This paper presents a statistical method for the calibration of an acoustic technique for the real-time evaluation of fruit firmness. The technique uses an experimental setup based on two standard piezoelectric transducers and exploits two novel stiffness indexes developed in the first part of this paper. Extensive experimental measurements show good correlation (r = 0.930, R^2 = 0.865) between the proposed non-destructive test and the traditional destructive Magness-Taylor test. An evaluation of the statistical significance (t-test) of the obtained regression model parameters has been performed and validates the method. The presented sorting analysis complements the physical detection techniques presented in the first part of the paper, allowing to classify individual kiwifruits with high accuracy and high prediction rate (∼90%). The technology is suitable for industrial real-time and in-line applications aiming to improve warehouse stock management and market stock uniformity.
E. Macrelli, A. Romani, R. P. Paganelli, E. Sangiorgi, M. Tartagni (2013). Piezoelectric transducers for real-time evaluation of fruit firmness. Part II: Statistical and sorting analysis. SENSORS AND ACTUATORS. A, PHYSICAL, 201, 497-503 [10.1016/j.sna.2013.07.037].
Piezoelectric transducers for real-time evaluation of fruit firmness. Part II: Statistical and sorting analysis
MACRELLI, ENRICO;ROMANI, ALDO;PAGANELLI, RUDI PAOLO;SANGIORGI, ENRICO;TARTAGNI, MARCO
2013
Abstract
This paper presents a statistical method for the calibration of an acoustic technique for the real-time evaluation of fruit firmness. The technique uses an experimental setup based on two standard piezoelectric transducers and exploits two novel stiffness indexes developed in the first part of this paper. Extensive experimental measurements show good correlation (r = 0.930, R^2 = 0.865) between the proposed non-destructive test and the traditional destructive Magness-Taylor test. An evaluation of the statistical significance (t-test) of the obtained regression model parameters has been performed and validates the method. The presented sorting analysis complements the physical detection techniques presented in the first part of the paper, allowing to classify individual kiwifruits with high accuracy and high prediction rate (∼90%). The technology is suitable for industrial real-time and in-line applications aiming to improve warehouse stock management and market stock uniformity.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.