• Non-destructive grading of pea...

Treffer: Non-destructive grading of peaches by near-infrared spectrometry

Title:
Non-destructive grading of peaches by near-infrared spectrometry
Authors:
CARLOMAGNO, G, CAPOZZO, L, ATTOLICO, G, DISTANTE, A
Source:
7th AITA Conference 2003: Workshop on Advanced Infrared Technology, 9-11 September 2003, Pisa, ItalyInfrared physics & technology. 46(1-2):23-29
Publisher Information:
Amsterdam: Elsevier, 2004.
Publication Year:
2004
Physical Description:
print, 8 ref
Original Material:
INIST-CNRS
Subject Terms:
Optics, Optique, Condensed state physics, Physique de l'état condensé, Sciences exactes et technologie, Exact sciences and technology, Physique, Physics, Generalites, General, Instruments, appareillage, composants et techniques communs à plusieurs branches de la physique et de l'astronomie, Instruments, apparatus, components and techniques common to several branches of physics and astronomy, Informatique en physique expérimentale, Computers in experimental physics, Analyse de données: algorithmes et implémentations; gestion de données, Data analysis: algorithms and implementation; data management, Instrumentation, équipements et techniques en infrarouge, onde submillimétrique, hyperfréquence et radiofréquence, Infrared, submillimeter wave, microwave and radiowave instruments, equipment and techniques, Spectromètres infrarouge, équipement auxiliaire et techniques, Infrared spectrometers, auxiliary equipment and techniques, Domaines interdisciplinaires: science des materiaux; rheologie, Cross-disciplinary physics: materials science; rheology, Rhéologie, Rheology, Types de matériaux, Material types, Infrared spectrometers, auxiliary equipment, and techniques, Analyse chimique, Chemical analysis, Analyse non destructive, Nondestructive analysis, Caméra CCD, CCD camera, Cámara CCD, Classification morphologique, Morphological classification, Clasificación morfológica, Classification spectrale, Spectral classification, Contrôle qualité, Quality control, Etude expérimentale, Experimental study, Facteur transmission, Transmittance, Factor transmisión, Fermeté, Firmness, Firmeza, Industrie alimentaire, Food industry, Industria alimenticia, Maturation, Ripening, Pêche(fruit), Peaches, Rayonnement IR proche, Near infrared radiation, Spectrométrie IR, Infrared spectroscopy, Sucre, Sugar, Azúcar, Traitement signal, Signal processing, Transformation ondelette, Wavelet transforms, Triage, Sorting
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Italian National Research Council, Institute of Intelligent Systems for Automation, Via Amendola, 122/D-O, 70126 Bari, Italy
ISSN:
1350-4495
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16236407
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Metrology

Physics of condensed state: structure, mechanical and thermal properties
Accession Number:
edscal.16236407
Database:
PASCAL Archive

Weitere Informationen

This paper describes an experimental study on non-destructive methods for sorting peaches according to their degree of ripeness. The method is based on near-infrared (NIR) transmittance spectrometry in the region between 730 and 900 nm. It estimates the ripeness in terms of internal sugar content and firmness. A station for acquiring the NIR signal has been designed and realized, carefully choosing between several options for each component. Four different stations have been realized and compared during the experimental phase. The signals acquired by the station have been pre-processed using a noise-reducing method based on a packets-wavelet transform. In addition, an outlier detection technique has been applied for identifying irregular behaviors inside each of the considered classes. Finally, a minimum distance classifier estimates the grade of each experimental data. The results obtained in classification show that this early version of the station enables the correct discrimination of peaches with a percentage of 82.5%.