Novel 2D-NMR Approach for the Classification of Balsamic Vinegars of Modena.

The aim of this work is to evaluate the possibility of using 2D-NMR for the construction of classification models for balsamic vinegars of Modena. The goal was to obtain an indirect indicator of authenticity and a quality control tool. The spectral data were analyzed by chemometric methods, aiming to discriminate the samples in relation to their origin. Application of general discriminant analysis (GDA) revealed a good discrimination; the two obtained models explained 83.9% and 97.3% of the total variance with a predictive capacity of 98.6% and 98.4%, respectively. The signals of 5-HMF, ß-glucose, 2,3-butanediol, 6-acetyl glucose, and different aliphatic signals of sugars were the most significant variables. These results are very promising for giving an important contribution in quality control and characterization of such very valuable foods.

An analytical approach to Sr isotope ratio determination in Lambrusco wines for geographical traceability purposes.

Geographical origin and authenticity of food are topics of interest for both consumers and producers. Among the different indicators used for traceability studies, (87)Sr/(86)Sr isotopic ratio has provided excellent results. In this study, two analytical approaches for wine sample pre-treatment, microwave and low temperature mineralisation, were investigated to develop accurate and precise analytical method for (87)Sr/(86)Sr determination. The two procedures led to comparable results (paired t-test, with t

Application of one- and two-dimensional NMR spectroscopy for the characterization of Protected Designation of Origin Lambrusco wines of Modena.

Lambrusco is a Protected Designation of Origin (PDO) red wine of Modena (Italy) produced according to the production regulation (Decreto Ministeriale (DM) July 27, 2009; GU no. 184-187-188, 13/08/2009). Here the use of (1)H NMR spectroscopy as molecular fingerprints of several PDO Lambrusco wines was proposed to serve as indicators of authenticity and quality control. Application of partial least squares discriminant analysis (PLS-DA) revealed a good varietal discrimination by analyzing the low-frequency spectral region. This model explains 68.8% of the variance for the Y vector (classification factor: varietal source). In particular, the signals of 2,3-butanediol, lactic, succinic and malic acids, and threonine were found to be the most statistically significant variables in the model. These findings seem to be very promising in the attempt to extend the study to geographical discrimination.

Chemical and functional characterization of Italian propolis obtained by different harvesting methods.

The composition and antioxidant activity of Italian poplar propolis obtained using three harvesting methods and extracted with different solvents were evaluated. Waxes, balsams, and resins contents were determined. Flavones and flavonols, flavanones and dihydroflavonols, and total phenolics were also analyzed. To characterize the phenolic composition, the presence of 15 compounds was verified through HPLC-MS/MS. The antioxidant activity was evaluated through 1,1-diphenyl-2-picrylhydrazyl radical and reducing power assays. The ability of propolis to inhibit lipid oxidation was monitored by analyzing hydroperoxide and TBARS formation in lipids incorporated into an oil-in-water (O/W) emulsion. Acetone shows the highest extraction capacity. Wedge propolis has the highest concentration of active phenolic compounds (TP = 359.1 ± 16.3 GAEs/g; TFF = 5.83 ± 0.42%; TFD = 7.34 ± 1.8%) and seems to be the most promising for obtaining high-value propolis more suitable to prepare high-quality dietary supplements (TBARS = 0.012 ± 0.009 mmol std/g; RP = 0.77 ± 0.07 TEs/g).

¹H-NMR simultaneous identification of health-relevant compounds in propolis extracts.

INTRODUCTION: Propolis is a resinous substance collected by bees from exudates of different plants that is rich in well-known health-relevant phenolic compounds such as flavonoids and phenolic acids. Propolis extracts are very complex matrices difficult to study. Different analytical methods are usable to analyse propolis extracts and to obtain chemical fingerprint but to our knowledge NMR has not previously been used for this purpose. OBJECTIVE: This study aims to demonstrate that it is possible to use ¹H-NMR for the simultaneous recognition of phenolic compounds in complex matrices, such as propolis extracts, using appropriate tools for spectra pre-treatment and analysis. METHODOLOGY: In this work 12 typical phenolic propolis compounds (apigenin, chrysin, galangin, kaempferol, quercetin, naringenin, pinocembrin, pinostrobin, caffeic acid, cinnamic acid, p-coumaric acid and ferulic acid) were considered as reference compounds and their presence in samples was verified by HPLC-MS. A simple ¹H-NMR sequence was used to obtain spectra of samples. Spectra were pre-treated by using an appropriate tool for spectra alignment and analysed by using software for the study of spectra originated from complex matrices. Sixty-five propolis samples were used to test the proposed identification procedure. RESULTS: Ten out of 12 considered compounds were identified as statistically significant in most of the samples. CONCLUSION: This work suggests that it is possible to efficiently use ¹H-NMR, coupled with appropriate spectral analytical tools, for the simultaneous detection of phenolic compounds in complex matrices.

Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance.

The importance of honey adulteration detection has recently increased owing to the limited production levels in recent years and the relative high price of honey; therefore, this illegal practice has become more and more attractive to producers. Hence, the need has arisen for more effective analytical methods aiming at detecting honey adulteration. The present research presents an effective method to detect adulteration in honey falsified by intentional addition of different concentrations of commercial sugar syrups, using one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) coupled with multivariate statistical analysis. Sixty-three authentic and 63 adulterated honey samples were analyzed. To prepare adulterated honeys, seven different sugar syrups normally used for nutrition of bees were used. The best discriminant model was obtained by 1D spectra, and leave-one-out cross-validation showed a predictive capacity of 95.2%. 2D NMR also furnished acceptable results (cross-validation correct classification 90.5%), although the (1)H NMR sequence is preferable because it is the simplest and fastest NMR technique.