Vibrational spectroscopy coupled to a multivariate analysis tiered approach for argentinean honey provenance confirmation

In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a “year-by-year” validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context

A Liposomal Formulation to Exploit the Bioactive Potential of an Extract from Graciano Grape Pomace

Antioxidant compounds with health benefits can be found in food processing residues, such as grape pomace. In this study, antioxidants were identified and quantified in an extract obtained from Graciano red grape pomace via a green process. The antioxidant activity of the extract was assessed by the DPPH and FRAP tests, and the phenolic content by the Folin–Ciocalteu test. Furthermore, nanotechnologies were employed to produce a safe and effective formulation that would exploit the antioxidant potential of the extract for skin applications. Anthocyanins, flavan-3-ols and flavanols were the main constituents of the grape pomace extract. Phospholipid vesicles, namely liposomes, were prepared and characterized. Cryo-TEM images showed that the extract-loaded liposomes were predominantly spherical/elongated, small, unilamellar vesicles. Light scattering results revealed that the liposomes were small (~100 nm), homogeneously dispersed, and stable during storage. The non-toxicity of the liposomal formulation was demonstrated in vitro in skin cells, suggesting its possible safe use. These findings indicate that an extract with antioxidant properties can be obtained from food processing residues, and a liposomal formulation can be developed to exploit its bioactive value, resulting in a promising healthy product

Geographical authentication of virgin olive oil by GC–MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC–MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries)

Stepwise strategy based on 1H-NMR fingerprinting in combination with chemometrics to determine the content of vegetable oils in olive oil mixtures

1H NMR fingerprinting of edible oils and a set of multivariate classification and regression models organised in a decision tree is proposed as a stepwise strategy to assure the authenticity and traceability of olive oils and their declared blends with other vegetable oils (VOs). Oils of the ‘virgin olive oil’ and ‘olive oil’ categories and their mixtures with the most common VOs, i.e. sunflower, high oleic sunflower, hazelnut, avocado, soybean, corn, refined palm olein and desterolized high oleic sunflower oils, were studied. Partial least squares (PLS) discriminant analysis provided stable and robust binary classification models to identify the olive oil type and the VO in the blend. PLS regression afforded models with excellent precisions and acceptable accuracies to determine the percentage of VO in the mixture. The satisfactory performance of this approach, tested with blind samples, confirm its potential to support regulations and control bodies

Coffee consumption modifies risk of estrogen-receptor negative breast cancer

10.1186/bcr2879Breast Cancer Research133R4

Short communication: Natural molecules for the control of Paenibacillus larvae, causal agent of American foulbrood in honey bees (Apis mellifera L.)

Aim of study: To evaluate the potential bactericidal activity of natural molecules against Paenibacillus larvae. Moreover, we investigated if molecules that exhibit antimicrobial activity were able to inhibit the proteolytic activity of the bacterium.Area of study: Isolates S1 and S2 were from Balcarce, Buenos Aires province, strain S3 from Rio Cuarto, Cordoba province, strain S4 from Concordia, Entre Rios province, strain S5 and S8 from Necochea, Buenos Aires, strain S6 and S7 from Mar del Plata, Buenos Aires, strain S9 from Modena, Italy and strain S10 from Emilia Reggio, Italy.Material and methods: Bacterial isolates identification was carried out by amplification of a specific 16S rRNA gene fragment of P. larvae using primers PL5 and PL4. Screening of the antimicrobial activity of thirteen molecules against four P. larvae isolates was conducted by the agar diffusion technique. The antimicrobial activity of selected molecules was evaluated by broth microdilution method.Main results: Menadione, lauric acid, monoglyceride of lauric acid and naringenin showed antimicrobial activity against ten P. larvae isolates. Menadione and lauric acid showed the strongest activities, with minimum inhibitory concentration mean values ranging 0.78-3.125 µg/mLand 25-50 µg/mL, respectively.Research highlights: Those concentrations are feasible to be applied at field level, and constitute promissory candidates to be evaluated using in vivo larval models