A. Ortiz, C. Fallola, J. Labrador, José Martín-Gallardo, P. Rodríguez, C. Trenzado, Amalia Pérez-Jiménez, Susana García-Torres, David Tejerina
{"title":"将近红外光谱仪(NIRS)作为鲈鱼肉(Tinca tinca)质量控制和可追溯性的工具。","authors":"A. Ortiz, C. Fallola, J. Labrador, José Martín-Gallardo, P. Rodríguez, C. Trenzado, Amalia Pérez-Jiménez, Susana García-Torres, David Tejerina","doi":"10.12706/itea.2023.014","DOIUrl":null,"url":null,"abstract":"The nutritional composition of the diet directly affects the final quality of tench meat ( Tinca tinca L.). Thus, in recent years there has been a commitment to replace the protein component of feed with more sustainable vegetable alternatives. The experimental design from which this study is derived consisted of substituting organic fish meal with different percentages of organic soybean meal and pregerminated soybean meal. Therefore, the objective of this study was to evaluate the potential of Near infrared spectroscopy (NIRS) in categorizing tench according to the feed they received during its fattening phase and the quantification of the main nutritional parameters. Different spectral pretreatments were used previous to the partial least squares regressions for qualitative (PLS-DA) and quantitative (PLSR) predictions. The best PLS-DA model showed an accuracy for classification of 97.5 % in cross-validation; while the best PLSR model showed a good predictive capacity for dry matter (g/100 g), fat (g/100 g Dry Matter), and γ -tocopherol (mg/g dry matter) (0.689 ≤ R 2 vc ≤ 0.804), suggesting the possibility of performing a rapid and in situ control of the traceability and quality of tench meat by means of NIRS technology.","PeriodicalId":516767,"journal":{"name":"Informacion Tecnica Economica Agraria","volume":"14 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Espectroscopia en el infrarrojo cercano (NIRS) como herramienta para el control de calidad y trazabilidad de la carne de tenca (Tinca tinca)\",\"authors\":\"A. Ortiz, C. Fallola, J. Labrador, José Martín-Gallardo, P. Rodríguez, C. Trenzado, Amalia Pérez-Jiménez, Susana García-Torres, David Tejerina\",\"doi\":\"10.12706/itea.2023.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The nutritional composition of the diet directly affects the final quality of tench meat ( Tinca tinca L.). Thus, in recent years there has been a commitment to replace the protein component of feed with more sustainable vegetable alternatives. The experimental design from which this study is derived consisted of substituting organic fish meal with different percentages of organic soybean meal and pregerminated soybean meal. Therefore, the objective of this study was to evaluate the potential of Near infrared spectroscopy (NIRS) in categorizing tench according to the feed they received during its fattening phase and the quantification of the main nutritional parameters. Different spectral pretreatments were used previous to the partial least squares regressions for qualitative (PLS-DA) and quantitative (PLSR) predictions. The best PLS-DA model showed an accuracy for classification of 97.5 % in cross-validation; while the best PLSR model showed a good predictive capacity for dry matter (g/100 g), fat (g/100 g Dry Matter), and γ -tocopherol (mg/g dry matter) (0.689 ≤ R 2 vc ≤ 0.804), suggesting the possibility of performing a rapid and in situ control of the traceability and quality of tench meat by means of NIRS technology.\",\"PeriodicalId\":516767,\"journal\":{\"name\":\"Informacion Tecnica Economica Agraria\",\"volume\":\"14 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informacion Tecnica Economica Agraria\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12706/itea.2023.014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informacion Tecnica Economica Agraria","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12706/itea.2023.014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Espectroscopia en el infrarrojo cercano (NIRS) como herramienta para el control de calidad y trazabilidad de la carne de tenca (Tinca tinca)
The nutritional composition of the diet directly affects the final quality of tench meat ( Tinca tinca L.). Thus, in recent years there has been a commitment to replace the protein component of feed with more sustainable vegetable alternatives. The experimental design from which this study is derived consisted of substituting organic fish meal with different percentages of organic soybean meal and pregerminated soybean meal. Therefore, the objective of this study was to evaluate the potential of Near infrared spectroscopy (NIRS) in categorizing tench according to the feed they received during its fattening phase and the quantification of the main nutritional parameters. Different spectral pretreatments were used previous to the partial least squares regressions for qualitative (PLS-DA) and quantitative (PLSR) predictions. The best PLS-DA model showed an accuracy for classification of 97.5 % in cross-validation; while the best PLSR model showed a good predictive capacity for dry matter (g/100 g), fat (g/100 g Dry Matter), and γ -tocopherol (mg/g dry matter) (0.689 ≤ R 2 vc ≤ 0.804), suggesting the possibility of performing a rapid and in situ control of the traceability and quality of tench meat by means of NIRS technology.