Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin.
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 standard normal variate, mean centring and a Savitzky-Golay filter with seven smoothing points, 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.


CONCLUSIONS
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.
Department of Science and Technology for Agriculture, Forest, Nature and Energy, Tuscia University, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy.
Roberto
R
Moscetti
Department of Science and Technology for Agriculture, Forest, Nature and Energy, Tuscia University, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy.
Emanuele
E
Radicetti
Department of Science and Technology for Agriculture, Forest, Nature and Energy, Tuscia University, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy.
Danilo
D
Monarca
Department of Science and Technology for Agriculture, Forest, Nature and Energy, Tuscia University, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy.
Massimo
M
Cecchini
Department for Innovation in Biological, Agro-food and Forest system, Tuscia University, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy.
Riccardo
R
Massantini
D000465:Algorithms; D002138:Calibration; D016000:Cluster Analysis; D031211:Corylus; D004041:Dietary Fats / Q000032:analysis; D016002:Discriminant Analysis; D005515:Food Labeling; D006801:Humans; D007558:Italy; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009754:Nuts / Q000737:chemistry / Q000145:classification*; D013045:Species Specificity; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D060388:Support Vector Machine
D004041:Dietary Fats
Corylus avellana L; chemometrics; cultivars; discriminant analysis; non-destructive techniques