Microbial contamination, seed browning, bad taste and lipid oxidation are primary causes of quality deterioration in stored hazelnuts, affecting their marketability. The feasibility of NIR spectroscopy to detect flawed kernels and estimate lipid oxidation in in-shell and shelled hazelnuts was investigated. ‘Mortarella’ hazelnuts were measured twice by NIR spectroscopy, first in-shell, and then as kernels. Afterwards, the kernels were evaluated visually, externally and internally, and by sensory evaluation with a subsequent measurement of fat oxidation. A satisfactory PLS model was created for the detection of flawed kernels. For lipid oxidation estimation the best performance of PLS models was obtained by first removing the flawed kernels from the calibration set. The PLS model for the K 232 extinction coefficient, that is indicative of lipid primary oxidation, was able to predict K 232 for both in-shell (R2=0.79) and shelled (R2= 0.85) hazelnuts. Our results suggest, for shelled hazelnuts, a two-step NIR procedure: a first PLS model to detect and separate flawed kernels and then a second PLS model to grade healthy kernels by lipid oxidation levels.

Non-destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR spectroscopy

PANNICO, ANTONIO;BASILE, BORIS;ROMANO, RAFFAELE;CIRILLO, CHIARA
2015

Abstract

Microbial contamination, seed browning, bad taste and lipid oxidation are primary causes of quality deterioration in stored hazelnuts, affecting their marketability. The feasibility of NIR spectroscopy to detect flawed kernels and estimate lipid oxidation in in-shell and shelled hazelnuts was investigated. ‘Mortarella’ hazelnuts were measured twice by NIR spectroscopy, first in-shell, and then as kernels. Afterwards, the kernels were evaluated visually, externally and internally, and by sensory evaluation with a subsequent measurement of fat oxidation. A satisfactory PLS model was created for the detection of flawed kernels. For lipid oxidation estimation the best performance of PLS models was obtained by first removing the flawed kernels from the calibration set. The PLS model for the K 232 extinction coefficient, that is indicative of lipid primary oxidation, was able to predict K 232 for both in-shell (R2=0.79) and shelled (R2= 0.85) hazelnuts. Our results suggest, for shelled hazelnuts, a two-step NIR procedure: a first PLS model to detect and separate flawed kernels and then a second PLS model to grade healthy kernels by lipid oxidation levels.
File in questo prodotto:
File Dimensione Formato  
Pannico et al 2015 (JFOE).pdf

solo utenti autorizzati

Tipologia: Documento in Post-print
Licenza: Accesso privato/ristretto
Dimensione 855.81 kB
Formato Adobe PDF
855.81 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/602052
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 31
  • ???jsp.display-item.citation.isi??? 28
social impact