Victoria Lafuente1, Luis J Herrera2, María del Mar Pérez3, Jesús Val4, Ignacio Negueruela5. 1. Consejo Superior de Investigaciones Cientifcias (CSIC), Nutrición Vegetak, Zaragoza, Spain. 2. Departamento de Arquitectura y Tecnología de los computadores, Universidad de Granada, Granada, Spain. 3. Departamento de Óptica, Universidad de Granada, Granada, Spain. 4. Estación Experimental de Aula Dei, CSIC, Plant Nutrition, Avda, Montañana 1005, Zaragoza, Spain. 5. Física aplicada, Universidad de Zaragoza, Zarogoza, Spain.
Abstract
BACKGROUND: In this work, near infrared spectroscopy (NIR) and an acoustic measure (AWETA) (two non-destructive methods) were applied in Prunus persica fruit 'Calrico' (n = 260) to predict Magness-Taylor (MT) firmness. METHODS: Separate and combined use of these measures was evaluated and compared using partial least squares (PLS) and least squares support vector machine (LS-SVM) regression methods. Also, a mutual-information-based variable selection method, seeking to find the most significant variables to produce optimal accuracy of the regression models, was applied to a joint set of variables (NIR wavelengths and AWETA measure). RESULTS: The newly proposed combined NIR-AWETA model gave good values of the determination coefficient (R(2)) for PLS and LS-SVM methods (0.77 and 0.78, respectively), improving the reliability of MT firmness prediction in comparison with separate NIR and AWETA predictions. The three variables selected by the variable selection method (AWETA measure plus NIR wavelengths 675 and 697 nm) achieved R(2) values 0.76 and 0.77, PLS and LS-SVM. CONCLUSION: These results indicated that the proposed mutual-information-based variable selection algorithm was a powerful tool for the selection of the most relevant variables.
BACKGROUND: In this work, near infrared spectroscopy (NIR) and an acoustic measure (AWETA) (two non-destructive methods) were applied in Prunus persica fruit 'Calrico' (n = 260) to predict Magness-Taylor (MT) firmness. METHODS: Separate and combined use of these measures was evaluated and compared using partial least squares (PLS) and least squares support vector machine (LS-SVM) regression methods. Also, a mutual-information-based variable selection method, seeking to find the most significant variables to produce optimal accuracy of the regression models, was applied to a joint set of variables (NIR wavelengths and AWETA measure). RESULTS: The newly proposed combined NIR-AWETA model gave good values of the determination coefficient (R(2)) for PLS and LS-SVM methods (0.77 and 0.78, respectively), improving the reliability of MT firmness prediction in comparison with separate NIR and AWETA predictions. The three variables selected by the variable selection method (AWETA measure plus NIR wavelengths 675 and 697 nm) achieved R(2) values 0.76 and 0.77, PLS and LS-SVM. CONCLUSION: These results indicated that the proposed mutual-information-based variable selection algorithm was a powerful tool for the selection of the most relevant variables.
Authors: Luis Javier Herrera; Carlos José Todero Peixoto; Oresti Baños; Juan Miguel Carceller; Francisco Carrillo; Alberto Guillén Journal: Entropy (Basel) Date: 2020-09-07 Impact factor: 2.524