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Wang P, Qin H, He D, Zou Z, Xu L, Tang Q. Developing colorimetric ammonia-sensing nanocomposite films based on potato starch/PVA and ZnCu-BTC nanorods for real-time monitoring food freshness. Int J Biol Macromol 2024; 277:134376. [PMID: 39094891 DOI: 10.1016/j.ijbiomac.2024.134376] [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: 05/26/2024] [Revised: 07/19/2024] [Accepted: 07/30/2024] [Indexed: 08/04/2024]
Abstract
Smart packaging material capable of real-time monitoring of food freshness is essential for ensuring food safe. At present, colorimetric ammonia-sensing smart film often possesses issues with complicated production, high cost, and inferior long-term colour stability. Herein, Zinc‑copper bimetallic organic framework (ZnCu-BTC, BTC = 1,3,5-benzenetricarboxylate acid) nanorods with colorimetric ammonia-responsiveness were synthesized by adopting facile aqueous solution method, which were then explored as nano inclusions in potato starch/polyvinyl alcohol (PS/PVA) composite film towards developing high-performance smart packaging material. The results demonstrated that the introduction of ZnCu-BTC nanorods within PS/PVA brought about remarkable improvement in blend compatibility, accompanied by a boost in tensile strength to 47.2 MPa, as well as enhanced ultraviolet (UV) blocking efficacy (over 95.0 %). Additionally, the barrier properties of PS/PVA film against water vapor and oxygen were fortified due to the addition of ZnCu-BTC. More importantly, the developed PS/PVA/ZnCu-BTC nanocomposite film displayed satisfactory antibacterial activity (over 99 %) against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), favorable colorimetric ammonia-sensing ability, and long-term colour stability. The ZnCu-BTC incorporated PS/PVA nanocomposite film could grant real-time detection of prawn freshness decline via remarkable colour change, indicating vast promise for smart food packaging applications.
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Affiliation(s)
- Pengpeng Wang
- Guangxi Key Laboratory of Electrochemical and Magneto-chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, PR China
| | - Haiping Qin
- Guangxi Key Laboratory of Electrochemical and Magneto-chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, PR China
| | - Danfeng He
- Qiongtai Normal University, Haikou 571127, PR China
| | - Zhiming Zou
- Guangxi Key Laboratory of Electrochemical and Magneto-chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, PR China.
| | - Lin Xu
- Biomaterials R&D Center, Zhuhai Institute of Advanced Technology, Chinese Academy of Sciences, Zhuhai 519003, PR China.
| | - Qun Tang
- Guangxi Key Laboratory of Electrochemical and Magneto-chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, PR China.
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Marques LP, Bernardo YAA, Conte-Junior CA. Applications of high-intensity ultrasound on shrimp: Potential, constraints, and prospects in the extraction and retrieval of bioactive compounds, safety, and quality. J Food Sci 2024; 89:3148-3166. [PMID: 38685866 DOI: 10.1111/1750-3841.17093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/15/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024]
Abstract
The global shrimp market holds substantial prominence within the food industry, registering a significant USD 24.7 billion in worldwide exportation in 2020. However, the production of a safe and high-quality product requires consideration of various factors, including the potential for allergenic reactions, occurrences of foodborne outbreaks, and risks of spoilage. Additionally, the exploration of the recovery of bioactive compounds (e.g., astaxanthin [AX], polyunsaturated fatty acids, and polysaccharides) from shrimp waste demands focused attention. Within this framework, this review seeks to comprehend and assess the utilization of high-intensity ultrasound (HIUS), both as a standalone method and combined with other technologies, within the shrimp industry. The objective is to evaluate its applications, limitations, and prospects, with a specific emphasis on delineating the impact of sonication parameters (e.g., power, time, and temperature) on various applications. This includes an examination of undesirable effects and identifying areas of interest for current and prospective research. HIUS has demonstrated promise in enhancing the extraction of bioactive compounds, such as AX, lipids, and chitin, while concurrently addressing concerns such as allergen reduction (e.g., tropomyosin), inactivation of pathogens (e.g., Vibrio parahaemolyticus), and quality improvement, manifesting in reduced melanosis scores and improved peelability. Nonetheless, potential impediments, particularly related to oxidation processes, especially those associated with lipids, pose a hindrance to its widespread implementation, potentially impacting texture properties. Consequently, further optimization studies remain imperative. Moreover, novel applications of sonication in shrimp processing, including brining, thawing, and drying, represent a promising avenue for expanding the utilization of HIUS in the shrimp industry.
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Affiliation(s)
- Lucas P Marques
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Yago A A Bernardo
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Graduate Program in Food Science (PPGCAL), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Carlos A Conte-Junior
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Graduate Program in Food Science (PPGCAL), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Graduate Program in Veterinary Hygiene (PPGHV), Faculty of Veterinary Medicine, Fluminense Federal University (UFF), Niterói, Brazil
- Graduate Program in Sanitary Surveillance (PPGVS), National Institute of Health Quality Control (INCQS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
- Graduate Program in Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- Graduate Program in Biochemistry (PPGBq), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
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Xu W, Ma Q, Sun J, Li Y, Wang J, Tang Y, Liu Y, Mu J, Wang W. Changes in quality characteristics of shrimp (Penaeus chinensis) during refrigerated storage and their correlation with protein degradation. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Volff V, de Almeida Rosa Kurosaki JK, Retcheski MC, Maximovski LV, Soares Escorsin KJ, Tormen L, Romão S, Pinto VZ, Bergler Bitencourt T, Cazarolli LH. In Vivo Yarrowia lipolytica Fermented Biomass Feed Supplementation for Shrimp and Its Effects Upon Frozen Meat Storage. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2022. [DOI: 10.1080/10498850.2022.2106604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
| | | | | | | | | | - Luciano Tormen
- Federal University of Fronteira Sul, Laranjeiras do Sul, Brazil
| | - Silvia Romão
- Federal University of Fronteira Sul, Laranjeiras do Sul, Brazil
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NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165498] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.
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