Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance
Abstract
:1. Introduction
2. Materials and Methods
2.1. Animals and Sample Collection
2.2. Meat Samples
2.3. Beef Steak Images
2.4. Development of a Beef Consumer Acceptance Index
2.5. Perception Index: Statistical Analysis and Structural Equation Modeling
2.6. NIRS Collection
2.7. Spectra data Analysis
3. Results and Discussion
3.1. Sociodemographic Characteristics
3.2. Perception Index
3.3. Estimating Consumers’ Perceptions with NIRS
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Scollan, N.; Hocquette, J.F.; Nuernberg, K.; Dannenberger, D.; Richardson, I.; Moloney, A. Innovations in beef production systems that enhance the nutritional and health value of beef lipids and their relationship with meat quality. Meat Sci. 2006, 74, 17–33. [Google Scholar] [CrossRef] [PubMed]
- Pencharz, P.B.; Elango, R.; Wolfe, R.R. Recent developments in understanding protein needs—How much and what kind should we eat? Appl. Physiol. Nutr. Metab. 2016, 41, 577–580. [Google Scholar] [CrossRef] [Green Version]
- Bender, A.E. Meat and Meat Products in Human Nutrition in Developing Countries; FAO: Rome, Italy, 1992; ISBN 9251031460. [Google Scholar]
- Polidori, P.; Vincenzetti, S.; Pucciarelli, S.; Polzonetti, V. CLAs in animal source foods: Healthy benefits for consumers. In Bioactive Molecules in Food. Reference Series in Phytochemistry; Mérillon, J.M., Ramawat, K., Eds.; Springer: Cham, Switzerland, 2018; ISBN 978-3-319-54528-8. [Google Scholar]
- Visvalingam, J.; Vahmani, P.; Rolland, D.C.; Dugan, M.E.R.; Yang, X. Anti-mutagenic Properties of Mono- and Dienoic Acid Biohydrogenation Products from Beef Fat. Lipids 2017, 52, 651–655. [Google Scholar] [CrossRef]
- Vahmani, P.; Meadus, W.J.; Rolland, D.C.; Duff, P.; Dugan, M.E.R. Trans10,cis15 18:2 Isolated from Beef Fat Does Not Have the Same Anti-Adipogenic Properties as Trans10,cis12–18:2 in 3T3-L1 Adipocytes. Lipids 2016, 51, 1231–1239. [Google Scholar] [CrossRef]
- Vahmani, P.; Meadus, W.J.; Da Silva, M.L.P.; Mitchell, A.D.; Mapiye, C.; Duff, P.; Rolland, D.C.; Dugan, M.E.R. A trans10-18:1 enriched fraction from beef fed a barley grain-based diet induces lipogenic gene expression and reduces viability of HepG2 cells. Biochem. Biophys. Rep. 2016, 7, 84–90. [Google Scholar] [CrossRef] [Green Version]
- Grunert, K.G.; Bredahl, L.; Brunsø, K. Consumer perception of meat quality and implications for product development in the meat sector—A review. Meat Sci. 2004, 66, 259–272. [Google Scholar] [CrossRef]
- Henchion, M.M.; McCarthy, M.; Resconi, V.C. Beef quality attributes: A systematic review of consumer perspectives. Meat Sci. 2017, 128, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Troy, D.J.; Kerry, J.P. Consumer perception and the role of science in the meat industry. Meat Sci. 2010, 86, 214–226. [Google Scholar] [CrossRef]
- Mancini, R.A. Meat color. In Improving the Sensory and Nutritional Quality of Fresh Meat; Woodhead Publishing Limited: Cambridge, UK, 2009; pp. 89–110. ISBN 9781845693435. [Google Scholar]
- Carpenter, C.E.; Cornforth, D.P.; Whittier, D. Consumer preferences for beef color and packaging did not affect eating satisfaction. Meat Sci. 2001, 57, 359–363. [Google Scholar] [CrossRef]
- Ardeshiri, A.; Rose, J.M. How Australian consumers value intrinsic and extrinsic attributes of beef products. Food Qual. Prefer. 2018, 65, 146–163. [Google Scholar] [CrossRef]
- Frank, D.; Joo, S.T.; Warner, R. Consumer acceptability of intramuscular fat. Korean J. Food Sci. Anim. Resour. 2016, 36, 699–708. [Google Scholar] [CrossRef] [PubMed]
- Font-i-Furnols, M.; Guerrero, L. Consumer preference, behavior and perception about meat and meat products: An overview. Meat Sci. 2014, 98, 361–371. [Google Scholar] [CrossRef] [PubMed]
- Morales, R.; Aguiar, A.P.S.; Subiabre, I.; Realini, C.E. Beef acceptability and consumer expectations associated with production systems and marbling. Food Qual. Prefer. 2013, 29, 166–173. [Google Scholar] [CrossRef]
- Ngapo, T.M.; Braña Varela, D.; Rubio Lozano, M.S. Mexican consumers at the point of meat purchase. Beef choice. Meat Sci. 2017, 134, 34–43. [Google Scholar] [CrossRef]
- Banović, M.; Chrysochou, P.; Grunert, K.G.; Rosa, P.J.; Gamito, P. The effect of fat content on visual attention and choice of red meat and differences across gender. Food Qual. Prefer. 2016, 52, 42–51. [Google Scholar] [CrossRef]
- Mancini, R.A.; Hunt, M.C. Current research in meat color. Meat Sci. 2005, 71, 100–121. [Google Scholar] [CrossRef]
- Font-i-Furnols, M.; Fulladosa, E.; Prevolnik Povše, M.; Čandek-Potokar, M. Future trends in non-invasive technologies suitable for quality determinations. In A Handbook of Reference Methods for Meat Quality Assessment (M Font-i-Furnols); Čandek-Potokar, M., Maltin, C., Povše, M.P., Eds.; SRUC: Edinburgh, Scotland, 2015; pp. 90–103. [Google Scholar]
- AMSA. Research Guidelines for Cookery, Sensory Evaluation, and Instrumental Tenderness Measurements of Meat; American Meat Science Association: Champaign, IL, USA, 2016; ISBN 8005172672. [Google Scholar]
- Prieto, N.; Pawluczyk, O.; Dugan, M.E.R.; Aalhus, J.L. A review of the principles and applications of near-infrared spectroscopy to characterize meat, fat, and meat products. Appl. Spectrosc. 2017, 71, 1403–1426. [Google Scholar] [CrossRef] [Green Version]
- Weeranantanaphan, J.; Downey, G.; Allen, P.; Sun, D.W. A review of near infrared spectroscopy in muscle food analysis: 2005–2010. J. Near Infrared Spectrosc. 2011, 19, 61–104. [Google Scholar] [CrossRef]
- OCDE. OCDE-FAO Perspectivas Agrícolas 2019-2028; OCDE-FAO Perspectivas Agrícolas; OECD: Roma, Italy, 2019; ISBN 978-92-64-18272-1. [Google Scholar]
- Realini, C.E.; Kallas, Z.; Pérez-Juan, M.; Gómez, I.; Olleta, J.L.; Beriain, M.J.; Albertí, P.; Sañudo, C.; Kallas, Z. Relative importance of cues underlying Spanish consumers’ beef choice and segmentation, and consumer liking of beef enriched with n-3 and CLA fatty acids. Food Qual. Prefer. 2014, 33, 74–85. [Google Scholar] [CrossRef]
- González-Quintero, R.; Sánchez-Pinzón, M.S.; Bolívar-Vergara, D.M.; Chirinda, N.; Arango, J.; Pantévez, H.A.; Correa-Londoño, G.; Barahona-Rosales, R. Technical and environmental characterization of Colombian beef cattle-fattening farms, with a focus on farm size and ways of improving production. Outlook Agric. 2019. [Google Scholar] [CrossRef]
- Cortez Passetti, R.A.; Akamine, J.; Garcia, M.; Mottin, C.; Lopes De Olivera, C.A.; Guerrero, A.; Campo, M.; Sañudo, C.; Nunes, I. Validation of photographs usage to evaluate meat visual acceptability of young bulls fi nished in feedlot fed with or without essential oils. Meat Sci. 2017, 123, 105–111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671–675. [Google Scholar] [CrossRef] [PubMed]
- Barragán-Hernández, W.Á.; Mahecha-Ledesma, L.; Angulo-Arizala, J.; Olivera-Angel, M. Clasificación de la calidad de la carne bovina mediante el uso de infrarrojo por transmitancia y técnicas multivariadas. XV Encuentro Nacional y VIII Internacional de los Investigadores de las Ciencias Pecuarias (ENICIP). Rev. Colomb. Cienc. Pecu. 2019, 32, 50. [Google Scholar]
- Mejía-Arango, L.J.; Estrada, C.M.; Salcedo-Amaya, G.J.; Higuita, J.M.; Granados, J.D. Informe de Calidad de Vida Medellìn 2016; Pregón SAS: Medellín, Colombia, 2016. [Google Scholar]
- Alinda, F.; Kavoi, M.; Migisha, J. Consumer willingness to pay for quality beef in Kampala, Uganda. J. Agric. Sci. Technol. 2016, 17, 59–77. [Google Scholar]
- Enciso, K.; Burkart, S.; Charrya, A.; Rodriguez, C.D.P.; Quiceno, J.J.M.; Ruiz, L.R.; Solis, J.F.G.; Quilac, N.J.V.; Lopez, N.A.; Velasco, S.M.; et al. Consumer Preferences and Market Segmentation for Differentiated Beef with Less Environmental Impact; CGSpace: Montpellier, France, 2016. [Google Scholar]
- Xue, H.; Mainville, D.; You, W.; Nayga, R.M. Consumer preferences and willingness to pay for grass-fed beef: Empirical evidence from in-store experiments. Food Qual. Prefer. 2010, 21, 857–866. [Google Scholar] [CrossRef]
- Hair, J.; Black, W.; Babin, B.; Anderson, R. Multivariate Data Analysis; Pearson, Education: Edinburgh, UK, 2017; ISBN 9781118895238. [Google Scholar]
- Bishu, K.G.; O’Reilly, S.; Lahiff, E.; Steiner, B. Cattle farmers’ perceptions of risk and risk management strategies: Evidence from Northern Ethiopia. J. Risk Res. 2016, 21, 579–598. [Google Scholar] [CrossRef] [Green Version]
- Beaujean, A.A. Latent Variable Modeling Using R: A Step-by-Step Guide; Routledge, Taylor & Francis Group: East Sussex, NY, USA, 2014; ISBN 9781315869780. [Google Scholar]
- Revelle, W. Package ‘psych’. Available online: https://personality-project.org/r/psych-manual.pdf (accessed on 8 January 2020).
- Rosseel, Y. Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). J. Stat. Softw. 2012, 48, 1–36. [Google Scholar] [CrossRef] [Green Version]
- Prieto, N.; López-Campos, Ó.; Aalhus, J.L.; Dugan, M.E.R.R.; Juárez, M.; Uttaro, B. Use of near infrared spectroscopy for estimating meat chemical composition, quality traits and fatty acid content from cattle fed sunflower or flaxseed. Meat Sci. 2014, 98, 279–288. [Google Scholar] [CrossRef] [PubMed]
- Anderson, S. Determination of fat, moisture, and protein in meat and meat products by using the FOSS FoodScanTM near-infrared spectrophotometer with FOSS artificial neural network calibration model and associated database: Collaborative study. J. AOAC Int. 2007, 90, 1073–1083. [Google Scholar]
- R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2009. [Google Scholar]
- Barnes, R.J.; Dhanoa, M.S.; Lister, S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 1989, 43, 772–777. [Google Scholar] [CrossRef]
- Naes, T.; Isaksson, T.; Fearn, T.; Davies, T. Introduction A User-Friendly Guide to Multivariate Calibration and Classification; NIR publications: Chichester, UK, 2002; ISBN 0952866625. [Google Scholar]
- Stevens, A.; Ramirez Lopez, L. An introduction to the prospectr package. R Packag. Vignette Rep. No. R Packag. Version 0.1 2014, 3, 1–22. [Google Scholar]
- Kucheryavskiy, S. Mdatools: Multivariate Data Analysis for Chemometrics, R Package Version 0.7.0. 2015. Available online: https://CRAN.R--project.org/package=mdatools (accessed on 8 January 2020).
- Williams, P.; Norris, K. Near-Infrared Technology in the Agricultural and Food Industries; American Association of Cereal Chemists, Inc.: Saint Paul, Minnesota, 1997; Volume 36, ISBN 091325049X. [Google Scholar]
- Mevik, B.-H.; Wehrens, R.; Liland, K.H.; Hiemstra, P. Package ‘pls’. Available online: https://cran.rediris.es/web/packages/pls/pls.pdf (accessed on 8 January 2020).
- Prieto, N.; López-Campos, Ó.; Zijlstra, R.T.; Uttaro, B.; Aalhus, J.L. Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy. Can. J. Anim. Sci. 2014, 94, 445–454. [Google Scholar] [CrossRef] [Green Version]
- Censo Nacional de Población y Vivienda (CNPV)-2018; DANE: Bogotá, Colombia, 2019.
- Kline, R.B. Principles and Practice of Structural Equation Modeling, 3rd ed.; Guiford: New York, NY, USA, 2011; ISBN 978-1-60623-876-9. [Google Scholar]
- Cozzolino, D.; Murray, I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT Food Sci. Technol. 2004, 37, 447–452. [Google Scholar] [CrossRef]
- Mamani-Linares, L.W.; Gallo, C.; Alomar, D. Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Sci. 2012, 90, 378–385. [Google Scholar] [CrossRef] [PubMed]
- Prieto, N.; Roehe, R.; Lavín, P.; Batten, G.; Andrés, S. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Sci. 2009, 83, 175–186. [Google Scholar] [CrossRef]
- Morsy, N.; Sun, D.W. Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef. Meat Sci. 2013, 93, 292–302. [Google Scholar] [CrossRef] [PubMed]
- Sierra, V.; Aldai, N.; Castro, P.; Osoro, K.; Coto-Montes, A.; Oliván, M. Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy. Meat Sci. 2008, 78, 248–255. [Google Scholar] [CrossRef]
- De Marchi, M.; Penasa, M.; Cecchinato, A.; Bittante, G. The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts. Meat Sci. 2013, 93, 329–335. [Google Scholar] [CrossRef]
- Moran, L.; Andres, S.; Allen, P.; Moloney, A.P. Visible and near infrared spectroscopy as an authentication tool: Preliminary investigation of the prediction of the ageing time of beef steaks. Meat Sci. 2018, 142, 52–58. [Google Scholar] [CrossRef]
- López-Maestresalas, A.; Insausti, K.; Jarén, C.; Pérez-Roncal, C.; Urrutia, O.; Beriain, M.J.; Arazuri, S. Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control 2019, 98, 465–473. [Google Scholar] [CrossRef]
- Berri, C.; Picard, B.; Lebret, B.; Andueza, D.; Lefèvre, F.; Le Bihan-Duval, E.; Beauclercq, S.; Chartrin, P.; Vautier, A.; Legrand, I.; et al. Predicting the quality of meat: Myth or reality? Foods 2019, 8, 436. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.; Lyon, B.G.; Windham, W.R.; Realini, C.E.; Pringle, T.D.D.; Duckett, S. Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study. Meat Sci. 2003, 65, 1107–1115. [Google Scholar] [CrossRef]
- Yancey, J.W.S.; Apple, J.K.; Meullenet, J.F.; Sawyer, J.T. Consumer responses for tenderness and overall impression can be predicted by visible and near-infrared spectroscopy, Meullenet-Owens razor shear, and Warner-Bratzler shear force. Meat Sci. 2010, 85, 487–492. [Google Scholar] [CrossRef]
- Magalhães, A.F.B.; de Almeida Teixeira, G.H.; Ríos, A.C.H.; dos Santos Silva, D.B.; Mota, L.F.M.; Muniz, M.M.M.; de Morais, C.D.L.M.; de Lima, K.M.G.; Júnior, L.C.C.; Baldi, F.; et al. Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy. J. Anim. Sci. 2018, 96, 4229–4237. [Google Scholar] [CrossRef]
- Cafferky, J.; Sweeney, T.; Allen, P.; Sahar, A.; Downey, G.; Cromie, A.R.; Hamill, R.M. Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum. Meat Sci. 2020, 159, 107915. [Google Scholar] [CrossRef] [PubMed]
- Prieto, N.; Andrés, S.; Giráldez, F.J.; Mantecón, A.R.; Lavín, P. Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. Meat Sci. 2008, 79, 692–699. [Google Scholar] [CrossRef] [PubMed]
- Cozzolino, D.; Murray, I. Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy. J. Near Infrared Spectrosc. 2002, 10, 37–44. [Google Scholar] [CrossRef]
- Ripoll, G.; Albertí, P.; Panea, B.; Olleta, J.L.; Sañudo, C. Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Sci. 2008, 80, 697–702. [Google Scholar] [CrossRef]
Sample (n = 400) | ||
---|---|---|
Gender | Male | 41.32% |
Female | 58.67% | |
Age (years) | <37 | 31.78% |
37–55 | 38.14% | |
55–74 | 28.11% | |
>74 | 1.95% | |
Income | Undeclared | 16.38% |
(times the statutory minimum wage) | <1 | 11.49% |
1–2 | 41.07% | |
3–4 | 21.02% | |
5–6 | 7.33% | |
>6 | 2.68% | |
Marital status | Married | 43.27% |
Separated | 6.35% | |
Single | 30.56% | |
Widow | 3.66% | |
Consensual union | 15.64% | |
Other | 0.4% | |
Educational status | Primary school | 8.55% |
Secondary school | 35.94% | |
Technological level | 21.51% | |
Professional | 22.00% | |
Postgraduate | 11.00% | |
Other | 0.90% | |
Employment | Housewife | 19.55% |
Unemployed | 0.7% | |
Employed | 40.58% | |
Retired | 11.49% | |
Student | 4.15% | |
Self-employed | 23.47% |
Likert Variable 1 | Communality | Cronbach’s Alpha | KMO 2 | Beef Consumers Perception 3 | Variance |
---|---|---|---|---|---|
Color | 0.90 | 0.894 | 0.85 | 0.88 | 79% |
Visible fat | 0.84 | 0.85 | 0.83 | ||
Overall appearance | 0.98 | 0.76 | 0.99 | ||
Willingness to buy | 0.84 | 0.89 | 0.84 | ||
Perception Index | Independent Variable | Factor Loading (FL) | p-value | ||
Visual beef consumers’ perception | Color | 0.902 | <0.001 | ||
Visual fat | 0.841 | <0.001 | |||
Overall appearance | 0.975 | <0.001 | |||
Willingness to buy | 0.843 | <0.001 |
Reflectance | Transmittance | ||||||||
---|---|---|---|---|---|---|---|---|---|
Mat. Process. 1 | C 2 | Calibration 3 (%) | External Validation 3 (%) | Mat. Process. | C | Calibration 3 (%) | External Validation 3 (%) | ||
Color | 1. Dislike very much | Raw | 3 | 0.000 | 0.000 | SNV&D | 7 | 8.30 | 0.000 |
2. Dislike | 14.30 | 16.70 | 45.50 | 0.000 | |||||
3. Neither like or dislike | 80.00 | 75.00 | 64.70 | 33.30 | |||||
4. Like | 29.70 | 18.80 | 46.90 | 76.90 | |||||
5. Like very much | 0.000 | 0.000 | 0.000 | 0.000 | |||||
Model accuracy | 28.40 | 32.50 | 38.10 | 40.00 | |||||
Visible fat | 1. Dislike very much | First derivative | 3 | 0.000 | 0.000 | Second derivative | 2 | 0.000 | 0.000 |
2. Dislike | 25.00 | 0.000 | 14.30 | 0.000 | |||||
3. Neither like or dislike | 60.00 | 28.60 | 60.90 | 77.80 | |||||
4. Like | 67.90 | 70.00 | 70.40 | 54.50 | |||||
5. Like very much | 25.00 | 0.000 | 0.000 | 0.000 | |||||
Model accuracy | 47.60 | 29.00 | 41.50 | 39.40 | |||||
Overall appearance | 1. Dislike very much | SNV&D | 5 | 10.00 | 0.000 | SNV&D | 7 | 0.000 | 0.000 |
2. Dislike | 14.30 | 0.000 | 25.00 | 33.30 | |||||
3. Neither like or dislike | 60.00 | 77.80 | 82.40 | 57.10 | |||||
4. Like | 62.50 | 60.00 | 54.30 | 53.30 | |||||
5. Like very much | 0.000 | 0.000 | 0.000 | 20.00 | |||||
Model accuracy | 43.90 | 39.40 | 42.70 | 42.40 | |||||
Visual Perception index | 1. Dislike very much | Raw | 7 | 0.000 | 0.000 | SNV&D | 4 | 0.000 | 0.000 |
2. Dislike | 22.20 | 0.000 | 0.000 | 0.000 | |||||
3. Neither like or dislike | 68.20 | 66.70 | 94.10 | 57.10 | |||||
4. Like | 56.30 | 55.00 | 56.40 | 56.30 | |||||
5. Like very much | 0.000 | 0.000 | 0.000 | 0.000 | |||||
Model accuracy | 43.10 | 42.50 | 45.80 | 40.60 |
Reflectance | Transmittance | ||||||||
---|---|---|---|---|---|---|---|---|---|
Mat. Process. 1 | C 2 | Calibration 3 (%) | External Validation3 (%) | Mat. Process. | C | Calibration 3 (%) | External Validation 3 (%) | ||
Color | 1. Dislike | SNV | 3 | 0.00 | 0.00 | SNV | 7 | 17.40 | 12.50 |
2. Neither like or dislike | 80.00 | 83.30 | 76.50 | 66.70 | |||||
3. Like | 28.80 | 27.80 | 50.00 | 64.70 | |||||
Model accuracy | 30.40 | 37.50 | 46.40 | 51.60 | |||||
Visible fat | 1. Dislike | First derivative | 7 | 13.30 | 20.00 | SNV&D | 10 | 35.70 | 16.70 |
2. Neither like or dislike | 92.00 | 57.10 | 82.60 | 55.60 | |||||
3. Like | 56.80 | 57.90 | 80.00 | 66.70 | |||||
Model accuracy | 60.20 | 51.60 | 73.20 | 54.60 | |||||
Overall appearance | 1. Dislike | Second derivative | 5 | 29.40 | 14.30 | First derivative | 7 | 16.70 | 16.70 |
2. Neither like or dislike | 73.30 | 66.70 | 76.50 | 71.40 | |||||
3. Like | 68.00 | 41.20 | 63.80 | 55.00 | |||||
Model accuracy | 61.70 | 43.80 | 56.10 | 51.50 | |||||
Visual perception index | 1. Dislike | First derivative | 6 | 0.00 | 14.30 | SNV | 7 | 11.10 | 16.70 |
2. Neither like or dislike | 72.70 | 77.80 | 76.50 | 71.40 | |||||
3. Like | 57.60 | 50.00 | 52.10 | 57.90 | |||||
Model accuracy | 49.0 | 50.00 | 48.20 | 53.10 | |||||
Willingness to buy | No | Raw | 1 | 0.00 | 0.00 | SNV | 4 | 33.30 | 33.30 |
Yes | 100 | 100 | 93.10 | 91.70 | |||||
Model accuracy | 69.60 | 70.00 | 75.60 | 75.80 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Barragán-Hernández, W.; Mahecha-Ledesma, L.; Angulo-Arizala, J.; Olivera-Angel, M. Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance. Foods 2020, 9, 984. https://doi.org/10.3390/foods9080984
Barragán-Hernández W, Mahecha-Ledesma L, Angulo-Arizala J, Olivera-Angel M. Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance. Foods. 2020; 9(8):984. https://doi.org/10.3390/foods9080984
Chicago/Turabian StyleBarragán-Hernández, Wilson, Liliana Mahecha-Ledesma, Joaquín Angulo-Arizala, and Martha Olivera-Angel. 2020. "Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance" Foods 9, no. 8: 984. https://doi.org/10.3390/foods9080984