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Shi BW, Zhao JM, Wang YK, Wang YX, Jiang YF, Yang GL, Wang J, Qiang T. Real-Time Detection of Yeast Growth on Solid Medium through Passive Microresonator Biosensor. BIOSENSORS 2024; 14:216. [PMID: 38785692 PMCID: PMC11117844 DOI: 10.3390/bios14050216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024]
Abstract
This study presents a biosensor fabricated based on integrated passive device (IPD) technology to measure microbial growth on solid media in real-time. Yeast (Pichia pastoris, strain GS115) is used as a model organism to demonstrate biosensor performance. The biosensor comprises an interdigital capacitor in the center with a helical inductive structure surrounding it. Additionally, 12 air bridges are added to the capacitor to increase the strength of the electric field radiated by the biosensor at the same height. Feasibility is verified by using a capacitive biosensor, and the change in capacitance values during the capacitance detection process with the growth of yeast indicates that the growth of yeast can induce changes in electrical parameters. The proposed IPD-based biosensor is used to measure yeast drop-added on a 3 mm medium for 100 h at an operating frequency of 1.84 GHz. The resonant amplitude of the biosensor varies continuously from 24 to 72 h due to the change in colony height during vertical growth of the yeast, with a maximum change of 0.21 dB. The overall measurement results also fit well with the Gompertz curve. The change in resonant amplitude between 24 and 72 h is then analyzed and reveals a linear relationship with time with a coefficient of determination of 0.9844, indicating that the biosensor is suitable for monitoring yeast growth. Thus, the proposed biosensor is proved to have potential in the field of microbial proliferation detection.
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Affiliation(s)
- Bo-Wen Shi
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (B.-W.S.); (J.-M.Z.); (Y.-K.W.); (Y.-X.W.); (Y.-F.J.)
| | - Jun-Ming Zhao
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (B.-W.S.); (J.-M.Z.); (Y.-K.W.); (Y.-X.W.); (Y.-F.J.)
| | - Yi-Ke Wang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (B.-W.S.); (J.-M.Z.); (Y.-K.W.); (Y.-X.W.); (Y.-F.J.)
| | - Yan-Xiong Wang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (B.-W.S.); (J.-M.Z.); (Y.-K.W.); (Y.-X.W.); (Y.-F.J.)
| | - Yan-Feng Jiang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (B.-W.S.); (J.-M.Z.); (Y.-K.W.); (Y.-X.W.); (Y.-F.J.)
| | - Gang-Long Yang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing 100190, China
| | - Jicheng Wang
- School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University, Wuxi 214122, China;
| | - Tian Qiang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (B.-W.S.); (J.-M.Z.); (Y.-K.W.); (Y.-X.W.); (Y.-F.J.)
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Xie M, Zhu Y, Li Z, Yan Y, Liu Y, Wu W, Zhang T, Li Z, Wang H. Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis. Talanta 2024; 268:125281. [PMID: 37832450 DOI: 10.1016/j.talanta.2023.125281] [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: 07/18/2023] [Revised: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.
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Affiliation(s)
- Maomei Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yiting Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zhiyao Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yueling Yan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yidan Liu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Wenbo Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Tong Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
| | - Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
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Yang Z, Zhang M, Li X, Xu Z, Chen Y, Xu X, Chen D, Meng L, Si X, Wang J. Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection. Photodiagnosis Photodyn Ther 2024; 45:103984. [PMID: 38244654 DOI: 10.1016/j.pdpdt.2024.103984] [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: 11/25/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/22/2024]
Abstract
Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.
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Affiliation(s)
- Zhe Yang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Minrui Zhang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Xianduo Li
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Zhipeng Xu
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Yi Chen
- Shandong Medical College, Jinan 250000, China
| | - Xiaoyu Xu
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Dongdong Chen
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Lingquan Meng
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Xiaoqing Si
- Department of dermatology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China.
| | - Jianning Wang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China.
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Zhang H, He Q, Yang C, Lu M, Liu Z, Zhang X, Li X, Dong C. Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:9684. [PMID: 38139529 PMCID: PMC10748152 DOI: 10.3390/s23249684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.
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Affiliation(s)
- Haowen Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Qinghai He
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China;
| | - Chongshan Yang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Min Lu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Zhongyuan Liu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Xiaojia Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China;
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
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Ke Q, Yin L, Jayan H, El-Seedi HR, Gómez PL, Alzamora SM, Zou X, Guo Z. Determination of Dicofol in Tea Using Surface-Enhanced Raman Spectroscopy Coupled Chemometrics. Molecules 2023; 28:5291. [PMID: 37513164 PMCID: PMC10386380 DOI: 10.3390/molecules28145291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
Dicofol is a highly toxic residual pesticide in tea, which seriously endangers human health. A method for detecting dicofol in tea by combining stoichiometry with surface-enhanced Raman spectroscopy (SERS) technology was proposed in this study. AuNPs were prepared, and silver shells were grown on the surface of AuNPs to obtain core-shell Au@AgNPs. Then, the core-shell Au@AgNPs were attached to the surface of a PDMS membrane by physical deposition to obtain a Au@AgNPs/PDMS substrate. The limit of detection (LOD) of this substrate for 4-ATP is as low as 0.28 × 10-11 mol/L, and the LOD of dicofol in tea is 0.32 ng/kg, showing high sensitivity. By comparing the modeling effects of preprocessing and variable selection algorithms, it is concluded that the modeling effect of Savitzky-Golay combined with competitive adaptive reweighted sampling-partial least squares regression is the best (Rp = 0.9964, RPD = 10.6145). SERS technology combined with stoichiometry is expected to rapidly detect dicofol in tea without labels.
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Affiliation(s)
- Qian Ke
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Limei Yin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, Jiangsu University, Zhenjiang 212013, China
| | - Heera Jayan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hesham R El-Seedi
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, BMC, Uppsala University, P.O. Box 591, SE 751 24 Uppsala, Sweden
- International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Paula L Gómez
- Consejo Nacional de Investigaciones Cientificasy Tecnicas (CONICET), University of Buenos Aires, Ciudad Autónoma de Buenos Aires C1428EGA, Argentina
| | - Stella M Alzamora
- Consejo Nacional de Investigaciones Cientificasy Tecnicas (CONICET), University of Buenos Aires, Ciudad Autónoma de Buenos Aires C1428EGA, Argentina
| | - Xiaobo Zou
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, Jiangsu University, Zhenjiang 212013, China
- Consejo Nacional de Investigaciones Cientificasy Tecnicas (CONICET), University of Buenos Aires, Ciudad Autónoma de Buenos Aires C1428EGA, Argentina
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
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Chen Y, Wu H, Liu Y, Wang Y, Lu C, Li T, Wei Y, Ning J. Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3093-3101. [PMID: 36418909 DOI: 10.1002/jsfa.12350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Yuyu Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Huiting Wu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- 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
| | - Chengye Lu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yuming Wei
- 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
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Rapid Screening of High-Yield Gellan Gum Mutants of Sphingomonas paucimobilis ATCC 31461 by Combining Atmospheric and Room Temperature Plasma Mutation with Near-Infrared Spectroscopy Monitoring. Foods 2022; 11:foods11244078. [PMID: 36553820 PMCID: PMC9777525 DOI: 10.3390/foods11244078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/23/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
In this study, an efficient mutagenesis and rapid screening method of high-yield gellan gum mutant by atmospheric and room temperature plasma (ARTP) treatment combined with Near-Infrared Spectroscopy (NIRS) was proposed. A NIRS model for the on-line detection of gellan gum yield was constructed by joint interval partial least squares (siPLS) regression on the basis of chemical determination and NIRS acquisition of gellan gum yield. Five genetically stable mutant strains were screened using the on-line NIRS detection of gellan gum yield in the fermentation from approximately 600 mutant strains induced by ARTP. Remarkably, compared with the original strain, the gellan gum yield of mutant strain 519 was 9.427 g/L (increased by 133.5%) under the optimal fermentation conditions, which was determined by single-factor and response surface optimization. Therefore, the method of ARTP mutation combined with the NIRS model can be used to screen high-yield mutant strains of gellan gum and other high-yield polysaccharide strains.
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Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Xu W, He Y, Li J, Deng Y, Zhou J, Xu E, Ding T, Wang W, Liu D. Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness. Meat Sci 2022; 194:108950. [PMID: 36087368 DOI: 10.1016/j.meatsci.2022.108950] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 08/12/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022]
Abstract
Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.
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Affiliation(s)
- Weidong Xu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yingchao He
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jiaheng Li
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yong Deng
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jianwei Zhou
- Ningbo Research Institute, Zhejiang University, Ningbo 315100, China; Zhejiang University Ningbo Institute of Technology, Ningbo 315100, China
| | - Enbo Xu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China
| | - Tian Ding
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China; Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
| | - Wenjun Wang
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Donghong Liu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China; Ningbo Research Institute, Zhejiang University, Ningbo 315100, China; Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan 314100, China.
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Cai R, Chen X, Zhang Y, Wang X, Zhou N. Systematic bio-fabrication of aptamers and their applications in engineering biology. SYSTEMS MICROBIOLOGY AND BIOMANUFACTURING 2022; 3:223-245. [PMID: 38013802 PMCID: PMC9550155 DOI: 10.1007/s43393-022-00140-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 10/27/2022]
Abstract
Aptamers are single-stranded DNA or RNA molecules that have high affinity and selectivity to bind to specific targets. Compared to antibodies, aptamers are easy to in vitro synthesize with low cost, and exhibit excellent thermal stability and programmability. With these features, aptamers have been widely used in biology and medicine-related fields. In the meantime, a variety of systematic evolution of ligands by exponential enrichment (SELEX) technologies have been developed to screen aptamers for various targets. According to the characteristics of targets, customizing appropriate SELEX technology and post-SELEX optimization helps to obtain ideal aptamers with high affinity and specificity. In this review, we first summarize the latest research on the systematic bio-fabrication of aptamers, including various SELEX technologies, post-SELEX optimization, and aptamer modification technology. These procedures not only help to gain the aptamer sequences but also provide insights into the relationship between structure and function of the aptamers. The latter provides a new perspective for the systems bio-fabrication of aptamers. Furthermore, on this basis, we review the applications of aptamers, particularly in the fields of engineering biology, including industrial biotechnology, medical and health engineering, and environmental and food safety monitoring. And the encountered challenges and prospects are discussed, providing an outlook for the future development of aptamers.
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Affiliation(s)
- Rongfeng Cai
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122 China
| | - Xin Chen
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122 China
| | - Yuting Zhang
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122 China
| | - Xiaoli Wang
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122 China
| | - Nandi Zhou
- The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122 China
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Deng J, Zhang X, Li M, Jiang H, Chen Q. Feasibility study on Raman spectra-based deep learning models for monitoring the contamination degree and level of aflatoxin B1 in edible oil. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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12
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Fourier transform near-infrared spectroscopy coupled with variable selection methods for fast determination of salmon fillets storage time. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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13
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Electronic nose signals-based deep learning models to realize high-precision monitoring of simultaneous saccharification and fermentation of cassava. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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14
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He Y, Xu W, Qu M, Zhang C, Wang W, Cheng F. Recent advances in the application of Raman spectroscopy for fish quality and safety analysis. Compr Rev Food Sci Food Saf 2022; 21:3647-3672. [PMID: 35794726 DOI: 10.1111/1541-4337.12968] [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: 01/28/2022] [Revised: 03/29/2022] [Accepted: 04/14/2022] [Indexed: 11/27/2022]
Abstract
Fish is one of the highly demanded aquatic products, and its quality and safety play a pivotal role in daily diet. However, the possible hazardous substance in perishable fish both in pre- and postharvest periods may decrease their values and pose a threat to public health. Laborious and expensive traditional methods drive the need of developing effective tools for detecting fish quality and safety properties in a rapid, nondestructive, and effective manner. Recent advances in Raman spectroscopy (RS) and surface-enhanced Raman scattering (SERS) have shown enormous potential in various aspects, which largely boost their applications in fish quality and safety evaluation. They have incomparable merits such as providing molecule fingerprint information and allowing for rapid, sensitive, and noninvasive detection with simple sample preparation. This review provides a comprehensive overview focusing on the applications of RS and SERS for fish quality assessment and safety inspection, highlighting the hazardous substance and illegal behavior both in preharvest (veterinary drug residues and environmental pollutants) and postharvest (freshness and illegal behavior) particularly. Moreover, challenges and prospects are also proposed to facilitate the vigorous development of RS and SERS. This review is aimed to emphasize potential opportunities for applying RS and SERS as promising techniques for routine food quality and safety detection. PRACTICAL APPLICATION: With these applications, it can be clearly indicated that RS and SERS are promising and powerful in fish quality and safety surveillance, thereby reducing the occurrence of commercial fraud and food safety issues. More efforts still should be concentrated on exploiting the high-performance Raman instruments, establishing a universal Raman database, developing reproducible SERS substrates and combing RS with other versatile spectral techniques to promote these technologies from laboratory to practice. It is hoped that this review should arouse more research interests in RS and SERS technologies for fish quality and safety surveillance, as well as provide more insights to make a breakthrough.
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Affiliation(s)
- Yingchao He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
| | - Weidong Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Maozhen Qu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
| | - Chao Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
| | - Wenjun Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Hangzhou, China
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
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15
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Zhu C, Jiang H, Chen Q. High Precisive Prediction of Aflatoxin B1 in Pressing Peanut Oil Using Raman Spectra Combined with Multivariate Data Analysis. Foods 2022; 11:foods11111565. [PMID: 35681315 PMCID: PMC9180714 DOI: 10.3390/foods11111565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 12/12/2022] Open
Abstract
This study proposes a label-free rapid detection method for aflatoxin B1 (AFB1) in pressing peanut oil based on Raman spectroscopy technology combined with appropriate chemometric methods. A DXR laser Raman spectrometer was used to acquire the Raman spectra of the pressed peanut oil samples, and the obtained spectra were preprocessed by wavelet transform (WT) combined with adaptive iteratively reweighted penalized least squares (airPLS). The competitive adaptive reweighted sampling (CARS) method was used to optimize the characteristic bands of the Raman spectra pretreated by the WT + airPLS, and a partial least squares (PLS) detection model for the AFB1 content was established based on the features optimized. The results obtained showed that the root mean square error of prediction (RMSEP) and determination coefficient of prediction (RP2) of the optimal CARS-PLS model in the prediction set were 22.6 µg/kg and 0.99, respectively. The results demonstrate that the Raman spectroscopy combined with appropriate chemometrics can be used to quickly detect the safety of edible oil with high precision. The overall results can provide a technical basis and method reference for the design and development of the portable Raman spectroscopy system for the quality and safety detection of edible oil storage, and also provide a green tool for fast on-site analysis for regulatory authorities of edible oil and production enterprises of edible oil.
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Affiliation(s)
- Chengyun Zhu
- School of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng 224007, China;
- Jiangsu Intelligent Optoelectronic Devices and Measurement and Control Engineering Research Center, Yancheng 224007, China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China;
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16
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Determination of lead in food by surface-enhanced Raman spectroscopy with aptamer regulating gold nanoparticles reduction. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108498] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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17
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Zhu C, Jiang H, Chen Q. Rapid determination of process parameters during simultaneous saccharification and fermentation (SSF) of cassava based on molecular spectral fusion (MSF) features. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120245. [PMID: 34364037 DOI: 10.1016/j.saa.2021.120245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/27/2021] [Accepted: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Simultaneous saccharification and fermentation (SSF) of cassava is one of the key steps in the production of fuel ethanol. In order to improve the monitoring efficiency of the ethanol production process and the product yield, this study puts forward a new idea for monitoring of the cassava SSF process based on the molecular spectroscopy fusion (MSF) technique. Savisky-Golay (SG) combined with standard normal variable (SNV) was used to preprocess the obtained Raman spectra and near-infrared (NIR) spectra. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic wavelengths of the preprocessed Raman spectra and the NIR spectra, and the optimized features were fused in the feature layer. The support vector machine (SVM) model of the process parameters during the cassava SSF based on the MSF features was established. The experimental results showed that compared with the best CARS-SVM model based on the single-molecule spectral features, the performance of the best CARS-SVM model based on fusion features has been significantly improved. For detection of the glucose content, the RMSEP, RP2 and RPD of the best CARS-SVM model were 5.398, 0.957 and 4.922, respectively. For detection of the ethanol content, the RMSEP, RP2 and RPD of the best CARS-SVM model were 4.394, 0.977 and 6.758, respectively. The obtained results reveal that the combination of MSF technique and appropriate chemometric methods can achieve high-precision quantitative detection of the process parameters during the cassava SSF. This study can provide technical basis and experimental reference for the development of portable spectrometer equipment for process monitoring of the cassava SSF.
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Affiliation(s)
- Chengyun Zhu
- School of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng 224007, PR China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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18
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OUP accepted manuscript. J Pharm Pharmacol 2022; 74:1040-1050. [DOI: 10.1093/jpp/rgab177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022]
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19
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Liu Z, Yang C, Luo X, Hu B, Dong C. Research on the online rapid sensing method of moisture content in famous green tea spreading. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Zhongyuan Liu
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
- Tea Research Institute The Chinese Academy of Agricultural Sciences Hangzhou China
| | - Chongshan Yang
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
- Tea Research Institute The Chinese Academy of Agricultural Sciences Hangzhou China
| | - Xin Luo
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
| | - Bin Hu
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
| | - Chunwang Dong
- Tea Research Institute The Chinese Academy of Agricultural Sciences Hangzhou China
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20
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Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104110] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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21
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Wang Y, Wang C, Dong F, Wang S. Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:4157-4168. [PMID: 34554149 DOI: 10.1039/d1ay00757b] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900-1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with Rc of 0.8716, RMSEC of 0.0793 g/100 g, RPDc of 2.398, and Rp of 0.8121 with RMSEP of 0.1481 g/100 g and RPDp of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.
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Affiliation(s)
- Yan Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
| | - Caixia Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
| | - Fujia Dong
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
| | - Songlei Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
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22
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Zhuang S, Renault N, Archer I. A brief review on recent development of multidisciplinary engineering in fermentation of Saccharomyces cerevisiae. J Biotechnol 2021; 339:32-41. [PMID: 34339775 DOI: 10.1016/j.jbiotec.2021.07.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 07/13/2021] [Accepted: 07/27/2021] [Indexed: 11/26/2022]
Abstract
Fermentation technology has unprecedented potential to upgrade state-of-art biotechnology and refine the processes used in existing ones, taking into account of complex technical, economic and environmental factors. Given the economic importance and ongoing challenges of biotech sector, multidisciplinary engineering technologies is poised to become an increasingly important tool along with the emergence of modern technology and innovation. This article reviews recent technology advancement in the field of fermentation using Saccharomyces cerevisiae. Interesting research progress has been made by leveraging multiple engineering fields such as electrical engineering, information engineering, electrochemical engineering and new material development, leading to recent development of novel real-time probes (electronic nose technology, analysis of yeast morphology and metabolites, timely control of glucose feed), improved understanding of electro-fermentation (enhanced electronic transfer provision), as well as application of cost-effective and sustainable materials (bioreactor vessel manufactured from textile, and yeast immobilisation support matrix made from abundant natural biomass). To the best of our knowledge, the subject is reviewed for the first time in recent years. Furthermore, this review also constitutes a futuristic S. cerevisiae fermentation process based on the recent advancement discussed.
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Affiliation(s)
- Shiwen Zhuang
- Industrial Biotechnology Innovation Centre (IBioIC), University of Strathclyde, Glasgow, G1 1XQ, United Kingdom; School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
| | - Neil Renault
- Industrial Biotechnology Innovation Centre (IBioIC), University of Strathclyde, Glasgow, G1 1XQ, United Kingdom; School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom
| | - Ian Archer
- Industrial Biotechnology Innovation Centre (IBioIC), University of Strathclyde, Glasgow, G1 1XQ, United Kingdom
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23
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Jiang H, He Y, Chen Q. Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA-based strategy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3328-3335. [PMID: 33222172 DOI: 10.1002/jsfa.10962] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near-infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA-based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage. RESULTS The results revealed that, compared with the full-spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g-1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively. CONCLUSION The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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24
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Feng Y, Tian X, Chen Y, Wang Z, Xia J, Qian J, Zhuang Y, Chu J. Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose. BIORESOUR BIOPROCESS 2021; 8:37. [PMID: 38650202 PMCID: PMC10991113 DOI: 10.1186/s40643-021-00391-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 04/29/2021] [Indexed: 02/08/2023] Open
Abstract
In this study, introduction of a viable cell sensor and electronic nose into ethanol fermentation was investigated, which could be used in real-time and on-line monitoring of the amount of living cells and product content, respectively. Compared to the conventional off-line biomass determination, the capacitance value exhibited a completely consistent trend with colony forming units, indicating that the capacitance value could reflect the living cells in the fermentation broth. On the other hand, in comparison to the results of off-line determination by high-performance liquid chromatography, the ethanol concentration measured by electronic nose presented an excellent consistency, so as to realize the on-line monitoring during the whole process. On this basis, a dynamic feeding strategy of glucose guided by the changes of living cells and ethanol content was developed. And consequently, the ethanol concentration, productivity and yield were enhanced by 15.4%, 15.9% and 9.0%, respectively. The advanced sensors adopted herein to monitor the key parameters of ethanol fermentation process could be readily extended to an industrial scale and other similar fermentation processes.
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Affiliation(s)
- Yao Feng
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
| | - Xiwei Tian
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China.
| | - Yang Chen
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
| | - Zeyu Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
| | - Jiangchao Qian
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P. O. Box 329#, Shanghai, 200237, China
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25
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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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26
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Cappozzo A, Duponchel L, Greselin F, Murphy TB. Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food. Anal Chim Acta 2021; 1153:338245. [PMID: 33714445 DOI: 10.1016/j.aca.2021.338245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/23/2020] [Accepted: 01/20/2021] [Indexed: 11/28/2022]
Abstract
Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths.
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Affiliation(s)
- Andrea Cappozzo
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
| | - Ludovic Duponchel
- Univ. Lille, CNRS, UMR 8516, LASIRE-Laboratoire avancé de spectroscopie pour les interactions, la réactivité et l'environnement, F-59000, Lille, France.
| | - Francesca Greselin
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
| | - Thomas Brendan Murphy
- School of Mathematics & Statistics and Insight Research Centre, University College Dublin, Dublin, Ireland.
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Jiang H, He Y, Xu W, Chen Q. Quantitative Detection of Acid Value During Edible Oil Storage by Raman Spectroscopy: Comparison of the Optimization Effects of BOSS and VCPA Algorithms on the Characteristic Raman Spectra of Edible Oils. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01939-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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28
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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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29
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Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats. Processes (Basel) 2021. [DOI: 10.3390/pr9030422] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance.
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30
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Fan N, Liu G, Wan G, Ban J, Yuan R, Sun Y, Li Y. A combination of near‐infrared hyperspectral imaging with two‐dimensional correlation analysis for monitoring the content of biogenic amines in mutton. Int J Food Sci Technol 2021. [DOI: 10.1111/ijfs.14950] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Naiyun Fan
- School of Food & Wine Ningxia University Yinchuan750021China
| | - Guishan Liu
- School of Food & Wine Ningxia University Yinchuan750021China
| | - Guoling Wan
- School of Food & Wine Ningxia University Yinchuan750021China
| | - Jingjing Ban
- School of Food & Wine Ningxia University Yinchuan750021China
| | - Ruirui Yuan
- School of Food & Wine Ningxia University Yinchuan750021China
| | - Yourui Sun
- School of Food & Wine Ningxia University Yinchuan750021China
| | - Yue Li
- School of Food & Wine Ningxia University Yinchuan750021China
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31
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Lin YK, Leong HY, Ling TC, Lin DQ, Yao SJ. Raman spectroscopy as process analytical tool in downstream processing of biotechnology. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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32
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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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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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Ren G, Wang Y, Ning J, Zhang Z. Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 237:118407. [PMID: 32361218 DOI: 10.1016/j.saa.2020.118407] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/19/2020] [Accepted: 04/21/2020] [Indexed: 06/11/2023]
Abstract
The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of 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, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, China.
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Ren G, Liu Y, Ning J, Zhang Z. Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion. Int J Food Sci Technol 2020. [DOI: 10.1111/ijfs.14624] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization Anhui Agricultural University Hefei 230036 China
| | - Ying Liu
- 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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