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Cheng H, Li J, Yang Y, Zhou G, Xu B, Yang L. Identifying freshness of various chilled pork cuts using rapid imaging analysis. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 39247997 DOI: 10.1002/jsfa.13865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/22/2024] [Accepted: 08/10/2024] [Indexed: 09/10/2024]
Abstract
BACKGROUND Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance. RESULTS This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively. CONCLUSION Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Haoran Cheng
- China Light Industry Key Laboratory of Meat Microbial Control and Utilization, Hefei University of Technology, Hefei, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
- Engineering Research Center of Bio-process, Ministry of Education, Hefei University of Technology, Hefei, China
| | - Jinglei Li
- China Light Industry Key Laboratory of Meat Microbial Control and Utilization, Hefei University of Technology, Hefei, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
- Engineering Research Center of Bio-process, Ministry of Education, Hefei University of Technology, Hefei, China
| | - Yulong Yang
- China Light Industry Key Laboratory of Meat Microbial Control and Utilization, Hefei University of Technology, Hefei, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
- Engineering Research Center of Bio-process, Ministry of Education, Hefei University of Technology, Hefei, China
| | - Gang Zhou
- China Light Industry Key Laboratory of Meat Microbial Control and Utilization, Hefei University of Technology, Hefei, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
- Engineering Research Center of Bio-process, Ministry of Education, Hefei University of Technology, Hefei, China
| | - Baocai Xu
- China Light Industry Key Laboratory of Meat Microbial Control and Utilization, Hefei University of Technology, Hefei, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
- Engineering Research Center of Bio-process, Ministry of Education, Hefei University of Technology, Hefei, China
| | - Liu Yang
- China Light Industry Key Laboratory of Meat Microbial Control and Utilization, Hefei University of Technology, Hefei, China
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
- Engineering Research Center of Bio-process, Ministry of Education, Hefei University of Technology, Hefei, China
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2
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Jia W, Ferragina A, Hamill R, Koidis A. Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI). Talanta 2024; 276:126199. [PMID: 38714010 DOI: 10.1016/j.talanta.2024.126199] [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: 12/13/2023] [Revised: 04/12/2024] [Accepted: 04/30/2024] [Indexed: 05/09/2024]
Abstract
Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.
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Affiliation(s)
- Wenyang Jia
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| | - Alessandro Ferragina
- Teagasc Food Research Centre, Food Quality and Sensory Science Department, Dublin, Ireland
| | - Ruth Hamill
- Teagasc Food Research Centre, Food Quality and Sensory Science Department, Dublin, Ireland
| | - Anastasios Koidis
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK.
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3
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Ding H, Tian J, Yu W, Wilson DI, Young BR, Cui X, Xin X, Wang Z, Li W. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods 2023; 12:4511. [PMID: 38137314 PMCID: PMC10742996 DOI: 10.3390/foods12244511] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/11/2023] [Accepted: 12/16/2023] [Indexed: 12/24/2023] Open
Abstract
Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.
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Affiliation(s)
- Haohan Ding
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; (H.D.); (X.X.)
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; (J.T.); (W.L.)
| | - Jiawei Tian
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; (J.T.); (W.L.)
| | - Wei Yu
- Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand; (W.Y.); (B.R.Y.)
| | - David I. Wilson
- Electrical and Electronic Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Brent R. Young
- Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand; (W.Y.); (B.R.Y.)
| | - Xiaohui Cui
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; (H.D.); (X.X.)
- School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
| | - Xing Xin
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; (H.D.); (X.X.)
| | - Zhenyu Wang
- Jiaxing Institute of Future Food, Jiaxing 314050, China;
| | - Wei Li
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; (J.T.); (W.L.)
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4
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Alshejari A, Kodogiannis VS, Leonidis S. Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:9451. [PMID: 38067823 PMCID: PMC10708854 DOI: 10.3390/s23239451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/18/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023]
Abstract
In the food industry, quality and safety issues are associated with consumers' health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multispectral imaging technique has been extensively used for inspection of various food products. In this paper, a stacking-based ensemble prediction system has been developed for the prediction of total viable counts of microorganisms in beef fillet samples, an essential cause to meat spoilage, utilizing multispectral imaging information. As the selection of important wavelengths from the multispectral imaging system is considered as an essential stage to the prediction scheme, a features fusion approach has been also explored, by combining wavelengths extracted from various feature selection techniques. Ensemble sub-components include two advanced clustering-based neuro-fuzzy network prediction models, one utilizing information from average reflectance values, while the other one from the standard deviation of the pixels' intensity per wavelength. The performances of neurofuzzy models were compared against established regression algorithms such as multilayer perceptron, support vector machines and partial least squares. Obtained results confirmed the validity of the proposed hypothesis to utilize a combination of feature selection methods with neurofuzzy models in order to assess the microbiological quality of meat products.
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Affiliation(s)
- Abeer Alshejari
- Department of Mathematical Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | | | - Stavros Leonidis
- Consulting & Systems Integration, Netcompany-Intrasoft, GR-57001 Thessaloniki, Greece;
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5
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Hassoun A, Anusha Siddiqui S, Smaoui S, Ucak İ, Arshad RN, Bhat ZF, Bhat HF, Carpena M, Prieto MA, Aït-Kaddour A, Pereira JA, Zacometti C, Tata A, Ibrahim SA, Ozogul F, Camara JS. Emerging Technological Advances in Improving the Safety of Muscle Foods: Framing in the Context of the Food Revolution 4.0. FOOD REVIEWS INTERNATIONAL 2022. [DOI: 10.1080/87559129.2022.2149776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Abdo Hassoun
- Univ. Littoral Côte d’Opale, UMRt 1158 BioEcoAgro, USC ANSES, INRAe, Univ. Artois, Univ. Lille, Univ. Picardie Jules Verne, Univ. Liège, Junia, Boulogne-sur-Mer, France
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
| | - Shahida Anusha Siddiqui
- Department of Biotechnology and Sustainability, Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Straubing, Germany
- German Institute of Food Technologies (DIL e.V.), Quakenbrück, Germany
| | - Slim Smaoui
- Laboratory of Microbial, Enzymatic Biotechnology and Biomolecules (LBMEB), Center of Biotechnology of Sfax, University of Sfax-Tunisia, Sfax, Tunisia
| | - İ̇lknur Ucak
- Faculty of Agricultural Sciences and Technologies, Nigde Omer Halisdemir University, Nigde, Turkey
| | - Rai Naveed Arshad
- Institute of High Voltage & High Current, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Zuhaib F. Bhat
- Division of Livestock Products Technology, SKUASTof Jammu, Jammu, Kashmir, India
| | - Hina F. Bhat
- Division of Animal Biotechnology, SKUASTof Kashmir, Kashmir, India
| | - María Carpena
- Nutrition and Bromatology Group, Analytical and Food Chemistry Department. Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
| | - Miguel A. Prieto
- Nutrition and Bromatology Group, Analytical and Food Chemistry Department. Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolonia, Bragança, Portugal
| | | | - Jorge A.M. Pereira
- CQM—Centro de Química da Madeira, Universidade da Madeira, Funchal, Portugal
| | - Carmela Zacometti
- Istituto Zooprofilattico Sperimentale Delle Venezie, Laboratorio di Chimica Sperimentale, Vicenza, Italy
| | - Alessandra Tata
- Istituto Zooprofilattico Sperimentale Delle Venezie, Laboratorio di Chimica Sperimentale, Vicenza, Italy
| | - Salam A. Ibrahim
- Food and Nutritional Sciences Program, North Carolina A&T State University, Greensboro, North Carolina, USA
| | - Fatih Ozogul
- Department of Seafood Processing Technology, Faculty of Fisheries, Cukurova University, Adana, Turkey
| | - José S. Camara
- CQM—Centro de Química da Madeira, Universidade da Madeira, Funchal, Portugal
- Departamento de Química, Faculdade de Ciências Exatas e Engenharia, Campus da Penteada, Universidade da Madeira, Funchal, Portugal
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6
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Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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7
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Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA. Application of Artificial Intelligence in Food Industry—a Guideline. FOOD ENGINEERING REVIEWS 2021. [PMCID: PMC8350558 DOI: 10.1007/s12393-021-09290-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.
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Affiliation(s)
- Nidhi Rajesh Mavani
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Jarinah Mohd Ali
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Suhaili Othman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, 43400 Selangor, Malaysia
| | - M. A. Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Haslaniza Hashim
- Department of Food Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Norliza Abd Rahman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
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8
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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9
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Liang N, Sun S, Zhang C, He Y, Qiu Z. Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food. Crit Rev Food Sci Nutr 2020; 62:2963-2984. [PMID: 33345592 DOI: 10.1080/10408398.2020.1862045] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.
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Affiliation(s)
- Ning Liang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Sashuang Sun
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Zhengjun Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
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10
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Taghadomi‐Saberi S, Masoumi AA, Sadeghi M, Zekri M. Integration of wavelet network and image processing for determination of total pigments in bitter orange (
Citrus aurantium
L.) peel during ripening. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13120] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Saeedeh Taghadomi‐Saberi
- Department of Biosystems Engineering, College of AgricultureIsfahan University of Technology (IUT) Isfahan Iran
| | - Amin A. Masoumi
- Department of Biosystems Engineering, College of AgricultureIsfahan University of Technology (IUT) Isfahan Iran
| | - Morteza Sadeghi
- Department of Biosystems Engineering, College of AgricultureIsfahan University of Technology (IUT) Isfahan Iran
| | - Maryam Zekri
- Department of Electrical and Computer EngineeringIsfahan University of Technology (IUT) Isfahan Iran
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11
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Kutsanedzie FYH, Guo Z, Chen Q. Advances in Nondestructive Methods for Meat Quality and Safety Monitoring. FOOD REVIEWS INTERNATIONAL 2019. [DOI: 10.1080/87559129.2019.1584814] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
| | - Zhiming Guo
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
| | - Quansheng Chen
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
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12
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Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage. FOOD BIOPROCESS TECH 2017. [DOI: 10.1007/s11947-016-1851-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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