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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7
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Medina I, Scholl S, Rädle M. Film Thickness and Glycerol Concentration Mapping of Falling Films Based on Fluorescence and Near-Infrared Technique. MICROMACHINES 2022; 13:2184. [PMID: 36557483 PMCID: PMC9785223 DOI: 10.3390/mi13122184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Falling film evaporation processes involve high fluid velocities with continuous variations in local film thickness, fluid composition, and viscosity. This contribution presents a parallel and complementary film thickness and concentration mapping distribution in falling films using a non-invasive fluorescence and near-infrared imaging technique. The experiments were performed with a mixture of glycerol/water with a mass fraction from 0 to 0.65 gglycgtotal-1 and operating ranges similar to evaporation processes. The measurement system was designed by integrating two optical measurement methods for experimental image analysis. The film thickness was evaluated using a VIS camera and high-power LEDs at 470 nm. The local glycerol concentration gglycgtotal-1 was determined using a NIR camera and high-power LEDs at 1050, 1300, 1450 and 1550 nm. A multiwavelength analysis with all NIR wavelengths was implemented with a better correlation for falling films at low flow velocity. The results show an improvement in the analysis of falling films with high flow velocities up to almost 500 mm/s by using only the 1450 nm wavelength and the fluorescence measurement. Simultaneous imaging analysis of film thickness and concentration in falling films provides further insight into understanding mass and heat transport and thus supports the optimization of falling film evaporators.
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Affiliation(s)
- Isabel Medina
- Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Stephan Scholl
- Institute for Chemical and Thermal Process Engineering, Technische Universität Braunschweig, 38106 Braunschweig, Germany
| | - Matthias Rädle
- Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
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8
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Zhang X, Li S, Shan Y, Li P, Jiang L, Liu X, Fan W. Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xinxin Zhang
- College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha 410128 P. R. China
| | - Shangke Li
- College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha 410128 P. R. China
| | - Yang Shan
- Hunan Agricultural Product Processing Institute Hunan Provincial Key Laboratory for Fruits and Vegetables Storage Processing and Quality Safety Hunan Academy of Agricultural Sciences Changsha 410125 P. R. China
| | - Pao Li
- College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha 410128 P. R. China
- Hunan Agricultural Product Processing Institute Hunan Provincial Key Laboratory for Fruits and Vegetables Storage Processing and Quality Safety Hunan Academy of Agricultural Sciences Changsha 410125 P. R. China
| | - Liwen Jiang
- College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha 410128 P. R. China
| | - Xia Liu
- College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha 410128 P. R. China
| | - Wei Fan
- College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha 410128 P. R. China
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9
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Sun J, Tian Y, Zhou X, Yao K, Tang N. Detection of soluble solid content in apples based on hyperspectral technology combined with deep learning algorithm. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16414] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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 Jiangsu University Zhenjiang 212013 China
| | - Yan Tian
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China
- School of Electronic Information Jiangsu University of Science and Technology Zhenjiang 212003 China
| | - Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China
| | - Ningqiu Tang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China
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10
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Zhang P, Shen B, Ji H, Wang H, Liu Y, Zhang X, Ren C. Nondestructive Prediction of Mechanical Parameters to Apple Using Hyperspectral Imaging by Support Vector Machine. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-021-02201-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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11
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Nondestructive Methods for the Quality Assessment of Fruits and Vegetables Considering Their Physical and Biological Variability. FOOD ENGINEERING REVIEWS 2022. [DOI: 10.1007/s12393-021-09300-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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12
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Liu P, Qiao Y, Hou B, Xi Z, Hu Y. Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Penghui Liu
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Yichen Qiao
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Bingru Hou
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Ziting Xi
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Yaohua Hu
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
- Key Laboratory of Agricultural Internet of Things Ministry of Agriculture Yangling China
- College of Mechanical and Electronic Engineering Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling China
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13
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Liu P, Zhang P, Ni F, Hu Y. Feasibility of nondestructive detection of apple crispness based on spectroscopy and machine vision. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Penghui Liu
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling China
| | - Peng Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling China
| | - Fupeng Ni
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling China
| | - Yaohua Hu
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture of the P.R. China Yangling China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling China
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14
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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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15
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Liu Y, Wang H, Fei Y, Liu Y, Shen L, Zhuang Z, Zhang X. Research on the Prediction of Green Plum Acidity Based on Improved XGBoost. SENSORS 2021; 21:s21030930. [PMID: 33573249 PMCID: PMC7866513 DOI: 10.3390/s21030930] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 11/19/2022]
Abstract
The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.
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16
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Li H, Zhu J, Jiao T, Wang B, Wei W, Ali S, Ouyang Q, Zuo M, Chen Q. Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 243:118765. [PMID: 32861202 DOI: 10.1016/j.saa.2020.118765] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/15/2020] [Accepted: 07/18/2020] [Indexed: 06/11/2023]
Abstract
This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700 nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N = 100, k = 500, ω = 14, σ = 10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (RP = 0.9053), and lower root mean square errors (RMSEP = 0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.
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Affiliation(s)
- Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiaji Zhu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Tianhui Jiao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Bing Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Shujat Ali
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Min Zuo
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, 100048 Beijing, PR China; School of Computer and Information Engineering, Beijing Technology and Business University, 100048, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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17
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Tian Y, Sun J, Zhou X, Wu X, Lu B, Dai C. Research on apple origin classification based on variable iterative space shrinkage approach with stepwise regression
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support vector machine algorithm and visible‐near infrared hyperspectral imaging. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13432] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Yan Tian
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang China
- School of Electronic Information, Jiangsu University of Science and Technology Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information 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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Song W, Jiang N, Wang H, Guo G. Evaluation of machine learning methods for organic apple authentication based on diffraction grating and image processing. J Food Compost Anal 2020. [DOI: 10.1016/j.jfca.2020.103437] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Ni F, Meng Q, Gu F, Hu Y. Building kinetic models for apple crispness to determine the optimal freshness preservation time during shelf life based on spectroscopy. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.14422] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Fupeng Ni
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Qingkui Meng
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Fang Gu
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
| | - Yaohua Hu
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling China
- Key Laboratory of Agricultural Internet of Things Ministry of Agriculture Yangling China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Northwest A&F University Yangling China
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20
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Weng S, Yu S, Dong R, Pan F, Liang D. Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2020. [DOI: 10.1080/10942912.2020.1716793] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Shuan Yu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Ronglu Dong
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Fangfang Pan
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
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21
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Ni F, Zhu X, Gu F, Hu Y. Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths. Food Sci Nutr 2019; 7:3654-3663. [PMID: 31763014 PMCID: PMC6848846 DOI: 10.1002/fsn3.1222] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/16/2019] [Accepted: 08/23/2019] [Indexed: 11/25/2022] Open
Abstract
Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x-loading weights (x-LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of "Fuji" and "Qinguan" apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for "Fuji" and "Qinguan" apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the "Fuji" apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x-LW for the "Qinguan" apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for "Fuji" and "Qinguan" apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both "Fuji" and "Qinguan" apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.
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Affiliation(s)
- Fupeng Ni
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
| | - Xiaowen Zhu
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
| | - Fang Gu
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
| | - Yaohua Hu
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
- Key Laboratory of Agricultural Internet of ThingsMinistry of AgricultureYanglingChina
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent ServiceYanglingChina
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22
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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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23
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Zhu X, Li G. Rapid detection and visualization of slight bruise on apples using hyperspectral imaging. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2019. [DOI: 10.1080/10942912.2019.1669638] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Xiaolin Zhu
- School of Internet of Things Engineering, Jiangnan University, Wuxi, China
- Engineering Research Center of IoT Technology Application, MOE, Wuxi, China
| | - Guanghui Li
- School of Internet of Things Engineering, Jiangnan University, Wuxi, China
- Engineering Research Center of IoT Technology Application, MOE, Wuxi, China
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Relationship between quality characteristics and skin color of ‘Fuji’ Apples (Malus domestica Borkh.). JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00112-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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25
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Zhang Y, Guo W. Moisture content detection of maize seed based on visible/near‐infrared and near‐infrared hyperspectral imaging technology. Int J Food Sci Technol 2019. [DOI: 10.1111/ijfs.14317] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yanmin Zhang
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling Shaanxi 712100 China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling Shaanxi 712100 China
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26
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Non-destructive detection of Flos Lonicerae treated by sulfur fumigation based on hyperspectral imaging. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9896-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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27
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Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9834-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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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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Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models. Sci Rep 2017; 7:7845. [PMID: 28798306 PMCID: PMC5552817 DOI: 10.1038/s41598-017-08509-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 07/11/2017] [Indexed: 11/08/2022] Open
Abstract
We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre = 0.9812, RPD = 5.17) and SSC (R pre = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.
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Liu Y, Sun Y, Xie A, Yu H, Yin Y, Li X, Duan X. Potential of Hyperspectral Imaging for Rapid Prediction of Anthocyanin Content of Purple-Fleshed Sweet Potato Slices During Drying Process. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-0950-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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31
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Discrimination of “Hayward” Kiwifruits Treated with Forchlorfenuron at Different Concentrations Using Hyperspectral Imaging Technology. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0603-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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32
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Mayorga-Martínez AA, Olvera-Trejo D, Elías-Zúñiga A, Parra-Saldívar R, Chuck-Hernández C. Non-destructive Assessment of Guava (Psidium guajava L.) Maturity and Firmness Based on Mechanical Vibration Response. FOOD BIOPROCESS TECH 2016. [DOI: 10.1007/s11947-016-1736-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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