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Xie A, Zhang Y, Wu H, Chen M. Monitoring the Aging and Edible Safety of Pork in Postmortem Storage Based on HSI and Wavelet Transform. Foods 2024; 13:1903. [PMID: 38928844 PMCID: PMC11202915 DOI: 10.3390/foods13121903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
The process of meat postmortem aging is a complex one, in which improved tenderness and aroma coincide with negative effects such as water loss and microbial growth. Determining the optimal postmortem storage time for meat is crucial but also challenging. A new visual monitoring technique based on hyperspectral imaging (HSI) has been proposed to monitor pork aging progress. M. longissimus thoracis from 15 pigs were stored at 4 °C for 12 days while quality indexes and HSI spectra were measured daily. Based on changes in physical and chemical indicators, 100 out of the 180 pieces of meat were selected and classified into rigor mortis, aged, and spoilt meat. Discrete wavelet transform (DWT) technology was used to improve the accuracy of classification. DWT separated approximate and detailed signals from the spectrum, resulting in a significant increase in classification speed and precision. The support vector machine (SVM) model with 70 band spectra achieved remarkable classification accuracy of 97.06%. The study findings revealed that the aging and microbial spoilage process started at the edges of the meat, with varying rates from one pig to another. Using HSI and visualization techniques, it was possible to evaluate and portray the postmortem aging progress and edible safety of pork during storage. This technology has the potential to aid the meat industry in making informed decisions on the optimal storage and cooking times that would preserve the quality of the meat and ensure its safety for consumption.
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Affiliation(s)
- Anguo Xie
- Zhang Zhongjing School of Chinese Medicine, Nanyang Institute of Technology, Nanyang 473000, China; (H.W.); (M.C.)
| | - Yu Zhang
- School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473000, China;
| | - Han Wu
- Zhang Zhongjing School of Chinese Medicine, Nanyang Institute of Technology, Nanyang 473000, China; (H.W.); (M.C.)
| | - Meng Chen
- Zhang Zhongjing School of Chinese Medicine, Nanyang Institute of Technology, Nanyang 473000, China; (H.W.); (M.C.)
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2
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Wang Y, Ou X, He HJ, Kamruzzaman M. Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chem X 2024; 21:101235. [PMID: 38420503 PMCID: PMC10900407 DOI: 10.1016/j.fochx.2024.101235] [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: 12/08/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.
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Affiliation(s)
- Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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3
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He HJ, Liu H, Wang Y, Chew KW, Ou X, Zhang M, Bi J. Fast quantitative analysis and chemical visualization of amylopectin and amylose in sweet potatoes via merging 1D spectra and 2D image. Int J Biol Macromol 2024; 260:129421. [PMID: 38228206 DOI: 10.1016/j.ijbiomac.2024.129421] [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: 08/11/2023] [Revised: 12/08/2023] [Accepted: 01/09/2024] [Indexed: 01/18/2024]
Abstract
The quantitative analysis and spatial chemical visualization of amylopectin and amylose in different varieties of sweet potatoes were studied by merging spectral and image information. Three-dimensional (3D) hyperspectral images carrying 1D spectra and 2D images of hundreds of the samples (amylopectin, n = 644; amylose, n = 665) in near-infrared (NIR) range of 950-1650 nm (426 wavelengths) were acquired. The NIR spectra were mined to correlate with the values of the two indexes using a linear algorithm, generating a best performance with correlation coefficients and root mean square error of prediction (rP and RMSEP) of 0.983 and 0.847 g/100 mg for amylopectin, and 0.975 and 0.500 g/100 mg for amylose, respectively. Then, 14 % of the wavelengths (60 for amylopectin, 61 for amylopectin) were selected to simplify the prediction with rP and RMSEP of 0.970 and 1.103 g/100 mg for amylopectin, and 0.952 and 0.684 g/100 mg for amylose, respectively, comparable to those of full-wavelength models. By transferring the simplified model to original images, the color chemical maps were created and the differences of the two indexes in spatial distribution were visualized. The integration of NIR spectra and 2D image could be used for the more comprehensive evaluation of amylopectin and amylose concentrations in sweet potatoes.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Hongjie Liu
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
| | - Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China.
| | - Mian Zhang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Jicai Bi
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
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4
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Cheng JH, Du R, Sun DW. Regulating bacterial biofilms in food and biomedicine: unraveling mechanisms and Innovating strategies. Crit Rev Food Sci Nutr 2024:1-17. [PMID: 38384205 DOI: 10.1080/10408398.2024.2312539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Bacterial biofilm has brought a lot of intractable problems in food and biomedicine areas. Conventional biofilm control mainly focuses on inactivation and removal of biofilm. However, with robust construction and enhanced resistance, the established biofilm is extremely difficult to eradicate. According to the mechanism of biofilm development, biofilm formation can be modulated by intervening in the key factors and regulatory systems. Therefore, regulation of biofilm formation has been proposed as an alternative way for effective biofilm control. This review aims to provide insights into the regulation of biofilm formation in food and biomedicine. The underlying mechanisms for early-stage biofilm establishment are summarized based on the key factors and correlated regulatory networks. Recent developments and applications of novel regulatory strategies such as anti/pro-biofilm agents, nanomaterials, functionalized surface materials and physical strategies are also discussed. The current review indicates that these innovative methods have contributed to effective biofilm control in a smart, safe and eco-friendly way. However, standard methodology for regulating biofilm formation in practical use is still missing. As biofilm formation in real-world systems could be far more complicated, further studies and interdisciplinary collaboration are still needed for simulation and experiments in the industry and other open systems.
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Affiliation(s)
- Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 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, China
| | - Rong Du
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 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, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, 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, China
- 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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5
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Guo Z, Zhang J, Sun J, Dong H, Huang J, Geng L, Li S, Jing X, Guo Y, Sun X. A multivariate algorithm for identifying contaminated peanut using visible and near-infrared hyperspectral imaging. Talanta 2024; 267:125187. [PMID: 37722342 DOI: 10.1016/j.talanta.2023.125187] [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: 06/21/2023] [Revised: 08/29/2023] [Accepted: 09/07/2023] [Indexed: 09/20/2023]
Abstract
In this study, a novel uniform manifold approximation and projection combined-improved simultaneous optimization genetic algorithm-convolutional neural network (UMAP-ISOGA-CNN) algorithm was proposed. The improved simultaneous optimization genetic algorithm (ISOGA) combined with convolutional neural network (CNN) to optimize the architecture, hyperparameters, and optimizer of the CNN model simultaneously. Additionally, a uniform manifold approximation and projection (UMAP) method was used to visualize the feature space of all feature layers of the ISOGA-CNN model. The UMAP-ISOGA-CNN algorithm combined with visible and near-infrared hyperspectral imaging was used to identify peanut kernels contaminated with Aspergillus flavus and to distinguish their storage time, which is essential for the food industry to monitor the freshness of products. Overall, the UMAP-ISOGA-CNN algorithm provides useful insights into the feature space of the ISOGA-CNN model, contributing to a better understanding of the model's internal mechanisms. This study has practical implications for the food industry and future research on deep learning optimization.
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Affiliation(s)
- Zhen Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jing Zhang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jiashuai Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Haowei Dong
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jingcheng Huang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Lingjun Geng
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Shiling Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Xiangzhu Jing
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Yemin Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
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6
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Zhang B, Ou Y, Yu S, Liu Y, Liu Y, Qiu W. Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging. PLANT METHODS 2023; 19:148. [PMID: 38115023 PMCID: PMC10729489 DOI: 10.1186/s13007-023-01123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/04/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. RESULTS Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. CONCLUSIONS This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.
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Affiliation(s)
- Baohua Zhang
- College of Engineering, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Taishan Street, Pukou District, Nanjing, Jiangsu, 210031, P.R. China
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Yunmeng Ou
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Shuwan Yu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Yuchen Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Ying Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Wei Qiu
- College of Engineering, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Taishan Street, Pukou District, Nanjing, Jiangsu, 210031, P.R. China.
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Cui Y, Yao Y, Yang R, Wang Y, Liang J, Ouyang S, Yu S, Zou H, Yan Y. Detection of Mildewed Nutmeg Internal Quality during Storage Using an Electronic Nose Combined with Chemical Profile Analysis. Molecules 2023; 28:6051. [PMID: 37630302 PMCID: PMC10457796 DOI: 10.3390/molecules28166051] [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: 06/29/2023] [Revised: 07/28/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
Internal mildewed nutmeg is difficult to perceive without cutting the nutmeg open and examining it carefully, which poses a significant risk to public health. At present, macroscopic identification and chromatographic analysis are applied to determine whether nutmeg is moldy or not. However, the former relies on a human panel, with the disadvantages of subjectivity and empirical dependence, whilst the latter is generally time-consuming and requires organic solvents. Therefore, it is urgent to develop a rapid and feasible approach for evaluating the quality and predicting mildew in nutmeg. In this study, the quality and odor characteristics of five groups of nutmeg samples with different degrees of mildew were analyzed by using the responses of an electronic nose combined with chemical profiling. The main physicochemical indicators, such as the levels of α-pinene, β-pinene, elemicin, and dehydro-di-isoeugenol, were determined. The results revealed that the contents of α-pinene, β-pinene, and elemicin changed significantly with the extension of storage time. Through the use of an electronic nose and HS-GC-MS technology to assess the overall odor characteristics of nutmeg samples, it was found that the production of volatile organic compounds (VOCs) such as ammonia/organic amines, carbon monoxide, ethanol, and hydrogen sulfide, as well as changes in the terpene and phenylpropene components of the nutmeg itself, may be the material basis for the changes in odor. The accuracy of the qualitative classification model for the degree of mildew in nutmeg was higher than 90% according to the electronic nose data combined with different machine learning algorithms. Quantitative models were established for predicting the contents of the chemical components, and models based on a BP neural network (BPNN), the support vector machine (SVM), and the random forest algorithm (RF) all showed good performance in predicting the concentrations of these chemical components, except for dehydro-di-isoeugenol. The BPNN performed effectively in predicting the storage time of nutmeg on the basis of the E-nose's responses, with an RMSE and R2 of 0.268 and 0.996 for the training set, and 0.317 and 0.993 for the testing set, respectively. The results demonstrated that the responses of the electronic nose (E-nose) had a high correlation with the internal quality of nutmeg. This work proposes a quick and non-destructive evaluation method for the quality of nutmeg, which has high accuracy in discriminating between different degrees of mold in nutmeg and is conducive to early detection and warning of moldy phenomena.
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Affiliation(s)
| | | | | | | | | | | | | | - Huiqin Zou
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Yonghong Yan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
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8
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He HJ, Wang Y, Wang Y, Liu H, Zhang M, Ou X. Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [ Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging. Food Chem X 2023; 18:100631. [PMID: 36926310 PMCID: PMC10010985 DOI: 10.1016/j.fochx.2023.100631] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/27/2023] [Accepted: 03/05/2023] [Indexed: 03/10/2023] Open
Abstract
This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (r P) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.,School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Yangyang Wang
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hongjie Liu
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
| | - Mian Zhang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
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9
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Sahachairungrueng W, Thompson AK, Terdwongworakul A, Teerachaichayut S. Non-Destructive Classification of Organic and Conventional Hens' Eggs Using Near-Infrared Hyperspectral Imaging. Foods 2023; 12:2519. [PMID: 37444257 DOI: 10.3390/foods12132519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares-discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.
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Affiliation(s)
- Woranitta Sahachairungrueng
- Department of Food Science, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Cranfield, Bedford MK43 0AL, UK
| | - Anupun Terdwongworakul
- Department of Agricultural Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
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10
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Fan M, Rakotondrabe TF, Chen G, Guo M. Advances in microbial analysis: based on volatile organic compounds of microorganisms in food. Food Chem 2023; 418:135950. [PMID: 36989642 DOI: 10.1016/j.foodchem.2023.135950] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/30/2022] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
In recent years, microbial volatile organic compounds (mVOCs) produced by microbial metabolism have attracted more and more attention because they can be used to detect food early contamination and flaws. So far, many analytical methods have been reported for the determination of mVOCs in food, but few integrated review articles discussing these methods are published. Consequently, mVOCs as indicators of food microbiological contamination and their generation mechanism including carbohydrate, amino acid, and fatty acid metabolism are introduced. Meanwhile, a detailed summary of the mVOCs sampling methods such as headspace, purge trap, solid phase microextraction, and needle trap is presented, and a systematic and critical review of the analytical methods (ion mobility spectrometry, electronic nose, biosensor, and so on) of mVOCs and their application in the detection of food microbial contamination is highlighted. Finally, the future concepts that can help improve the detection of food mVOCs are prospected.
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Affiliation(s)
- Minxia Fan
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China.
| | - Tojofaniry Fabien Rakotondrabe
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Guilin Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Mingquan Guo
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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11
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Current Developments of Analytical Methodologies for Aflatoxins' Determination in Food during the Last Decade (2013-2022), with a Particular Focus on Nuts and Nut Products. Foods 2023; 12:foods12030527. [PMID: 36766055 PMCID: PMC9914313 DOI: 10.3390/foods12030527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/09/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
This review aims to provide a clear overview of the most important analytical development in aflatoxins analysis during the last decade (2013-2022) with a particular focus on nuts and nuts-related products. Aflatoxins (AFs), a group of mycotoxins produced mainly by certain strains of the genus Aspergillus fungi, are known to impose a serious threat to human health. Indeed, AFs are considered carcinogenic to humans, group 1, by the International Agency for Research on Cancer (IARC). Since these toxins can be found in different food commodities, food control organizations worldwide impose maximum levels of AFs for commodities affected by this threat. Thus, they represent a cumbersome issue in terms of quality control, analytical result reliability, and economical losses. It is, therefore, mandatory for food industries to perform analysis on potentially contaminated commodities before the trade. A full perspective of the whole analytical workflow, considering each crucial step during AFs investigation, namely sampling, sample preparation, separation, and detection, will be presented to the reader, focusing on the main challenges related to the topic. A discussion will be primarily held regarding sample preparation methodologies such as partitioning, solid phase extraction (SPE), and immunoaffinity (IA) related methods. This will be followed by an overview of the leading analytical techniques for the detection of aflatoxins, in particular liquid chromatography (LC) coupled to a fluorescence detector (FLD) and/or mass spectrometry (MS). Moreover, the focus on the analytical procedure will not be specific only to traditional methodologies, such as LC, but also to new direct approaches based on imaging and the ability to detect AFs, reducing the need for sample preparation and separative techniques.
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12
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Sun Z, Pan H, Zuo M, Li J, Liang L, Ho CT, Zou X. Non-destructive assessment of equivalent umami concentrations in salmon using hyperspectral imaging technology combined with multivariate algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121890. [PMID: 36126621 DOI: 10.1016/j.saa.2022.121890] [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: 07/19/2022] [Revised: 09/05/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
This study utilized equivalent umami concentrations (EUC) to characterize umami intensity in salmon with different freeze-thaw times. A rapid and non-destructive method was established to determine EUC values in salmon which is based on hyperspectral imaging (HSI) system combined with multiple characteristic variable screening methods. The established CARS-PLS model showed greater advantages in correlating the reference values of spectral data with EUC in salmon with Rc of 0.9012, Rp of 0.9009, RMSECV of 0.82, and RMSEP of 0.88. The model was employed pixel-wise to visualize the distribution of EUC with different freeze-thaw times, which demonstrated the reduction of EUC value with the increasing of freeze-thaw times. Therefore, this reseearch showed hyperspectral imaging (HSI) system combined with chemometrics possesses a substantial capability to predict and visualize the EUC of salmon, which would provide an intuitive understanding of salmon quality prediction and detection.
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Affiliation(s)
- Zongbao Sun
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China.
| | - Haodong Pan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
| | - Min Zuo
- National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, 100048, China
| | - Junkui Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
| | - Liming Liang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
| | - Chi-Tang Ho
- Department of Food Science, Rutgers University, New Brunswick, NJ 08903, USA.
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
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13
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Zou W, Peng Y, Yang D, Zuo J, Li Y, Guo Q. An Intelligent Detector for Sensing Pork Freshness In Situ Based on a Multispectral Technique. BIOSENSORS 2022; 12:998. [PMID: 36354507 PMCID: PMC9688451 DOI: 10.3390/bios12110998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Fresh pork is prone to spoilage during storage, transportation, and sale, resulting in reduced freshness. The total viable count (TVC) and total volatile basic nitrogen (TVB-N) content are key indicators for evaluating the freshness of fresh pork, and when they reach unacceptable limits, this seriously threatens dietary safety. To realize the on-site, low-cost, rapid, and non-destructive testing and evaluation of fresh pork freshness, a miniaturized detector was developed based on a cost-effective multi-channel spectral sensor. The partial least squares discriminant analysis (PLS-DA) model was used to distinguish fresh meat from deteriorated meat. The detector consists of microcontroller, light source, multi-channel spectral sensor, heat-dissipation modules, display system, and battery. In this study, the multispectral data of pork samples with different freshness levels were collected by the developed detector, and its ability to distinguish pork freshness was based on different spectral shape features (SSF) (spectral ratio (SR), spectral difference (SD), and normalized spectral intensity difference (NSID)) were compared. The experimental results show that compared with the original multispectral modeling, the performance of the model based on spectral shape features is significantly improved. The model established by optimizing the spectral shape feature variables has the best performance, and the discrimination accuracy of its prediction set is 91.67%. In addition, the validation accuracy of the optimal model was 86.67%, and its sensitivity and variability were 87.50% and 85.71%, respectively. The results show that the detector developed in this study is cost-effective, compact in its structure, stable in its performance, and suitable for the on-site digital rapid non-destructive testing of freshness during the storage, transportation, and sale of fresh pork.
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14
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Tang T, Zhang M, Mujumdar AS. Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology. Compr Rev Food Sci Food Saf 2022; 21:5171-5198. [PMID: 36156851 DOI: 10.1111/1541-4337.13039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 01/28/2023]
Abstract
Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.
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Affiliation(s)
- Tiantian Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, Jiangsu, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Montreal, Quebec, Canada
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15
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Detection of moisture content in salted sea cucumbers by hyperspectral and low field nuclear magnetic resonance based on deep learning network framework. Food Res Int 2022; 156:111174. [DOI: 10.1016/j.foodres.2022.111174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 11/23/2022]
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16
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Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2021.12.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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17
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Ai N, Jiang Y, Omar S, Wang J, Xia L, Ren J. Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods. Molecules 2022; 27:335. [PMID: 35056650 PMCID: PMC8780011 DOI: 10.3390/molecules27020335] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 11/17/2022] Open
Abstract
Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.
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Affiliation(s)
- Ning Ai
- College of Biological, Chemical Science and Engineering, Jiaxing University, Jiaxing 314001, China;
- Zhejiang Province Key Laboratory of Biomass Fuel, Hangzhou 310014, China;
| | - Yibo Jiang
- Zhejiang Province Key Laboratory of Biomass Fuel, Hangzhou 310014, China;
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Sainab Omar
- Chemical Engineering and Applied Chemistry, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (S.O.); (J.W.)
| | - Jiawei Wang
- Chemical Engineering and Applied Chemistry, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (S.O.); (J.W.)
| | - Luyue Xia
- Zhejiang Province Key Laboratory of Biomass Fuel, Hangzhou 310014, China;
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jie Ren
- College of Biological, Chemical Science and Engineering, Jiaxing University, Jiaxing 314001, China;
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18
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Cao K, Wei W, Xing S, Ai X, Zhao Z, Zhang C. Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.16081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Kai Cao
- College of Quality and Technology Supervision Hebei University Baoding China
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Wensong Wei
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Shujuan Xing
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Xin Ai
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Zhilei Zhao
- College of Quality and Technology Supervision Hebei University Baoding China
| | - Chunjiang Zhang
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
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19
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Bonifazi G, Capobianco G, Gasbarrone R, Serranti S. Contaminant detection in pistachio nuts by different classification methods applied to short-wave infrared hyperspectral images. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108202] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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20
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Review on Active and Passive Remote Sensing Techniques for Road Extraction. REMOTE SENSING 2021. [DOI: 10.3390/rs13214235] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.
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21
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Jiang S, Wang F, Li Q, Sun H, Wang H, Yao Z. Environment and food safety: a novel integrative review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:54511-54530. [PMID: 34431060 PMCID: PMC8384557 DOI: 10.1007/s11356-021-16069-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/16/2021] [Indexed: 04/12/2023]
Abstract
Environment protection and food safety are two critical issues in the world. In this review, a novel approach which integrates statistical study and subjective discussion was adopted to review recent advances on environment and food safety. Firstly, a scientometric-based statistical study was conducted based on 4904 publications collected from the Web of Science Core Collection database. It was found that the research on environment and food safety was growing steadily from 2001 to 2020. Interestingly, the statistical analysis of most-cited papers, titles, abstracts, keywords, and research areas revealed that the research on environment and food safety was diverse and multidisciplinary. In addition to the scientometric study, strategies to protect environment and ensure food safety were critically discussed, followed by a discussion on the emerging research topics, including emerging contaminates (e.g., microplastics), rapid detection of contaminants (e.g., biosensors), and environment friendly food packaging materials (e.g., biodegradable polymers). Finally, current challenges and future research directions were proposed.
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Affiliation(s)
- Shanxue Jiang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing, 100048, China
| | - Fang Wang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing, 100048, China
| | - Qirun Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Haishu Sun
- Department of Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Huijiao Wang
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China.
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China.
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing, 100048, China.
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22
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Shi Y, Wang X, Borhan MS, Young J, Newman D, Berg E, Sun X. A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies. Food Sci Anim Resour 2021; 41:563-588. [PMID: 34291208 PMCID: PMC8277176 DOI: 10.5851/kosfa.2021.e25] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 11/09/2022] Open
Abstract
Increasing meat demand in terms of both quality and quantity in conjunction with
feeding a growing population has resulted in regulatory agencies imposing
stringent guidelines on meat quality and safety. Objective and accurate rapid
non-destructive detection methods and evaluation techniques based on artificial
intelligence have become the research hotspot in recent years and have been
widely applied in the meat industry. Therefore, this review surveyed the key
technologies of non-destructive detection for meat quality, mainly including
ultrasonic technology, machine (computer) vision technology, near-infrared
spectroscopy technology, hyperspectral technology, Raman spectra technology, and
electronic nose/tongue. The technical characteristics and evaluation methods
were compared and analyzed; the practical applications of non-destructive
detection technologies in meat quality assessment were explored; and the current
challenges and future research directions were discussed. The literature
presented in this review clearly demonstrate that previous research on
non-destructive technologies are of great significance to ensure
consumers’ urgent demand for high-quality meat by promoting automatic,
real-time inspection and quality control in meat production. In the near future,
with ever-growing application requirements and research developments, it is a
trend to integrate such systems to provide effective solutions for various grain
quality evaluation applications.
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Affiliation(s)
- Yinyan Shi
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA.,College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Md Saidul Borhan
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
| | - Jennifer Young
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - David Newman
- Department of Animal Science, Arkansas State University, Jonesboro, AR 72467, USA
| | - Eric Berg
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Xin Sun
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
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23
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Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model. Food Res Int 2021; 147:110577. [PMID: 34399549 DOI: 10.1016/j.foodres.2021.110577] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 06/24/2021] [Accepted: 06/27/2021] [Indexed: 11/23/2022]
Abstract
Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The objectives of this study were to evaluate the feasibility of using hyperspectral imaging (HSI) and deep learning approaches, firstly to identify dead and live forms of C. sporogenes spores and secondly, to estimate the concentration of spores on culture media plates and ready-to-eat mashed potato (food matrix). C. sporogenes spores were inoculated by either spread plating or drop plating on sheep blood agar (SBA) and tryptic soy agar (TSA) plates and by spread plating on the surface of mashed potato. Reflectance in the spectral range of 547-1701 nm from the region of interest was used for principal component analysis (PCA). PCA was successful in distinguishing dead and live spores and different levels of inoculum (102 to 106 CFU/ml) on both TSA and SBA plates, however, was not efficient on the mashed potato (food matrix). Hence, deep learning classification frameworks namely 1D- convolutional neural networks (CNN) and random forest (RF) model were used. CNN model outperformed the RF model and the accuracy for quantification of spores was improved by 4% and 8% in the presence and absence, respectively of dead spores. The screening system used in this study was a combination of HSI and deep learning modelling, which resulted in an overall accuracy of 90-94% when the dead/inactivated spores were present and absent, respectively. The only discrepancy detected was during the prediction of samples with low inoculum levels (<102 CFU/ml). In summary, it was evident that HSI in combination with a deep learning approach showed immense potential as a tool to detect and quantify spores on nutrient media as well as on specific food matrix (mashed potato). However, the presence of dead spores in any sample is postulated to affect the accuracy and would need replicates and confirmatory assays.
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24
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Ferone M, Gowen A, Fanning S, Scannell AGM. Microbial detection and identification methods: Bench top assays to omics approaches. Compr Rev Food Sci Food Saf 2020; 19:3106-3129. [PMID: 33337061 DOI: 10.1111/1541-4337.12618] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/20/2020] [Accepted: 07/20/2020] [Indexed: 12/26/2022]
Abstract
Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.
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Affiliation(s)
- Mariateresa Ferone
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD School of Biosystems and Food Engineering, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland
| | - Aoife Gowen
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD School of Biosystems and Food Engineering, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland
| | - Séamus Fanning
- UCD Institute of Food and Health, Dublin, Ireland.,UCD-Centre for Food Safety, Dublin, Ireland.,UCD School of Public Health, Physiotherapy and Sport Science University College Dublin, Dublin, Ireland
| | - Amalia G M Scannell
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland.,UCD-Centre for Food Safety, Dublin, Ireland
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25
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Cisneros-Carrillo H, Hernandez-Aguilar C, Dominguez-Pacheco A, Cruz-Orea A, Zepeda-Bautista R. Thermal analysis and artificial vision of laser irradiation on corn. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03402-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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26
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Zhang W, Dong Z, Zhu L, Hou Y, Qiu Y. Direct Observation of the Release of Nanoplastics from Commercially Recycled Plastics with Correlative Raman Imaging and Scanning Electron Microscopy. ACS NANO 2020; 14:7920-7926. [PMID: 32441911 DOI: 10.1021/acsnano.0c02878] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Nanoplastics (NPs), mainly originated from weathering of microplastics, are ubiquitous throughout the world. However, the environmentally released NPs are still under debate due to the lack of direct proof for the chemical identification of individual nanoparticles. Here, we show an observational evidence of release of heterogeneous NPs from recycled PVC powders (RPP) using a nondestructive analytical method, namely, correlative Raman imaging and scanning electron (RISE) microscopy. The technology achieves direct chemical identification of individual nanoparticles on RPP surface that are as small as 360 nm including nano-PVC and nano-CaCO3 in complexes with pigments. After washing and filtering through a 1 μm poly(ether sulfone) filter, we clearly distinguish nano-PVC from the other components in an air-dried filtrate. Furthermore, the automated 2D mapping of RISE enables the acquisition of the 2D chemical information on a selected area (e.g., 5 μm × 5 μm) and the display of the different components of nanoparticle aggregates without colloidal separation. Our findings give direct evidence and detailed insights in the potential release of nanoplastics from the recycled plastic products. The RISE method will help us intuitively understand the origin, occurrence, and fate of NPs in the environment.
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Affiliation(s)
- Wen Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, Ministry of Education Key Laboratory of Yangtze River Water Environment, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhiqiang Dong
- State Key Laboratory of Pollution Control and Resource Reuse, Ministry of Education Key Laboratory of Yangtze River Water Environment, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ling Zhu
- State Key Laboratory of Pollution Control and Resource Reuse, Ministry of Education Key Laboratory of Yangtze River Water Environment, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Yuanzhang Hou
- State Key Laboratory of Pollution Control and Resource Reuse, Ministry of Education Key Laboratory of Yangtze River Water Environment, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Yuping Qiu
- State Key Laboratory of Pollution Control and Resource Reuse, Ministry of Education Key Laboratory of Yangtze River Water Environment, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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27
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Shen F, Huang Y, Jiang X, Fang Y, Li P, Liu Q, Hu Q, Liu X. On-line prediction of hazardous fungal contamination in stored maize by integrating Vis/NIR spectroscopy and computer vision. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 229:118012. [PMID: 31927238 DOI: 10.1016/j.saa.2019.118012] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 12/18/2019] [Accepted: 12/27/2019] [Indexed: 06/10/2023]
Abstract
This work presents a fusion scheme to combine visible/near infrared spectroscopy and computer vision for on-line detection of Aspergillus spp. and Fusarium spp. contamination in stored maize. Spectroscopy and image information of 270 groups of maize kernels were collected at speed of 0.15 m/s. Principal component analysis indicated fungi growth on maize could be monitored by both techniques. Spectroscopy method was found sensitive for infection level identification, while computer vision was more effective for fungal strain recognition. Linear discriminant analysis based on fusion of spectral and image features provided 100% accuracy for discrimination of samples infected by different strains after stored for 12 d, which is at least 5.6% higher than single-type features. Classification rate of samples with different infection levels achieved 92.2%, also 5.5% and 10.0% higher than single technique. Moreover, data fusion improved colony counts prediction in samples by partial least squares regression, with root mean-square error of prediction value being reduced by 25.0% and 17.4%, respectively. This study demonstrated the superiority of data fusion for fungal detection in grain during on-line processing.
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Affiliation(s)
- Fei Shen
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Yi Huang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Xuesong Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Yong Fang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China.
| | - Peng Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Qin Liu
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Qiuhui Hu
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Xingquan Liu
- School of Agriculture and Food Science, Zhejiang A&F University, Hangzhou 311300, China
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Rahman G, Sohag H, Chowdhury R, Wahid KA, Dinh A, Arcand M, Vail S. SoilCam: A Fully Automated Minirhizotron using Multispectral Imaging for Root Activity Monitoring. SENSORS (BASEL, SWITZERLAND) 2020; 20:E787. [PMID: 32023975 PMCID: PMC7038518 DOI: 10.3390/s20030787] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/18/2020] [Accepted: 01/29/2020] [Indexed: 01/23/2023]
Abstract
A minirhizotron is an in situ root imaging system that captures components of root system architecture dynamics over time. Commercial minirhizotrons are expensive, limited to white-light imaging, and often need human intervention. The implementation of a minirhizotron needs to be low cost, automated, and customizable to be effective and widely adopted. We present a newly designed root imaging system called SoilCam that addresses the above mentioned limitations. The imaging system is multi-modal, i.e., it supports both conventional white-light and multispectral imaging, with fully automated operations for long-term in-situ monitoring using wireless control and access. The system is capable of taking 360° images covering the entire area surrounding the tube. The image sensor can be customized depending on the spectral imaging requirements. The maximum achievable image quality of the system is 8 MP (Mega Pixel)/picture, which is equivalent to a 2500 DPI (dots per inch) image resolution. The length of time in the field can be extended with a rechargeable battery and solar panel connectivity. Offline image-processing software, with several image enhancement algorithms to eliminate motion blur and geometric distortion and to reconstruct the 360° panoramic view, is also presented. The system is tested in the field by imaging canola roots to show the performance advantages over commercial systems.
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Affiliation(s)
- Gazi Rahman
- Department of Electrical and Computer Engineering, University of Saskatchewan, SK, S7N 5A9, Canada; (H.S.); (R.C.); (K.A.W.); (A.D.)
| | - Hanif Sohag
- Department of Electrical and Computer Engineering, University of Saskatchewan, SK, S7N 5A9, Canada; (H.S.); (R.C.); (K.A.W.); (A.D.)
| | - Rakibul Chowdhury
- Department of Electrical and Computer Engineering, University of Saskatchewan, SK, S7N 5A9, Canada; (H.S.); (R.C.); (K.A.W.); (A.D.)
| | - Khan A. Wahid
- Department of Electrical and Computer Engineering, University of Saskatchewan, SK, S7N 5A9, Canada; (H.S.); (R.C.); (K.A.W.); (A.D.)
| | - Anh Dinh
- Department of Electrical and Computer Engineering, University of Saskatchewan, SK, S7N 5A9, Canada; (H.S.); (R.C.); (K.A.W.); (A.D.)
| | - Melissa Arcand
- Department of Soil Science, University of Saskatchewan, SK, S7N 5A8, Canada;
| | - Sally Vail
- Research Scientist, Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, SK S7N 0X2, Canada;
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29
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Inkinen J, Ahonen M, Iakovleva E, Karppinen P, Mielonen E, Mäkinen R, Mannonen K, Koivisto J. Contamination detection by optical measurements in a real-life environment: A hospital case study. JOURNAL OF BIOPHOTONICS 2020; 13:e201960069. [PMID: 31613045 PMCID: PMC7065611 DOI: 10.1002/jbio.201960069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/30/2019] [Accepted: 10/06/2019] [Indexed: 05/05/2023]
Abstract
Organic dirt on touch surfaces can be biological contaminants (microbes) or nutrients for those but is often invisible by the human eye causing challenges for evaluating the need for cleaning. Using hyperspectral scanning algorithm, touch surface cleanliness monitoring by optical imaging was studied in a real-life hospital environment. As the highlight, a human eye invisible stain from a dirty chair armrest was revealed manually with algorithms including threshold levels for intensity and clustering analysis with two excitation lights (green and red) and one bandpass filter (wavelength λ = 500 nm). The same result was confirmed by automatic k-means clustering analysis from the entire dirty data of visible light (red, green and blue) and filters 420 to 720 nm with 20 nm increments. Overall, the collected touch surface samples (N = 156) indicated the need for cleaning in some locations by the high culturable bacteria and adenosine triphosphate counts despite the lack of visible dirt. Examples of such locations were toilet door lock knobs and busy registration desk armchairs. Thus, the studied optical imaging system utilizing the safe visible light area shows a promising method for touch surface cleanliness evaluation in real-life environments.
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Affiliation(s)
- Jenni Inkinen
- Aalto University, School of Science, Department of Applied PhysicsComplex Systems and MaterialsAaltoFinland
| | - Merja Ahonen
- Satakunta University of Applied Sciences, Faculty of TechnologyWANDER Nordic Water and Materials InstituteRaumaFinland
| | - Evgenia Iakovleva
- Aalto University, School of Science, Department of Applied PhysicsComplex Systems and MaterialsAaltoFinland
| | - Pasi Karppinen
- Aalto University, School of Science, Department of Applied PhysicsComplex Systems and MaterialsAaltoFinland
| | - Eelis Mielonen
- Aalto University, School of Science, Department of Applied PhysicsComplex Systems and MaterialsAaltoFinland
| | - Riika Mäkinen
- Satakunta University of Applied Sciences, Faculty of TechnologyWANDER Nordic Water and Materials InstituteRaumaFinland
| | - Katriina Mannonen
- Satakunta University of Applied Sciences, Faculty of TechnologyWANDER Nordic Water and Materials InstituteRaumaFinland
| | - Juha Koivisto
- Aalto University, School of Science, Department of Applied PhysicsComplex Systems and MaterialsAaltoFinland
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Zhuang J, Liu W, Yang L, Kang J, Zhang X. Bioluminescent Imaging and Tracking of Bacterial Transport in Soils. Methods Mol Biol 2020; 2081:53-65. [PMID: 31721118 DOI: 10.1007/978-1-4939-9940-8_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Bioimaging instrumentation can be used to observe environmental phenomena such as the transport, retention, and distribution of bacteria in soils in situ in a real-time, nondestructive manner. Bacteria designed to emit bioluminescence light signals are injected into a transparent column packed with soils, and then the column is placed into a bioimaging instrument, such as a PerkinElmer IVIS Spectrum, while it is connected through thin teflon tubes to other parts of the column system located outside of the imaging chamber, including a fraction collector for collecting effluent solution and a pump for introducing bacterial suspension or experimental solution. After self-correction of soil autofluorescence and bioluminescence and setup of required imaging parameters, the transport experiment is initiated by introducing the bacterial suspension to the soil column while the spatiotemporal distribution of bioluminescent bacteria in the entire soil column is imaged. Finally, the images are processed to analyze bacterial migration in the soil under various environmental conditions in comparison with the breakthrough and elution curves of the bacteria obtained by analyzing the effluent samples.
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Affiliation(s)
- Jie Zhuang
- Department of Biosystems Engineering and Soil Science, Center for Environmental Biotechnology, The University of Tennessee, Knoxville, TN, USA. .,Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China.
| | - Weipeng Liu
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China.,The University of Chinese Academy of Sciences, Beijing, China
| | - Liqiong Yang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China.,The University of Chinese Academy of Sciences, Beijing, China
| | - Jia Kang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China.,The University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoming Zhang
- College of Desert Control Science, Inner Mongolia Agricultural University, Hohhot, China
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31
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Sun J, Shi X, Zhang H, Xia L, Guo Y, Sun X. Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13263] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jianfei Sun
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Xiaojie Shi
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Hui Zhang
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Lianming Xia
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Yemin Guo
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
| | - Xia Sun
- School of Agricultural Engineering and Food ScienceShandong University of Technology Zibo Shandong China
- Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability Zibo Shandong China
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32
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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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33
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Aheto JH, Huang X, Tian X, Ren Y, Bonah E, Alenyorege EA, Lv R, Dai C. Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13225] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Joshua H. Aheto
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xingyi Huang
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xiaoyu Tian
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Yi Ren
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Suzhou Polytechnic Institute of Agriculture; Suzhou China
| | - Ernest Bonah
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Laboratory Services Department; Food and Drugs Authority; Accra Ghana
| | - Evans A. Alenyorege
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Faculty of Agriculture; University for Development Studies; Tamale Ghana
| | - Riqin Lv
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Biological Science and Food Engineering; Chuzhou University; No. 1528 Fengle Avenue, Yu District, Zhangzhou City China
| | - Chunxia Dai
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang Jiangsu China
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34
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Liu B, Zhang M, Sun Y, Wang Y. Current intelligent segmentation and cooking technology in the central kitchen food processing. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13149] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bo Liu
- State Key Laboratory of Food Science and TechnologyJiangnan University Wuxi Jiangsu China
- Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and TechnologyJiangnan University Wuxi Jiangsu China
| | - Min Zhang
- State Key Laboratory of Food Science and TechnologyJiangnan University Wuxi Jiangsu China
- International Joint Laboratory on Food SafetyJiangnan University Wuxi Jiangsu China
| | - Yanan Sun
- State Key Laboratory of Food Science and TechnologyJiangnan University Wuxi Jiangsu China
| | - Yu‐Chuan Wang
- Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and TechnologyJiangnan University Wuxi Jiangsu China
- International Joint Laboratory on Food SafetyJiangnan University Wuxi Jiangsu China
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35
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Liu JJ, Cai Z, Liao Y, Zhao L, Moulin J, Hartmann C. Validation of a laser based in‐line aflatoxin sorting technology in Spanish type raw peanut in factory‐scale production conditions. J Food Saf 2018. [DOI: 10.1111/jfs.12611] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
| | | | | | - Lina Zhao
- Nestlé R&D (China) Ltd. Beijing China
| | | | - Christoph Hartmann
- Nestlé R&D (China) Ltd. Beijing China
- Nestlé Research Centre Lausanne Switzerland
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36
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Hameed S, Xie L, Ying Y. Conventional and emerging detection techniques for pathogenic bacteria in food science: A review. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.05.020] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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37
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Pasquini C. Near infrared spectroscopy: A mature analytical technique with new perspectives – A review. Anal Chim Acta 2018; 1026:8-36. [DOI: 10.1016/j.aca.2018.04.004] [Citation(s) in RCA: 363] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 04/05/2018] [Accepted: 04/06/2018] [Indexed: 12/19/2022]
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38
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Khoshnoudi-Nia S, Moosavi-Nasab M, Nassiri SM, Azimifar Z. Determination of Total Viable Count in Rainbow-Trout Fish Fillets Based on Hyperspectral Imaging System and Different Variable Selection and Extraction of Reference Data Methods. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1320-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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39
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Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves. SENSORS 2018; 18:s18072077. [PMID: 29958467 PMCID: PMC6068755 DOI: 10.3390/s18072077] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 06/16/2018] [Accepted: 06/26/2018] [Indexed: 11/17/2022]
Abstract
Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400⁻1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400⁻1000 nm (visible range) and 900⁻1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way.
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40
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Wu Q, Xie L, Xu H. Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques. Food Chem 2018; 252:228-242. [DOI: 10.1016/j.foodchem.2018.01.076] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 12/06/2017] [Accepted: 01/09/2018] [Indexed: 12/29/2022]
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41
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Lodhi V, Chakravarty D, Mitra P. Hyperspectral Imaging for Earth Observation: Platforms and Instruments. J Indian Inst Sci 2018. [DOI: 10.1007/s41745-018-0070-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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42
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Spectral Detection Techniques for Non-Destructively Monitoring the Quality, Safety, and Classification of Fresh Red Meat. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1256-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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43
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Wu Z, Xu E, Jin Z, Irudayaraj J. An ultrasensitive aptasensor based on fluorescent resonant energy transfer and exonuclease-assisted target recycling for patulin detection. Food Chem 2018; 249:136-142. [PMID: 29407916 DOI: 10.1016/j.foodchem.2018.01.025] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 09/06/2017] [Accepted: 01/02/2018] [Indexed: 11/17/2022]
Abstract
Patulin (PAT) is a type of mycotoxin which can compromise both food quality and human health. Due to its harmful effects, strict monitoring of PAT contents in food systems is necessary. A novel kind of bioassay was proposed for determining PAT based on the fluorescent resonant energy transfer (FRET) strategy. The exonuclease-catalyzed target recycling strategy was employed to improve the sensitivity of the FRET system. The results showed that the linear range extends from 0.01 ng/mL to 100 ng/mL. Furthermore, the average recoveries ranged from 93.33% to 105.21%, confirming the reliability of this method. The total analysis time for our assay developed is about 50 min. Compared to traditional analytical methods, the developed assay is more stable and has a significantly lower detection of limit (0.003 ng/mL). We believe the approach developed in this study would be useful for high-throughput screening of PAT in food industry and government laboratory.
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Affiliation(s)
- Zhengzong Wu
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; Department of Agriculture & Biological Engineering, Bindley Bioscience Center, Purdue Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA
| | - Enbo Xu
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China
| | - Zhengyu Jin
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China.
| | - Joseph Irudayaraj
- Department of Agriculture & Biological Engineering, Bindley Bioscience Center, Purdue Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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44
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Xing F, Yao H, Liu Y, Dai X, Brown RL, Bhatnagar D. Recent developments and applications of hyperspectral imaging for rapid detection of mycotoxins and mycotoxigenic fungi in food products. Crit Rev Food Sci Nutr 2017; 59:173-180. [DOI: 10.1080/10408398.2017.1363709] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Fuguo Xing
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing, P. R. China
- Geosystems Research Institute, Mississippi State University, Stennis Space Center, MS, USA
- Southern Regional Research Center, Agricultural Research Service-United States Department of Agriculture, New Orleans, LA, USA
| | - Haibo Yao
- Geosystems Research Institute, Mississippi State University, Stennis Space Center, MS, USA
| | - Yang Liu
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing, P. R. China
| | - Xiaofeng Dai
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing, P. R. China
| | - Robert L. Brown
- Southern Regional Research Center, Agricultural Research Service-United States Department of Agriculture, New Orleans, LA, USA
| | - Deepak Bhatnagar
- Southern Regional Research Center, Agricultural Research Service-United States Department of Agriculture, New Orleans, LA, USA
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45
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Vejarano R, Siche R, Tesfaye W. Evaluation of biological contaminants in foods by hyperspectral imaging: A review. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1338729] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ricardo Vejarano
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo (UNT), Ciudad Universitaria, Trujillo, Peru
- Facultad de Ingeniería, Universidad Privada del Norte (UPN), Trujillo, Peru
| | - Raúl Siche
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo (UNT), Ciudad Universitaria, Trujillo, Peru
| | - Wendu Tesfaye
- Departamento de Química y Tecnología de Alimentos, Universidad Politécnica de Madrid (UPM), Madrid, Spain
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46
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47
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Sun M, Zhang D, Liu L, Wang Z. How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method. Food Chem 2017; 218:413-421. [DOI: 10.1016/j.foodchem.2016.09.023] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 07/18/2016] [Accepted: 09/05/2016] [Indexed: 10/21/2022]
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