TY - JOUR
T1 - A rapid and non-invasive method for authenticating the origin of pistachio samples by NIR spectroscopy and chemometrics
AU - Vitale, Raffaele
AU - Bevilacqua, Marta
AU - Bucci, Remo
AU - Magrì, Andrea D.
AU - Magrì, Antonio L.
AU - Marini, Federico
PY - 2013/2/5
Y1 - 2013/2/5
N2 - In this study, near-infrared spectroscopy coupled to chemometrics is used to build an analytical protocol to authenticate the origin of pistachio nuts (Pistacia vera L.), a high value-added food product. In particular, 483 samples from six different origins (Sicily, India, Iran, Syria, Turkey and U.S.A.) were analyzed by NIR spectroscopy. Spectra were recorded on half seeds cut longitudinally in reflectance mode. Spectral data were then processed by chemometrics to build classification models by SIMCA and PLS-DA. The discriminant approach resulted in classification accuracies higher than 90% for most of the classes. On the other hand, SIMCA built class-models with high sensitivity and specificities, the only exception being the two categories Turkey and Iran, whose heterogeneity resulted in a poorer specificity (anyway higher than 80%). In particular, the results obtained for the samples coming from Bronte (Sicily), the only PDO pistachio production in Europe – 95.5% non-error rate in PLS-DA, 90% sensitivity and 97% specificity in SIMCA, as evaluated on the external test set – are very promising from the viewpoint of the authentication of this product. In general, the results show that the coupling of NIR spectroscopy to chemometric classification techniques can be a valuable tool for tracing the origin of pistachio nuts, providing a reliable authentication in a rapid, relatively cheap and non-invasive way.
AB - In this study, near-infrared spectroscopy coupled to chemometrics is used to build an analytical protocol to authenticate the origin of pistachio nuts (Pistacia vera L.), a high value-added food product. In particular, 483 samples from six different origins (Sicily, India, Iran, Syria, Turkey and U.S.A.) were analyzed by NIR spectroscopy. Spectra were recorded on half seeds cut longitudinally in reflectance mode. Spectral data were then processed by chemometrics to build classification models by SIMCA and PLS-DA. The discriminant approach resulted in classification accuracies higher than 90% for most of the classes. On the other hand, SIMCA built class-models with high sensitivity and specificities, the only exception being the two categories Turkey and Iran, whose heterogeneity resulted in a poorer specificity (anyway higher than 80%). In particular, the results obtained for the samples coming from Bronte (Sicily), the only PDO pistachio production in Europe – 95.5% non-error rate in PLS-DA, 90% sensitivity and 97% specificity in SIMCA, as evaluated on the external test set – are very promising from the viewpoint of the authentication of this product. In general, the results show that the coupling of NIR spectroscopy to chemometric classification techniques can be a valuable tool for tracing the origin of pistachio nuts, providing a reliable authentication in a rapid, relatively cheap and non-invasive way.
KW - Pistachio (.) nuts
KW - Near infrared spectroscopy (NIR)
KW - Classification
KW - Partial least squares-discriminant analysis (PLS-DA)
KW - Soft independent modeling of class analogies (SIMCA)
U2 - 10.1016/j.chemolab.2012.11.019
DO - 10.1016/j.chemolab.2012.11.019
M3 - Journal article
SN - 0169-7439
VL - 121
SP - 90
EP - 99
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
ER -