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Cheng H, Zhang Z, Cheng Y, Guan J. Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in 'Yali' pear. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124688. [PMID: 38941754 DOI: 10.1016/j.saa.2024.124688] [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: 12/27/2023] [Revised: 04/22/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in 'Yali' pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm-2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm-2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in 'Yali' pear, which would be helpful for supporting postharvest decision systems.
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Affiliation(s)
- Hong Cheng
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China
| | - Zishen Zhang
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China; College of Horticulture, Xinjiang Agruicultural University, Urumqi 830000, Xinjiang, China
| | - Yudou Cheng
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China
| | - Junfeng Guan
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China.
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Qi H, Luo J, Chen G, Zhang J, Chen F, Li H, Shen C, Zhang C. Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:4309-4319. [PMID: 38305465 DOI: 10.1002/jsfa.13316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. RESULTS This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. CONCLUSION The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Jiahao Luo
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Gang Chen
- Zhejiang Dekfeller Intelligent Machinery Manufacturing Co., Ltd, Hangzhou, China
| | - Jianyi Zhang
- Zhejiang Dekfeller Intelligent Machinery Manufacturing Co., Ltd, Hangzhou, China
| | - Fengnong Chen
- School of Automation, School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China
| | - Hongyang Li
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Cong Shen
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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3
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Ouyang H, Tang L, Ma J, Pang T. Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears. PLANTS (BASEL, SWITZERLAND) 2024; 13:1163. [PMID: 38674571 PMCID: PMC11055027 DOI: 10.3390/plants13081163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/12/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024]
Abstract
Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG-GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG-SVR model. The SG-CARS-GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG-CARS-GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear.
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Affiliation(s)
- Hongkun Ouyang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (L.T.); (J.M.)
| | | | | | - Tao Pang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (L.T.); (J.M.)
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4
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Mulowayi AM, Shen ZH, Nyimbo WJ, Di ZF, Fallah N, Zheng SH. Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis. Sci Rep 2024; 14:8514. [PMID: 38609452 PMCID: PMC11014857 DOI: 10.1038/s41598-024-59151-y] [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: 12/12/2023] [Accepted: 04/08/2024] [Indexed: 04/14/2024] Open
Abstract
The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.
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Affiliation(s)
- Arcel Mutombo Mulowayi
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Zhen Hui Shen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Engineering College, Fujian Jiangxia University, Fuzhou, 350108, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Witness Joseph Nyimbo
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Zhi Feng Di
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Nyumah Fallah
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Shu He Zheng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China.
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5
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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: 5] [Impact Index Per Article: 5.0] [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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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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7
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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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8
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Qi YP, He PJ, Lan DY, Xian HY, Lü F, Zhang H. Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 153:20-30. [PMID: 36041267 DOI: 10.1016/j.wasman.2022.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/13/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.
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Affiliation(s)
- Ya-Ping Qi
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Pin-Jing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
| | - Dong-Ying Lan
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hao-Yang Xian
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China.
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9
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Chen Y, Sun W, Jiu S, Wang L, Deng B, Chen Z, Jiang F, Hu M, Zhang C. Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:841452. [PMID: 35923875 PMCID: PMC9340214 DOI: 10.3389/fpls.2022.841452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and F 1-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders.
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Affiliation(s)
- Yuzhen Chen
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, China
| | - Wanxia Sun
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Songtao Jiu
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Lei Wang
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Bohan Deng
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zili Chen
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Fei Jiang
- Shanghai Citrus Research Institute, Shanghai, China
| | - Menghan Hu
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, China
| | - Caixi Zhang
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
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10
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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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11
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Gao Q, Wang P, Niu T, He D, Wang M, Yang H, Zhao X. Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging. Food Chem 2022; 370:131013. [PMID: 34509150 DOI: 10.1016/j.foodchem.2021.131013] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/08/2021] [Accepted: 08/29/2021] [Indexed: 11/04/2022]
Abstract
Malus micromalus Makino has great commercial and nutritional value. The regression and classification models were investigated by using near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics to improve the efficiency of non-destructive detection. The successive projections algorithm (SPA), interval random frog, and competitive adaptive reweighted sampling were employed to extract effective wavelengths sensitive to changes of soluble solid content (SSC) and firmness index (FI) information. Two types of assessment models based on full spectrum and effective wavelengths, namely partial least squares regression and extreme learning machine, were established to predict SSC and FI. In addition, the classification models based on the support vector machine improved by the grey wolf optimizer (GWO-SVM) and partial least squares discrimination analysis were constructed to differentiate maturity stage. The SPA-ELM and SPA-GWO-SVM models achieved satisfactory performance. The results illustrate that NIR-HSI is feasible for evaluation of the quality of Malus micromalus Makino.
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Affiliation(s)
- Qiang Gao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China.
| | - Peng Wang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
| | - Tong Niu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
| | - Dongjian He
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China.
| | - Meili Wang
- College of Information Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China.
| | - Huijun Yang
- College of Information Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
| | - Xiaoqiang Zhao
- School of Communication and Information, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China.
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12
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Guo L, Hunag W, Zheng W, Chen F, Wang W, Zhang D, Hu Z, Chu Y. Indirect quantitative analysis of soluble solid content in citrus by the leaves using hyperspectral imaging combined with machine learning. APPLIED OPTICS 2022; 61:491-497. [PMID: 35200888 DOI: 10.1364/ao.440669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Due to the effect of bagging on fruit growth, non-destructive and in situ soluble solid content (SSC) in citrus detection remains a challenge. In this work, a new method for accurately quantifying SSC in citrus using hyperspectral imaging of citrus leaves was proposed. Sixty-five Ehime Kashi No. 28 citruses with surrounding leaves picked at two different times were picked for the experiment. Using the principal components analysis combined with Gaussian process regression model, the correlation coefficients of prediction-real value by citrus and its leaves in cross-validation were 0.972 and 0.986, respectively. In addition, the relationship between citrus leaves and SSC content was further explored, and the possible relationship between chlorophyll in leaves and SSC of citrus was analyzed. Comparing the quantitative analysis results by citrus and its leaves, the results show that the proposed method is a non-destructive and reliable method for determining the SSC by citrus leaves and has broad application prospects in indirect detection of citrus.
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13
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Zhang Y, Yang X, Cai Z, Fan S, Zhang H, Zhang Q, Li J. Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method. Foods 2021; 10:foods10122983. [PMID: 34945536 PMCID: PMC8700705 DOI: 10.3390/foods10122983] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/13/2021] [Accepted: 11/30/2021] [Indexed: 11/24/2022] Open
Abstract
Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.
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Affiliation(s)
- Yifei Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Correspondence: (X.Y.); (J.L.)
| | - Zhonglei Cai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Haiyun Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
| | - Jiangbo Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
- Correspondence: (X.Y.); (J.L.)
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14
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Wu X, Li G, He F. Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing. Foods 2021; 10:1315. [PMID: 34200438 PMCID: PMC8226885 DOI: 10.3390/foods10061315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/29/2021] [Accepted: 06/01/2021] [Indexed: 12/17/2022] Open
Abstract
The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.
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Affiliation(s)
- Xin Wu
- Department of Agricultural Engineering, College of Engineering and Technology, Southwest University, Chongqing 400715, China; (X.W.); (F.H.)
- Department of Electronics and Internet of Things, Chongqing College of Electronic Engineering, Chongqing 401331, China
| | - Guanglin Li
- Department of Agricultural Engineering, College of Engineering and Technology, Southwest University, Chongqing 400715, China; (X.W.); (F.H.)
| | - Fengyun He
- Department of Agricultural Engineering, College of Engineering and Technology, Southwest University, Chongqing 400715, China; (X.W.); (F.H.)
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15
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Wang Z, Fan S, Wu J, Zhang C, Xu F, Yang X, Li J. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 254:119666. [PMID: 33744703 DOI: 10.1016/j.saa.2021.119666] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/28/2021] [Accepted: 02/28/2021] [Indexed: 05/28/2023]
Abstract
Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930-2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full-spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full-spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is Rpre = 0.9311 ± 0.0094 and RMSEP = 1.2131 ± 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed.
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Affiliation(s)
- Zheli Wang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Jingzhu Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Chi Zhang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Fengying Xu
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
| | - Jiangbo Li
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
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16
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Wu X, Li G, Liu X, He F. Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics. Food Energy Secur 2021. [DOI: 10.1002/fes3.289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Xin Wu
- College of Engineering and Technology Southwest University Chongqing China
- Chongqing College of Electronic Engineering Chongqing China
| | - Guanglin Li
- College of Engineering and Technology Southwest University Chongqing China
| | - Xuwen Liu
- College of Engineering and Technology Southwest University Chongqing China
| | - Fengyun He
- College of Engineering and Technology Southwest University Chongqing China
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17
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Uyeh DD, Ha Y, Park T. Animal feed formulation: Rapid and non-destructive measurement of components from waste by-products. Anim Feed Sci Technol 2021. [DOI: 10.1016/j.anifeedsci.2021.114848] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Yang X, Liu G, He J, Kang N, Yuan R, Fan N. Determination of sugar content in Lingwu jujube by NIR-hyperspectral imaging. J Food Sci 2021; 86:1201-1214. [PMID: 33770419 DOI: 10.1111/1750-3841.15674] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 02/07/2021] [Accepted: 02/09/2021] [Indexed: 11/30/2022]
Abstract
Near infrared hyperspectral imaging (NIR-HSI) with a spectral range of 900 to 1700 nm was for the first time used to predict the changes of sugar content in Lingwu jujube during storage. Monte Carlo method was adopted to detect outliers, and multiple scattering correction (MSC), standard normal variate transformation (SNV), and Baseline were used to optimize modeling. Competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), and interval random frog (IRF) were used to select optimal wavelengths. In addition, partial least square regression (PLSR) and support vector machine (SVM) modeling based on optimal wavelengths were compared. The results showed that 30, 30, and 24 wavelengths were selected by CARS; 106, 87, and 112 feature wavelengths were selected by iVISSA; and 96, 71, and 83 optimal wavelengths were selected by IRF for sucrose, fructose, and glucose, respectively. The CARS-PLSR models provided the best results for fructose and glucose, and iVISSA-SVM model was better for sucrose. The results indicated that NIR-HSI model may be used as a rapid and nondestructive method for the determination of sugar content in jujubes.
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Affiliation(s)
- Xiaoyu Yang
- School of Food and Wine, Ningxia University, Yinchuan, China
| | - Guishan Liu
- School of Food and Wine, Ningxia University, Yinchuan, China
| | - Jianguo He
- School of Food and Wine, Ningxia University, Yinchuan, China
| | - Ningbo Kang
- School of Food and Wine, Ningxia University, Yinchuan, China
| | - Ruirui Yuan
- School of Food and Wine, Ningxia University, Yinchuan, China
| | - Naiyun Fan
- School of Food and Wine, Ningxia University, Yinchuan, China
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19
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Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging. SENSORS 2020; 20:s20061611. [PMID: 32183206 PMCID: PMC7146463 DOI: 10.3390/s20061611] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 11/17/2022]
Abstract
Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
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20
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Wang H, Zhang R, Peng Z, Jiang Y, Ma B. Measurement of SSC in processing tomatoes (
Lycopersicon esculentum Mill
.) by applying Vis‐NIR hyperspectral transmittance imaging and multi‐parameter compensation models. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13100] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Huting Wang
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Ruoyu Zhang
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Zhen Peng
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Yinglan Jiang
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Benxue Ma
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
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21
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Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol 2019; 10:197-220. [DOI: 10.1146/annurev-food-032818-121155] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.
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Affiliation(s)
- Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland;,
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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22
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Li J, Fan S, Huang W. Assessment of multiregion local models for detection of SSC of whole peach (
Amygdalus persica
L.) by combining both hyperspectral imaging and wavelength optimization methods. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12914] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Jiangbo Li
- Beijing Research Center of Intelligent Equipment for Agriculture Beijing China
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture Beijing China
| | - Wenqian Huang
- Beijing Research Center of Intelligent Equipment for Agriculture Beijing China
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23
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Comparison and Optimization of Models for Determination of Sugar Content in Pear by Portable Vis-NIR Spectroscopy Coupled with Wavelength Selection Algorithm. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1326-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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24
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25
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Ye D, Sun L, Zou B, Zhang Q, Tan W, Che W. Non-destructive prediction of protein content in wheat using NIRS. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 189:463-472. [PMID: 28843880 DOI: 10.1016/j.saa.2017.08.055] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 08/09/2017] [Accepted: 08/19/2017] [Indexed: 06/07/2023]
Abstract
A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples. Additionally, improved simulated annealing (ISA) combined with PLSR was employed to select most effective variables from spectral data, the data's dimensionality reduced from 100 to 57, and the standard error of prediction (SEP) decreased from 0.0716 to 0.0565 for prediction set, as well as the correlation coefficients (R2) between the predicted and actual protein content of wheat increased from 0.9989 to 0.9994. In order to reduce the dimensionality of the data further, successive projections algorithm (SPA) was then used, the combination of these two methods was called ISA-SPA. The results indicated that calibration model built using ISA-SPA on 14 effective variables achieved the optimal performance for prediction of protein content in wheat comparing with other developed PLSR models (ISA or SPA) by comprehensively considering the accuracy, robustness, and complexity of models. The coefficient of determination increased to 0.9986 and the SEP decreased to 0.0528, respectively.
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Affiliation(s)
- Dandan Ye
- Key Laboratory of Electronics Engineering, Heilongjiang University, Harbin, College of Heilongjiang Province, China; Room 503, Building A8, Heilongjiang University, No. 74, Xuefu road, Nangang District, Harbin 150080, China
| | - Laijun Sun
- Key Laboratory of Electronics Engineering, Heilongjiang University, Harbin, College of Heilongjiang Province, China; Room 503, Building A8, Heilongjiang University, No. 74, Xuefu road, Nangang District, Harbin 150080, China.
| | - Borui Zou
- Key Laboratory of Electronics Engineering, Heilongjiang University, Harbin, College of Heilongjiang Province, China
| | - Qian Zhang
- Key Laboratory of Electronics Engineering, Heilongjiang University, Harbin, College of Heilongjiang Province, China; Room 503, Building A8, Heilongjiang University, No. 74, Xuefu road, Nangang District, Harbin 150080, China
| | - Wenyi Tan
- Key Laboratory of Electronics Engineering, Heilongjiang University, Harbin, College of Heilongjiang Province, China
| | - Wenkai Che
- Key Laboratory of Electronics Engineering, Heilongjiang University, Harbin, College of Heilongjiang Province, China
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26
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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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27
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Jia S, Li H, Wang Y, Tong R, Li Q. Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen. SENSORS 2017; 17:s17102252. [PMID: 28974005 PMCID: PMC5677396 DOI: 10.3390/s17102252] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 11/16/2022]
Abstract
Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People's Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874-1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.
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Affiliation(s)
- Shengyao Jia
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
| | - Hongyang Li
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China.
| | - Yanjie Wang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
| | - Renyuan Tong
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
| | - Qing Li
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
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Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7010109] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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