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Jiao L, Li Y, Hu J, Zhao S, Zhang X, Benjakul S, Zhang B. Curcumin-loaded food-grade nano-silica hybrid material exhibiting improved photodynamic effect and its application for the preservation of small yellow croaker (Larimichthys polyactis). Food Res Int 2024; 188:114492. [PMID: 38823875 DOI: 10.1016/j.foodres.2024.114492] [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: 02/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Two types of curcumin-loaded food-grade nano-silica (F-SiO2) hybrid materials were successfully synthesized using the rotary evaporation method (F-SiO2@Cur) and the adsorption method (Cur@F-SiO2). The microstructure and spectral analyses confirmed that the curcumin in F-SiO2@Cur was loaded within the nanopores in a non-aggregate form rather than being adsorbed onto the surface (Cur@F-SiO2). Additionally, F-SiO2@Cur exhibited remarkable water solubility (1510 ± 50.33 µg/mL) and photostability (a photodegradation ratio of only 59.22 %). Importantly, F-SiO2@Cur obtained a higher capacity for the generation of singlet oxygen (1O2) compared to control groups. Consequently, F-SiO2@Cur-mediated photodynamic inactivation (PDI) group attained the highest score in sensory evaluation and the best color protection effect in PDI experiment of small yellow croaker (Larimichthys polyactis) at 4 °C. Moreover, F-SiO2@Cur could effectively controlled total volatile basic nitrogen (TVB-N) content, pH, and total viable count (TVC), thereby prolonging the shelf life. Therefore, F-SiO2@Cur-mediated PDI is an effective fresh-keeping technology for aquatic products.
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Affiliation(s)
- Long Jiao
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China
| | - Yuwei Li
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China
| | - Jiajie Hu
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China
| | - Shuyi Zhao
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China; Pisa Marine Graduate School, Zhejiang Ocean University, Zhoushan 316022, China
| | - Xiaoye Zhang
- School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China.
| | - Soottawat Benjakul
- International Center of Excellence in Seafood Science and Innovation, Faculty of Agro-Industry, Prince of Songkla University, Songkhla 90112, Thailand
| | - Bin Zhang
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China; Pisa Marine Graduate School, Zhejiang Ocean University, Zhoushan 316022, China.
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Qiu R, Zhao Y, Kong D, Wu N, He Y. Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121838. [PMID: 36108407 DOI: 10.1016/j.saa.2022.121838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.
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Affiliation(s)
- Ruicheng Qiu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, China
| | - Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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Zhang Z, Chen J, Zou L, Tang J, Zheng J, Luo M, Wang G, Liang D, Li Y, Chen B, Yan H, Ding W. Preparation, Characterization, and Staphylococcus aureus Biofilm Elimination Effect of Baicalein-Loaded β-Cyclodextrin-Grafted Chitosan Nanoparticles. Int J Nanomedicine 2022; 17:5287-5302. [PMID: 36411767 PMCID: PMC9675332 DOI: 10.2147/ijn.s383182] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND AND PURPOSE Infections caused by Staphylococcus aureus (S. aureus) colonization in medical implants are resistant to antibiotics due to the formation of bacterial biofilm internal. Baicalein (BA) has been confirmed as an inhibitor of bacterial biofilm with less pronounced effects owing to its poor solubility and absorption. Studies have found that β-cyclodextrin-grafted chitosan (CD-CS) can improve drug efficiency as a drug carrier. Therefore, this research aims to prepare BA-loaded CD-CS nanoparticles (CD-CS-BA-NPs) for S. aureus biofilm elimination enhancement. METHODS CD-CS-BA-NPs were prepared via the ultrasonic method. The NPs were characterized using the X-ray diffraction (XRD), Thermo gravimetric analyzer (TGA), Transmission electron microscopy (TEM) and Malvern Instrument. The minimum inhibitory concentration (MIC) of the NPs were investigated. The biofilm models in vivo and in vitro were constructed to assess the S. aureus biofilm elimination ability of the NPs. The Confocal laser method (CLSM) and the Live/Dead kit were employed to explore the mechanism of the NPs in promoting biofilm elimination. RESULTS CD-CS-BA-NPs have an average particle size of 424.5 ± 5.16 nm, a PDI of 0.2 ± 0.02, and a Zeta potential of 46.13 ± 1.62 mV. TEM images revealed that the NPs were spherical with uniform distribution. XRD and TGA analysis verified the formation and the thermal stability of the NPs. The NPs with a MIC of 12.5 ug/mL exhibited a better elimination effect on S. aureus biofilm both in vivo and in vitro. The mechanism study demonstrated that the NPs may permeate into the biofilm more easily, thereby improving the biofilm elimination effect of BA. CONCLUSION CD-CS-BA-NPs were successfully prepared with enhanced elimination of S. aureus biofilm, which may serve as a reference for future development of anti-biofilm agents.
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Affiliation(s)
- Zhongbin Zhang
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
- Key Laboratory of Common Technology of Chinese Medicine Preparations, Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Jinqing Chen
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Linghui Zou
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Jing Tang
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Jiaxin Zheng
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Meijiao Luo
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Gang Wang
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Dan Liang
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
- Key Laboratory of Common Technology of Chinese Medicine Preparations, Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Yuyang Li
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Ben Chen
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Hongjun Yan
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
| | - Wenya Ding
- Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
- Key Laboratory of Common Technology of Chinese Medicine Preparations, Guangxi University of Chinese Medicine, Nanning, People’s Republic of China
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
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4
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Seafood Processing, Preservation, and Analytical Techniques in the Age of Industry 4.0. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031703] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Fish and other seafood products are essential dietary components that are highly appreciated and consumed worldwide. However, the high perishability of these products has driven the development of a wide range of processing, preservation, and analytical techniques. This development has been accelerated in recent years with the advent of the fourth industrial revolution (Industry 4.0) technologies, digitally transforming almost every industry, including the food and seafood industry. The purpose of this review paper is to provide an updated overview of recent thermal and nonthermal processing and preservation technologies, as well as advanced analytical techniques used in the seafood industry. A special focus will be given to the role of different Industry 4.0 technologies to achieve smart seafood manufacturing, with high automation and digitalization. The literature discussed in this work showed that emerging technologies (e.g., ohmic heating, pulsed electric field, high pressure processing, nanotechnology, advanced mass spectrometry and spectroscopic techniques, and hyperspectral imaging sensors) are key elements in industrial revolutions not only in the seafood industry but also in all food industry sectors. More research is still needed to explore how to harness the Industry 4.0 innovations in order to achieve a green transition toward more profitable and sustainable food production systems.
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Moosavi-Nasab M, Khoshnoudi-Nia S. Combining Knowledge- and Data-Driven Fuzzy Approach to Evaluate Shelf-Life of Various Seafood Products. FOOD QUALITY AND SAFETY 2021. [DOI: 10.1093/fqsafe/fyab022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Abstract
Due to the complexity of the deterioration process of seafood products, relying on one indicator is not adequate to determine the quality of such products. Usually, shelf-life was estimated based on various indicators complicating the decision-making process. Decision Support Systems are considered as a good solution. The current study aims to establish a simple and novel fuzzy model based on a combination of knowledge- and data-driven approaches to define a fuzzy quality deterioration index (FQDI) in various seafood products (rainbow trout, threadfin bream, and white shrimp samples) during cold storage. Total volatile basic nitrogen (TVB-N) and psychrotrophic microorganisms counts (PMCs) were determined based on traditional methods. The sensory analysis was performed by a data-driven fuzzy approach. Overall, the shelf-life of the rainbow trout fillet was estimated to be 8 days, based on all the freshness parameters. However, the shelf-life of the Japanese threadfin bream fillet was 5–7 days according to the microbial and chemical parameters, respectively. This time for shrimp samples was 6–8 days using sensory score and TVB-N contents. The results of data-driven fuzzy approach showed all of the quality properties were considered as the ‘Important’–‘Very Important’ (defuzzification score >75). The TVB-N and PMCs were the most and weakest freshness quality properties (defuzzified-values: 84.64 and 78.75, respectively). Based on FQDI, the shelf-life of the rainbow trout, Japanese threadfin bream, and shrimp samples were estimated to be 8, 5, and 7 days, respectively. This method was able to successfully provide a comprehensive deterioration index for evaluating the seafood shelf-life. Such a total index can be considered as a comprehensive output (y variable) to predict seafood freshness by rapid and nondestructive method.
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Affiliation(s)
- Marzieh Moosavi-Nasab
- Department of Food Science and Technology, School of Agriculture, Shiraz University, Shiraz, Iran
- Seafood Processing Research Group, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, Shiraz, Iran
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Yu HD, Qing LW, Yan DT, Xia G, Zhang C, Yun YH, Zhang W. Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness. Food Chem 2021; 348:129129. [PMID: 33515952 DOI: 10.1016/j.foodchem.2021.129129] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 01/01/2023]
Abstract
The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.
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Affiliation(s)
- Hai-Dong Yu
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China
| | - Li-Wei Qing
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China
| | - Dan-Ting Yan
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China
| | - Guanghua Xia
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China; Hainan Engineering Research Center of Aquatic Resources Efficient Utilization in South China Sea, Hainan University, Haikou 570228, China
| | - Chenghui Zhang
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China
| | - Yong-Huan Yun
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China; Hainan Engineering Research Center of Aquatic Resources Efficient Utilization in South China Sea, Hainan University, Haikou 570228, China.
| | - Weimin Zhang
- College of Food Science and Engineering, Hainan University, 58 Renmin Road, Haikou 570228, China.
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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Moosavi-Nasab M, Khoshnoudi-Nia S, Azimifar Z, Kamyab S. Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis. Sci Rep 2021; 11:5094. [PMID: 33658634 PMCID: PMC7930251 DOI: 10.1038/s41598-021-84659-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 01/25/2021] [Indexed: 11/09/2022] Open
Abstract
Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430-1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R2p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R2p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.
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Affiliation(s)
- Marzieh Moosavi-Nasab
- Seafood Processing Research Group, Department of Food Science and Technology, School of Agriculture, Shiraz University, P.O. Box 71441-65186, Shiraz, Iran.
| | - Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, P.O. Box 71441-65186, Shiraz, Iran
| | - Zohreh Azimifar
- Department of Computer Science and Engineering, Shiraz University, P.O. Box 71936-16548, Shiraz, Iran
| | - Shima Kamyab
- Department of Computer Science and Engineering, Shiraz University, P.O. Box 71936-16548, Shiraz, Iran
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Effect of active edible coating on quality properties of green-raisin and ranking the samples using fuzzy approach. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2020. [DOI: 10.1007/s11694-020-00595-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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10
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Khoshnoudi-Nia S, Moosavi-Nasab M. Prediction of various freshness indicators in fish fillets by one multispectral imaging system. Sci Rep 2019; 9:14704. [PMID: 31605023 PMCID: PMC6789145 DOI: 10.1038/s41598-019-51264-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 09/29/2019] [Indexed: 01/16/2023] Open
Abstract
In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R2P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R2p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R2p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R2p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R2P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.
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Affiliation(s)
- Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, PO Box: 71441-65186, Shiraz, Iran.
| | - Marzieh Moosavi-Nasab
- Seafood Processing Research Group & Department of Food Science and Technology, School of Agriculture, Shiraz University, PO Box: 71441-65186, Shiraz, Iran.
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11
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Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine. SENSORS 2019; 19:s19204355. [PMID: 31600914 PMCID: PMC6832974 DOI: 10.3390/s19204355] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/04/2019] [Accepted: 10/07/2019] [Indexed: 11/17/2022]
Abstract
Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.
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Bonah E, Huang X, Aheto JH, Osae R. Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization. Foodborne Pathog Dis 2019; 16:712-722. [PMID: 31305129 PMCID: PMC6785170 DOI: 10.1089/fpd.2018.2617] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Microbial food safety is a persistent and exacting global issue due to the multiplicity and complexity of foods and food production systems. Foodborne illnesses caused by foodborne bacterial pathogens frequently occur, thus endangering the safety and health of human beings. Factors such as pretreatments, that is, culturing, enrichment, amplification make the traditional routine identification and enumeration of large numbers of bacteria in a complex microbial consortium complex, expensive, and time-consuming. Therefore, the need for rapid point-of-use detection systems for foodborne bacterial pathogens with high sensitivity and specificity is crucial in food safety control. Hyperspectral imaging (HSI) as a powerful testing technology provides a rapid, nondestructive approach for pathogen detection. This article reviews some fundamental information about HSI, including instrumentation, data acquisition, image processing, and data analysis-the current application of HSI for the detection, classification, and discrimination of various foodborne pathogens. The merits and demerits of HSI for pathogen detection as well as current and future trends are discussed. Therefore, the purpose of this review is to provide a brief overview of HSI, and further lay emphasis on the emerging trend and importance of this technique for foodborne pathogen detection.
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Affiliation(s)
- Ernest Bonah
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
- Laboratory Services Department, Food and Drugs Authority, Cantonments, Ghana
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Joshua Harrington Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Richard Osae
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
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Liu Y, Wang Q, Gao X, Xie A. Total phenolic content prediction in
Flos Lonicerae
using hyperspectral imaging combined with wavelengths selection methods. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13224] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yunhong Liu
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Qingqing Wang
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Xiuwei Gao
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Anguo Xie
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
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14
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Wang Q, Liu Y, Gao X, Xie A, Yu H. Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00180-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Khoshnoudi‐Nia S, Moosavi‐Nasab M. Comparison of various chemometric analysis for rapid prediction of thiobarbituric acid reactive substances in rainbow trout fillets by hyperspectral imaging technique. Food Sci Nutr 2019; 7:1875-1883. [PMID: 31139402 PMCID: PMC6526668 DOI: 10.1002/fsn3.1043] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 03/13/2019] [Accepted: 03/27/2019] [Indexed: 12/31/2022] Open
Abstract
This study explores the potential application of hyperspectral imaging (HSI; 430-1,010 nm) coupled with different linear and nonlinear models for rapid nondestructive evaluation of thiobarbituric acid-reactive substances (TBARS) value in rainbow trout (Oncorhynchus mykiss) fillets during 12 days of cold storage (4 ± 2°C). HSI data and TBARS value of fillets were obtained in the laboratory. The primary prediction models were established based on linear partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). In full spectral range, the prediction capability of LS-SVM ( R P 2 = 0.829; RMSEP = 0.128 mg malondialdehyde [MDA]/kg) was better than PLSR ( R P 2 = 0.748; RMSEP = 0.155 mg MDA/kg) model and LS-SVM model exhibited satisfactory prediction performance ( R P 2 > 0.82). To simplify the calibration models, a combination of uninformative variable elimination and backward regression (UB) was used as variable selection. Nine wavelengths were selected. Various chemometric analysis methods including linear PLSR and multiple linear regression and nonlinear LS-SVM and back-propagation artificial neural network (BP-ANN) were compared. The simplified models showed better capability than those were built based on the whole dataset in prediction of TBARS values. Moreover, the nonlinear models were preferred over linear models. Among the four chemometric algorithms, the best and weakest models were LS-SVM and PLSR model, respectively. UB-LS-SVM model was the optimal models for predicting TBARS value in rainbow trout fillets ( R P 2 = 0.831; RMSEP = 0.125 mg MDA/kg). The establishing of lipid-oxidation prediction model in rainbow trout fish was complicated, due to the fluctuations of TBARS values during storage. Therefore, further researches are needed to improve the prediction results and applicability of HIS technique for prediction of TBARS value in rainbow trout fish.
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Affiliation(s)
- Sara Khoshnoudi‐Nia
- Seafood Processing Research Group & Department of Food Science and Technology, School of AgricultureShiraz UniversityShirazIran
| | - Marzieh Moosavi‐Nasab
- Seafood Processing Research Group & Department of Food Science and Technology, School of AgricultureShiraz UniversityShirazIran
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Nondestructive Determination of Microbial, Biochemical, and Chemical Changes in Rainbow Trout (Oncorhynchus mykiss) During Refrigerated Storage Using Hyperspectral Imaging Technique. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01494-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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