Assessing the differences of two vineyards soils' by NIR spectroscopy and chemometrics.

Soil properties influence greatly the status of vine plants which consequently influences the quality of wine. Therefore, in the context of viticulture management, it is extremely important to assess the physical and chemical parameters of vineyards soils. In this study, the soils of two vineyards were analysed by near-infrared (NIR) spectroscopy and established analytical reference procedures. The main objective of this study was to verify if NIR spectroscopy is a potential tool to discriminate the soils of both vineyards as well as to quantify differences of soil's parameters. For that, a total of eight sampling spots were selected at each vineyard taking into consideration the soil type and sampled at different depths. The data analysis was performed using analysis of variance (ANOVA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression. The ANOVA results revealed that 12 out of the 18 parameters analysed through the reference procedures can be considered statistically different (p < 0.05). Regarding PCA, the obtained results revealed a clear separation between the scores of both vineyards either considering NIR spectra or the chemical parameters. The PLS-DA model was able to obtain 100 % of correct predictions for the discrimination of both vineyards. PLS regression analysis using NIR spectra revealed R2P and RER values higher than 0.85 and 10, respectively, for 8 (pH (H2O), N, Ca2+, Mg2+, SB, CEC, ECEC and GSB) of the 18 chemical parameters evaluated. Concluding, these results demonstrate that it is possible to discriminate the soils of the different vineyards through NIR spectroscopy as well as to quantify several chemical parameters through soils NIR spectra in a rapid, accurate, cost-effective, simple and environmentally friendly way when compared to the reference procedures.

Chromatographic method for the simultaneous quantification of dapsone and clofazimine in nanoformulations.

The low bioavailability and nonspecific distribution of dapsone and clofazimine, commonly applied in combination for the treatment of leprosy, can produce toxic effects. Nanotechnological approaches enhance the delivery of these drugs. Therefore, a high-performance liquid chromatography method was developed for the simultaneous determination of dapsone and clofazimine loaded in nanoformulations for quality control purposes. Chromatographic separation was achieved on a reversed-phase Kinetex core-shell C18 column, followed by spectrophotometric detection at 280 nm. Considering the different physicochemical properties of dapsone and clofazimine, elution was performed in gradient mode using an aqueous acetate buffer (50 mmol/L, pH 4.8) and an increasing acetonitrile content from 27 to 63% v/v at a flow rate of 1.0 mL/min with retention times of 6.2 and 14.0 min, respectively. The method was validated according to the European Medicines Agency guideline and it was found to be specific, accurate (99.6-114.0%), and precise for intra- (RSD ≤ 1.8%) and interday assays (RSD ≤ 12.5%). Both drugs showed stability after 24 h at room temperature and over three freeze-thaw cycles with recoveries ≥86.2%. Low temperature (4°C) in the autosampler caused the precipitation of clofazimine and must be avoided. The validated method was successfully applied in the quantification of both drugs in nanoformulations.

Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy.

Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries. In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (Rp(2)) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, (+)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid, (+)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures.