Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers
Fish burgers as new products require their shelf life investigated. Sensory results usually do not follow a homogeneous profile, as it measures human perception. Once the sensory and physicochemical monitoring of the shelf life takes time and considerable investment, the Near Infrared spectroscopy...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Mato Grosso do Sul
2019-12-01
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| Series: | Orbital: The Electronic Journal of Chemistry |
| Subjects: | |
| Online Access: | https://periodicos.ufms.br/index.php/orbital/article/view/15790 |
| _version_ | 1828057832687140864 |
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| author | Caroline Marques Carla Cristina Lise Vanderlei Aparecido de Lima Marina Leite Mitterer-Daltoé |
| author_facet | Caroline Marques Carla Cristina Lise Vanderlei Aparecido de Lima Marina Leite Mitterer-Daltoé |
| author_sort | Caroline Marques |
| collection | DOAJ |
| description |
Fish burgers as new products require their shelf life investigated. Sensory results usually do not follow a homogeneous profile, as it measures human perception. Once the sensory and physicochemical monitoring of the shelf life takes time and considerable investment, the Near Infrared spectroscopy comes as a fast instrumental technique, which can access multiple parameters from the sample at the same time. In order to replace traditional methods improving mathematical modeling, the objective of this study is the estimation of the data preprocessing and homogeneity (Kolmogorov–Smirnov) influence in the quality parameters of Partial Least Squares modeling. Calibration and validation models were evaluated by means of correlation coefficient, Rank, robustness and Residual Prediction Deviation. All the preprocessing available on the software Opus Lab® were tested and compared. 72 readings/8 samples of refrigerated grass carp burgers originated the data regarding its water activity, rancid taste, pH and reactive substances of thiobarbituric acid results. The preprocessing methods accessible were Standard Normal Variate, Multiplicative Scatter Correction, 2nd derivative, 1st derivative, Straight Line Subtraction and Min/Max. Each chosen preprocessing generated a model with different parameters. The homogeneity of data proved to have a direct influence on the robustness, confirming the challenge to fit sensory results in Partial Least Squares prediction models. New possibilities to investigate meat products were shown.
DOI: http://dx.doi.org/10.17807/orbital.v11i6.1234
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| first_indexed | 2024-04-10T21:17:24Z |
| format | Article |
| id | doaj.art-60104e8a33fd4f8ba046ecf4b0ef7676 |
| institution | Directory Open Access Journal |
| issn | 1984-6428 |
| language | English |
| last_indexed | 2024-04-10T21:17:24Z |
| publishDate | 2019-12-01 |
| publisher | Universidade Federal de Mato Grosso do Sul |
| record_format | Article |
| series | Orbital: The Electronic Journal of Chemistry |
| spelling | doaj.art-60104e8a33fd4f8ba046ecf4b0ef76762023-01-20T10:51:38ZengUniversidade Federal de Mato Grosso do SulOrbital: The Electronic Journal of Chemistry1984-64282019-12-01116Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish BurgersCaroline Marques0Carla Cristina Lise1Vanderlei Aparecido de Lima2Marina Leite Mitterer-Daltoé3Graduate Program in Chemical and Biochemical Technology Processes, Chemistry Department, Federal University of TechnologyGraduate Program in Chemical and Biochemical Technology Processes, Chemistry Department, Federal University of TechnologyGraduate Program in Chemical and Biochemical Technology Processes, Chemistry Department, Federal University of TechnologyGraduate Program in Chemical and Biochemical Technology Processes, Chemistry Department, Federal University of Technology Fish burgers as new products require their shelf life investigated. Sensory results usually do not follow a homogeneous profile, as it measures human perception. Once the sensory and physicochemical monitoring of the shelf life takes time and considerable investment, the Near Infrared spectroscopy comes as a fast instrumental technique, which can access multiple parameters from the sample at the same time. In order to replace traditional methods improving mathematical modeling, the objective of this study is the estimation of the data preprocessing and homogeneity (Kolmogorov–Smirnov) influence in the quality parameters of Partial Least Squares modeling. Calibration and validation models were evaluated by means of correlation coefficient, Rank, robustness and Residual Prediction Deviation. All the preprocessing available on the software Opus Lab® were tested and compared. 72 readings/8 samples of refrigerated grass carp burgers originated the data regarding its water activity, rancid taste, pH and reactive substances of thiobarbituric acid results. The preprocessing methods accessible were Standard Normal Variate, Multiplicative Scatter Correction, 2nd derivative, 1st derivative, Straight Line Subtraction and Min/Max. Each chosen preprocessing generated a model with different parameters. The homogeneity of data proved to have a direct influence on the robustness, confirming the challenge to fit sensory results in Partial Least Squares prediction models. New possibilities to investigate meat products were shown. DOI: http://dx.doi.org/10.17807/orbital.v11i6.1234 https://periodicos.ufms.br/index.php/orbital/article/view/15790grass carphamburgersNIRPLSpredictive modelsstatistics |
| spellingShingle | Caroline Marques Carla Cristina Lise Vanderlei Aparecido de Lima Marina Leite Mitterer-Daltoé Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers Orbital: The Electronic Journal of Chemistry grass carp hamburgers NIR PLS predictive models statistics |
| title | Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers |
| title_full | Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers |
| title_fullStr | Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers |
| title_full_unstemmed | Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers |
| title_short | Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers |
| title_sort | data preprocessing and homogeneity the influence on robustness and modeling by pls via nir of fish burgers |
| topic | grass carp hamburgers NIR PLS predictive models statistics |
| url | https://periodicos.ufms.br/index.php/orbital/article/view/15790 |
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