Beef marbling measurement using spectral imaging: A multiple linear regression approach
This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a...
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| Format: | Article |
| Language: | English |
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Universidad Nacional de Tumbes
2023-12-01
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| Series: | Manglar |
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| Online Access: | https://revistas.untumbes.edu.pe/index.php/manglar/article/view/410 |
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| author | Victor Aredo Lía Ethel Velásquez Castillo Nikol Siche |
| author_facet | Victor Aredo Lía Ethel Velásquez Castillo Nikol Siche |
| author_sort | Victor Aredo |
| collection | DOAJ |
| description | This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a few selected wavelengths. Data came from 44 beef samples and consisted of their spectral signatures (75 wavelengths) from hyperspectral reflectance images (400-1000 nm) and their marbling scores assigned by evaluators. The wavelengths that presented regression coefficients with the highest absolute values in the PLSR model, were used to calibrate the MLR model by a backward stepwise approach (p < 0.05). The coefficient of determination for prediction (R2p) and the standard error of prediction (SEP) were evaluated. The MLR model was suitable for practical use because it required only 12 wavelengths for reliable predictions (R2p = 0.824 > 0.8; SEP = 11.4% < 15%). A model is proposed for the objective and simple measurement of beef marbling score using multispectral imaging technology. |
| format | Article |
| id | doaj-art-7b4f8863a90a4d029a06e9b7b81c476a |
| institution | Kabale University |
| issn | 1816-7667 2414-1046 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Universidad Nacional de Tumbes |
| record_format | Article |
| series | Manglar |
| spelling | doaj-art-7b4f8863a90a4d029a06e9b7b81c476a2025-01-03T01:30:05ZengUniversidad Nacional de TumbesManglar1816-76672414-10462023-12-01204333339https://doi.org/10.57188/manglar.2023.038Beef marbling measurement using spectral imaging: A multiple linear regression approachVictor Aredo0https://orcid.org/0000-0002-3293-7696Lía Ethel Velásquez Castillo1https://orcid.org/0000-0001-8091-5611Nikol Siche2https://orcid.org/0000-0002-7174-8337Departamento de Operaciones Unitarias. Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Av. República de Venezuela s/n, Lima, Peru.Department of Food Engineering, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, SP 13635-900, Brazil.Escuela de Ingeniería Zootecnista, Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, Av. Juan Pablo II s/n, Trujillo, Peru.This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a few selected wavelengths. Data came from 44 beef samples and consisted of their spectral signatures (75 wavelengths) from hyperspectral reflectance images (400-1000 nm) and their marbling scores assigned by evaluators. The wavelengths that presented regression coefficients with the highest absolute values in the PLSR model, were used to calibrate the MLR model by a backward stepwise approach (p < 0.05). The coefficient of determination for prediction (R2p) and the standard error of prediction (SEP) were evaluated. The MLR model was suitable for practical use because it required only 12 wavelengths for reliable predictions (R2p = 0.824 > 0.8; SEP = 11.4% < 15%). A model is proposed for the objective and simple measurement of beef marbling score using multispectral imaging technology.https://revistas.untumbes.edu.pe/index.php/manglar/article/view/410hyperspectral imaging; partial least squares regression; predictive model; meat quality; multispectral imaging; multiple linear regression. |
| spellingShingle | Victor Aredo Lía Ethel Velásquez Castillo Nikol Siche Beef marbling measurement using spectral imaging: A multiple linear regression approach Manglar hyperspectral imaging; partial least squares regression; predictive model; meat quality; multispectral imaging; multiple linear regression. |
| title | Beef marbling measurement using spectral imaging: A multiple linear regression approach |
| title_full | Beef marbling measurement using spectral imaging: A multiple linear regression approach |
| title_fullStr | Beef marbling measurement using spectral imaging: A multiple linear regression approach |
| title_full_unstemmed | Beef marbling measurement using spectral imaging: A multiple linear regression approach |
| title_short | Beef marbling measurement using spectral imaging: A multiple linear regression approach |
| title_sort | beef marbling measurement using spectral imaging a multiple linear regression approach |
| topic | hyperspectral imaging; partial least squares regression; predictive model; meat quality; multispectral imaging; multiple linear regression. |
| url | https://revistas.untumbes.edu.pe/index.php/manglar/article/view/410 |
| work_keys_str_mv | AT victoraredo beefmarblingmeasurementusingspectralimagingamultiplelinearregressionapproach AT liaethelvelasquezcastillo beefmarblingmeasurementusingspectralimagingamultiplelinearregressionapproach AT nikolsiche beefmarblingmeasurementusingspectralimagingamultiplelinearregressionapproach |