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Albano-Gaglio M, Mishra P, Erasmus SW, Tejeda JF, Brun A, Marcos B, Zomeño C, Font-I-Furnols M. Visible and near-infrared spectral imaging combined with robust regression for predicting firmness, fatness, and compositional properties of fresh pork bellies. Meat Sci 2024; 219:109645. [PMID: 39265383 DOI: 10.1016/j.meatsci.2024.109645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/05/2024] [Accepted: 09/02/2024] [Indexed: 09/14/2024]
Abstract
Belly is a widely consumed pork product with very variable properties. Meat industry needs real-time quality assessment for maintaining superior pork quality throughout the production. This study explores the potential of using visible and near-infrared (VNIR,386-1015 nm) spectral imaging for predicting firmness, fatness and chemical compositional properties in pork belly samples, offering robust spectral calibrations. A total of 182 samples with wide variations in firmness and compositional properties were analysed using common laboratory analyses, whereas spectral images were acquired with a VNIR spectral imaging system. Exploratory analysis of the studied properties was performed, followed by a robust regression approach called iterative reweighted partial least-squares regression to model and predict these belly properties. The models were also used to generate spatial maps of predicted chemical compositional properties. Chemical properties such as fat, dry matter, protein, ashes, iodine value, along with firmness measures as flop distance and angle, were predicted with excellent, very good and fair models, with a ratio prediction of standard deviation (RPD) of 4.93, 3.91, 2.58, 2.54, 2.41, 2.53 and 2.51 respectively. The methodology developed in this study showed that a short wavelength spectral imaging system can yield promising results, being a potential benefit for the pork industry in automating the analysis of fresh pork belly samples. VNIR spectral imaging emerges as a non-destructive method for pork belly characterization, guiding process optimization and marketing strategies. Moreover, future research can explore advanced data analytics approaches such as deep learning to facilitate the integration of spectral and spatial information in joint modelling.
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Affiliation(s)
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands
| | - Sara W Erasmus
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands
| | | | - Albert Brun
- IRTA-Food Quality and Technology, Finca Camps i Armet, 17121 Monells, Spain
| | - Begonya Marcos
- IRTA-Food Quality and Technology, Finca Camps i Armet, 17121 Monells, Spain
| | - Cristina Zomeño
- IRTA-Food Quality and Technology, Finca Camps i Armet, 17121 Monells, Spain
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2
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Alotaibi RF, AlTilasi HH, Al-Mutairi AM, Alharbi HS. Chromatographic and spectroscopic methods for the detection of cocoa butter in cocoa and its derivatives: A review. Heliyon 2024; 10:e31467. [PMID: 38882372 PMCID: PMC11176802 DOI: 10.1016/j.heliyon.2024.e31467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 06/18/2024] Open
Abstract
Currently, there is fierce competition in the cocoa industry to develop products that possess distinctive sensory characteristics and flavours. This is because cocoa and its derivatives provide numerous health and functional advantages, which is essential to their economics. The fatty acid and triglyceride composition of cocoa determines its quality. This review emphasises the necessity of developing precise, adaptable analytical techniques to identify and quantify cocoa butter in cocoa and its derived products, from cocoa beans to chocolate bars. Key chromatographic and spectroscopic techniques play crucial roles in understanding the fundamental principles underlying the production of cocoa with desirable flavours. This significantly impacts the sustainability, traceability, and authenticity of cocoa products while also supporting the battle against adulteration.
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Affiliation(s)
- Razan F Alotaibi
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Hissah H AlTilasi
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Adibah M Al-Mutairi
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Hibah S Alharbi
- Saudi Food and Drug Authority, Riyadh, 0112038222, Kingdom of Saudi Arabia
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3
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Ma H, Guo J, Liu G, Xie D, Zhang B, Li X, Zhang Q, Cao Q, Li X, Ma F, Li Y, Wan G, Li Y, Wu D, Ma P, Guo M, Yin J. Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review. Crit Rev Food Sci Nutr 2024:1-23. [PMID: 38523442 DOI: 10.1080/10408398.2024.2329956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.
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Affiliation(s)
- Haiyang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Jiajun Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guishan Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Delang Xie
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Bingbing Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaojun Li
- School of Electronic and Electrical Engineering, Ningxia University, Yinchuan, China
| | - Qian Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Qingqing Cao
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaoxue Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Fang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Yang Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guoling Wan
- College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Yan Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Di Wu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Ping Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Mei Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Junjie Yin
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
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4
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Måge I, Wubshet SG, Wold JP, Solberg LE, Böcker U, Dankel K, Lintvedt TA, Kafle B, Cattaldo M, Matić J, Sorokina L, Afseth NK. The role of biospectroscopy and chemometrics as enabling technologies for upcycling of raw materials from the food industry. Anal Chim Acta 2023; 1284:342005. [PMID: 37996160 DOI: 10.1016/j.aca.2023.342005] [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: 03/29/2023] [Revised: 09/25/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023]
Abstract
It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes.
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Affiliation(s)
- Ingrid Måge
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway.
| | - Sileshi Gizachew Wubshet
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Jens Petter Wold
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Lars Erik Solberg
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Ulrike Böcker
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Katinka Dankel
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Tiril Aurora Lintvedt
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; Norwegian University of Life Sciences, Faculty of Science and Technology, 1432, Ås, Norway
| | - Bijay Kafle
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; Norwegian University of Life Sciences, Faculty of Science and Technology, 1432, Ås, Norway
| | - Marco Cattaldo
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; Universidad Politécnica de Valencia, Department of Applied Statistics, Operations Research and Quality, 46022, Valencia, Spain
| | - Josipa Matić
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Liudmila Sorokina
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; University of Oslo, Department of Chemistry, 0371, Oslo, Norway
| | - Nils Kristian Afseth
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
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Campos RL, Yoon SC, Chung S, Bhandarkar SM. Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:7014. [PMID: 37631551 PMCID: PMC10459470 DOI: 10.3390/s23167014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/28/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023]
Abstract
A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000-1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm2 and 5 × 5 mm2. The FM-detection technique achieved impressive results at both the spectral pixel level and the foreign material object level. At the spectral pixel level, the model achieved a precision of 100%, a recall of over 93%, an F1 score of 96.8%, and a balanced accuracy of 96.9%. When combining the rich 1D spectral data with 2D spatial information, the FM-detection accuracy at the object level reached 96.5%. In summary, the impressive results obtained through this study demonstrate its effectiveness at accurately identifying and localizing FMs. Furthermore, the technique's potential for generalization and application to other agriculture and food-related domains highlights its broader significance.
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Affiliation(s)
| | - Seung-Chul Yoon
- U.S. National Poultry Research Center, Agricultural Research Service, U.S. Department of Agriculture, Athens, GA 30605, USA
| | - Soo Chung
- Department of Biosystems Engineering, Integrated Major in Global Smart Farm, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea;
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6
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Kharbach M, Alaoui Mansouri M, Taabouz M, Yu H. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches. Foods 2023; 12:2753. [PMID: 37509845 PMCID: PMC10379817 DOI: 10.3390/foods12142753] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/30/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
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Affiliation(s)
- Mourad Kharbach
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
- Department of Computer Sciences, University of Helsinki, 00560 Helsinki, Finland
| | - Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, 90014 Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Mohammed Taabouz
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Rabat BP 6203, Morocco
| | - Huiwen Yu
- Shenzhen Hospital, Southern Medical University, Shenzhen 518005, China
- Chemometrics group, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
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Robert C, Bain WE, Craigie C, Hicks TM, Loeffen M, Fraser-Miller SJ, Gordon KC. Fusion of three spectroscopic techniques for prediction of fatty acid in processed lamb. Meat Sci 2023; 195:109005. [DOI: 10.1016/j.meatsci.2022.109005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022]
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8
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Qu C, Li Y, Du S, Geng Y, Su M, Liu H. Raman spectroscopy for rapid fingerprint analysis of meat quality and security: Principles, progress and prospects. Food Res Int 2022; 161:111805. [DOI: 10.1016/j.foodres.2022.111805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/06/2022] [Accepted: 08/18/2022] [Indexed: 11/28/2022]
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Li R, Zhu Q, Wang X, Wang H. Mulberry leaf polyphenols alleviated high-fat diet-induced obesity in mice. Front Nutr 2022; 9:979058. [PMID: 36185673 PMCID: PMC9521161 DOI: 10.3389/fnut.2022.979058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
Mulberry leaf is an important medicinal food plant, which is rich in polyphenol compounds. Mulberry leaf polyphenols (MLP) possess significant lipid-lowering and antioxidant effects, and healthcare functions. In this study, the polyphenol content of mulberry leaf ethanol extract was measured using HPLC. The analysis of mulberry leaf extract resulted in the identification of 14 compounds, of which Chlorogenic acid and Quercitrin were the highest. A high-fat diet (HFD)-induced obese mouse model was developed and treated with MLP for 12 weeks to explore their effect on lipid metabolism in HFD-induced obese mice. The results showed that the MLP could inhibit the weight gain and fat cell volume increase in the HFD-induced obese mice in a dose-dependent manner. Further analysis revealed that the MLP decelerated the fatty acid composition in the adipose tissues of HFD-induced obese mice, and significantly increased the polyunsaturated-to-saturated fatty acid (PUFA/SFA) ratio. The real-time quantitative PCR (RT-qPCR) results indicated that the MLP significantly inhibited the down regulation of uncoupling protein (UCP) 1 (UCP1), UCP3, and PR domain zinc finger protein 16 (PRDM16) caused by the HFD. These beneficial effects of MLP on HFD-induced obese mice might be attributed to their ability to change the fatty acid composition of adipose tissue and increase the expression of thermogenesis genes. Overall, the study results suggested that the MLP could serve as potential lipid-lowering and weight-loss functional food and healthcare products.
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Affiliation(s)
- Rui Li
- Chongqing Academy of Animal Sciences, Chongqing, China
| | - Qubo Zhu
- Southwest University, Chongqing, China
- National Center of Technology Innovation for Pigs, Chongqing, China
| | - Xiaoyan Wang
- Chongqing Academy of Animal Sciences, Chongqing, China
| | - Haiyan Wang
- Chongqing Academy of Animal Sciences, Chongqing, China
- *Correspondence: Haiyan Wang,
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Cui H, Liu L, Liu X, Wang Y, Luo N, Tan X, Zhu Y, Liu R, Zhao G, Wen J. A selected population study reveals the biochemical mechanism of intramuscular fat deposition in chicken meat. J Anim Sci Biotechnol 2022; 13:54. [PMID: 35546408 PMCID: PMC9097349 DOI: 10.1186/s40104-022-00705-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/07/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Increasing intramuscular fat (IMF) is an important strategy to improve meat quality, but the regulation mechanism of IMF deposition needs to be systematically clarified. RESULTS A total of 520 chickens from a selected line with improved IMF content and a control line were used to investigate the biochemical mechanism of IMF deposition in chickens. The results showed that the increased IMF would improve the flavor and tenderness quality of chicken meat. IMF content was mainly determined both by measuring triglyceride (TG) and phospholipid (PLIP) in muscle tissue, but only TG content was found to be decisive for IMF deposition. Furthermore, the increase in major fatty acid (FA) components in IMF is mainly derived from TGs (including C16:0, C16:1, C18:1n9c, and C18:2n6c, etc.), and the inhibition of certain very-long-chain FAs would help to IMF/TG deposition. CONCLUSIONS Our study elucidated the underlying biochemical mechanism of IMF deposition in chicken: Prevalent accumulation of long-chain FAs and inhibitions of medium-chain FAs and very long chain FA would jointly result in the increase of TGs with the FA biosynthesis and cellular uptake ways. Our findings will guide the production of high-quality chicken meat.
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Affiliation(s)
- Huanxian Cui
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lu Liu
- College of Animal Science and Technology, College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, China
| | - Xiaojing Liu
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yongli Wang
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Na Luo
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xiaodong Tan
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yuting Zhu
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Jie Wen
- State Key Laboratory of Animal Nutrition; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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11
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Saini RK, Prasad P, Shang X, Keum YS. Advances in Lipid Extraction Methods-A Review. Int J Mol Sci 2021; 22:13643. [PMID: 34948437 PMCID: PMC8704327 DOI: 10.3390/ijms222413643] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 02/07/2023] Open
Abstract
Extraction of lipids from biological tissues is a crucial step in lipid analysis. The selection of appropriate solvent is the most critical factor in the efficient extraction of lipids. A mixture of polar (to disrupt the protein-lipid complexes) and nonpolar (to dissolve the neutral lipids) solvents are precisely selected to extract lipids efficiently. In addition, the disintegration of complex and rigid cell-wall of plants, fungi, and microalgal cells by various mechanical, chemical, and enzymatic treatments facilitate the solvent penetration and extraction of lipids. This review discusses the chloroform/methanol-based classical lipid extraction methods and modern modifications of these methods in terms of using healthy and environmentally safe solvents and rapid single-step extraction. At the same time, some adaptations were made to recover the specific lipids. In addition, the high throughput lipid extraction methodologies used for liquid chromatography-mass spectrometry (LC-MS)-based plant and animal lipidomics were discussed. The advantages and disadvantages of various pretreatments and extraction methods were also illustrated. Moreover, the emerging green solvents-based lipid extraction method, including supercritical CO2 extraction (SCE), is also discussed.
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Affiliation(s)
| | - Parchuri Prasad
- Institute of Biological Chemistry, Washington State University, Pullman, WA 99164, USA;
| | - Xiaomin Shang
- Jilin Provincial Key Laboratory of Nutrition and Functional Food, Jilin University, Changchun 130062, China;
| | - Young-Soo Keum
- Department of Crop Science, Konkuk University, Seoul 143-701, Korea;
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12
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Sun R, Wu T, Guo H, Xu J, Chen J, Tao N, Wang X, Zhong J. Lipid profile migration during the tilapia muscle steaming process revealed by a transactional analysis between MS data and lipidomics data. NPJ Sci Food 2021; 5:30. [PMID: 34782644 PMCID: PMC8593017 DOI: 10.1038/s41538-021-00115-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/14/2021] [Indexed: 11/08/2022] Open
Abstract
In this work, lipid profile migration from muscle to juice during the tilapia muscle steaming process was revealed by a transactional analysis of data from ultra-high-performance liquid chromatography coupled with Q Exactive (UHPLC-QE) Orbitrap mass spectrometry (MS) and lipidomics. Firstly, the lipids in tilapia muscles and juices at different steaming time points were extracted and examined by UHPLC-QE Orbitrap mass spectrometry. Secondly, a transactional analysis procedure was developed to analyze the data from UHPLC-QE Orbitrap MS and lipidomics. Finally, the corrected lipidomics data and the normalized MS data were used for lipid migration analysis. The results suggested that the transactional analysis procedure was efficient to significantly decrease UHPLC-QE Orbitrap MS workloads and delete the false-positive data (22.4-36.7%) in lipidomics data, which compensated the disadvantages of the current lipidomics method. The lipid changes could be disappearance, full migration into juice, appearance in juice, appearance in muscle, appearance in both muscle and juice, and retention in the muscle. Moreover, the results showed 9 (compared with 52), 5 (compared with 116), and 10 (compared with 178) of lipid class (compared with individual lipid) variables showed significant differences among the different steaming times (0, 10, 30, and 60 min) in all the muscles, juices, and muscle-juice systems, respectively. These results showed significant lipid profile migration from muscle to juice during the tilapia steaming process.
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Affiliation(s)
- Rui Sun
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Tingting Wu
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Hao Guo
- Chongqing Institute of Forensic Science, Chongqing, 400021, China
| | - Jiamin Xu
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Jiahui Chen
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Ningping Tao
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Xichang Wang
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Jian Zhong
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian, 116034, Liaoning Province, China.
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Leighton PL, Segura JD, Lam SD, Marcoux M, Wei X, Lopez-Campos OD, Soladoye P, Dugan ME, Juarez M, PRIETO NURIA. Prediction of carcass composition and meat and fat quality using sensing technologies: A review. MEAT AND MUSCLE BIOLOGY 2021. [DOI: 10.22175/mmb.12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Consumer demand for high-quality healthy food is increasing, thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.
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Near-Infrared Reflectance Spectroscopy for Predicting the Phospholipid Fraction and the Total Fatty Acid Composition of Freeze-Dried Beef. SENSORS 2021; 21:s21124230. [PMID: 34203102 PMCID: PMC8233715 DOI: 10.3390/s21124230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 02/04/2023]
Abstract
Research on fatty acids (FA) is important because their intake is related to human health. NIRS can be a useful tool to estimate the FA of beef but due to the high moisture and the high absorbance of water makes it difficult to calibrate the analyses. This work evaluated near-infrared reflectance spectroscopy as a tool to assess the total fatty acid composition and the phospholipid fraction of fatty acids of beef using freeze-dried meat. An average of 22 unrelated pure breed young bulls from 15 European breeds were reared on a common concentrate-based diet. A total of 332 longissimus thoracis steaks were analysed for fatty acid composition and a freeze-dried sample was subjected to near-infrared spectral analysis. 220 samples (67%) were used as a calibration set with the remaining 110 (33%) being used for validation of the models obtained. There was a large variation in the total FA concentration across the animals giving a good data set for the analysis and whilst the coefficient of variation was nearly 68% for the monounsaturated FA it was only 27% for the polyunsaturated fatty acids (PUFA). PLS method was used to develop the prediction models. The models for the phospholipid fraction had a low R2p and high standard error, while models for neutral lipid had the best performance, in general. It was not possible to obtain a good prediction of many individual PUFA concentrations being present at low concentrations and less variable than other FA. The best models were developed for Total FA, saturated FA, 9c18:1 and 16:1 with R2p greater than 0.76. This study indicates that NIRS is a feasible and useful tool for screening purposes and it has the potential to predict most of the FA of freeze-dried beef.
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15
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Liu XM, Zhang Y, Zhou Y, Li GH, Zeng BQ, Zhang JW, Feng XS. Progress in Pretreatment and Analysis of Fatty Acids in Foods: An Update since 2012. SEPARATION & PURIFICATION REVIEWS 2021. [DOI: 10.1080/15422119.2019.1673776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Xiao-Min Liu
- School of Pharmacy, China Medical University, Shenyang, China
| | - Yuan Zhang
- Department of Pharmacy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Zhou
- Department of Pharmacy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guo-Hui Li
- Department of Pharmacy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ben-Qing Zeng
- Department of Pharmacy, The First People’s Hospital of Longquanyi District, Chengdu, China
| | - Jian-Wei Zhang
- Department of Abdominal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xue-Song Feng
- School of Pharmacy, China Medical University, Shenyang, China
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16
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Barragán-Hernández W, Mahecha-Ledesma L, Burgos-Paz W, Olivera-Angel M, Angulo-Arizala J. Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches. J Anim Sci 2021; 98:5939743. [PMID: 33099624 DOI: 10.1093/jas/skaa342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/19/2020] [Indexed: 02/06/2023] Open
Abstract
This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.
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Affiliation(s)
- Wilson Barragán-Hernández
- Red de Ganadería y Especies Menores, Centro de Investigación El Nus, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), San Roque, Antioquia, Colombia
| | - Liliana Mahecha-Ledesma
- Facultad de ciencias agrarias, Grupo de investigación en ciencias animales-GRICA, Universidad de Antioquia, Medellín, Colombia
| | - William Burgos-Paz
- Red de Ganadería y Especies Menores, Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), Mosquera, Cundinamarca, Colombia
| | - Martha Olivera-Angel
- Facultad de ciencias agrarias, Grupo de investigación Biogénesis, Universidad de Antioquia, Medellín, Colombia
| | - Joaquín Angulo-Arizala
- Facultad de ciencias agrarias, Grupo de investigación en ciencias animales-GRICA, Universidad de Antioquia, Medellín, Colombia
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17
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Difford GF, Horn SS, Dankel KR, Ruyter B, Dagnachew BS, Hillestad B, Sonesson AK, Afseth NK. The heritable landscape of near-infrared and Raman spectroscopic measurements to improve lipid content in Atlantic salmon fillets. Genet Sel Evol 2021; 53:12. [PMID: 33546581 PMCID: PMC7866706 DOI: 10.1186/s12711-021-00605-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/18/2021] [Indexed: 12/19/2022] Open
Abstract
Background Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. Results Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27–0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values (\documentclass[12pt]{minimal}
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\begin{document}$${r}_{\text{g}}$$\end{document}rg = 0.91–0.98) obtained with the reference method using Lin’s concordance correlation coefficient (CCC = 0.63–0.90), and were significantly heritable (\documentclass[12pt]{minimal}
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\begin{document}$${h}^{2}$$\end{document}h2 = 0.52–0.67). Conclusions Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.
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Affiliation(s)
- Gareth F Difford
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway.
| | - Siri S Horn
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway
| | - Katinka R Dankel
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway
| | - Bente Ruyter
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway
| | - Binyam S Dagnachew
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway
| | | | - Anna K Sonesson
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway
| | - Nils K Afseth
- Nofima, Norwegian Institute for Food, Fisheries and Aquaculture Research, NO-1433, Ås, Norway
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18
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Ruiz-Capillas C, Herrero AM. Development of Meat Products with Healthier Lipid Content: Vibrational Spectroscopy. Foods 2021; 10:foods10020341. [PMID: 33562823 PMCID: PMC7914705 DOI: 10.3390/foods10020341] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/31/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
This review focuses on the importance of developing meat products with healthier lipid content and strategies such as the use of structured lipids to develop these enriched products. The review also conducts a critical analysis of the use of vibrational spectroscopy as a tool to further these developments. Meat and meat products are extensively recognized and consumed in the world. They are an important nutritional contribution in our diet. However, their consumption has also been associated with some negative consequences for health due to some of its components. There are new trends in the design of healthy meat products focusing mainly on improving their composition. From among the different strategies, improving lipid content is the one that has received the most attention. A novel development is the formation of lipid materials based on structured lipids such emulsion gels (EGs) or oil-bulking agents (OBAs) that offer attractive applications in the reformulation of health-enhanced meat products. A deeper interpretation is required of the complicated relationship between the structure of their components and their properties in order to obtain structured lipids and healthier meat products with improved lipid content and acceptable characteristics. To this end, vibrational spectroscopy techniques (Raman and infrared spectroscopy) have been demonstrated to be suitable in the elucidation of the structural characteristics of lipid materials based on structured lipids (EGs or OBAs) and the corresponding reformulated health-enhanced meat products into which these fat replacers have been incorporated. Future research on these structures and how they correlate to certain technological properties could help in selecting the best lipid material to achieve specific technological properties in healthier meat products with improved lipid content.
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19
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Nakashima Y, Shiba N. Nondestructive measurement of intramuscular fat content of fresh beef meat by a hand-held magnetic resonance sensor. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2021. [DOI: 10.1080/10942912.2021.1999261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yoshito Nakashima
- Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Nobuya Shiba
- Livestock and Forage Research Division, National Agriculture and Food Research Organization (NARO), Tohoku Agricultural Research Center, Morioka, Japan
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20
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Wu T, Guo H, Lu Z, Zhang T, Zhao R, Tao N, Wang X, Zhong J. Reliability of LipidSearch software identification and its application to assess the effect of dry salting on the long-chain free fatty acid profile of tilapia muscles. Food Res Int 2020; 138:109791. [PMID: 33288177 DOI: 10.1016/j.foodres.2020.109791] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/01/2020] [Accepted: 10/04/2020] [Indexed: 12/16/2022]
Abstract
Dry salting has important effects on food lipids. In this work, the reliability of LipidSearch software identification and its application to assess the effect of dry salting on the long-chain free fatty acid profile of tilapia muscles were studied by ultra-high-performance liquid chromatography-Q-Extractive Orbitrap mass spectrometry and LipidSearch software. Compared with the standard reference identification method, the LipidSearch software identification method was suggested to be a reliable identification method for long-chain free fatty acid identification. During the dry salting process, tilapia muscles with low muscle-to-salt mass ratios of 3-8 might have stable and similar free fatty acid profile changes, and the free fatty acid amounts decreased and then increased with time. This work could provide useful information to evaluate the development and application of LipidSearch software as well as a way to analyze the effect of dry salting on the free fatty acids change of aquatic products.
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Affiliation(s)
- Tingting Wu
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Hao Guo
- Chongqing Institute of Forensic Science, Chongqing 400021, China
| | - Zhiwen Lu
- Shanghai Gaojing Detection Technology Co., Ltd., Shanghai 200438, China
| | - Ting Zhang
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Ruofei Zhao
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Ningping Tao
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Xichang Wang
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Jian Zhong
- National R & D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
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21
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Nakashima Y. Development of a hand-held magnetic resonance sensor for the nondestructive quantification of fat and lean meat of fresh tuna. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2020. [DOI: 10.1007/s11694-020-00539-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Abstract
For the in-situ nondestructive fat quantification of fresh tuna meat, an original lightweight (5.7 kg) hand-held sensor that consists of a planar radio-frequency coil and a single-sided magnetic circuit was developed as a subunit of a time-domain proton magnetic resonance (MR) scanner system. The investigation depth of the sensor unit is 12 mm, which is sufficient to probe the meat section beneath thick skin with scales and the underlying subcutaneous fat layer of large fish such as tuna. The scanner was successfully applied in a laboratory to a fillet of a bluefin tuna (Thunnus thynnus) to measure meat sections 12 mm beneath the skin. The required measurement time was 100 s for each section. The results of MR scan at 11 locations on the fillet were compared with those of conventional destructive food analysis. Reasonable agreement with an error (root-mean-square residual) of as small as 1.8 wt% was obtained for fat quantification. The time-domain MR relaxometry for the same tuna fillet also allowed lean meat quantification with a small root-mean-square residual of 6.7 wt%.
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22
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Fernandes AFA, Dórea JRR, Rosa GJDM. Image Analysis and Computer Vision Applications in Animal Sciences: An Overview. Front Vet Sci 2020; 7:551269. [PMID: 33195522 PMCID: PMC7609414 DOI: 10.3389/fvets.2020.551269] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.
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Affiliation(s)
| | | | - Guilherme Jordão de Magalhães Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
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23
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Zhang W, Ma J, Sun DW. Raman spectroscopic techniques for detecting structure and quality of frozen foods: principles and applications. Crit Rev Food Sci Nutr 2020; 61:2623-2639. [DOI: 10.1080/10408398.2020.1828814] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Wenyang Zhang
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
- State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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24
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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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25
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Kang JW, Lim SY, Galindo LH, Yoon H, Dasari RR, So PTC, Kim HM. Analysis of subcutaneous swine fat via deep Raman spectroscopy using a fiber-optic probe. Analyst 2020; 145:4421-4426. [PMID: 32441278 PMCID: PMC7329574 DOI: 10.1039/d0an00707b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Since the fat content of pork is a deciding factor in meat quality grading, the use of a noninvasive subcutaneous probe for real-time in situ monitoring of the fat components is of importance to vendors and other interested parties. In this work, we developed a spectroscopic method using a fiber-optic probe for subcutaneous fat analysis that utilizes spatially offset Raman spectroscopy (SORS). Here, normalized Raman spectra were acquired as a function of spatial offset, and the relative composition of fat-to-skin was determined. We found that the Raman intensity ratio varied disproportionately depending on the fat content and that the variations of the slope were correlated to the thickness of the fat layer. Furthermore, ordinary least square (OLS) regression using two components indicated that the depth-resolved SORS spectra reflected the relative thickness of the fat layer. We concluded that the local distribution of subcutaneous fat could be measured noninvasively using a pair of fiber-optic probes.
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Affiliation(s)
- Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Soo Yeong Lim
- Department of Chemistry, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea
| | - Luis H. Galindo
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hongman Yoon
- Division of Convergence Technology, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si Gyeonggi-do, 10408, Republic of Korea
| | - Ramachandra R. Dasari
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Peter T. C. So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hyung Min Kim
- Department of Chemistry, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea
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San Martín E, Avenoza A, Peregrina JM, Busto JH. Solvent-based strategy improves the direct determination of key parameters in edible fats and oils by 1 H NMR. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:1726-1734. [PMID: 31821564 DOI: 10.1002/jsfa.10193] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 12/04/2019] [Accepted: 12/05/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Edible fats and oils are very important in nutrition and as a main source of energy and are also essential nutrients. There are several methods for the analysis of edible fats and oils, but nowadays nuclear magnetic resonance (NMR) is emerging as a powerful tool (albeit complex and high-tech demanding) to identify, quantify, and differentiate many types of food, including fats and oils. In this sense, the challenges of this technique are the simplification of methodology and taking advantage of a 400 MHz NMR instrument. RESULTS Through an adequate mixture of solvents, we have developed a methodology to quantify essential parameters in edible fats and oils, including 1,2-diacylglycerol, 1,3-diacylglycerol, and 1-monoacylglycerol, by using a single experiment and without the need for matrix derivatization. CONCLUSION This methodology has been successfully applied to the analysis of olive, sunflower, corn, sesame, and peanut oils, as well as butter, walnut, salmon, and spicy pork sausage. Moreover, the evolution of thermal oxidation and lipolysis of virgin olive oil and sunflower has been analyzed. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Emilio San Martín
- Departamento de Química, Centro de Investigación en Síntesis Química (CISQ), Universidad de La Rioja, Logroño, Spain
| | - Alberto Avenoza
- Departamento de Química, Centro de Investigación en Síntesis Química (CISQ), Universidad de La Rioja, Logroño, Spain
| | - Jesús M Peregrina
- Departamento de Química, Centro de Investigación en Síntesis Química (CISQ), Universidad de La Rioja, Logroño, Spain
| | - Jesús H Busto
- Departamento de Química, Centro de Investigación en Síntesis Química (CISQ), Universidad de La Rioja, Logroño, Spain
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Kamal T, Cheng S, Khan IA, Nawab K, Zhang T, Song Y, Wang S, Nadeem M, Riaz M, Khan MAU, Zhu B, Tan M. Potential uses of LF‐NMR and MRI in the study of water dynamics and quality measurement of fruits and vegetables. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.14202] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tariq Kamal
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
- Department of Agriculture University of Swabi Swabi Pakistan
| | - Shasha Cheng
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
| | - Imtiaz Ali Khan
- Department of Agriculture University of Swabi Swabi Pakistan
| | - Khalid Nawab
- Department of Agricultural Extension Education and Communication The University of Agriculture Peshawar Peshawar Pakistan
| | - Tan Zhang
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
| | - Yukun Song
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
| | - Siqi Wang
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
| | - Muhammad Nadeem
- Department of Plant Protection The University of Agriculture Peshawar Peshawar Pakistan
| | - Muhammad Riaz
- Department of Plant Breeding and Genetics The University of Agriculture Peshawar Peshawar Pakistan
| | | | - Bei‐Wei Zhu
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
| | - Mingqian Tan
- National Engineering Research Center of Seafood, School of Food Science and Technology Dalian Polytechnic University Dalian People's Republic of China
- Engineering Research Center of Seafood of Ministry of Education of China Dalian People's Republic of China
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Nakashima Y. Non-Destructive Quantification of Lipid and Water in Fresh Tuna Meat by a Single-Sided Nuclear Magnetic Resonance Scanner. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2019. [DOI: 10.1080/10498850.2019.1569742] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Yoshito Nakashima
- Research Institute for Geo-Resources and Environment, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan
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29
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Spectral Detection Techniques for Non-Destructively Monitoring the Quality, Safety, and Classification of Fresh Red Meat. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1256-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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30
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Kucha CT, Liu L, Ngadi MO. Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review. SENSORS 2018; 18:s18020377. [PMID: 29382092 PMCID: PMC5855493 DOI: 10.3390/s18020377] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/07/2018] [Accepted: 01/10/2018] [Indexed: 12/31/2022]
Abstract
Fat is one of the most important traits determining the quality of pork. The composition of the fat greatly influences the quality of pork and its processed products, and contribute to defining the overall carcass value. However, establishing an efficient method for assessing fat quality parameters such as fatty acid composition, solid fat content, oxidative stability, iodine value, and fat color, remains a challenge that must be addressed. Conventional methods such as visual inspection, mechanical methods, and chemical methods are used off the production line, which often results in an inaccurate representation of the process because the dynamics are lost due to the time required to perform the analysis. Consequently, rapid, and non-destructive alternative methods are needed. In this paper, the traditional fat quality assessment techniques are discussed with emphasis on spectroscopic techniques as an alternative. Potential spectroscopic techniques include infrared spectroscopy, nuclear magnetic resonance and Raman spectroscopy. Hyperspectral imaging as an emerging advanced spectroscopy-based technology is introduced and discussed for the recent development of assessment for fat quality attributes. All techniques are described in terms of their operating principles and the research advances involving their application for pork fat quality parameters. Future trends for the non-destructive spectroscopic techniques are also discussed.
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Affiliation(s)
- Christopher T Kucha
- Department of Bioresource Engineering, McGill University, Macdonald Campus 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, QC H9X 3V9, Canada.
| | - Li Liu
- Department of Bioresource Engineering, McGill University, Macdonald Campus 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, QC H9X 3V9, Canada.
| | - Michael O Ngadi
- Department of Bioresource Engineering, McGill University, Macdonald Campus 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, QC H9X 3V9, Canada.
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