Geographical origin discrimination of lentils (Lens culinaris Medik.) using ¹H NMR fingerprinting and multivariate statistical analyses

Highlights

• Geographic origin of lentils was discriminated by ¹H NMR fingerprint and chemometrics.

• ¹H NMR was used in an untargeted approach.

• Different supervised methods were tested.

• External validation procedures were applied on the supervised models.

• LDA gave 100% classification and test set prediction performances.

Abstract

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted ¹H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated.

Introduction

Lentil (Lens culinaris Medik.) is the fourth most important pulse crop in the world after bean (Phaseolus vulgaris L.), pea (Pisum sativum L.), and chickpea (Cicer arietinum L.). Lentils are characterised by a high energy value and a high content of complex carbohydrates, proteins, dietary fibers, vitamins, minerals (de Almeida Costa et al., 2006, Wang and Daun, 2006, Wang et al., 2009) even if some anti-nutritional constituents are also present (Thavarajah et al., 2010, Wang et al., 2009).

FAOSTAT reported that the world production of lentils was about 4.9 million of tons, primarily coming from Canada, India, Australia and Turkey; in particular, about a quarter of the production is from India but most of it is consumed in the domestic market, while Canada is the largest export producer of lentils in the world (FAOSTAT database, 2014).

In Italy during the last years the lentil production declined from 14 k tons in the 60’s to 1.9 k tons in 2014 due to several causes; therefore, as consequence, Italy annually imports about 29.6 million kg of lentils, mainly coming from Canada, USA, Turkey and China (Bacchi et al., 2010, Piergiovanni, 2000). However, Italian lentils, being cultivated mainly in specific localities, present unique and characteristic sensory and nutritional properties giving them a higher value; in fact, many Italian lentils gained international and national marks linked to their geographical origins, such as “protected geographical indication” (PGI), “traditional agricultural food products” (PAT) and Slow Food Presidium. Such labels allow to improve the commercial value of the food products, by guaranteeing a high quality level, and protect their typicality. Nevertheless, unscrupulous producers, driven by high illicit profits, often sell products that recall the “Italian Sounding” but are actually obtained blending or substituting the Italian products with foreign ones having low qualitative levels and commercial values.

Obviously, this kind of problems concerns not only the lentil production but all the traditional foods from raw materials to finished products. Therefore, it is clear why there is an increasing demand to have analytical methods able to certify the declared geographical origin of food products, in order to protect consumers and honest producers from fraud and unfair competition, respectively; consequently, during recent years, several food authentication techniques have been proposed (de la Guardia & Gonzalvez Illueca, 2013).

Among these techniques, the Nuclear Magnetic Resonance (NMR) has been considered a versatile and useful tool, due to its ability to provide a complete view of food metabolites, providing qualitative and quantitative information either on major and minor compounds (Mannina, Sobolev, & Viel, 2012). NMR has been regarded, in combination with multivariate statistical analysis, as a powerful tool for determining food quality and geographical origin, especially when used as untargeted method, where the whole spectra are used as fingerprints without assigning particular resonances to specific metabolites (Baiano et al., 2012, Ferrara et al., 2013, Fiehn, 2001, Longobardi et al., 2012, Longobardi et al., 2013, Mannina et al., 2001, Vlahov et al., 2003).

As far as lentil authenticity is concerned, some studies are reported in literature. In particular, accessions of lentils from different countries were examined on the basis of some morphological characters by discriminant analysis and canonical analysis, showing regional grouping, even if misclassifications of individuals within groups were frequent (Erskine, Adham, & Holly, 1989). Moreover, the proteome of lentil seeds was used to identify specific markers and discriminate different plant landraces, through multivariate statistical analyses (Scippa et al., 2010).

In addition, DNA-based methods combined with high resolution melting analysis (Bosmali, Ganopoulos, Madesis, & Tsaftaris, 2012) were used to identify a particular lentil variety amongst other Greek varieties or admixtures, reaching a clear discrimination.

However, only few studies on geographical differentiation of lentil samples have been done; in particular, Diffuse Reflectance Fourier Transform Infrared Spectroscopy combined with discriminant analysis was proved to be convenient and fast, but the study, involving 27 samples grouped in two classes, i.e. “Greek” and “imported”, was carried out without performing a validation procedure, reducing the real applicability of the proposed method (Kouvoutsakis, Mitsi, Tarantilis, Polissiou, & Pappas, 2014). Other studies involved stable isotope ratios of δ¹³C, δ¹⁵N, whose values may depend on several factors, such as climatic parameters typical of the region (Zhang, Emeriau, & Martin, 1991); however, the δ²H, δ¹⁸O, δ³⁴S ratios are most linked to geographical origin (Rossmann et al., 1999, Stöckigt et al., 2005, Ziegler et al., 1976) and were analysed, in combination with chemometrics, to successfully discriminate geographical origin of lentils (Longobardi et al., 2015).

To the authors’ knowledge, no study based on “NMR fingerprinting - multivariate statistical analysis” approach has been reported; thus, in this paper different statistical strategies, i.e. Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA), k-Nearest Neighbor (k-NN), Partial Least Squares-Discriminant Analysis (PLS-DA), and Soft Independent Modelling of Class Analogy (SIMCA) were tested on ¹H NMR data of lentil samples aiming at discriminating them on the basis of their different geographical origin, i.e. Italy and Canada.