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Lin XW, Liu RH, Wang S, Yang JW, Tao NP, Wang XC, Zhou Q, Xu CH. Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37406208 DOI: 10.1021/acs.jafc.3c01841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.
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Affiliation(s)
- Xiao-Wen Lin
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
| | - Run-Hui Liu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
| | - Song Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
| | - Jie-Wen Yang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
| | - Ning-Ping Tao
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China
| | - Xi-Chang Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China
| | - Qun Zhou
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Chang-Hua Xu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China
- Ministry of Agriculture, Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Shanghai 201306, China
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Shanghai 201306, China
- Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
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Shi C, Zi Y, Huang S, Chen J, Wang X, Zhong J. Development and application of lipidomics for food research. ADVANCES IN FOOD AND NUTRITION RESEARCH 2023; 104:1-42. [PMID: 37236729 DOI: 10.1016/bs.afnr.2022.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.
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Affiliation(s)
- Cuiping Shi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ye Zi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Shudan Huang
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jiahui Chen
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Xichang Wang
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jian Zhong
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.
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Lu CH, Li BQ, Jing Q, Pei D, Huang XY. A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine. Food Chem 2023; 420:136161. [PMID: 37080110 DOI: 10.1016/j.foodchem.2023.136161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.
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Affiliation(s)
- Cong-Hui Lu
- CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory of Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bao-Qiong Li
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China.
| | - Quan Jing
- CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory of Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong Pei
- CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory of Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Yunnan Olive Health Industry Innovation Research and Development Co., Ltd, Lijiang 674100, China.
| | - Xin-Yi Huang
- CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory of Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Wu Y, Jiang X, Chen Y, Liu T, Ni Z, Yi H, Lu R. Rapid estimation approach for glycosylated serum protein of human serum based on the combination of deep learning and TD-NMR technology. ANAL SCI 2023; 39:957-968. [PMID: 36897540 DOI: 10.1007/s44211-023-00303-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/13/2023] [Indexed: 03/11/2023]
Abstract
Rapid and precise estimation of glycosylated serum protein (GSP) of human serum is of great importance for the treatment and diagnosis of diabetes mellitus. In this study, we propose a novel method for estimation of GSP level based on the combination of deep learning and time domain nuclear magnetic resonance (TD-NMR) transverse relaxation signal of human serum. Specifically, a principal component analysis (PCA)-enhanced one-dimensional convolutional neural network (1D-CNN) is proposed to analyze the TD-NMR transverse relaxation signal of human serum. The proposed algorithm is proved by accurate estimation of GSP level for the collected serum samples. Furthermore, the proposed algorithm is compared with 1D-CNN without PCA, long short-term memory network (LSTM) and some conventional machine learning algorithms. The results indicate that PCA-enhanced 1D-CNN (PC-1D-CNN) has the minimum error. This study proves that proposed method is feasible and superior to estimate GSP level of human serum using TD-NMR transverse relaxation signals.
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Affiliation(s)
- Yuchen Wu
- Jiangsu Key Laboratory for Design and Manufacture of Micro Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Xiaowen Jiang
- Jiangsu Key Laboratory for Design and Manufacture of Micro Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Yi Chen
- Jiangsu Key Laboratory for Design and Manufacture of Micro Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Tingyu Liu
- School of Mechanical Engineering, Southeast University, Nanjing, 211189, China
| | - Zhonghua Ni
- Jiangsu Key Laboratory for Design and Manufacture of Micro Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Hong Yi
- Jiangsu Key Laboratory for Design and Manufacture of Micro Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Rongsheng Lu
- Jiangsu Key Laboratory for Design and Manufacture of Micro Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
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5
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Yuan L, Meng X, Xin K, Ju Y, Zhang Y, Yin C, Hu L. A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 288:122120. [PMID: 36473296 DOI: 10.1016/j.saa.2022.122120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 11/07/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.
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Affiliation(s)
- Libo Yuan
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Xiangru Meng
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Kehui Xin
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Ying Ju
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Yan Zhang
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Chunling Yin
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Leqian Hu
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China.
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LF-NMR intelligent evaluation for lipid oxidation indices of polar compound distribution, fatty acid unsaturation, and dynamic viscosity: Preference and mechanism. Food Res Int 2022; 161:111807. [DOI: 10.1016/j.foodres.2022.111807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/28/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022]
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Sun Q, Zhang M, Bhandari B, Raghavan V. Establishment of novel standardised operating procedures for LF‐NMR: used in rapid detection of typical fruit and vegetable. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.15291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Qing Sun
- State Key Laboratory of Food Science and Technology Jiangnan University Wuxi Jiangsu 214122 China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology Jiangnan University Wuxi Jiangsu 214122 China
- Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology Jiangnan University Wuxi Jiangsu 214122 China
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences University of Queensland Brisbane QLD Australia
| | - Vijaya Raghavan
- Department of Bioresource Engineering Faculty of Agricultural and Environmental Sciences McGill University Sainte‐Anne‐de‐Bellevue QC H9X 3V9 Canada
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8
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Su N, Weng S, Wang L, Xu T. Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration. BIOSENSORS 2021; 11:bios11120492. [PMID: 34940249 PMCID: PMC8699652 DOI: 10.3390/bios11120492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/23/2021] [Accepted: 11/25/2021] [Indexed: 11/16/2022]
Abstract
The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.
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Affiliation(s)
- Ning Su
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
| | - Liusan Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
- Correspondence: (L.W.); (T.X.)
| | - Taosheng Xu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
- Correspondence: (L.W.); (T.X.)
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Gao M, Xie J, Yao H, Yan Y, Li F, Wang S, Shi W, Lu Y, Deng S, Xu C. An in‐situ method to track the quality change of frozen surimi as a whole: Multi‐molecular infrared spectroscopy in combination with LF‐NMR. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.16055] [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)
- Ming‐Hui Gao
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Qinpu Biotechnology Pte Ltd Shanghai China
| | - Jun Xie
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Qinpu Biotechnology Pte Ltd Shanghai China
| | - Hui Yao
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Qinpu Biotechnology Pte Ltd Shanghai China
| | - Yu Yan
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
| | - Fei‐Li Li
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Qinpu Biotechnology Pte Ltd Shanghai China
| | - Song Wang
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Qinpu Biotechnology Pte Ltd Shanghai China
| | - Wen‐Zheng Shi
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Engineering Research Center of Aquatic‐Product Processing & Preservation Shanghai China
- Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai) Ministry of Agriculture Shanghai China
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai) Shanghai China
| | - Ying Lu
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Engineering Research Center of Aquatic‐Product Processing & Preservation Shanghai China
- Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai) Ministry of Agriculture Shanghai China
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai) Shanghai China
| | - Shang‐Gui Deng
- College of Food and Pharmacy Zhejiang Ocean University Zhoushan China
| | - Chang‐Hua Xu
- College of Food Science & Technology Shanghai Ocean University Shanghai P.R. China
- Shanghai Engineering Research Center of Aquatic‐Product Processing & Preservation Shanghai China
- Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai) Ministry of Agriculture Shanghai China
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai) Shanghai China
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Liu C, Wang X. The physicochemical properties and stability of flaxseed oil emulsions: effects of emulsification methods and the ratio of soybean protein isolate to soy lecithin. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:6407-6416. [PMID: 33969885 DOI: 10.1002/jsfa.11311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/27/2021] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND The properties and stability of emulsion rely greatly on the emulsification method and emulsifier. In this study, different emulsification methods (high-speed homogenization, ultrasonic treatment and their combination) were employed for the preparation of emulsions stabilized by soybean protein isolate (SPI) and soy lecithin (SLT) at three ratios. The microstructure, hydrodynamic average diameter, ζ-potential, creaming stability and low-field nuclear magnetic resonance relaxation behaviors of emulsions were investigated. RESULTS The results indicated that the influence of emulsification method was closely related to the ratio of SPI/SLT. Overall, the SPI-SLT-stabilized emulsion treated by ultrasound showed better stability and uniformity, while the combined treatment of high-speed homogenization and ultrasound was helpful in improving the uniformity and stability of SPI-stabilized Pickering emulsion. However, the SLT-stabilized emulsions all exhibited worse uniformity in terms of particle size distribution and polydispersity index. CONCLUSION These results will be helpful for selecting an appropriate emulsification method and emulsifier to improve the stability of emulsions. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Conghui Liu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xin Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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A novel non-destructive detection of deteriorative dried longan fruits using machine learning algorithms based on low field nuclear magnetic resonance. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-01190-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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12
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Xing M, Wang S, Lin J, Xia F, Feng J, Shen G. Composition Profiling and Authenticity Assessment of Camellia Oil Using High Field and Low Field 1H NMR. Molecules 2021; 26:4738. [PMID: 34443325 PMCID: PMC8400449 DOI: 10.3390/molecules26164738] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/28/2021] [Accepted: 08/03/2021] [Indexed: 11/17/2022] Open
Abstract
Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.
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Affiliation(s)
- Meijun Xing
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (M.X.); (S.W.); (F.X.); (J.F.)
| | - Shenghao Wang
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (M.X.); (S.W.); (F.X.); (J.F.)
| | - Jianzhong Lin
- Technology Center of Xiamen Customs, Xiamen 361012, China;
| | - Feng Xia
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (M.X.); (S.W.); (F.X.); (J.F.)
| | - Jianghua Feng
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (M.X.); (S.W.); (F.X.); (J.F.)
| | - Guiping Shen
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (M.X.); (S.W.); (F.X.); (J.F.)
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13
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Su N, Pan F, Wang L, Weng S. Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods. BIOSENSORS-BASEL 2021; 11:bios11080261. [PMID: 34436063 PMCID: PMC8395004 DOI: 10.3390/bios11080261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 12/27/2022]
Abstract
The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.
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Affiliation(s)
- Ning Su
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
| | - Fangfang Pan
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
| | - Liusan Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
- Correspondence: (L.W.); (S.W.)
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
- Correspondence: (L.W.); (S.W.)
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14
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Hou X, Wang X, Hu Y, Chen Y, Huang G, Nie S. A One-Dimensional U-Net-Based Calibration-Transfer Method for Low-Field Nuclear Magnetic Resonance Signals. Anal Chem 2021; 93:10469-10476. [PMID: 34270205 DOI: 10.1021/acs.analchem.1c00765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The reconstruction of the statistical analysis model of an instrument is a time-consuming and expensive process. Herein, the feasibility of spectral model calibration-transfer application to the same type of low-field nuclear magnetic resonance (LF-NMR) instrument was investigated using a one-dimensional U-net (1D U-net). Unlike conventional calibration-transfer algorithms such as direct standardization (DS), the 1D U-net network can reduce the error between the master and slave instruments through iterative cycles. The calibration-transfer ability was verified; three experiments that entailed the use of edible oil and copper sulfate (CuSO4) samples were implemented. The analysis of the spectral responses and feature analysis of the edible oil samples revealed that the signal of the slave instrument calibrated using the 1D U-net most resembled the signal of the master instrument, and its relative residual value was reduced to 0.0045. Further analysis of the CuSO4 concentration prediction showed that on the support vector regression (SVR) model constructed using the master instrument, the signal of the slave instrument calibrated by the 1D U-net was more similar to the response of the master instrument, and its root mean square error (RMSE) was only 0.01606 mmol/L. Thus, 1D U-net is a viable candidate for calibration-transfer applications to LF-NMR instruments.
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Affiliation(s)
- Xuewen Hou
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xin Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Ying Hu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yang Chen
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Gang Huang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Shengdong Nie
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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15
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Song H, Li F, Guang P, Yang X, Pan H, Huang F. Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models. J Food Prot 2021; 84:1315-1320. [PMID: 33710323 DOI: 10.4315/jfp-20-447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS
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Affiliation(s)
- Han Song
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Feng Li
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Peiwen Guang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Xinhao Yang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Huanyu Pan
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
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16
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The Most Important Parameters to Differentiate Tempranillo and Tempranillo Blanco Grapes and Wines through Machine Learning. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02049-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Song P, Wang Z, Song P, Yue X, Bai Y, Feng L. Evaluating the effect of aging process on the physicochemical characteristics of rice seeds by low field nuclear magnetic resonance and its imaging technique. J Cereal Sci 2021. [DOI: 10.1016/j.jcs.2021.103190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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18
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Hou X, Wang G, Wang X, Ge X, Fan Y, Jiang R, Nie S. Rapid screening for hazelnut oil and high-oleic sunflower oil in extra virgin olive oil using low-field nuclear magnetic resonance relaxometry and machine learning. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2389-2397. [PMID: 33011981 DOI: 10.1002/jsfa.10862] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/30/2020] [Accepted: 10/04/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND As extra virgin olive oil (EVOO) has high commercial value, it is routinely adulterated with other oils. The present study investigated the feasibility of rapidly identifying adulterated EVOO using low-field nuclear magnetic resonance (LF-NMR) relaxometry and machine learning approaches (decision tree, K-nearest neighbor, linear discriminant analysis, support vector machines and convolutional neural network (CNN)). RESULTS LF-NMR spectroscopy effectively distinguished pure EVOO from that which was adulterated with hazelnut oil (HO) and high-oleic sunflower oil (HOSO). The applied CNN algorithm had an accuracy of 89.29%, a precision of 81.25% and a recall of 81.25%, and enabled the rapid (2 min) discrimination of pure EVOO that was adulterated with HO and HOSO in the volumetric ratio range of 10-100%. CONCLUSIONS LF-NMR coupled with the CNN algorithm is a viable candidate for rapid EVOO authentication. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Xuewen Hou
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Guangli Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xin Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xinmin Ge
- School of Geosciences, China University of Petroleum, Qingdao, China
| | - Yiren Fan
- School of Geosciences, China University of Petroleum, Qingdao, China
| | - Rui Jiang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Shengdong Nie
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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19
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Nasir IM, Rashid M, Shah JH, Sharif M, Awan MYH, Alkinani MH. An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set. Curr Med Imaging 2021; 17:136-147. [PMID: 32324518 DOI: 10.2174/1573405616666200423085826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 03/24/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. OBJECTIVE Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system. METHODS A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method. RESULTS The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. CONCLUSION The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.
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Affiliation(s)
| | - Muhammad Rashid
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Jamal Hussain Shah
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | | | - Monagi H Alkinani
- College of Computer Science and Engineering, Department of Computer Science and Artificial Intelligence, University of Jeddah, Saudi Arabia
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20
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Wang C, Wang X, Liu C, Liu C. Application of LF-NMR to the characterization of camellia oil-loaded pickering emulsion fabricated by soy protein isolate. Food Hydrocoll 2021. [DOI: 10.1016/j.foodhyd.2020.106329] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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21
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Chitrakar B, Zhang M, Bhandari B. Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic. Food Control 2021; 125:108010. [PMID: 33679006 PMCID: PMC7914018 DOI: 10.1016/j.foodcont.2021.108010] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/05/2021] [Accepted: 02/21/2021] [Indexed: 12/24/2022]
Abstract
Coronavirus disease-19 (COVID-19) is a contagious disease caused by a novel corona virus (SARS-CoV-2). No medical intervention has yet succeeded, though vaccine success is expected soon. However, it may take months or years to reach the vaccine to the whole population of the world. Therefore, the technological preparedness is worth to discuss for the smooth running of food processing activities. We have explained the impact of the COVID-19 pandemic on the food supply chain (FSC) and then discussed the technological interventions to overcome these impacts. The novel and smart technologies during food processing to minimize human-to-human and human-to-food contact were compiled. The potential virus-decontamination technologies were also discussed. Finally, we concluded that these technologies would make food processing activities smarter, which would ultimately help to run the FSC smoothly during COVID-19 pandemic.
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Affiliation(s)
- Bimal Chitrakar
- State Key Laboratory of Food Science and Technology, Jiangnan University, 214122, Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, 214122, Wuxi, Jiangsu, China.,International Joint Laboratory on Food Safety, Jiangnan University, 214122, Wuxi, Jiangsu, China
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia
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22
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23
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Jia XX, Li S, Han DP, Chen RP, Yao ZY, Ning BA, Gao ZX, Fan ZC. Development and perspectives of rapid detection technology in food and environment. Crit Rev Food Sci Nutr 2021; 62:4706-4725. [PMID: 33523717 DOI: 10.1080/10408398.2021.1878101] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Food safety become a hot issue currently with globalization of food trade and food supply chains. Chemical pollution, microbial contamination and adulteration in food have attracted more attention worldwide. Contamination with antibiotics, estrogens and heavy metals in water environment and soil environment have also turn into an enormous threat to food safety. Traditional small-scale, long-term detection technologies have been unable to meet the current needs. In the monitoring process, rapid, convenient, accurate analysis and detection technologies have become the future development trend. We critically synthesizing the current knowledge of various rapid detection technology, and briefly touched upon the problem which still exist in research process. The review showed that the application of novel materials promotes the development of rapid detection technology, high-throughput and portability would be popular study directions in the future. Of course, the ultimate aim of the research is how to industrialization these technologies and apply to the market.
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Affiliation(s)
- Xue-Xia Jia
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China.,State Key Laboratory of Food Nutrition and Safety, China International Scientific & Technological Cooperation Base for Health Biotechnology, College of Food Engineering and Biotechnology, Tianjin University of Science & Technology, Tianjin, P.R. China
| | - Shuang Li
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Dian-Peng Han
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Rui-Peng Chen
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Zi-Yi Yao
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Bao-An Ning
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Zhi-Xian Gao
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Zhen-Chuan Fan
- State Key Laboratory of Food Nutrition and Safety, China International Scientific & Technological Cooperation Base for Health Biotechnology, College of Food Engineering and Biotechnology, Tianjin University of Science & Technology, Tianjin, P.R. China
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24
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Tan Y, Chen B, Ren C, Guo M, Wang J, Shi K, Wu X, Feng Y. Rapid identification model based on decision tree algorithm coupling with 1H NMR and feature analysis by UHPLC-QTOFMS spectrometry for sandalwood. J Chromatogr B Analyt Technol Biomed Life Sci 2020; 1161:122449. [PMID: 33246279 DOI: 10.1016/j.jchromb.2020.122449] [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: 01/28/2020] [Revised: 10/25/2020] [Accepted: 11/04/2020] [Indexed: 12/01/2022]
Abstract
Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus (Dalbergia and Pterocarpus) and confused species (Gluta sp.) of sandalwood were quickly and efficiently identified. Rapid identification model based on 1H NMR and decision tree (DT) algorithm was firstly developed for the identification of sandalwood, and the accuracy was improved by introducing the AdaBoost algorithm. The accuracy of the final model was above 95%. And the feature components between different species of sandalwood were further explored using UHPLC-QTOFMS and NMR spectrometry. The results showed that 183 compounds were identified, among which 99 were known components, 84 were unknown components. The 1H NMR and 13C NMR signals of 505 samples were assigned, among them, 14 compounds were attributed, characteristic chemical shift intervals with great differences in the model were analysed. Furthermore, the fragmentation pattern of different compounds from sandalwood, in both positive and negative ion ESI modes, was summarized. The results showed a potential and rapid tool based on DT, NMR spectroscopy and UHPLC-QTOFMS, which had performed great potential for rapid identification and feature analysis of sandalwood.
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Affiliation(s)
- Youzhen Tan
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Biying Chen
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Cui Ren
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Mingxin Guo
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Juanxia Wang
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Kexing Shi
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Xia Wu
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China
| | - Yifan Feng
- New Drug Reserach And Development Center, Guangdong Pharmaceutical University, Guangzhou, Guangdong, PR China.
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25
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Li Y, Wang X, Li C, Huang W, Gu K, Wang Y, Yang B, Li Y. Exploration of chemical markers using a metabolomics strategy and machine learning to study the different origins of Ixeris denticulata (Houtt.) Stebb. Food Chem 2020; 330:127232. [PMID: 32535318 DOI: 10.1016/j.foodchem.2020.127232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 04/05/2020] [Accepted: 06/01/2020] [Indexed: 01/16/2023]
Abstract
As a generally edible plant, Ixeris denticulata (Houtt.) Stebb is widely distributed in China. Its medicinal value has attracted much attention in recent years. However, the chemical markers that cause quality and taste differences in I. denticulata from different regions are currently unclear. In this study, samples from 8 different origins were collected and analysed by UPLC-Q-TOF/MS. A metabolomics data processing strategy and machine learning method were established to explore the reasons for the difference in quality and taste of different origins from the perspective of chemical composition. With the established strategy, 10 characteristic chemical markers were identified that could be used to distinguish the origins of I. denticulata. The strategy proposed in this study could provide a certain basis for quality control and reasonable consumption of I. denticulata and additional food and medicinal homologous species.
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Affiliation(s)
- Ying Li
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China
| | - Xing Wang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China
| | - Chunyan Li
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China
| | - Wei Huang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China
| | - Kun Gu
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China
| | - Yuming Wang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China
| | - Bin Yang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China.
| | - Yubo Li
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Tuanbo New City, Jinghai District, Tianjin 301617, China.
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26
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Neves MDG, Poppi RJ. Authentication and identification of adulterants in virgin coconut oil using ATR/FTIR in tandem with DD-SIMCA one class modeling. Talanta 2020; 219:121338. [DOI: 10.1016/j.talanta.2020.121338] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/12/2020] [Accepted: 05/29/2020] [Indexed: 11/28/2022]
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27
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Convolutional neural network based approach for classification of edible oils using low-field nuclear magnetic resonance. J Food Compost Anal 2020. [DOI: 10.1016/j.jfca.2020.103566] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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28
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Wang X, Wang G, Hou X, Nie S. A Rapid Screening Approach for Authentication of Olive Oil and Classification of Binary Blends of Olive Oils Using Low-Field Nuclear Magnetic Resonance Spectra and Support Vector Machine. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01799-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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29
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Zheng P, Wang N, Wang J, Mao X, Lai C, Zhong C, Li W, Luo Y. Classification of bottled mineral waters using solution cathode glow discharge optical emission spectroscopy and chemometrics methods. Microchem J 2019. [DOI: 10.1016/j.microc.2019.104216] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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