BACKGROUND: This study investigates the possibility of using near infrared spectroscopy for the authentication of the ‘Nocciola Romana’ hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. RESULTS: The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively,were achieved. Various pretreatments, such as standardnormal variate,meancentringanda Savitzky–Golayfilter with sevensmoothingpoints, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. CONCLUSION: Near infrared spectroscopy could classify hazelnut according to the PDO ‘Nocciola Romana’ designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions.

Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin

RADICETTI E
Secondo
;
2015

Abstract

BACKGROUND: This study investigates the possibility of using near infrared spectroscopy for the authentication of the ‘Nocciola Romana’ hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. RESULTS: The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively,were achieved. Various pretreatments, such as standardnormal variate,meancentringanda Savitzky–Golayfilter with sevensmoothingpoints, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. CONCLUSION: Near infrared spectroscopy could classify hazelnut according to the PDO ‘Nocciola Romana’ designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions.
2015
Moscetti, R.; Radicetti, E; Monarca, D.; Cecchini, M.; Massantini, R.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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: https://hdl.handle.net/11392/2459082
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 6
  • Scopus 32
  • ???jsp.display-item.citation.isi??? 28
social impact