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Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food.
Marín-Méndez, Juan-Jesús; Luri Esplandiú, Paula; Alonso-Santamaría, Miriam; Remirez-Moreno, Berta; Urtasun Del Castillo, Leyre; Echavarri Dublán, Jaione; Almiron-Roig, Eva; Sáiz-Abajo, María-José.
Affiliation
  • Marín-Méndez JJ; National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain.
  • Luri Esplandiú P; National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain.
  • Alonso-Santamaría M; National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain.
  • Remirez-Moreno B; National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain.
  • Urtasun Del Castillo L; National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain.
  • Echavarri Dublán J; National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain.
  • Almiron-Roig E; Centre for Nutrition Research, University of Navarra. Pamplona, Spain.
  • Sáiz-Abajo MJ; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
Curr Res Food Sci ; 9: 100799, 2024.
Article in En | MEDLINE | ID: mdl-39040225
ABSTRACT
Knowledge of the energy and macronutrient content of complex foods is essential for the food industry and to implement population-based dietary guidelines. However, conventional methodologies are time-consuming, require the use of chemical products and the sample cannot be recovered. We hypothesize that the nutritional value of heterogeneous food products can be readily measured instead by using hyperspectral imaging systems (NIR and VIS-NIR) combined with mathematical models previously fitted with spectral profiles.118 samples from different food products were collected for building the predictive models using their hyperspectral imaging data as predictors and their nutritional values as dependent variables. Ten different models were screened (Multivariate Linear regression, Lasso regression, Rigde regression, Elastic Net regression, K-Neighbors regression, Decision trees regression, Partial Least Square, Support Vector Machines, Gradient Boosting regression and Random Forest regression). The best results were obtained with Ridge regression for all parameters. The best performance was for estimating the protein content with a RMSE of 1.02 and a R2 equal to 0.88 in a test set, following by moisture (RMSE of 2.21 and R2 equal to 0.85), energy value (RMSE of 21.84 and R2 equal to 0.76) and total fat (RMSE of 2.17 and R2 equal to 0.72). The performance with carbohydrates (RMSE of 2.12 and R2 equal to 0.61) and ashes (RMSE of 0.25 and R2 equal to 0.38) was worse. This study shows that it is possible to predict the energy and nutrient values of processed complex foods, using hyperspectral imaging systems combined with supervised machine learning methods.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Curr Res Food Sci Year: 2024 Document type: Article Affiliation country: España

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Curr Res Food Sci Year: 2024 Document type: Article Affiliation country: España