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This work aimed to propose a methodology to be used as a fast screening method to detect adulteration in oil capsules using Near Infrared (NIR) spectroscopy through Principal Components Analysis (PCA). The experiment consisted in measuring the NIR spectra from 25 different intact oil capsules from different oil/mixtures and, to provide a trustworthy comparison, the oil capsule internal content was extracted to be evaluated by High-Resolution Gas Chromatography (HRGC). In order to be useful, the proposed methodology demands for a known certified set of samples to be used as a target. The ability to detect adulteration was verified comparing between NIR and GC PCA scores disposal, in which 6 samples did not match. Therefore, the methodology suggests that these 6 samples would be evaluated by HRGC in place of the whole set of 25 samples. Thus, NIR spectroscopy could be used as a fast screening method to indicate which samples should be analyzed by gas chromatography, considering that each coinciding sample, i.e. disposed of in agreement to the HRGC PCA score, is already certified by HRGC regarding on their content.

Samples of chia seeds (Salvia hispanica L.) were degreased by cold pressing and the residue obtained named degreased chia seeds (DCS) had the antioxidants extract obtained. A 22 experimental design was employed in order to evaluate the factors ethanol concentration and extraction time. The optimized conditions were extraction time 15 min with ethanol 70% where the activity (RSA), evaluated by the DPPH (2,2-diphenyl-1-picryl-hydrazyl) method was 94.76%. For this extract, the total phenolics content was 359.64 mg EGA/100 g. A pseudo-univariate calibration model is proposed as an alternative methodology to determine the %RSA by using the relative concentration from multivariate curve resolution with alternating least squares (MCR-ALS) from near-infrared (NIR) spectra of the antioxidant extract from DCS. The results suggest the possibility of %RSA determination directly from NIR spectra from extracts from DCS by using the multivariate resolution method MCR-ALS.

Human milk (HM) is vital for newborns and its importance allied to growing donations has contributed to the expansion of HM banks. However, compositional analysis in HM banks faces many challenges due to traditional methodologies, making it unfeasible for routine inspection. Therefore, this research aims to develop predictive models for the direct determination of the proximal composition in HM samples. The models were developed using spectra acquired with a portable near-infrared spectrometer (MicroNIR) coupled with partial least squares regression and included samples in different lactation phases (colostrum, transition, and mature) and forms (raw and pasteurized). A total of 408 samples were analyzed to give reliability to the models. The performance of the models was estimated by a complete multivariate analytical validation, which indicated satisfactory results with accuracy (represented by the adjust with correlation coefficients ranging from 0.64 to 0.90, and close results for calibration/prediction errors for each parameter). Using the proposed method, moisture, ash, protein, lipids, carbohydrates, and energetic value were successfully predicted. The method is simple, fast, and robust and can be used routinely in compositional analysis of HM banks as an alternative to the traditional methodologies, offering an immediate response with a single and a quick measurement.