1
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Fengou LC, Lytou AE, Tsekos G, Tsakanikas P, Nychas GJE. Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud. Food Chem 2024; 440:138184. [PMID: 38100963 DOI: 10.1016/j.foodchem.2023.138184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.
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Affiliation(s)
- Lemonia-Christina Fengou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - Anastasia E Lytou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - George Tsekos
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - Panagiotis Tsakanikas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - George-John E Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
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2
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Kolosov D, Fengou LC, Carstensen JM, Schultz N, Nychas GJ, Mporas I. Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094233. [PMID: 37177437 PMCID: PMC10181489 DOI: 10.3390/s23094233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023]
Abstract
Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.
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Affiliation(s)
- Dimitrios Kolosov
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Lemonia-Christina Fengou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
| | | | | | - George-John Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
| | - Iosif Mporas
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
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3
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Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.
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4
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Hassoun A, Aït-Kaddour A, Abu-Mahfouz AM, Rathod NB, Bader F, Barba FJ, Biancolillo A, Cropotova J, Galanakis CM, Jambrak AR, Lorenzo JM, Måge I, Ozogul F, Regenstein J. The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Crit Rev Food Sci Nutr 2022; 63:6547-6563. [PMID: 35114860 DOI: 10.1080/10408398.2022.2034735] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | - Adnan M Abu-Mahfouz
- Council for Scientific and Industrial Research, Pretoria, South Africa
- Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
| | - Nikheel Bhojraj Rathod
- Department of Post-Harvest Management of Meat, Poultry and Fish, Post-Graduate Institute of Post-Harvest Management, Raigad, Maharashtra, India
| | - Farah Bader
- Saudi Goody Products Marketing Company Ltd, Jeddah, Saudi Arabia
| | - Francisco J Barba
- Nutrition and Bromatology Area, Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, University of Valencia, València, Spain
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L'Aquila, Coppito, L'Aquila, Italy
| | - Janna Cropotova
- Department of Biological Sciences in Ålesund, Norwegian University of Science and Technology, Ålesund, Norway
| | - Charis M Galanakis
- Research & Innovation Department, Galanakis Laboratories, Chania, Greece
- Food Waste Recovery Group, ISEKI Food Association, Vienna, Austria
| | - Anet Režek Jambrak
- Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia
| | - José M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
- Área de Tecnología de los Alimentos, Facultad de Ciencias de Ourense, Universidad de Vigo, Ourense, Spain
| | - Ingrid Måge
- Fisheries and Aquaculture Research, Nofima - Norwegian Institute of Food, Ås, Norway
| | - Fatih Ozogul
- Department of Seafood Processing Technology, Faculty of Fisheries, Cukurova University, Adana, Turkey
| | - Joe Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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5
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Priyashantha H, Höjer A, Saedén KH, Lundh Å, Johansson M, Bernes G, Geladi P, Hetta M. Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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6
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Zheng X, Shi X, Wang B. A Review on the General Cheese Processing Technology, Flavor Biochemical Pathways and the Influence of Yeasts in Cheese. Front Microbiol 2021; 12:703284. [PMID: 34394049 PMCID: PMC8358398 DOI: 10.3389/fmicb.2021.703284] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/12/2021] [Indexed: 12/05/2022] Open
Abstract
Cheese has a long history and this naturally fermented dairy product contains a range of distinctive flavors. Microorganisms in variety cheeses are an essential component and play important roles during both cheese production and ripening. However, cheeses from different countries are still handmade, the processing technology is diverse, the microbial community structure is complex and the cheese flavor fluctuates greatly. Therefore, studying the general processing technology and relationship between microbial structure and flavor formation in cheese is the key to solving the unstable quality and standardized production of cheese flavor on basis of maintaining the flavor of cheese. This paper reviews the research progress on the general processing technology and key control points of natural cheese, the biochemical pathways for production of flavor compounds in cheeses, the diversity and the role of yeasts in cheese. Combined with the development of modern detection technology, the evolution of microbial structure, population evolution and flavor correlation in cheese from different countries was analyzed, which is of great significance for the search for core functional yeast microorganisms and the industrialization prospect of traditional fermented cheese.
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Affiliation(s)
| | - Xuewei Shi
- Food College, Shihezi University, Shihezi, China
| | - Bin Wang
- Food College, Shihezi University, Shihezi, China
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7
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Zhu D, Kebede B, McComb K, Hayman A, Chen G, Frew R. Milk biomarkers in relation to inherent and external factors based on metabolomics. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2020.12.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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8
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Khan A, Munir MT, Yu W, Young BR. Near‐infrared spectroscopy and data analysis for predicting milk powder quality attributes. INT J DAIRY TECHNOL 2020. [DOI: 10.1111/1471-0307.12734] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Asma Khan
- Department of Chemical & Materials Engineering Faculty of Engineering The University of Auckland Symond Street Auckland1010New Zealand
| | - Muhammad Tajammal Munir
- College of Engineering and Technology American University of the Middle East Kuwait1010Kuwait
| | - Wei Yu
- Department of Chemical & Materials Engineering Faculty of Engineering The University of Auckland Symond Street Auckland1010New Zealand
| | - Brent R. Young
- Department of Chemical & Materials Engineering Faculty of Engineering The University of Auckland Symond Street Auckland1010New Zealand
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9
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Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing. Int Dairy J 2020. [DOI: 10.1016/j.idairyj.2019.104623] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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10
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11
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Panikuttira B, Payne FA, O'Shea N, Tobin JT, O'Callaghan DJ, O'Donnell CP. Investigation of an in‐line prototype fluorescence and infrared backscatter sensor to monitor rennet‐induced coagulation of skim milk at different protein concentrations. Int J Food Sci Technol 2019. [DOI: 10.1111/ijfs.14267] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Bhavya Panikuttira
- School of Biosystems and Food Engineering University College Dublin Belfield, D4 Dublin Ireland
- Food Chemistry and Technology Department Teagasc Food Research Centre Moorepark Fermoy, Cork Ireland
| | - Fred A. Payne
- Biosystems and Agricultural Engineering Department University of Kentucky Lexington KY40546Kentucky
| | - Norah O'Shea
- Food Chemistry and Technology Department Teagasc Food Research Centre Moorepark Fermoy, Cork Ireland
| | - John T. Tobin
- Food Chemistry and Technology Department Teagasc Food Research Centre Moorepark Fermoy, Cork Ireland
| | - Donal J. O'Callaghan
- Food Chemistry and Technology Department Teagasc Food Research Centre Moorepark Fermoy, Cork Ireland
| | - Colm P. O'Donnell
- School of Biosystems and Food Engineering University College Dublin Belfield, D4 Dublin Ireland
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12
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O'Shea N, O'Callaghan TF, Tobin JT. The application of process analytical technologies (PAT) to the dairy industry for real time product characterization - process viscometry. INNOV FOOD SCI EMERG 2019. [DOI: 10.1016/j.ifset.2019.05.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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13
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Predictive modelling of instant whole milk powder functional performance across three industrial plants. J FOOD ENG 2019. [DOI: 10.1016/j.jfoodeng.2019.01.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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14
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Bista A, Hogan SA, O'Donnell CP, Tobin JT, O'Shea N. Evaluation and validation of an inline Coriolis flowmeter to measure dynamic viscosity during laboratory and pilot-scale food processing. INNOV FOOD SCI EMERG 2019. [DOI: 10.1016/j.ifset.2019.05.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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15
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Szymańska E. Modern data science for analytical chemical data – A comprehensive review. Anal Chim Acta 2018; 1028:1-10. [DOI: 10.1016/j.aca.2018.05.038] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 04/24/2018] [Accepted: 05/13/2018] [Indexed: 01/25/2023]
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16
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Tschudi J, O'Farrell M, Hestnes Bakke KA. Inline Spectroscopy: From Concept to Function. APPLIED SPECTROSCOPY 2018; 72:1298-1309. [PMID: 29945460 DOI: 10.1177/0003702818788374] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The field of applied spectroscopy is strongly dominated by publications presenting proof-of-concepts, lab set-ups, and demonstrations. In contrast, the corresponding number of commercial successes of inline spectroscopy is surprisingly lower. This article discusses inline spectroscopy from an instrumentation perspective. It is the authors' firm belief that the success of inline spectroscopy lies in the understanding of how the design and implementation of the optical instrumentation affects the data quality, and how this in turn will limit or enhance the performance of the prediction model. This article emphasizes the need for a strong, multidisciplinary design team, whose design process is rooted in first principles, to bridge the technology "valley of death" and convert research in applied spectroscopy into commercially successful solutions.
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17
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Feed-Forward Prediction of Product Qualities in Enzymatic Protein Hydrolysis of Poultry By-products: a Spectroscopic Approach. FOOD BIOPROCESS TECH 2018. [DOI: 10.1007/s11947-018-2161-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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18
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Daoutidis P, Lee JH, Harjunkoski I, Skogestad S, Baldea M, Georgakis C. Integrating operations and control: A perspective and roadmap for future research. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.04.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Mörschbächer AP, Dullius A, Dullius CH, Bandt CR, Kuhn D, Brietzke DT, Malmann Kuffel FJ, Etgeton HP, Altmayer T, Gonçalves TE, Oreste EQ, Ribeiro AS, de Souza CFV, Hoehne L. Validation of an analytical method for the quantitative determination of selenium in bacterial biomass by ultraviolet–visible spectrophotometry. Food Chem 2018; 255:182-186. [DOI: 10.1016/j.foodchem.2018.02.057] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 09/11/2017] [Accepted: 02/11/2018] [Indexed: 01/18/2023]
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20
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Henihan LE, O’Donnell CP, Esquerre C, Murphy EG, O’Callaghan DJ. Quality Assurance of Model Infant Milk Formula Using a Front-Face Fluorescence Process Analytical Tool. FOOD BIOPROCESS TECH 2018. [DOI: 10.1007/s11947-018-2112-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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21
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Panikuttira B, O'Shea N, Tobin JT, Tiwari BK, O'Donnell CP. Process analytical technology for cheese manufacture. Int J Food Sci Technol 2018. [DOI: 10.1111/ijfs.13806] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bhavya Panikuttira
- School of Biosystems and Food Engineering; University College Dublin; Belfield D4 Dublin Ireland
| | - Norah O'Shea
- Food Chemistry and Technology Department; Teagasc Food Research Centre; Moorepark, Fermoy Co.Cork Ireland
| | - John T. Tobin
- Food Chemistry and Technology Department; Teagasc Food Research Centre; Moorepark, Fermoy Co.Cork Ireland
| | - Brijesh K. Tiwari
- Food Chemistry and Technology Department; Teagasc Food Research Centre; Ashtown D15 Dublin Ireland
| | - Colm P. O'Donnell
- School of Biosystems and Food Engineering; University College Dublin; Belfield D4 Dublin Ireland
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22
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Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression. SENSORS 2018; 18:s18040975. [PMID: 29587403 PMCID: PMC5948626 DOI: 10.3390/s18040975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 03/20/2018] [Accepted: 03/20/2018] [Indexed: 12/03/2022]
Abstract
Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation.
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24
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Munir MT, Wilson DI, Depree N, Boiarkina I, Prince-Pike A, Young BR. Real-time product release and process control challenges in the dairy milk powder industry. Curr Opin Food Sci 2017. [DOI: 10.1016/j.cofs.2017.08.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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25
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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: 52] [Impact Index Per Article: 7.4] [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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26
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Boiarkina I, Depree N, Yu W, Wilson DI, Young BR. Fault diagnosis of an industrial plant using a Monte Carlo analysis coupled with systematic troubleshooting. Food Control 2017. [DOI: 10.1016/j.foodcont.2017.02.061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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27
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Hisazumi J, Kleinebudde P. In-line monitoring of multi-layered film-coating on pellets using Raman spectroscopy by MCR and PLS analyses. Eur J Pharm Biopharm 2017; 114:194-201. [DOI: 10.1016/j.ejpb.2017.01.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 01/16/2017] [Accepted: 01/16/2017] [Indexed: 10/20/2022]
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28
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Mung D, Li L. Development of Chemical Isotope Labeling LC-MS for Milk Metabolomics: Comprehensive and Quantitative Profiling of the Amine/Phenol Submetabolome. Anal Chem 2017; 89:4435-4443. [PMID: 28306241 DOI: 10.1021/acs.analchem.6b03737] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Milk is a complex sample containing a variety of proteins, lipids, and metabolites. Studying the milk metabolome represents an important application of metabolomics in the general area of nutritional research. However, comprehensive and quantitative analysis of milk metabolites is a challenging task due to the wide range of variations in chemical/physical properties and concentrations of these metabolites. We report an analytical workflow for in-depth profiling of the milk metabolome based on chemical isotope labeling (CIL) and liquid chromatography mass spectrometry (LC-MS) with a focus of using dansylation labeling to target the amine/phenol submetabolome. An optimal sample preparation method, including the use of methanol at a 3:1 ratio of solvent to milk for protein precipitation and dichloromethane for lipid removal, was developed to detect and quantify as many metabolites as possible. This workflow was found to be generally applicable to profile milk metabolomes of different species (cow, goat, and human) and types. Results from experimental replicate analysis (n = 5) of 1:1, 2:1, and 1:2 12C-/13C-labeled cow milk samples showed that 95.7%, 94.3%, and 93.2% of peak pairs, respectively, had ratio values within ±50% accuracy range and 90.7%, 92.6%, and 90.8% peak pairs had RSD values of less than 20%. In the metabolomic analysis of 36 samples from different categories of cow milk (brands, batches, and fat percentages) with experimental triplicates, a total of 7104 peak pairs or metabolites could be detected with an average of 4573 ± 505 (n = 108) pairs detected per LC-MS run. Among them, 3820 peak pairs were commonly detected in over 80% of the samples with 70 metabolites positively identified by mass and retention time matches to the dansyl standard library and 2988 pairs with their masses matched to the human metabolome libraries. This unprecedentedly high coverage of the amine/phenol submetabolome illustrates the complexity of the milk metabolome. Since milk and milk products are consumed in large quantities on a daily basis, the intake of these milk metabolites even at low concentrations can be cumulatively high. The high-coverage analysis of the milk metabolome using CIL LC-MS should be very useful in future research involving the study of the effects of these metabolites on human health. It should also be useful in the dairy industry in areas such as improving milk production, developing new processing technologies, developing improved nutritional products, quality control, and milk product authentication.
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Affiliation(s)
- Dorothea Mung
- Department of Chemistry, University of Alberta , Edmonton, Alberta T6G 2G2, Canada
| | - Liang Li
- Department of Chemistry, University of Alberta , Edmonton, Alberta T6G 2G2, Canada
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29
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Vrancken C, Longhurst PJ, Wagland ST. Critical review of real-time methods for solid waste characterisation: Informing material recovery and fuel production. WASTE MANAGEMENT (NEW YORK, N.Y.) 2017; 61:40-57. [PMID: 28139367 DOI: 10.1016/j.wasman.2017.01.019] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 12/16/2016] [Accepted: 01/15/2017] [Indexed: 06/06/2023]
Abstract
Waste management processes generally represent a significant loss of material, energy and economic resources, so legislation and financial incentives are being implemented to improve the recovery of these valuable resources whilst reducing contamination levels. Material recovery and waste derived fuels are potentially valuable options being pursued by industry, using mechanical and biological processes incorporating sensor and sorting technologies developed and optimised for recycling plants. In its current state, waste management presents similarities to other industries that could improve their efficiencies using process analytical technology tools. Existing sensor technologies could be used to measure critical waste characteristics, providing data required by existing legislation, potentially aiding waste treatment processes and assisting stakeholders in decision making. Optical technologies offer the most flexible solution to gather real-time information applicable to each of the waste mechanical and biological treatment processes used by industry. In particular, combinations of optical sensors in the visible and the near-infrared range from 800nm to 2500nm of the spectrum, and different mathematical techniques, are able to provide material information and fuel properties with typical performance levels between 80% and 90%. These sensors not only could be used to aid waste processes, but to provide most waste quality indicators required by existing legislation, whilst offering better tools to the stakeholders.
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Affiliation(s)
- C Vrancken
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
| | - P J Longhurst
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
| | - S T Wagland
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
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30
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Laske S, Paudel A, Scheibelhofer O, Sacher S, Hoermann T, Khinast J, Kelly A, Rantannen J, Korhonen O, Stauffer F, De Leersnyder F, De Beer T, Mantanus J, Chavez PF, Thoorens B, Ghiotti P, Schubert M, Tajarobi P, Haeffler G, Lakio S, Fransson M, Sparen A, Abrahmsen-Alami S, Folestad S, Funke A, Backx I, Kavsek B, Kjell F, Michaelis M, Page T, Palmer J, Schaepman A, Sekulic S, Hammond S, Braun B, Colegrove B. A Review of PAT Strategies in Secondary Solid Oral Dosage Manufacturing of Small Molecules. J Pharm Sci 2017; 106:667-712. [DOI: 10.1016/j.xphs.2016.11.011] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 10/14/2016] [Accepted: 11/08/2016] [Indexed: 12/14/2022]
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31
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Zhang Y, Munir MT, Yu W, Young BR. Modelling batch bioreactions with continuous process simulators. KOREAN J CHEM ENG 2016. [DOI: 10.1007/s11814-016-0244-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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32
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Nychas GJE, Panagou EZ, Mohareb F. Novel approaches for food safety management and communication. Curr Opin Food Sci 2016. [DOI: 10.1016/j.cofs.2016.06.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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33
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Zettel V, Ahmad MH, Beltramo T, Hermannseder B, Hitzemann A, Nache M, Paquet-Durand O, Schöck T, Hecker F, Hitzmann B. Supervision of Food Manufacturing Processes Using Optical Process Analyzers - An Overview. CHEMBIOENG REVIEWS 2016. [DOI: 10.1002/cben.201600013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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34
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Munir MT, Zhang Y, Yu W, Wilson DI, Young BR. Virtual milk for modelling and simulation of dairy processes. J Dairy Sci 2016; 99:3380-3395. [PMID: 26971156 DOI: 10.3168/jds.2015-10449] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2015] [Accepted: 01/21/2016] [Indexed: 11/19/2022]
Abstract
The modeling of dairy processing using a generic process simulator suffers from shortcomings, given that many simulators do not contain milk components in their component libraries. Recently, pseudo-milk components for a commercial process simulator were proposed for simulation and the current work extends this pseudo-milk concept by studying the effect of both total milk solids and temperature on key physical properties such as thermal conductivity, density, viscosity, and heat capacity. This paper also uses expanded fluid and power law models to predict milk viscosity over the temperature range from 4 to 75°C and develops a succinct regressed model for heat capacity as a function of temperature and fat composition. The pseudo-milk was validated by comparing the simulated and actual values of the physical properties of milk. The milk thermal conductivity, density, viscosity, and heat capacity showed differences of less than 2, 4, 3, and 1.5%, respectively, between the simulated results and actual values. This work extends the capabilities of the previously proposed pseudo-milk and of a process simulator to model dairy processes, processing different types of milk (e.g., whole milk, skim milk, and concentrated milk) with different intrinsic compositions, and to predict correct material and energy balances for dairy processes.
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Affiliation(s)
- M T Munir
- Chemical and Materials Engineering Department, Industrial Information and Control Centre (I2C2), The University of Auckland, New Zealand 1023.
| | - Y Zhang
- Chemical and Materials Engineering Department, Industrial Information and Control Centre (I2C2), The University of Auckland, New Zealand 1023
| | - W Yu
- Chemical and Materials Engineering Department, Industrial Information and Control Centre (I2C2), The University of Auckland, New Zealand 1023
| | - D I Wilson
- Chemical and Materials Engineering Department, Industrial Information and Control Centre (I2C2), The University of Auckland, New Zealand 1023
| | - B R Young
- Chemical and Materials Engineering Department, Industrial Information and Control Centre (I2C2), The University of Auckland, New Zealand 1023
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35
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Zettel V, Ahmad MH, Hitzemann A, Nache M, Paquet-Durand O, Schöck T, Hecker F, Hitzmann B. Optische Prozessanalysatoren für die Lebensmittelindustrie. CHEM-ING-TECH 2016. [DOI: 10.1002/cite.201500097] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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