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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [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: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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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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Safdar LB, Dugina K, Saeidan A, Yoshicawa GV, Caporaso N, Gapare B, Umer MJ, Bhosale RA, Searle IR, Foulkes MJ, Boden SA, Fisk ID. Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging. Food Energy Secur 2023; 12:e498. [PMID: 38440412 PMCID: PMC10909436 DOI: 10.1002/fes3.498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 03/06/2024] Open
Abstract
A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.
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Affiliation(s)
- Luqman B. Safdar
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
- International Flavour Research Centre (Adelaide), School of Agriculture, Food and Wine and Waite Research InstituteUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | - Kateryna Dugina
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
| | - Ali Saeidan
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
| | - Guilherme V. Yoshicawa
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | | | - Brighton Gapare
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - M. Jawad Umer
- Cotton Research InstituteChinese Academy of Agricultural SciencesAnyangChina
| | - Rahul A. Bhosale
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - Iain R. Searle
- School of Biological SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - Scott A. Boden
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | - Ian D. Fisk
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
- International Flavour Research Centre (Adelaide), School of Agriculture, Food and Wine and Waite Research InstituteUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
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Md Saleh R, Kulig B, Arefi A, Hensel O, Sturm B. Prediction of total carotenoids, color and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rosalizan Md Saleh
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Industrial Crops Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang, Selangor Malaysia
| | - Boris Kulig
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Arman Arefi
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Oliver Hensel
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Barbara Sturm
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Leibniz Institute for Agricultural Engineering and Bioeconomy(ATB) Max‐Eyth‐Allee 100 14469 Potsdam Germany
- Humboldt Universität zu Berlin Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences 10115 Berlin Germany
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Real-Time and Online Inspection of Multiple Pork Quality Parameters Using Dual-Band Visible/Near-Infrared Spectroscopy. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01801-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 312] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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A Review Towards Hyperspectral Imaging for Real-Time Quality Control of Food Products with an Illustrative Case Study of Milk Powder Production. FOOD BIOPROCESS TECH 2020. [DOI: 10.1007/s11947-020-02433-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Wang S, Liu B, Xu L, Tamura T, Kyouno N, Liu X, Zhang H, Akiyama Y, Chen JY. Application of compressed sensing for selecting relevant variables for a model to predict the quality of Japanese fermented soy sauce. INNOV FOOD SCI EMERG 2020. [DOI: 10.1016/j.ifset.2019.102241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Bruning B, Liu H, Brien C, Berger B, Lewis M, Garnett T. The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat ( Triticum aestivum). FRONTIERS IN PLANT SCIENCE 2019; 10:1380. [PMID: 31737009 PMCID: PMC6831646 DOI: 10.3389/fpls.2019.01380] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/07/2019] [Indexed: 05/25/2023]
Abstract
Quantifying plant water content and nitrogen levels and determining water and nitrogen phenotypes is important for crop management and achieving optimal yield and quality. Hyperspectral methods have the potential to advance high throughput phenotyping efforts by providing a rapid, accurate, and nondestructive alternative for estimating biochemical and physiological plant traits. Our study (i) acquired hyperspectral images of wheat plants using a high throughput phenotyping system, (ii) developed regression models capable of predicting water and nitrogen levels of wheat plants, and (iii) applied the regression coefficients from the best-performing models to hyperspectral images in order to develop prediction maps to visualize nitrogen and water distribution within plants. Hyperspectral images were collected of four wheat (Triticum aestivum) genotypes grown in nine soil nutrient conditions and under two water treatments. Five multivariate regression methods in combination with 10 spectral preprocessing techniques were employed to find a model with strong predictive performance. Visible and near infrared wavelengths (VNIR: 400-1,000nm) alone were not sufficient to accurately predict water and nitrogen content (validation R2 = 0.56 and R2 = 0.59, respectively) but model accuracy was improved when shortwave-infrared wavelengths (SWIR: 1,000-2,500nm) were incorporated (validation R2 = 0.63 and R2 = 0.66, respectively). Wavelength reduction produced equivalent model accuracies while reducing model size and complexity (validation R2 = 0.69 and R2 = 0.66 for water and nitrogen, respectively). Developed distribution maps provided a visual representation of the concentration and distribution of water within plants while nitrogen maps seemed to suffer from noise. The findings and methods from this study demonstrate the high potential of high-throughput hyperspectral imagery for estimating and visualizing the distribution of plant chemical properties.
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Affiliation(s)
- Brooke Bruning
- Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food & Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Huajian Liu
- Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food & Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Chris Brien
- Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food & Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Bettina Berger
- Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food & Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Megan Lewis
- Ecology and Evolutionary Biology, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Trevor Garnett
- Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food & Wine, University of Adelaide, Urrbrae, SA, Australia
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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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Dixit Y, Casado-Gavalda MP, Cama-Moncunill R, Cama-Moncunill X, Markiewicz-Keszycka M, Cullen PJ, Sullivan C. Developments and Challenges in Online NIR Spectroscopy for Meat Processing. Compr Rev Food Sci Food Saf 2017; 16:1172-1187. [PMID: 33371583 DOI: 10.1111/1541-4337.12295] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 06/22/2017] [Accepted: 07/13/2017] [Indexed: 11/30/2022]
Abstract
Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near-Infrared (NIR) spectroscopy has been identified as a fast and cost-effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.
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Affiliation(s)
- Y Dixit
- School of Food Science and Environmental Health, Dublin Inst. of Technology, Dublin 1, Ireland
| | - Maria P Casado-Gavalda
- School of Food Science and Environmental Health, Dublin Inst. of Technology, Dublin 1, Ireland
| | - R Cama-Moncunill
- School of Food Science and Environmental Health, Dublin Inst. of Technology, Dublin 1, Ireland
| | - X Cama-Moncunill
- School of Food Science and Environmental Health, Dublin Inst. of Technology, Dublin 1, Ireland
| | | | - P J Cullen
- School of Food Science and Environmental Health, Dublin Inst. of Technology, Dublin 1, Ireland.,Dept. of Chemical and Environmental Engineering, Univ. of Nottingham, UK
| | - Carl Sullivan
- School of Food Science and Environmental Health, Dublin Inst. of Technology, Dublin 1, Ireland
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12
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Emerging non-destructive terahertz spectroscopic imaging technique: Principle and applications in the agri-food industry. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.06.001] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Geng Z, Zhao S, Tao G, Han Y. Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety. Food Control 2017. [DOI: 10.1016/j.foodcont.2017.02.045] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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