1
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Lu N, Ma J, Lin Y, Cheng JH, Sun DW. A fluorescent Aptasensor based on magnetic-separation strategy with gold nanoclusters for Deoxynivalenol (DON) detection. Food Chem 2024; 459:140341. [PMID: 39121528 DOI: 10.1016/j.foodchem.2024.140341] [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: 02/03/2024] [Revised: 06/15/2024] [Accepted: 07/03/2024] [Indexed: 08/12/2024]
Abstract
A highly sensitive method based on MBs-cDNA@Apt-AuNCs519 was developed for deoxynivalenol (DON) detection in wheat. The MBs-cDNA@Apt-AuNCs519 was established using green emission gold nanoclusters (AuNCs519) with aggregation-induced emission properties as signal probes and combining amino-modified DON-aptamer (Apt), biotin-modified DNA strand (the partially complementary to Apt (cDNA)), and streptavidin-modified magnetic beads (MBs). The Apt-AuNCs519 were well connected with MBs-cDNA without DON but dissociated from MBs-cDNA@Apt-AuNCs519 with the addition of DON, leading to a noticeable reduction in the fluorescent intensity of the aptasensor. Moreover, this fluorescence aptasensor showed two linear relationships in the concentration range of 0.1-50 ng/mL and 50-5000 ng/mL with a limit of detection of 3.73 pg/mL with good stability, reproducibility and specificity. The results were consistent with high-performance liquid chromatography and enzyme-linked immunosorbent assay methods, further indicating the potential of this method for accurate trace detection of DON in wheat.
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Affiliation(s)
- Nian Lu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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2
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Wei Q, Pan C, Pu H, Sun DW, Shen X, Wang Z. Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging. Food Chem 2024; 456:139868. [PMID: 38870825 DOI: 10.1016/j.foodchem.2024.139868] [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: 10/14/2023] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 06/15/2024]
Abstract
The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P21) and a positive correlation with the proportion of immobilized water (P22). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (RP2) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P21 with RP2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P22 with RP2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method.
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Affiliation(s)
- Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Chaoying Pan
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | | | - Zhe Wang
- Hefei Hualing Co., Ltd, Hefei 230000, China
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3
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Ma Y, Bi J, Feng S, Wu Z, Yi J. Higher molecular weight pectin inhibits ice crystal growth and its effect on the microstructural and physical properties of pectin cryogels. Carbohydr Polym 2024; 340:122312. [PMID: 38858011 DOI: 10.1016/j.carbpol.2024.122312] [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: 02/23/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 06/12/2024]
Abstract
Understanding the formation of ice crystals is essential for tailoring the microstructure and physical properties of cryogels. This study investigated the effects and mechanisms of pectin molecular weight (Mw) on impacting ice crystal formation. Pectin fractions various Mw (10.13-212.20 kDa) were prepared by hydrothermal method. The solution of high Mw pectin fractions exhibited higher contact angle, lower water freedom, and stronger adsorption of water molecules. The splat experiment and molecular dynamic (MD) results confirmed that higher Mw pectin have stronger ice crystal growth inhibition activity than lower Mw pectin. Furthermore, the pore size distribution of the cryogel increased from 98-203 μm to 105-267 μm as the molecular weight decreased from 212.2 kDa to 121.0 kDa. Additionally, in the higher Mw pectin cryogel, stronger mechanical strength was observed. These findings suggested that changing the molecular weight of pectin has the potential to regulate the ice crystal growth, microstructure and physical properties of frozen products.
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Affiliation(s)
- Youchuan Ma
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS)/Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing, China; College of Mechanical Engineering, Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry & Food Machinery and Equipment, Tianjin University of Science and Technology, Tianjin, China
| | - Jinfeng Bi
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS)/Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing, China.
| | - Shuhan Feng
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS)/Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhonghua Wu
- College of Mechanical Engineering, Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry & Food Machinery and Equipment, Tianjin University of Science and Technology, Tianjin, China
| | - Jianyong Yi
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS)/Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing, China.
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4
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Lv M, Pu H, Sun DW. A tailored dual core-shell magnetic SERS substrate with precise shell-thickness control for trace organophosphorus pesticides residues detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 316:124336. [PMID: 38678838 DOI: 10.1016/j.saa.2024.124336] [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: 03/01/2024] [Revised: 04/17/2024] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
For addressing the challenges of strong affinity SERS substrate to organophosphorus pesticides (OPs), herein, a rapid water-assisted layer-by-layer heteronuclear growth method was investigated to grow uniform UiO-66 shell with controllable thickness outside the magnetic core and provide abundant defect sites for OPs adsorption. By further assembling the tailored Au@Ag, a highly sensitive SERS substrate Fe3O4-COOH@UiO-66/Au@Ag (FCUAA) was synthesized with a SERS enhancement factor of 2.11 × 107. The substrate's suitability for the actual vegetable samples (cowpeas and peppers) was confirmed under both destructive and non-destructive detection conditions, showing a strong SERS response to fenthion and triazophos, with limits of detection of 1.21 × 10-5 and 2.96 × 10-3 mg/kg in the vegetables under destructive conditions, and 0.13 and 1.39 ng/cm2 for non-destructive detection, respectively. The FCUAA substrate had high SERS performance, effective adsorption capability for OPs, and demonstrated good applicability, thus exhibiting great potential for rapid detection of trace OPs residues in the food industry.
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Affiliation(s)
- Mingchun Lv
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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5
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Zhu Z, Mai J, Li T, Sun DW, Zeng Q, Liu X, Wang Z. In-situ investigation of supercooling behaviour during high-pressure shift freezing of pure water and sucrose solution. Food Chem 2024; 447:138980. [PMID: 38564849 DOI: 10.1016/j.foodchem.2024.138980] [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: 10/13/2023] [Revised: 02/23/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024]
Abstract
Supercooling is a main controllable factor for the fundamental understanding the high-pressure shift freezing (HPSF). In the study, a self-developed device based on the diamond anvil cell (DAC) and confocal Raman microscopy was utilized to realize an in-situ investigation of supercooling behaviour during HPSF of the pure water and sucrose solution. The spectra were used to determine the freezing point which is shown as a spectral phase marker (SD). The hydrogen bond strengths of water and sucrose solution under supercooling states were estimated by peak position and peak area ratio of sub-peaks. The results showed that the OH stretching bands had redshift under supercooling states. Moreover, the addition of sucrose molecules could strengthen the hydrogen bonding strength of water molecules under supercooling states. Thus, the DAC combined with Raman spectroscopy could be considered a novel strategy for a deep understanding of the supercooling behaviour during HPSF.
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Affiliation(s)
- Zhiwei Zhu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jiayu Mai
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Tian Li
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | | | | | - Zhe Wang
- Hefei Hualing Co., Ltd, Hefei 230000, China
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6
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Yu Z, Zhao Y, Xie Y. Ensuring food safety by artificial intelligence-enhanced nanosensor arrays. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 111:139-178. [PMID: 39103212 DOI: 10.1016/bs.afnr.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.
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Affiliation(s)
- Zhilong Yu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, P.R. China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, P.R. China.
| | - Yali Zhao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, P.R. China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, P.R. China
| | - Yunfei Xie
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, P.R. China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, P.R. China
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7
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Pu H, Yu J, Luo J, Paliwal J, Sun DW. Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:124015. [PMID: 38359515 DOI: 10.1016/j.saa.2024.124015] [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: 11/21/2023] [Revised: 01/31/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024]
Abstract
Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jingxiao Yu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jie Luo
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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8
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Lv M, Pu H, Sun DW. A durian-shaped multilayer core-shell SERS substrate for flow magnetic detection of pesticide residues on foods. Food Chem 2024; 433:137389. [PMID: 37690135 DOI: 10.1016/j.foodchem.2023.137389] [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: 07/04/2023] [Revised: 08/18/2023] [Accepted: 08/31/2023] [Indexed: 09/12/2023]
Abstract
A new type of durian-shaped Fe3O4@Au@Ag@Au (DFAAA) multilayer core-shell composite was prepared as an efficient surface-enhanced Raman scattering (SERS) substrate. The optimization process and SERS enhancement mechanism of the substrate were further explained with finite-difference time-domain simulation. The dense and uniform spiny array on the DFAAA surface had abundant "hot spots", greatly improving sensitivity, uniformity and reproducibility, with a Raman enhancement factor of 3.01 × 107 and storage-life of 30 d. A "flow magnetic detection method" was proposed to realize rapid and flexible detection of pesticide residues on the surface of different foods including fish and apple. The limit of detection of malachite green and thiram on the fish and apple surfaces were 0.13 and 0.18 ng/cm2, respectively. With its high SERS performance and good magnetic, the DFAAA possessed great application prospects as a facile SERS substrate for rapid and non-destructive detection of trace pesticide residues on foods.
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Affiliation(s)
- Mingchun Lv
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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9
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Li M, Zhang L, Jiang LL, Zhao ZB, Long YH, Chen DM, Bin J, Kang C, Liu YJ. Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning. Talanta 2024; 267:125212. [PMID: 37741265 DOI: 10.1016/j.talanta.2023.125212] [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: 07/07/2023] [Revised: 08/16/2023] [Accepted: 09/14/2023] [Indexed: 09/25/2023]
Abstract
Apple ring rot caused by Botryosphaeria dothidea can cause fruit decay during the growth and storage stages of apple fruit. Understanding the infection process and cellular defense response at the cellular micro-level holds immense importance in the field of prevention and control. Consequently, there is a pressing need to develop suitable chemical imaging analysis methods. Here we proposed a label-free, high-throughput imaging method for cellular investigation of apple fruit ring rot infected by Botryosphaeria dothidea, based on confocal Raman microspectroscopic imaging technology combined with multivariate curve resolution-alternating least squares algorithm (MCR-ALS). We conducted Raman measurements on every apple fruit and obtain an image cube. This cube was then unfolded into an augmented matrix in a column-wise manner. We proceeded with simultaneous MCR-ALS analysis, resolving the single-substance spectrum and concentration profile from the mixed signals. Lastly, the accurate and pure molecular imaging of low methoxyl pectin, high methoxyl pectin, cellulose, lignin, and phenols were realized by refolding the resolved concentration data to construct the composition image. Thereafter, we realized the study of the spatial-temporal changes distribution of the above substances in the cuticle and cell wall of green and red apples at different stages of infection. The imaging method proposed in this paper is expected to provide a chemical imaging strategy for studying pathogen infection process and fruit defense response at the cellular level. In addition, by utilizing a fiber-optic probe near-infrared reflection spectrometer in conjunction with machine learning, we developed a rapid and non-destructive classification method. This method allows for the timely identification of apples exhibiting early infection by Botryosphaeria dothidea. Notably, both principal component analysis-quadratic discriminant analysis and support vector machine achieved a classification accuracy of 100%.
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Affiliation(s)
- Mei Li
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, 550025, China
| | - Lu Zhang
- Engineering and Technology Research Center of Kiwifruit, Guizhou University, Guiyang, 550025, China
| | - Ling-Li Jiang
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, 550025, China
| | - Zhi-Bo Zhao
- Engineering and Technology Research Center of Kiwifruit, Guizhou University, Guiyang, 550025, China
| | - You-Hua Long
- Engineering and Technology Research Center of Kiwifruit, Guizhou University, Guiyang, 550025, China
| | - Dong-Mei Chen
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, 550025, China
| | - Jun Bin
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, 550025, China
| | - Chao Kang
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, 550025, China.
| | - Ya-Juan Liu
- Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA & State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences & the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, 511436, China.
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10
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Cheng JH, Zhang X, Ma J, Sun DW. Fluorescent polythymidine-templated copper nanoclusters aptasensor for sensitive detection of tropomyosin in processed shrimp products. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123271. [PMID: 37714106 DOI: 10.1016/j.saa.2023.123271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/05/2023] [Accepted: 08/16/2023] [Indexed: 09/17/2023]
Abstract
Tropomyosin (TM) is the main allergen in shellfish. Developing a novel, simple and accurate method to track and detect TM in food products is necessary. In this work, a label-free fluorescent aptasensor based on polythymidine (poly(T))-templated copper nanoclusters (CuNCs) was designed for sensitive detection of TM in processed shrimp products. Magnetic beads (MBs), aptamer and cDNA were used to construct an MBs-aptamer@cDNA complex as a detection probe, and with the presence of TM, the poly(T)-templated CuNCs attached at the end of the cDNA as the fluorescent signal was released from the complex to turn on the fluorescence. Under optimal conditions, the poly(T)-templated CuNCs aptasensor achieved a linear range from 0.1 to 50 μg/mL (R2 = 0.9980), a low limit of detection of 0.0489 μg/mL and an excellent recovery percentage of 105.29%-108.91% in the complex food matrix, providing a new approach for food safety assurance.
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Affiliation(s)
- Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Xinxue Zhang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510640, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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11
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Wei Q, Pan C, Wang T, Pu H, Sun DW. A three-dimensional gold nanoparticles spherical liquid array for SERS sensitive detection of pesticide residues in apple. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123357. [PMID: 37776705 DOI: 10.1016/j.saa.2023.123357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/10/2023] [Accepted: 09/04/2023] [Indexed: 10/02/2023]
Abstract
High-performance plasmonic substrates have recently attracted much research attention. Herein, a three-dimensional gold nanoparticles (AuNPs) spherical liquid array (SLA) with high "hot spots" and tunable nanometer gap by optimizing the proportion of AuNPs colloids over chloroform was synthesized based on a water-oil interfacial self-assembly strategy. The substrate demonstrated excellent surface-enhanced Raman scattering (SERS) performance using tetrathiafulvalene and rhodamine 6G (R6G) as probe molecules. With a simple extraction and soaking pretreatment process, the SLA exhibited high sensitivity for analysing triazophos on apple peels, with a limit of detection (LOD) of 0.005 µg/mL and recovery ranging from 96 to 110 %. Particularly, the chloroform produced an inherent characteristic peak at 665 cm-1, which was used as the internal standard to correct SERS signal fluctuation, leading to an improvement of the corresponding coefficient R2 from 0.97 to 0.99, thus improving the reproducibility. Therefore the SLA substrate possesses the potential for quantitative analysis of food contaminants.
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Affiliation(s)
- Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Chaoying Pan
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Tengfei Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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12
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Wang B, Jia Y, Li Y, Wang Z, Wen L, He Y, Xu X. Dehydration-rehydration vegetables: Evaluation and future challenges. Food Chem X 2023; 20:100935. [PMID: 38144748 PMCID: PMC10739932 DOI: 10.1016/j.fochx.2023.100935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/19/2023] [Accepted: 10/08/2023] [Indexed: 12/26/2023] Open
Abstract
In this review, the rehydration kinetics model, the quality factors affecting of vegetables during rehydration process, the future challenges and development direction of rehydration process were comprehensively analyzed. Based on the fitting equation for the change in moisture content during rehydration, a suitable rehydration model can be selected to describe the rehydration process of vegetables. Optimal pre-treatment, drying and rehydration methods were selected by considering quality, energy consumption and environmental aspects, and new technologies were developed to improve the quality characteristics of rehydrated vegetables. It is necessary to classify vegetables according to their shape and type to establish the criteria of rehydration processing through mathematical modeling. Industrial production from pre-treatment to product packaging will be precisely adjusted through process parameters. Furthermore, improvements the quality of rehydrated vegetables can be considered in terms of the structural and compositional aspects of the cell wall and cell membrane.
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Affiliation(s)
- Bixiang Wang
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
| | - Yuanlong Jia
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
| | - Yue Li
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
| | - Zhitong Wang
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
| | - Liankui Wen
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
| | - Yang He
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
| | - Xiuying Xu
- Department of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China
- National Engineering Research Center for Wheat and Corn Deep Processing, Changchun 130118, China
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13
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Wu Z, Sun DW, Pu H. CRISPR/Cas12a and G-quadruplex DNAzyme-driven multimodal biosensor for visual detection of Aflatoxin B1. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123121. [PMID: 37579713 DOI: 10.1016/j.saa.2023.123121] [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: 05/02/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 08/16/2023]
Abstract
Aflatoxin B1 (AFB1) contamination severely threatens human and animal health, it is thus critical to construct a strategy for its rapid, accurate, and visual detection. Herein, a multimodal biosensor was proposed based on CRISPR/Cas12a cleaved G-quadruplex (G4) for AFB1 detection. Briefly, specific binding of AFB1 to the aptamer occupied the binding site of the complementary DNA (cDNA), and cDNA then activated Cas12a to cleave G4 into fragments. Meanwhile, the intact G4-DNAzyme could catalyze 3, 3', 5, 5'-tetramethylbenzidine (TMB) to form colourimetric/SERS/fluorescent signal-enhanced TMBox, and the yellow solution produced by TMBox under acidic conditions could be integrated with a smartphone application for visual detection. The colourimetric/SERS/fluorescent biosensor yielded detection limits of 0.85, 0.79, and 1.65 pg·mL-1, respectively, and was applied for detecting AFB1 in peanut, maize, and badam samples. The method is suitable for visual detection in naturally contaminated peanut samples and has prospective applications in the food industry.
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Affiliation(s)
- Zhihui Wu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
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14
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Jing S, Wu L, Siciliano AP, Chen C, Li T, Hu L. The Critical Roles of Water in the Processing, Structure, and Properties of Nanocellulose. ACS NANO 2023; 17:22196-22226. [PMID: 37934794 DOI: 10.1021/acsnano.3c06773] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
The cellulose industry depends heavily on water owing to the hydrophilic nature of cellulose fibrils and its potential for sustainable and innovative production methods. The emergence of nanocellulose, with its excellent properties, and the incorporation of nanomaterials have garnered significant attention. At the nanoscale level, nanocellulose offers a higher exposure of hydroxyl groups, making it more intimate with water than micro- and macroscale cellulose fibers. Gaining a deeper understanding of the interaction between nanocellulose and water holds the potential to reduce production costs and provide valuable insights into designing functional nanocellulose-based materials. In this review, water molecules interacting with nanocellulose are classified into free water (FW) and bound water (BW), based on their interaction forces with surface hydroxyls and their mobility in different states. In addition, the water-holding capacity of cellulosic materials and various water detection methods are also discussed. The review also examines water-utilization and water-removal methods in the fabrication, dispersion, and transport of nanocellulose, aiming to elucidate the challenges and tradeoffs in these processes while minimizing energy and time costs. Furthermore, the influence of water on nanocellulose properties, including mechanical properties, ion conductivity, and biodegradability, are discussed. Finally, we provide our perspective on the challenges and opportunities in developing nanocellulose and its interplay with water.
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Affiliation(s)
- Shuangshuang Jing
- Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Lianping Wu
- Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Amanda P Siciliano
- Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Chaoji Chen
- Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Teng Li
- Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Liangbing Hu
- Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States
- Center for Materials Innovation, University of Maryland, College Park, Maryland 20742, United States
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15
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Yuan T, Jiang W, Ye Y, Hai Y, Yi D. Confocal microscopy based on dual blur depth measurement. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2023; 40:2002-2007. [PMID: 38038065 DOI: 10.1364/josaa.499900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/05/2023] [Indexed: 12/02/2023]
Abstract
In this paper, we propose a confocal microscopy based on dual blur depth measurement (DBCM). The first blur is defocus blur, and the second blur is artificial convolutional blur. First, the DBCM blurs the defocus image using a known Gaussian kernel and calculates the edge gradient ratio between it and the re-blurred image. Then, the axial measurement of edge positions is based on a calibration measurement curve. Finally, depth information is inferred from the edges using the original image. Experiments show that the DBCM can achieve depth measurement in a single image. In a 10×/0.25 objective, the error measured for a step sample of 4.7397 µm is 0.23 µm. The relative error rate is 4.8%.
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16
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Lv M, Pu H, Sun DW. Preparation of Fe 3O 4@UiO-66(Zr)@Ag NPs core-shell-satellite structured SERS substrate for trace detection of organophosphorus pesticides residues. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 294:122548. [PMID: 36947914 DOI: 10.1016/j.saa.2023.122548] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/10/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) technology has been revived and developed with the introduction of metal-organic frameworks (MOFs), while more valuable properties of MOFs for SERS substrates remain largely unexplored. This work constructed a new SERS substrate Fe3O4@UiO-66(Zr)@Ag nanoparticles (FUAs) with excellent SERS detection sensitivity, uniformity, reproducibility and stability, exhibiting a high Raman enhancement factor (5.62 × 106), low limit of detection (LOD, 2.11 × 10-11 M) and RSD (12.41 %) for 4-NBT, and maintaining 81 % SERS activity within 60 days. The FUAs took full advantage of the strong affinity of UiO-66(Zr) for organophosphorus pesticides (OPs) to realize trace OPs detection. The LODs of phoxim, triazophos and methyl parathion in apple juice were 0.041, 0.021 and 0.0031 mg/L, respectively, with good linearities ranging from 0.02 or 0.1-50 mg/L, meeting the requirements of the food control standards, indicating that the potentials and prospects of the FUAs SERS substrate for trace detecting OPs in foods.
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Affiliation(s)
- Mingchun Lv
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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17
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Pu H, Yu J, Sun DW, Wei Q, Li Q. Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122771. [PMID: 37244024 DOI: 10.1016/j.saa.2023.122771] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 05/29/2023]
Abstract
The geographical indication of pericarpium citri reticulatae (PCR) is very important in grading the quality and price of PCRs. Therefore, terahertz time-domain spectroscopy (THz-TDS) technology combined with convolutional neural networks (CNN) was proposed to distinguish PCRs of different origins without damage in this study. The one-dimensional CNN (1D-CNN) model with an accuracy of 82.99% based on spectral data processed with SNV was established. The two-dimensional image features were transformed from unprocessed spectral data using the gramian angular field (GAF), the Markov transition field (MTF) and the recurrence plot (RP), which were used to build a two-dimensional CNN (2D-CNN) model with an accuracy of 78.33%. Further, the CNN models with different fusion methods were developed for fusing spectra data and image data. In addition, the adding spectra and images based on the CNN (Add-CNN) model with an accuracy of 86.17% performed better. Eventually, the Add-CNN model based on ten frequencies extracted using permutation importance (PI) achieved the identification of PCRs from different origins. Overall, the current study would provide a new method for identifying PCRs of different origins, which was expected to be used for the traceability of PCRs products.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Jingxiao Yu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qian Li
- Shenzhen Institute of Terahertz Technology and Innovation, Shenzhen, Guangdong 518102, China
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18
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Wang Z, Liu X, Du H, Sang Y, Xiao H, Tian G. Effect of boiling on water mobility, quality and structure characteristics of Mactra veneriformis during hot air drying. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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19
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Liu Y, Pu H, Li Q, Sun DW. Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122035. [PMID: 36332396 DOI: 10.1016/j.saa.2022.122035] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/27/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Pericarpium Citri Reticulatae (PCR) in longer storage years possess higher medicinal values, but their differentiation is difficult due to similar morphological characteristics. Therefore, this study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with a convolutional neural network (CNN) to identify PCR samples stored from 1 to 20 years. The absorption coefficient and refractive index spectra in the range of 0.2-1.5 THz were acquired. Partial least squares discriminant analysis, random forest, least squares support vector machines, and CNN were used to establish discriminant models, showing better performance of the CNN model than the others. In addition, the output data points of the CNN intermediate layer were visualized, illustrating gradual changes in these points from overlapping to clear separation. Overall, THz-TDS combined with CNN models could realize rapid identification of different year PCRs, thus providing an efficient alternative method for PCR quality inspection.
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Affiliation(s)
- Yao Liu
- School of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics (e) Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qian Li
- Shenzhen Institute of Terahertz Technology and Innovation, Shenzhen, Guangdong 518102, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics (e) Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
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20
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He H, Sun DW, Pu H, Wu Z. A SERS-Fluorescence dual-signal aptasensor for sensitive and robust determination of AFB1 in nut samples based on Apt-Cy5 and MNP@Ag-PEI. Talanta 2023; 253:123962. [PMID: 36208559 DOI: 10.1016/j.talanta.2022.123962] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 12/13/2022]
Abstract
Food aflatoxin B1 (AFB1) contamination greatly threatens human health and its sensitive determination is imperative. In this study, a surface-enhanced Raman scattering (SERS) and fluorescence dual-signal aptasensor was constructed for sensitive AFB1 detection in peanuts, walnuts, and almonds samples. Fluorescent dye cy5 was used as fluorophore and Raman reporter, while polyethyleneimine modified Ag coating magnetic nanoparticles (MNP@Ag-PEI) were utilized to absorb the cy5 modified aptamer (apt-cy5). Results indicated that linear ranges of 0.001-1000 ng/mL and 0.2-20,000 ng/mL with detection limits of 0.45 pg/mL and 0.135 ng/mL for the SERS and fluorescence methods were obtained, respectively, and AFB1 detection in the nut samples using the aptasensor achieved satisfactory recoveries of 95.2%-108.6% for SERS and 94.7%-109.7% for fluorescence. Compared with other mono signal detection, the established aptasensor facilely fused the merits of the two signals and improved the detection accuracy and flexibility.
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Affiliation(s)
- Haoyang He
- School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China
| | - Zhihui Wu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, 510006, China
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21
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Wu Z, Sun DW, Pu H, Wei Q. A dual signal-on biosensor based on dual-gated locked mesoporous silica nanoparticles for the detection of Aflatoxin B1. Talanta 2023. [DOI: 10.1016/j.talanta.2022.124027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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22
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Li Q, Lei T, Sun DW. Analysis and detection using novel terahertz spectroscopy technique in dietary carbohydrate-related research: Principles and application advances. Crit Rev Food Sci Nutr 2023; 63:1793-1805. [PMID: 36647744 DOI: 10.1080/10408398.2023.2165032] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
As one of the main functional substances, carbohydrates account for a large proportion of the human diet. Conventional analysis and detection methods of dietary carbohydrates and related products are destructive, time-consuming, and labor-intensive. In order to improve the efficiency of measurement and ensure food nutrition and consumer health, rapid and nondestructive quality evaluation techniques are needed. In recent years, terahertz (THz) spectroscopy, as a novel detection technology with dual characteristics of microwave and infrared, has shown great potential in dietary carbohydrate analysis. The current review aims to provide an up-to-date overview of research advances in using the THz spectroscopy technique in analysis and detection applications related to dietary carbohydrates. In the review, the principles of the THz spectroscopy technique are introduced. Advances in THz spectroscopy for quantitative and qualitative analysis and detection in dietary carbohydrate-related research studies from 2013 to 2022 are discussed, which include analysis of carbohydrate concentrations in liquid and powdery foods, detection of foreign body and chemical residues in carbohydrate food products, authentication of natural carbohydrate produce, monitoring of the fermentation process in carbohydrate food production and examination of crystallinity in carbohydrate polymers. In addition, applications in dietary carbohydrate-related detection research using other spectroscopic techniques are also briefed for comparison, and future development trends of THz spectroscopy in this field are finally highlighted.
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Affiliation(s)
- Qingxia Li
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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23
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Wei Q, Dong Q, Sun DW, Pu H. Synthesis of recyclable SERS platform based on MoS 2@TiO 2@Au heterojunction for photodegradation and identification of fungicides. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121895. [PMID: 36228505 DOI: 10.1016/j.saa.2022.121895] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) substrates based on metal/semiconductors have attracted much attention due to their excellent photocatalytic activity and SERS performance. However, they generally exhibit low light utilization and photocatalytic efficiencies. Herein, molybdenum disulfide coated titanium dioxide modified with gold nanoparticles (MoS2@TiO2@Au) as a heterojunction-based recyclable SERS platform was fabricated for the efficient determination of fungicides. Results showed that the MoS2@TiO2@Au platform could rapidly degrade 90.7% crystal violet in 120 min under solar light irradiation and enable reproducible and sensitive SERS analysis of three fungicides (methylene blue, malachite green, and crystal violet) and in-situ monitor of the photodegradation process. The platform could also be reused five times due to the unique integrated merits of the MoS2@TiO2@Au heterojunction. Meanwhile, experiments in determining methylene blue in prawn protein solution achieved a limit of detection of 1.509 μg/L. Therefore, it is hoped that this work could expand detection applications of photocatalytic materials.
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Affiliation(s)
- Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qirong Dong
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
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24
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Jayan H, Sun DW, Pu H, Wei Q. Mesoporous silica coated core-shell nanoparticles substrate for size-selective SERS detection of chloramphenicol. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121817. [PMID: 36084581 DOI: 10.1016/j.saa.2022.121817] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/17/2022] [Accepted: 08/28/2022] [Indexed: 06/15/2023]
Abstract
With the growing popularity of the non-destructive technique, surface-enhanced Raman spectroscopy (SERS) demands a highly sensitive and reproducible plasmonic nanoparticles substrate. In this study, a novel bimetallic core-shell nanoparticles (Au@Ag@mSiO2NP) substrate consisting of a gold core, silver shell, and a mesoporous silica coating was synthesized. The mesoporous coating structure was created by employing template molecules such as surfactant and their subsequent removal allowing selective screening based on the size of analyte molecules. Results showed that the plasmonic substrate could selectively enhance small molecules by preventing large macromolecules to reach the exciting zone of the substrate core, achieving the detection of chloramphenicol in milk samples with a detection limit of 6.68 × 10-8 M. Moreover, the mesoporous coating provided additional stability to the Au@Ag nanoparticles, leading to the reusability of the substrate. Thus, this work offered a simple and smart Au@Ag@mSiO2NP substrate for effective SERS detection of analytes.
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Affiliation(s)
- Heera Jayan
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland(1).
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
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25
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Jayan H, Sun DW, Pu H, Wei Q. Surface-enhanced Raman spectroscopy combined with stable isotope probing to assess the metabolic activity of Escherichia coli cells in chicken carcass wash water. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121549. [PMID: 35792480 DOI: 10.1016/j.saa.2022.121549] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/31/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Rapid evaluation of the metabolic activity of microorganisms is crucial in the assessment of the disinfection ability of various antimicrobial agents in the food industry. In this study, surface-enhanced Raman spectroscopy combined with isotope probing was employed for the analysis of the disinfection of single bacterial cells in the chicken carcass wash water. The Raman signals from single Escherichia coli O157:H7 cells were enhanced by in situ synthesis of silver nanoparticles. The ΔCD of the cells grown in presence of 0.5% hydrogen peroxide and 50 ppm chlorine was 5.86 ± 1.86% and 5.1 ± 2.3%, respectively, which showed significant reduction compared with cells grown in the absence of disinfecting agents (19.86 ± 2.51%) after 2 h of incubation. The study proved that the proposed method had the potential to assess the metabolic activity of microorganisms in other food products and optimize the disinfection process.
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Affiliation(s)
- Heera Jayan
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
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26
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Wu Z, Sun DW, Pu H, Wei Q. A novel fluorescence biosensor based on CRISPR/Cas12a integrated MXenes for detecting Aflatoxin B1. Talanta 2022; 252:123773. [DOI: 10.1016/j.talanta.2022.123773] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 12/26/2022]
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