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Dung CD, Trueman SJ, Wallace HM, Farrar MB, Gama T, Tahmasbian I, Bai SH. Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:114166-114182. [PMID: 37858016 PMCID: PMC10663281 DOI: 10.1007/s11356-023-30344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023]
Abstract
Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.
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Affiliation(s)
- Cao Dinh Dung
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Potato, Vegetable and Flower Research Center - Institute of Agricultural Science for Southern Vietnam, Thai Phien Village, Ward 12, Da Lat, Lam Dong, Vietnam
| | - Stephen J Trueman
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Helen M Wallace
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Michael B Farrar
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Tsvakai Gama
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
| | - Iman Tahmasbian
- Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD, 4350, Australia
| | - Shahla Hosseini Bai
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia.
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Xie C, Wang C, Zhao M, Zhao L. Prediction of acrylamide content in potato chips using near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 301:122982. [PMID: 37315502 DOI: 10.1016/j.saa.2023.122982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/02/2023] [Accepted: 06/03/2023] [Indexed: 06/16/2023]
Abstract
Acrylamide (ACR), a neurotoxin with carcinogenic properties that can affect fertility, is commonly found in fried and baked foods such as potato chips. This study was carried out to predict the ACR content in fried and baked potato chips using near-infrared (NIR) spectroscopy. Effective wavenumbers were identified using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA). Six wavenumbers (12799 cm-1, 12007 cm-1, 10944 cm-1, 10943 cm-1, 5801 cm-1, and 4332 cm-1) were selected using the ratio (λi/λj) and difference (λi-λj) of any two wavenumbers from the CARS and SPA results. First, partial least squares (PLS) models were established based on full spectral wavebands (12799-4000 cm-1), and the prediction models were subsequently redeveloped based on effective wavenumbers to predict ACR content. Results showed that the full and selected wavenumbers-based PLS models obtained the coefficient of determination (R2) of 0.7707 and 0.6670, respectively, and the root mean square errors of prediction (RMSEP) of 53.0442 μg/kg and 64.3810 μg/kg, respectively, in the prediction sets. The results of this work demonstrate the suitability of NIR spectroscopy as a non-destructive method for predicting ACR content in potato chips.
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Affiliation(s)
- Chuanqi Xie
- State Key Laboratory of Bioreactor Engineering, School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China; State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, The Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Changyan Wang
- State Key Laboratory of Bioreactor Engineering, School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China
| | - Mengyao Zhao
- State Key Laboratory of Bioreactor Engineering, School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China.
| | - Liming Zhao
- State Key Laboratory of Bioreactor Engineering, School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China; Shanghai Collaborative Innovation Center for Biomanufacturing Technology (SCICBT), Shanghai 200237, China.
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ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum. Foods 2022; 11:foods11223568. [PMID: 36429160 PMCID: PMC9689878 DOI: 10.3390/foods11223568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/27/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022] Open
Abstract
Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.
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HOU Y, ZHAO P, ZHANG F, YANG S, RADY A, WIJEWARDANE NK, HUANG J, LI M. Fourier-transform infrared spectroscopy and machine learning to predict amino acid content of nine commercial insects. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.100821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Yinchen HOU
- Henan University of Animal Husbandry and Economy, China
| | | | - Fan ZHANG
- China Agricultural University, People’s Republic of China
| | - Shengru YANG
- Henan University of Animal Husbandry and Economy, China
| | | | | | | | - Mengxing LI
- University of Nebraska-Lincoln, USA; University of Nebraska-Lincoln, USA
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Fourier-transform infrared spectroscopy and machine learning to predict fatty acid content of nine commercial insects. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-020-00694-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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6
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Determination of soluble solids content and firmness in plum using hyperspectral imaging and chemometric algorithms. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13597] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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7
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Zhou L, Zhang C, Qiu Z, He Y. Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.115901] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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8
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Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.biteb.2020.100386] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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9
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Rapid assessment of pork freshness using miniaturized NIR spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2020. [DOI: 10.1007/s11694-019-00360-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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10
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A Clustering-Based Partial Least Squares Method for Improving the Freshness Prediction Model of Crucian Carps Fillets by Hyperspectral Image Technology. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01541-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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11
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Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00129-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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12
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Jiménez-Carvelo AM, González-Casado A, Bagur-González MG, Cuadros-Rodríguez L. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review. Food Res Int 2019; 122:25-39. [PMID: 31229078 DOI: 10.1016/j.foodres.2019.03.063] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/31/2022]
Abstract
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.
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Affiliation(s)
- Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain.
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - M Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
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Zhang D, Gao Z, Liang B, Li J, Cai Y, Wang Z, Gao M, Jiao B, Huang R, Liu M. Eyes Closed Elevates Brain Intrinsic Activity of Sensory Dominance Networks: A Classifier Discrimination Analysis. Brain Connect 2019; 9:221-230. [DOI: 10.1089/brain.2018.0644] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Delong Zhang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Zhenni Gao
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Bishan Liang
- Guangdong Polytechnic Normal University, Guangzhou, China
| | - Junchao Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Yuxuan Cai
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Zengjian Wang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Mengxia Gao
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Hong Kong
| | - Bingqing Jiao
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Ming Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
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Bodner G, Nakhforoosh A, Arnold T, Leitner D. Hyperspectral imaging: a novel approach for plant root phenotyping. PLANT METHODS 2018; 14:84. [PMID: 30305838 PMCID: PMC6169016 DOI: 10.1186/s13007-018-0352-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 09/24/2018] [Indexed: 05/22/2023]
Abstract
BACKGROUND Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. RESULTS Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000-1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. CONCLUSIONS Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.
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Affiliation(s)
- Gernot Bodner
- Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
| | - Alireza Nakhforoosh
- Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
- Agriculture and Agri-Food Canada, Brandon Research and Development Centre, Brandon, MB R7A 5Y3 Canada
| | - Thomas Arnold
- Carinthian Tech Research AG, Europastraße 12, High Tech Campus Villach, 9524 Villach/St. Magdalen, Austria
| | - Daniel Leitner
- Computational Science Center, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
- Simulationswerkstatt, Ortmayrstrasse 20, 4060 Leonding, Austria
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Using a Combination of Spectral and Textural Data to Measure Water-Holding Capacity in Fresh Chicken Breast Fillets. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8030343] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8020212] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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17
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Su WH, Sun DW. Multispectral Imaging for Plant Food Quality Analysis and Visualization. Compr Rev Food Sci Food Saf 2018; 17:220-239. [DOI: 10.1111/1541-4337.12317] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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Nondestructive Freshness Discriminating of Shrimp Using Visible/Near-Infrared Hyperspectral Imaging Technique and Deep Learning Algorithm. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1050-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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19
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Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy. Meat Sci 2017; 135:142-147. [PMID: 29032278 DOI: 10.1016/j.meatsci.2017.09.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 09/08/2017] [Accepted: 09/27/2017] [Indexed: 11/22/2022]
Abstract
In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20°C) and stored for 7 and 32days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively.
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Hassoun A, Karoui R. Quality evaluation of fish and other seafood by traditional and nondestructive instrumental methods: Advantages and limitations. Crit Rev Food Sci Nutr 2017; 57:1976-1998. [PMID: 26192079 DOI: 10.1080/10408398.2015.1047926] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Although being one of the most vulnerable and perishable products, fish and other seafoods provide a wide range of health-promoting compounds. Recently, the growing interest of consumers in food quality and safety issues has contributed to the increasing demand for sensitive and rapid analytical technologies. Several traditional physicochemical, textural, sensory, and electrical methods have been used to evaluate freshness and authentication of fish and other seafood products. Despite the importance of these standard methods, they are expensive and time-consuming, and often susceptible to large sources of variation. Recently, spectroscopic methods and other emerging techniques have shown great potential due to speed of analysis, minimal sample preparation, high repeatability, low cost, and, most of all, the fact that these techniques are noninvasive and nondestructive and, therefore, could be applied to any online monitoring system. This review describes firstly and briefly the basic principles of multivariate data analysis, followed by the most commonly traditional methods used for the determination of the freshness and authenticity of fish and other seafood products. A special focus is put on the use of rapid and nondestructive techniques (spectroscopic techniques and instrumental sensors) to address several issues related to the quality of these products. Moreover, the advantages and limitations of each technique are reviewed and some perspectives are also given.
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Affiliation(s)
- Abdo Hassoun
- a Université d'Artois, Institut Régional en Agroalimentaire et Biotechnologie Charles Violette, Equipe Qualité et Sécurité des Aliments (QSA), Unité Ingénierie de Formulation des Aliments et Altération (IFAA), Faculté des Sciences Jean-Perrin , Lens , France
| | - Romdhane Karoui
- a Université d'Artois, Institut Régional en Agroalimentaire et Biotechnologie Charles Violette, Equipe Qualité et Sécurité des Aliments (QSA), Unité Ingénierie de Formulation des Aliments et Altération (IFAA), Faculté des Sciences Jean-Perrin , Lens , France
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Sun Y, Liu Y, Yu H, Xie A, Li X, Yin Y, Duan X. Non-Destructive Prediction of Moisture Content and Freezable Water Content of Purple-Fleshed Sweet Potato Slices during Drying Process Using Hyperspectral Imaging Technique. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0722-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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22
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Cheng JH, Nicolai B, Sun DW. Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. Meat Sci 2016; 123:182-191. [PMID: 27750085 DOI: 10.1016/j.meatsci.2016.09.017] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 09/26/2016] [Accepted: 09/29/2016] [Indexed: 12/20/2022]
Abstract
Muscle foods are very important for a well-balanced daily diet. Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods.
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Affiliation(s)
- Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Bart Nicolai
- MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. FOOD ENGINEERING REVIEWS 2016. [DOI: 10.1007/s12393-016-9147-1] [Citation(s) in RCA: 111] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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24
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Li JL, Sun DW, Cheng JH. Recent Advances in Nondestructive Analytical Techniques for Determining the Total Soluble Solids in Fruits: A Review. Compr Rev Food Sci Food Saf 2016; 15:897-911. [DOI: 10.1111/1541-4337.12217] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Revised: 05/22/2016] [Accepted: 05/24/2016] [Indexed: 12/13/2022]
Affiliation(s)
- Jiang-Lin Li
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Academy of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Da-Wen Sun
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Academy of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre; Univ. College Dublin, Natl. Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Academy of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
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Rojas-Moraleda R, Valous NA, Gowen A, Esquerre C, Härtel S, Salinas L, O’Donnell C. A frame-based ANN for classification of hyperspectral images: assessment of mechanical damage in mushrooms. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2376-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ropodi A, Panagou E, Nychas GJ. Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends Food Sci Technol 2016. [DOI: 10.1016/j.tifs.2016.01.011] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Pan TT, Sun DW, Cheng JH, Pu H. Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation. Compr Rev Food Sci Food Saf 2016; 15:529-541. [DOI: 10.1111/1541-4337.12191] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Revised: 12/12/2015] [Accepted: 12/16/2015] [Indexed: 01/06/2023]
Affiliation(s)
- Ting-Tiao Pan
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Hongbin Pu
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
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28
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Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.05.006] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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29
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Cheng JH, Sun DW. Recent Applications of Spectroscopic and Hyperspectral Imaging Techniques with Chemometric Analysis for Rapid Inspection of Microbial Spoilage in Muscle Foods. Compr Rev Food Sci Food Saf 2015. [DOI: 10.1111/1541-4337.12141] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Jun-Hu Cheng
- College of Light Industry and Food Science; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Light Industry and Food Science; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; Natl. Univ. of Ireland; Belfield Dublin 4 Ireland
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