1
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Sun J, Yao K, Cheng J, Xu M, Zhou X. Nondestructive detection of saponin content in Panax notoginseng powder based on hyperspectral imaging. J Pharm Biomed Anal 2024; 242:116015. [PMID: 38364344 DOI: 10.1016/j.jpba.2024.116015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/16/2024] [Accepted: 02/03/2024] [Indexed: 02/18/2024]
Abstract
This study investigated the feasibility of using hyperspectral imaging (HSI) technique to detect the saponin content in Panax notoginseng (PN) powder. The reflectance hyperspectral images of PN powder samples were collected in the spectral range of 400.6-999.9 nm. Savitzky-golay (SG) smoothing combined with detrending correction was utilized to preprocess the original spectral data. Two model population analysis (MPA) based methods, namely bootstrapping soft shrinkage (BOSS) and iteratively retains informative variables (IRIV) were employed to extract feature wavelengths from the full spectra. A generalized normal distribution optimization based extreme learning machine (GNDO-ELM) model was proposed to establish calibration model between spectra and saponin content, and compared with existing methods (GA-ELM, PSO-ELM and SSA-ELM). The result showed that the IRIV-GNDO-ELM model gave the best performance, with coefficient of determination for prediction (R2P) of 0.953 and root mean square error for prediction (RMSEP) of 0.115%. Therefore, it is possible to determine the saponin content of PN powder by using HSI technique.
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Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Kunshan Yao
- School of Electrical and Information Engineering of Changzhou Institute of Technology, Changzhou 213032, China.
| | - Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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2
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Zhou X, Zhao C, Sun J, Cao Y, Yao K, Xu M. A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging. Food Chem 2023; 409:135251. [PMID: 36586261 DOI: 10.1016/j.foodchem.2022.135251] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/31/2022]
Abstract
The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.
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Affiliation(s)
- Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Chunjiang Zhao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Yan Cao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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3
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Zou Z, Wu Q, Long T, Zou B, Zhou M, Wang Y, Liu B, Luo J, Yin S, Zhao Y, Xu L. Classification and Adulteration of Mengding Mountain Green Tea Varieties Based on Fluorescence Hyperspectral Image Method. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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4
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Rapid authentication of green tea grade by excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric methods. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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5
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Wang Y, Yang J, Yu S, Fu H, He S, Yang B, Nan T, Yuan Y, Huang L. Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2022. [DOI: 10.3389/fsufs.2022.1036892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fruits of Zanthoxylum bungeanum Maxim (Red “Huajiao,” RHJ) and Z. schinifolium Sieb. et Zucc. (Green “Huajiao,” GHJ) are famous spices around the world. Antioxidant capability (AOC), total alkylamides content (TALC) and volatile oil content (VOC) in HJ are three important quality indicators and lack rapid and effective methods for detection. Non-destructive, time-saving, and effective technology of hyperspectral imaging (HSI) combined with chemometrics was adopted to improve the indicators prediction in this study. Results showed that the three chemical indexes exhibited significant differences between different regions and varieties (P < 0.05). Specifically, the mass percentages of TALC were 11–22% in RHJ group and 21–36% in GHJ group. The mass percentages of VOC content were 23–31% and 16–24% in RHJ and GHJ groups, respectively. More importantly, these indicators could be well predicted based on the full or effective HSI wavelengths via model adaptive space shrinkage (MASS) and iteratively variable subset optimization (IVSO) selections combined with wavelet transform (WT) method for noise reduction. The best prediction results of AOC, TALC, and VOC indicators were achieved with the highest residual predictive deviation (RPD) values of 7.43, 7.82, and 3.73 for RHJ, respectively, and 6.82, 2.66, and 4.64 for GHJ, respectively. The above results highlight the great potential of HSI assisted with chemometrics in the rapid and effective prediction of chemical indicators of Zanthoxylum spices.
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Wei K, Chen B, Li Z, Chen D, Liu G, Lin H, Zhang B. Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7764. [PMID: 36298114 PMCID: PMC9609479 DOI: 10.3390/s22207764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/25/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.
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Affiliation(s)
- Kaihua Wei
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Bojian Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zejian Li
- Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Dongmei Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guangyu Liu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hongze Lin
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
- Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing 312000, China
| | - Baihua Zhang
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325000, China
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Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics. Foods 2022; 11:foods11152344. [PMID: 35954110 PMCID: PMC9368096 DOI: 10.3390/foods11152344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/27/2022] [Accepted: 08/03/2022] [Indexed: 12/04/2022] Open
Abstract
Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.
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8
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Yao K, Sun J, Chen C, Xu M, Zhou X, Cao Y, Tian Y. Non-destructive detection of egg qualities based on hyperspectral imaging. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111024] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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9
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Hu Y, Kang Z. The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27041196. [PMID: 35208985 PMCID: PMC8876823 DOI: 10.3390/molecules27041196] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/06/2022] [Accepted: 02/07/2022] [Indexed: 11/16/2022]
Abstract
Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.
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10
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Nirere A, Sun J, Atindana VA, Hussain A, Zhou X, Yao K. A comparative analysis of hybrid SVM and LS‐SVM classification algorithms to identify dried wolfberry fruits quality based on hyperspectral imaging technology. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16320] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Adria Nirere
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | | | - Ahmad Hussain
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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11
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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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12
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Sun T, Hu X, Tian J, Gu Q, Huang D, Luo H, Huang D. Combination of Spectral and Spatial Information of Hyperspectral Imaging for the Prediction of the Moisture Content and Visualizing Distribution in Daqu. JOURNAL OF THE AMERICAN SOCIETY OF BREWING CHEMISTS 2022. [DOI: 10.1080/03610470.2021.2008221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Ting Sun
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, China
- Chengdu Technological University, Chengdu, China
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Qiang Gu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Danping Huang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong, China
| | - Huibo Luo
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong, China
| | - Dan Huang
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong, China
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13
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PAN T, YAN R, CHEN Q. Geographical origin of green tea identification using LASSO and ANOVA. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.41922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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14
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Xu M, Sun J, Yao K, Wu X, Shen J, Cao Y, Zhou X. Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging. J Food Sci 2021; 87:326-338. [PMID: 34940982 DOI: 10.1111/1750-3841.16004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 11/01/2021] [Accepted: 11/12/2021] [Indexed: 11/27/2022]
Abstract
Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68-1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter-based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD-RC-LSSVM model obtained the best prediction accuracy for TSS, with R p 2 of 0.93, with R M S E P of 0.6 %, with R E R of 18.14 and R P D p of 3.69. The overall results indicated that the VMD-RC algorithm can be used as an alternative to handle high-dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. PRACTICAL APPLICATION: Traditional methods of evaluating grape quality attributes are destructive, time-consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.
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Affiliation(s)
- Min Xu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.,School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu, 213164, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Kunshan Yao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Jifeng Shen
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Yan Cao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Xin Zhou
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
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15
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Sun J, Zhang L, Zhou X, Yao K, Tian Y, Nirere A. A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Lin Zhang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Yan Tian
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Adria Nirere
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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16
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Song Y, Wang X, Xie H, Li L, Ning J, Zhang Z. Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 252:119522. [PMID: 33582437 DOI: 10.1016/j.saa.2021.119522] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.
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Affiliation(s)
- Yan Song
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Xiaozhong Wang
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Hanlei Xie
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
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17
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Xu M, Sun J, Zhou X, Tang N, Shen J, Wu X. Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image. J Food Sci 2021; 86:2011-2023. [PMID: 33885160 DOI: 10.1111/1750-3841.15715] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/10/2021] [Accepted: 03/13/2021] [Indexed: 12/16/2022]
Abstract
Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved denoising algorithm based on ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) is proposed to couple with the hyperspectral image (HSI) of grape varieties in this study. First, the hyperspectral data of grape varieties are collected by using HSI instrument, and denoised by the proposed EEMD-DWT and other denoising algorithms. CARS-SPA (competitive adaptive reweighed sampling coupled with successive projections algorithm) is introduced to select the effective wavelengths and a discriminative model is constructed by using support vector machine (SVM). Finally, Monte Carlo experiments verified that EEMD-DWT was an effective and powerful spectra denoising method, and the SVM model constructed by combining with CARS-SPA had an excellent identification accuracy (99.3125%). The results suggested that the key wavelengths selected by using CARS-SPA and EEMD-DWT could be an alternative to the deal with HSI, and its potential to become a method for identifying grape varieties. PRACTICAL APPLICATION: Traditional grape varieties identification methods are destructive and time consuming. Therefore, HSI technology is applied to realize fast and nondestructive identification of grape varieties in this study. The research results indicate that it is feasible to combine HSI technology with machine learning algorithm to discriminate grape varieties. It is of great significance for grape grading and the promotion of excellent varieties, and also provides reference for grape industry producers to identify grape varieties quickly and accurately.
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Affiliation(s)
- Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China.,School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Ningqiu Tang
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Jifeng Shen
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
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18
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Yao K, Sun J, Zhang L, Zhou X, Tian Y, Tang N, Wu X. Nondestructive detection for egg freshness based on hyperspectral imaging technology combined with harris hawks optimization support vector regression. J Food Saf 2021. [DOI: 10.1111/jfs.12888] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Lin Zhang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Yan Tian
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Ningqiu Tang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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Ren G, Sun Y, Li M, Ning J, Zhang Z. Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:3950-3959. [PMID: 32329077 DOI: 10.1002/jsfa.10439] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 03/12/2020] [Accepted: 04/24/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy. RESULTS Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (Rp ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin. CONCLUSION The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Yemei Sun
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
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Yao K, Sun J, Zhou X, Nirere A, Tian Y, Wu X. Nondestructive detection for egg freshness grade based on hyperspectral imaging technology. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13422] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Adria Nirere
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Yan Tian
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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21
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Ren G, Wang Y, Ning J, Zhang Z. Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 230:118079. [PMID: 31982655 DOI: 10.1016/j.saa.2020.118079] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 01/12/2020] [Accepted: 01/16/2020] [Indexed: 06/10/2023]
Abstract
From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (Rp) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China.
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Xia Z, Liu W, Zheng F, Huang W, Xing Z, Peng W, Tang T, Luo J, Yi L, Wang Y. VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury. ASN Neuro 2020; 12:1759091420910957. [PMID: 32146828 PMCID: PMC7066589 DOI: 10.1177/1759091420910957] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Metabolomics is an emerging tool to uncover the complex pathogenesis of disease, as well as the multitargets of traditional Chinese medicines, with chemometric analysis being a key step. However, conventional algorithms are not suitable for directly analyzing data at all times. The variable iterative space shrinkage approach-partial least squares-discriminant analysis, a novel algorithm for data mining, was first explored to screen metabolic varieties to reveal the multitargets of Xuefu Zhuyu decoction (XFZY) against traumatic brain injury (TBI) by the 7th day. Rat plasma from Sham, Vehicle, and XFZY groups was used for gas chromatography/mass spectrometry-based metabolomics. This method showed an improved discrimination ability (area under the curve = 93.64%). Threonine, trans-4-hydroxyproline, and creatinine were identified as the direct metabolic targets of XFZY against TBI. Five metabolic pathways affected by XFZY in TBI rats, were enriched using Metabolic Pathway Analysis web tool (i.e., phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; galactose metabolism; alanine, aspartate, and glutamate metabolism; and tryptophan metabolism). In conclusion, metabolomics coupled with variable iterative space shrinkage approach-partial least squares-discriminant analysis model may be a valuable tool for identifying the holistic molecular mechanisms involved in the effects of traditional Chinese medicine, such as XFZY.
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Affiliation(s)
- Zian Xia
- Department of Integrated Traditional Chinese and Western Medicine, Xiangya Hospital, Central South University
| | - Wenbin Liu
- Yunnan Food Safety Research Institute, Kunming University of Science and Technology
| | - Fei Zheng
- College of Electrical and Information Engineering, Hunan University
| | - Wei Huang
- Department of Integrated Traditional Chinese and Western Medicine, Xiangya Hospital, Central South University
| | - Zhihua Xing
- Department of Integrated Traditional Chinese and Western Medicine, Xiangya Hospital, Central South University
| | - Weijun Peng
- Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University
| | - Tao Tang
- Department of Integrated Traditional Chinese and Western Medicine, Xiangya Hospital, Central South University
| | - Jiekun Luo
- Department of Integrated Traditional Chinese and Western Medicine, Xiangya Hospital, Central South University
| | - Lunzhao Yi
- Yunnan Food Safety Research Institute, Kunming University of Science and Technology
| | - Yang Wang
- Department of Integrated Traditional Chinese and Western Medicine, Xiangya Hospital, Central South University
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Hu J, Xiao F, Jin G. Zirconium doping level modulation combined with chalconylthiourea organic frameworks induced enhancement of luminescence applied to cell imaging. NEW J CHEM 2020. [DOI: 10.1039/d0nj02327b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Derivatives of a zirconium metal–organic framework as the center polymer material with a chalconylthiourea polymer (CT) were applied to cell imaging.
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Affiliation(s)
- Jianpeng Hu
- Department of Urology
- Affiliated People's Hospital of Jiangsu University
- Zhenjiang
- P. R. China
| | - Fuyan Xiao
- School of Pharmacy
- Jiangsu University
- Zhenjiang 212013
- P. R. China
| | - Guofan Jin
- School of Pharmacy
- Jiangsu University
- Zhenjiang 212013
- P. R. China
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