Food Analytical Methods

, Volume 12, Issue 11, pp 2572–2581 | Cite as

Geographical discrimination of saffron (Crocus sativus L.) using ICP-MS elemental data and class modeling of PDO Zafferano dellAquila produced in Abruzzo (Italy)

  • Angelo Antonio D’ArchivioEmail author
  • Maria Laura Di Vacri
  • Marco Ferrante
  • Maria Anna Maggi
  • Stefano Nisi
  • Fabrizio Ruggieri


PDO (protected designation of origin) Zafferano dellAquila (AQ), Iranian (IR), and commercial (CS) saffron samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The ICP-MS data of 30 elements were handled by unsupervised (principal component analysis (PCA)) and supervised (linear discriminant analysis (LDA)) multivariate statistical methods to identify a subset of discriminant variables useful for geographical classification. Moreover, class modeling of AQ saffron was performed by both UNEQ (unequal disperses classes) and SIMCA (soft independent modeling of class analogy) methods. A good differentiation of the AQ, IR, and CS samples was obtained by the LDA based on four selected elements: Sr, Ca, Mo, and Fe. A UNEQ class model for the PDO AQ saffron based on the above four elements provided 100% sensitivity (all authentic AQ saffron were accepted) and 100% specificity (all IR and CS were rejected), while slightly worse results were obtained using the SIMCA (89% sensitivity and 96% specificity).


Saffron Elemental analysis Classification Class-modeling Authentication 


Compliance with Ethical Standards

Conflict of Interest

Angelo Antonio D’Archivio declares that he has no conflict of interest. Maria Laura Di Vacri declares that she has no conflict of interest. Marco Ferrante declares that he has no conflict of interest. Maria Anna Maggi declares that she has no conflict of interest. Stefano Nisi declares that he has no conflict of interest. Fabrizio Ruggieri declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable


  1. Alavizadeh SH, Hosseinzadeh H (2014) Bioactivity assessment and toxicity of crocin: a comprehensive review. Food Chem. Toxicol. 64:65–80CrossRefGoogle Scholar
  2. Anastasaki E, Kanakis C, Pappas C, Maggi L, del Campo CP, Carmona M, Alonso GL, Polissiou MG (2009) Geographical differentiation of saffron by GC-MS/FID and chemometrics. Eur Food Res Technol 229:899–905. CrossRefGoogle Scholar
  3. Anastasaki E, Kanakis C, Pappas C, Maggi L, del Campo CP, Carmona M, Alonso GL, Polissiou MG (2010) Differentiation of saffron from four countries by mid-infrared spectroscopy and multivariate analysis. Eur Food Res Technol 230:571–577. CrossRefGoogle Scholar
  4. Ballabio D, Todeschini R (2009) Multivariate classification for qualitative analysis. In: Sun D–W (ed) Infrared spectroscopy for food quality analysis and control, Elsevier Academic Press, San Diego, pp 83–104CrossRefGoogle Scholar
  5. Böhme K, Calo-Mata P, Barros-Velázquez J, Ortea I (2019) Recent applications of omics-based technologies to main topics in food authentication. TrAC - Trends Anal Chem 110:221–232. CrossRefGoogle Scholar
  6. Caballero-Ortega H, Pereda-Miranda R, Abdullaev FI (2007) HPLC quantification of major active components from 11 different saffron (Crocus sativus L.) sources. Food Chem 100:1126–1131. CrossRefGoogle Scholar
  7. Cagliani LR, Culeddu N, Chessa M, Consonni R (2015) NMR investigations for a quality assessment of Italian PDO saffron (Crocus sativus L.). Food Control 50:342–348. CrossRefGoogle Scholar
  8. Carmona M, Zalacain A, Sánchez AM, Novella JL, Alonso GL (2006) Crocetin esters, picrocrocin and its related compounds present in Crocus sativus stigmas and Gardenia jasminoides fruits. Tentative identification of seven new compounds by LC-ESI-MS. J Agric Food Chem 54:973–979. CrossRefPubMedGoogle Scholar
  9. Carmona M, Sánchez AM, Ferreres F, Zalacain A, Tomás-Barberán F, Alonso GL (2007) Identification of the flavonoid fraction in saffron spice by LC/DAD/MS/MS: comparative study of samples from different geographical origins. Food Chem 100:445–450. CrossRefGoogle Scholar
  10. Cubero-Leon E, Peñalver R, Maquet A (2014) Review on metabolomics for food authentication. Food Res Int 60:95–107. CrossRefGoogle Scholar
  11. D’Archivio AA, Maggi MA (2017) Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV–visible spectra of aqueous extracts. Food Chem 219:408–413. CrossRefPubMedGoogle Scholar
  12. D’Archivio AA, Giannitto A, Incani A, Nisi S (2014) Analysis of the mineral composition of Italian saffron by ICP-MS and classification of geographical origin. Food Chem 157:485–489. CrossRefPubMedGoogle Scholar
  13. D’Archivio AA, Giannitto A, Maggi MA, Ruggieri F (2016) Geographical classification of Italian saffron (Crocus sativus L.) based on chemical constituents determined by high-performance liquid-chromatography and by using linear discriminant analysis. Food Chem 212:110–116. CrossRefPubMedGoogle Scholar
  14. Derde MP, Massart DL (1986) UNEQ: a disjoint modelling technique for pattern recognition based on normal distribution. Anal Chim Acta 184:33–51. CrossRefGoogle Scholar
  15. Dong H, Xiao K, Xian Y, Wu Y (2018) Authenticity determination of honeys with non-extractable proteins by means of elemental analyzer (EA) and liquid chromatography (LC) coupled to isotope ratio mass spectroscopy (IRMS). Food Chem 240:717–724. CrossRefPubMedGoogle Scholar
  16. Drivelos SA, Georgiou CA (2012) Multi-element and multi-isotope-ratio analysis to determine the geographical origin of foods in the European Union. TrAC - Trends Anal. Chem. 40:38–51CrossRefGoogle Scholar
  17. Esteki M, Simal-Gandara J, Shahsavari Z, Zandbaaf S, Dashtaki E, Vander Heyden Y (2018) A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control 93:165–182CrossRefGoogle Scholar
  18. Forina M, Oliveri P, Lanteri S, Casale M (2008) Class-modeling techniques, classic and new, for old and new problems. Chemom Intell Lab Syst 93:132–148. CrossRefGoogle Scholar
  19. Forina M, Lanteri S, Armanino C, et al (2010) V-PARVUS 2010. Dipartimento di Tecnologie Farmaceutiche ed Alimentari. University of Genova,
  20. Ghorbani M (2008) The efficiency of saffron’s marketing channel in Iran. World Appl Sci J 4:523–527 doi: ISSN 1818-4952Google Scholar
  21. Härdle W, Simar L (2003) Applied multivariate statistical analysis, Springer-Verlag, Berlin HeidelbergGoogle Scholar
  22. Jia LH, Liu Y, Li YZ (2011) Determination of the major metal elements including heavy metals in saffron from Tibet and Henan by ICPAES or ICPMS. J Chinese Pharm Sci 20:297–301. CrossRefGoogle Scholar
  23. Kelly S, Heaton K, Hoogewerff J (2005) Tracing the geographical origin of food: the application of multi-element and multi-isotope analysis. Trends Food Sci Technol. 16:555–567CrossRefGoogle Scholar
  24. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148. CrossRefGoogle Scholar
  25. Luykx DMAM, van Ruth SM (2008) An overview of analytical methods for determining the geographical origin of food products. Food Chem 107:897–911. CrossRefGoogle Scholar
  26. Maggi L, Carmona M, Sánchez AM, Alonso GL (2010) Saffron flavor: compounds involved, biogenesis and human perception. Funct Plant Sci Biotechnol 4:45–55Google Scholar
  27. Maggi L, Carmona M, Kelly SD, Marigheto N, Alonso GL (2011) Geographical origin differentiation of saffron spice (Crocus sativus L. stigmas) - preliminary investigation using chemical and multi-element (H, C, N) stable isotope analysis. Food Chem 128:543–548. CrossRefPubMedGoogle Scholar
  28. Masi E, Taiti C, Heimler D, Vignolini P, Romani A, Mancuso S (2016) PTR-TOF-MS and HPLC analysis in the characterization of saffron (Crocus sativus L.) from Italy and Iran. Food Chem 192:75–81. CrossRefPubMedGoogle Scholar
  29. Melnyk JP, Wang S, Marcone MF (2010) Chemical and biological properties of the world’s most expensive spice: Saffron. Food Res Int 43:1981–1989CrossRefGoogle Scholar
  30. Moore JC, Spink J, Lipp M (2012) Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. J Food Sci 77:R118–R126CrossRefGoogle Scholar
  31. Priscila del Campo C, Garde-Cerdán T, Sánchez AM, Maggi L, Carmona M, Alonso GL (2009) Determination of free amino acids and ammonium ion in saffron (Crocus sativus L.) from different geographical origins. Food Chem 114:1542–1548. CrossRefGoogle Scholar
  32. Ríos JL, Recio MC, Giner RM, Meñez S (1996) An update review of saffron and its active constituents. Phyther Res 10:189–193CrossRefGoogle Scholar
  33. Snee RD (1977) Validation of regression models: methods and examples. Technometrics 19:415–428. CrossRefGoogle Scholar
  34. Sun S, Guo B, Wei Y, Fan M (2012) Classification of geographical origins and prediction of δ13C and δ15N values of lamb meat by near infrared reflectance spectroscopy. Food Chem 135:508–514. CrossRefPubMedGoogle Scholar
  35. Sun S, Guo B, Wei Y (2016) Origin assignment by multi-element stable isotopes of lamb tissues. Food Chem 213:675–681. CrossRefPubMedGoogle Scholar
  36. Wold S (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52. CrossRefGoogle Scholar
  37. Wold S, Sjostrom M (1977) SIMCA: a method for analysing chemical data in terms of similarity and analogy. In B. R. Kowalski (Ed.), Chemometrics, theory and application. ACS Symp Ser 52:243-1265. doi: CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Scienze Fisiche e ChimicheUniversità degli Studi dell’AquilaL’AquilaItaly
  2. 2.Pacific Northwest National LaboratoryRichlandUSA
  3. 3.Laboratorio Nazionale del Gran SassoIstituto Nazionale di Fisica NucleareL’AquilaItaly
  4. 4.Trace Research CentreTeramoItaly
  5. 5.Hortus NovusL’AquilaItaly

Personalised recommendations