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Feng CH, Makino Y, Yoshimura M, Rodríguez-Pulido FJ. Real-time prediction of pre-cooked Japanese sausage color with different storage days using hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2018; 98:2564-2572. [PMID: 29030975 DOI: 10.1002/jsfa.8746] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/05/2017] [Accepted: 10/11/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Redness can greatly influence the freshness of sausages. A precise, rapid and noncontact analytical method or tool is needed to quantify the color. Hyperspectral imaging (HSI) is an emerging technique that integrates spectroscopy and imaging to obtain the spectral and spatial information simultaneously. In the present study, the redness of cooked sausages stored up to 57 days was predicted using HSI in tandem with multivariate data analysis. The mean spectra of the sausages were extracted from the hyperspectral images. Partial least squares regression (PLSR) and forward stepwise multiple regression (FSMR) models were used to develop the relavent spectral profiles with the redness of the cooked sausages. RESULTS Ten important wavelengths were selected based on the regression coefficient values from the PLSR model. The PLSR model established using the full wavelengths presented a good performance, with Rc of 0.934 and a root mean square error of calibration of 0.642 (redness ranged between 14.99 and 21.48). The prediction maps for demonstrating evolution of redness in sausages were developed for the first time using R statistics (R Foundation for Statistical Computing) and Matlab (MathWorks Inc., Natick, MA, USA). CONCLUSION HSI combined with PLSR and FSMR can be used to quantify and visualize evolution of sausage redness under different storage days. © 2017 Society of Chemical Industry.
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Affiliation(s)
- Chao-Hui Feng
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- College of Food Science, Sichuan Agricultural University, Yucheng District, Ya'an, Sichuan, China
| | - Yoshio Makino
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Masatoshi Yoshimura
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Francisco J Rodríguez-Pulido
- Food Color & Quality Laboratory, Department of Nutrition and Food Science, Facultad de Farmacia, Universidad de Sevilla, Spain
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Schilling MW, Suman SP, Zhang X, Nair MN, Desai MA, Cai K, Ciaramella MA, Allen PJ. Proteomic approach to characterize biochemistry of meat quality defects. Meat Sci 2017; 132:131-138. [PMID: 28454727 DOI: 10.1016/j.meatsci.2017.04.018] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 04/19/2017] [Accepted: 04/19/2017] [Indexed: 02/06/2023]
Abstract
Proteomics can be used to characterize quality defects including pale, soft, and exudative (PSE) meat (pork and poultry), woody broiler breast meat, reddish catfish fillets, meat toughness, and beef myoglobin oxidation. PSE broiler meat was characterized by 15 proteins that differed in abundance in comparison to normal broiler breast meat, and eight proteins were differentially expressed in woody breast meat in comparison to normal breast meat. Hemoglobin was the only protein that was differentially expressed between red and normal catfish fillets. However, inducing low oxygen and/or heat stress conditions to catfish fillets did not lead to the production of red fillets. Proteomic data provided information pertaining to the protein differences that exist in meat quality defects. However, these data need to be evaluated in conjunction with information pertaining to genetics, nutrition, environment of the live animal, muscle to meat conversion, meat quality analyses and sensory attributes to understand causality, protein biomarkers, and ultimately how to prevent quality defects.
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Affiliation(s)
- M W Schilling
- Department of Food Science, Nutrition and Health Promotion, Mississippi State University, Mississippi State, MS 39762, United States.
| | - S P Suman
- Department of Animal and Food Sciences, University of Kentucky, Lexington, KY 40546, United States
| | - X Zhang
- Department of Food Science, Nutrition and Health Promotion, Mississippi State University, Mississippi State, MS 39762, United States
| | - M N Nair
- Department of Animal and Food Sciences, University of Kentucky, Lexington, KY 40546, United States
| | - M A Desai
- Reed Food Technology, Pearl, MS 39208, United States
| | - K Cai
- Department of Food Science, Nutrition and Health Promotion, Mississippi State University, Mississippi State, MS 39762, United States
| | - M A Ciaramella
- New York Sea Grant, College of Agriculture and Life Sciences, Cornell University, Stony Brook, NY 11794, United States
| | - P J Allen
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, United States
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