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Xia H, Chen W, Hu D, Miao A, Qiao X, Qiu G, Liang J, Guo W, Ma C. Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion. Food Chem 2024; 440:138242. [PMID: 38154280 DOI: 10.1016/j.foodchem.2023.138242] [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: 09/04/2023] [Revised: 11/25/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.
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Affiliation(s)
- Hongling Xia
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Wei Chen
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Die Hu
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Aiqing Miao
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Xiaoyan Qiao
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Guangjun Qiu
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Jianhua Liang
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Weiqing Guo
- GRINM (Guangdong) Institute for Advanced Materials and Technology, Foshan, Guangdong Province 528000, PR China.
| | - Chengying Ma
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China.
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2
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Tang T, Luo Q, Yang L, Gao C, Ling C, Wu W. Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology. Foods 2023; 13:25. [PMID: 38201054 PMCID: PMC10778318 DOI: 10.3390/foods13010025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.
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Affiliation(s)
- Ting Tang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Qing Luo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Liu Yang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Changlun Gao
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Caijin Ling
- Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Weibin Wu
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
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3
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TaqMan Probes for Plant Species Identification and Quantification in Food and Feed Traceability. Methods Mol Biol 2023; 2638:301-314. [PMID: 36781651 DOI: 10.1007/978-1-0716-3024-2_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
In the last few years, the traceability and labeling of processed food and feeds have gained increasing importance due to the impact that mislabeling and product fraud may have on human/animal health or on the quality of final products, such as milk, cheese, and meat, as a consequence of animal dietary. The presence of contaminants or possible frauds due to the use of alternative plant materials in food and feeds can greatly impact the economy; therefore, they are becoming important targets for product certification by competent institutional services. This is especially relevant when complex matrixes are considered, in which the visual identification of the different components is quite difficult or even impossible. Despite the existence of mandatory traceability requirements for the analysis of feed/food composition addressed by European Community regulations, the labels do not always provide a sufficient guarantee about the ingredients and additive composition of those products. In this sense, the development of new methodologies that aim to assess the traceability of feed and food complex matrixes is crucial. In this chapter, a general protocol is presented for the establishment of quantitative real-time PCR-based techniques based on TaqMan assays applied to feed/food traceability, with a special focus on applications in the areas of food and feed security (e.g., for the detection of plant species involved in allergenic reactions), fraud detection (e.g., genetically modified organisms), and certification (e.g., protected denomination of origin).
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4
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Huang H, Fei X, Hu X, Tian J, Ju J, Luo H, Huang D. Analysis of the spectral and textural features of hyperspectral images for the nondestructive prediction of amylopectin and amylose contents of sorghum. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.105018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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5
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Zhang Z, Wu J, Chen Y, Wang J, Xu J. Distinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1752. [PMID: 36554157 PMCID: PMC9778404 DOI: 10.3390/e24121752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/26/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the LSEFSTE100 and LSES&P500 are higher than LSESZI, which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy.
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Affiliation(s)
- Zelin Zhang
- School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
- Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430061, China
| | - Jun Wu
- School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
- Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430061, China
| | - Yufeng Chen
- School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
| | - Ji Wang
- School of Liberal Arts and Humanities, Sichuan Vocational College of Finance and Economics, Chengdu 610101, China
| | - Jinyu Xu
- School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
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Lin H, He X, Chen H, Li Z, Yin C, Shi Y. A residual dense comprehensively regulated convolutional neural network to identify spectral information for egg quality traceability. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3780-3789. [PMID: 36124761 DOI: 10.1039/d2ay01371a] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the egg market, due to the different nutritional values of eggs laid by hens under different feeding conditions, it is common for low-quality eggs to be counterfeited as high-quality eggs. This paper proposes a residual dense comprehensively regulated convolutional neural network (RDCR-Net) to identify the quality of eggs laid by hens under different feeding conditions. Firstly, a hyperspectral system is used to obtain the spectral information of eggs. Secondly, due to the complex structure of the spectral information, a comprehensively regulated convolution (CRConv) is proposed to extract features hidden in the spectral information through feature transformation in multiple spaces. Thirdly, due to the limited availability of spectral information training samples, deep networks may suffer from feature degradation. The residual dense comprehensively regulated block (RDCR-Block) is proposed to tightly connect multiple CRConv layers with residual dense connections. Finally, the RDCR-Block is taken as the central unit, and the RDCR-Net is designed to identify egg spectral information. In the comparison of multi-model results, the RDCR-Net obtains the best classification performance with 96.29% accuracy, 97.53% precision, 97.14% recall, and 96.19% kappa coefficient. In summary, the RDCR-Net effectively extracts the deep features of spectral information, achieves high accuracy in identifying eggs laid by hens under different feeding conditions, and provides a new method for egg quality traceability.
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Affiliation(s)
- Hualing Lin
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China.
- Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin, 132012, China
| | - Xinyu He
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China.
- Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin, 132012, China
| | - Haoming Chen
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China.
- Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin, 132012, China
| | - Ziyang Li
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China.
- Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin, 132012, China
| | - Chongbo Yin
- School of Bioengineering, Chongqing University, Chongqing, 400000, China
| | - Yan Shi
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China.
- Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin, 132012, China
- Institute of Advanced Sensor Technology, Northeast Electric Power University, Jilin, 132012, China
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7
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NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea. Foods 2022; 11:foods11192976. [PMID: 36230052 PMCID: PMC9563823 DOI: 10.3390/foods11192976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/29/2022] Open
Abstract
Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (RP = 0.9407, RPD = 3.00), FAA (RP = 0.9110, RPD = 2.21) and TP/FAA (RP = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.
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Compression and reinforce variation with convolutional neural networks for hyperspectral image classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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9
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Huang H, Hu X, Tian J, Peng X, Luo H, Huang D, Zheng J, Wang H. Rapid and nondestructive determination of sorghum purity combined with deep forest and near-infrared hyperspectral imaging. Food Chem 2022; 377:131981. [PMID: 34979401 DOI: 10.1016/j.foodchem.2021.131981] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 12/26/2021] [Accepted: 12/28/2021] [Indexed: 11/04/2022]
Abstract
This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity.
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Affiliation(s)
- Haoping Huang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
| | - Xinghui Peng
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Huibo Luo
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Dan Huang
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Jia Zheng
- Wuliangye Co., Ltd., Yibin 644000, China
| | - Hong Wang
- Wuliangye Co., Ltd., Yibin 644000, China
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10
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Cai H, Zhong Z, Li Z, Zhang X, Fu H, Yang B, Zhang L. Metabolomics in quality formation and characterisation of tea products: a review. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.15767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hongli Cai
- College of Life Sciences and Medicine Zhejiang Sci‐Tech University Hangzhou 310018 China
| | - Zhuoheng Zhong
- College of Life Sciences and Medicine Zhejiang Sci‐Tech University Hangzhou 310018 China
| | - Zhanming Li
- School of Grain Science and Technology Jiangsu University of Science and Technology Zhenjiang 212004 China
| | - Xiaojing Zhang
- State Key Laboratory of Food Science and Technology School of Food Science and Technology Jiangnan University Wuxi Jiangsu 214122 China
| | - Hongwei Fu
- College of Life Sciences and Medicine Zhejiang Sci‐Tech University Hangzhou 310018 China
| | - Bingxian Yang
- College of Life Sciences and Medicine Zhejiang Sci‐Tech University Hangzhou 310018 China
| | - Lin Zhang
- College of Life Sciences and Medicine Zhejiang Sci‐Tech University Hangzhou 310018 China
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11
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An Optimized Detection Method for Chinese Red Huajiao Geographical Origin Determination, Based on Electronic Tongue and Ensemble Recognition Algorithm. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02285-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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12
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Krauz L, Páta P, Kaiser J. Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging. SENSORS 2022; 22:s22020603. [PMID: 35062571 PMCID: PMC8781588 DOI: 10.3390/s22020603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/28/2021] [Accepted: 01/10/2022] [Indexed: 12/05/2022]
Abstract
Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.
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Affiliation(s)
- Lukáš Krauz
- Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech Republic; (P.P.); (J.K.)
- Correspondence: ; Tel.: +420-22435-2113
| | - Petr Páta
- Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech Republic; (P.P.); (J.K.)
| | - Jan Kaiser
- Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech Republic; (P.P.); (J.K.)
- FOMEI s.r.o., U Libeňského pivovaru 2015, 180 00 Prague, Czech Republic
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13
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Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection. Foods 2021; 11:foods11010008. [PMID: 35010134 PMCID: PMC8750721 DOI: 10.3390/foods11010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022] Open
Abstract
Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.
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14
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Mao Y, Li N, Shi B, Zhao L, Cheng S, Tian S, Wang H. Geographical origin determination of Red Huajiao in China using the electronic nose combined with ensemble recognition algorithm. J Food Sci 2021; 86:4922-4931. [PMID: 34642944 DOI: 10.1111/1750-3841.15933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 12/29/2022]
Abstract
Red Huajiao was the most important Zanthoxylum species in China, and its quality was highly determined the geographical region. This study was aimed to establish a determination method for the geographical origin recognition of Red Huajiao by using the electronic nose and ensemble recognition algorithm. Six origins of samples were detected by the electronic nose, and two categories of electronic nose sensors characteristic values, named as "optimized characteristic value" and "filtered characteristic value," were obtained by the principal component analysis and discrimination index method and Filter-Wrapper method. Based on the two categories of characteristic values, 22 kinds of model analysis methods, which belonged to five categories of ensemble recognition algorithms were used to recognize the geographical origin. The total recognition accuracy rate of the two categories of characteristic values were 83.9% and 85.7%, respectively. Furthermore, during 22 kinds of model analysis method, the ensemble Subspace KNN and Bagged Trees methods in Ensemble Learning algorithm exhibited the best distinguishing ability with the accuracy rate more than 90%. Therefore, the electronic nose combined with Ensemble Learning would be promising for the geographical origin determination application. PRACTICAL APPLICATION: This work demonstrates that the Red Huajiao can be simply and rapidly determined by using electronic nose combined with ensemble recognition algorithm, allowing to effectively distinguish geographical origin of Red Huajiao, which can provide an important reference for the quality assessment of Huajiao.
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Affiliation(s)
- Yuezhong Mao
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Na Li
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Bolin Shi
- China National Institute of Standardization, Beijing, China
| | - Lei Zhao
- China National Institute of Standardization, Beijing, China
| | - Shiwen Cheng
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Shiyi Tian
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Houyin Wang
- China National Institute of Standardization, Beijing, China
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15
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Jiang X, Hu X, Huang H, Tian J, Bu Y, Huang D, Luo H. Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13844] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Xinna Jiang
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Xinjun Hu
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Haoping Huang
- 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
| | - Youhua Bu
- School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China
| | - Dan Huang
- College of Bioengineering Sichuan University of Science and Engineering Zigong China
| | - Huibo Luo
- College of Bioengineering Sichuan University of Science and Engineering Zigong China
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16
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Huang H, Hu X, Tian J, Jiang X, Luo H, Huang D. Rapid detection of the reducing sugar and amino acid nitrogen contents of Daqu based on hyperspectral imaging. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103970] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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17
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Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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18
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Rapid and nondestructive prediction of amylose and amylopectin contents in sorghum based on hyperspectral imaging. Food Chem 2021; 359:129954. [PMID: 33964659 DOI: 10.1016/j.foodchem.2021.129954] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 02/06/2023]
Abstract
The contents of amylose and amylopectin in sorghum directly affects the quality and yield of liquor. Hyperspectral imaging (HSI) is an emerging technology widely applied in the content analysis of food ingredients. In this study, the effects of different preprocessing methods on visible-light and near-infrared spectral data were analyzed, and the prediction accuracies of these spectral data were compared. Principal components analysis (PCA) and successive projections algorithm (SPA) were combined to extract the characteristic wavelengths. Using both the full and characteristic wavelengths, partial least square regression (PLSR) and cascade forest (CF) models were developed to predict the contents of amylose and amylopectin in different varieties of sorghum. The average RPD values of the CF models established by the characteristic wavelengths were 4.7622 and 5.5889, respectively. These results corroborated the utility of HSI in achieving the rapid and nondestructive prediction of amylose and amylopectin contents in different varieties of sorghum.
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Ren G, Li T, Wei Y, Ning J, Zhang Z. Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Ren G, Wang Y, Ning J, Zhang Z. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2135-2142. [PMID: 32981110 DOI: 10.1002/jsfa.10836] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/28/2020] [Accepted: 09/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 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, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Saha D, Manickavasagan A. Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Curr Res Food Sci 2021; 4:28-44. [PMID: 33659896 PMCID: PMC7890297 DOI: 10.1016/j.crfs.2021.01.002] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/15/2021] [Accepted: 01/26/2021] [Indexed: 11/29/2022] Open
Abstract
Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed. Artificial neural network has been intensively used for Hyperspectral image (HSI) analysis. Support vector machines and random forest techniques are gaining momentum for HSI analysis. Deep learning applications has potential for implementation in real time HSI analysis. Lifelong machine learning needs further research to incorporate the seasonal variations in food quality.
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Affiliation(s)
- Dhritiman Saha
- School of Engineering, University of Guelph, N1G2W1, Canada
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Li L, Wang Y, Jin S, Li M, Chen Q, Ning J, Zhang Z. Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 246:118991. [PMID: 33068895 DOI: 10.1016/j.saa.2020.118991] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/18/2020] [Accepted: 09/21/2020] [Indexed: 06/11/2023]
Abstract
Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, 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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Ren G, Ning J, Zhang Z. Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118918. [PMID: 32942112 DOI: 10.1016/j.saa.2020.118918] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 05/05/2023]
Abstract
The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.
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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
| | - 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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Ren G, Gan N, Song Y, Ning J, Zhang Z. Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105600] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics. SENSORS 2020; 20:s20195451. [PMID: 32977413 PMCID: PMC7582835 DOI: 10.3390/s20195451] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/16/2020] [Accepted: 09/20/2020] [Indexed: 12/24/2022]
Abstract
Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.
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Ren G, Ning J, Zhang Z. Intelligent assessment of tea quality employing visible-near infrared spectra combined with a hybrid variable selection strategy. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105085] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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