Geographical characterization of polyfloral and acacia honeys by nuclear magnetic resonance and chemometrics

Attempted confirmation of the provenance of Corsican PDO honey using FT-IR spectroscopy and multivariate data analysis

This study investigated the potential of Fourier-transform infrared (FT-IR) spectroscopy and chemometric techniques to produce a mathematical model that would confirm or refute the provenance of honeys claiming to be Corsican. Authentic honey samples from two harvest seasons (2004/2005 and 2005/2006) were collected from Ireland (n=2), Italy (n=30), Austria (n=40), Germany (n=36), mainland France (n=46), and Corsica (n=219). Prior to scanning, samples were diluted with distilled water to a standard solids content (70 degrees Brix). Spectra (2500-12500 nm) were recorded at room temperature using a FT-IR spectrometer equipped with a germanium attenuated total reflectance (ATR) accessory. Standard normal variate (SNV) and first- and second-derivative data pretreatments were applied to the recorded spectra, which were processed using factorial discriminant analysis (FDA) and partial least-squares (PLS) regression analysis. Overall correct classification figures of 82% (FDA) and 87% (PLS) were obtained for a separate validation set comprising samples from both harvests.

Nuclear magnetic resonance and chemometrics to assess geographical origin and quality of traditional food products

In this globalization era, the opening of the markets has put at almost everybody's disposal a wide variety of foods, allowing everybody to taste food flavors and aromas from different nations. Notwithstanding this opportunity, countries try to preserve their markets by developing protection policies. A few countries have adopted different denominations to label their "typical food" products in order to give them additional value. Besides, the term "typical food" is widely thought of as something anchored to the local traditions, with geographical meaning and made with typical raw materials. Then a "typical food" starts to be considered "traditional" when it is made following specific and old recipes. As a matter of fact, these products acquire particular organoleptic characteristics that are not reproducible when produced in different places. In this review, NMR studies coupled to multivariate statistical analysis are presented with the aim of determining geographical origin and key quality characteristics.

1H nuclear magnetic resonance spectra of chloroform extracts of honey for chemometric determination of its botanical origin

In this work, we present a new NMR study, coupled with chemometric analysis, on nonvolatile organic honey components. The extraction method is simple and reproducible. The 1H NMR spectra of chloroform extracts acquired with a fast and new pulse sequence were used to characterize and differentiate by chemometric analysis 118 honey samples of four different botanical origins (chestnut, acacia, linden, and polyfloral). The spectra collection, processing, and analysis require only 30 min. The 1H spectrum provides a fingerprint for each honey type, showing many characteristic peaks in all spectral regions. Principal component analysis (PCA) and projection to latent structures by partial least squares-discriminant analysis (PLS-DA) were performed on selected signals of the spectra to discriminate the different botanical types and to identify characteristic metabolites for each honey type. A distinct discrimination among samples was achieved. According to the distance to model criterion, there was no overlap between the four models, which proved to be specific for each honey type. The PLS-DA model obtained has a correlation coefficient R2 of 0.67 and a validation correlation coefficient Q2 of 0.77. The discriminant analysis allowed us to classify correctly 100% of the samples. A classification index can be calculated and used to determine the floral origin of honey as an alternative to the melissopalinology test and possibly to determine the percentage of various botanical species in polyfloral samples. Preliminary data on the identification of marker compounds for each botanical origin are presented.

A Study of the Metabolome of Ricinus communis for Forensic Applications

Investigations were undertaken to ascertain the appropriateness of studying the metabolome of Ricinus communis for cultivar and provenance determination. Seeds from 14 R. communis specimens (a total of 56 seeds) collected from the east coast of Australia were analyzed by high pressure liquid chromatography with UV detection (HPLC-UV), liquid chromatography–mass spectrometry (LC-MS), and 1H NMR spectroscopy. The collected data were then analyzed using principle component analysis (PCA). For HPLC-UV analysis, six R. communis specimens were unambiguously identified by PCA as belonging to separate classes relating to specimen. LC-MS data allowed unique ions to be identified for four specimens. Conversely 10 specimens were unambiguously segregated in the PCA of the 1H NMR data. The ratio of ricinine 1 to demethylricinine analogues 2 and 3 was found to be important for specimen determination. These combined analyses suggested that a combination of HPLC-UV and 1H NMR in conjunction with PCA could allow for specimen differentiation.

Triple concentrated tomato paste: discrimination between Italian and Chinese products

(1)H NMR spectroscopy was applied to discriminate triple concentrated tomato paste coming from two different countries. Notwithstanding different tomato cultivars and ripening stages employed to obtain the final product, significant discrimination between Italian and Chinese samples was obtained by combining NMR data and principal component analysis. Supervised orthogonal projection to latent structure discriminant analysis (OPLS-DA) technique was used to build robust classification models, while S-plot was employed to identify statistically significant variables responsible for class separation. Citrate content resulted in being the most relevant chemical compound for Chinese and Italian sample differentiation. In order to highlight other compounds able to contribute to sample differentiation, citrate content was excluded, and a new classification model was built. This latter model indicated aspartate, glutamine, and sugars as important variables in sample differentiation.