Lafuente V, Herrera LJ, Pérez MDM, Val J, Negueruela I. 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.
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2015;
95:2033-2040. [PMID:
25224468 DOI:
10.1002/jsfa.6916]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Revised: 09/05/2014] [Accepted: 09/10/2014] [Indexed: 06/03/2023]
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.
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