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Firmness prediction in Prunus persica ‘Calrico’ peaches by visible/short‐wave near infrared spectroscopy and acoustic measurements using optimised linear and non‐linear chemometric models
Author(s) -
Lafuente Victoria,
Herrera Luis J,
Pérez María del Mar,
Val Jesús,
Negueruela Ignacio
Publication year - 2014
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.6916
Subject(s) - partial least squares regression , support vector machine , measure (data warehouse) , linear regression , feature selection , near infrared spectroscopy , mathematics , regression analysis , coefficient of determination , prunus , variable elimination , statistics , pattern recognition (psychology) , analytical chemistry (journal) , biological system , chemistry , artificial intelligence , computer science , optics , botany , data mining , chromatography , physics , inference , biology
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. © 2014 Society of Chemical Industry