Search

This study focused on optimizing a cold water extraction method to obtain bioactive compounds from chamomile (Matricaria chamomilla L.), addressing increasing consumer demand for natural products and nutraceuticals. A full-factorial design was employed to evaluate the effects of temperature, time, and chamomile amount on the polyphenolic profile of extracts. The samples were characterized by HPLC-DAD and UV-Vis coupled with chemometrics; the analysis showed that extraction time negatively affected extract quality, as did the interaction between time and temperature. In addition, a significant positive quadratic effect for temperature and a positive coefficient for chamomile amount was found. ASCA was used to assess the UV-Vis profile, offering an alternative untargeted method for understanding the variable effects. The optimal extraction conditions (25 °C, 32 min, and 2.5 g of chamomile) produced samples high in hydroxybenzoic and hydroxycinnamic acids and flavanol derivatives. Using A face-centered design, this study also monitored antioxidant activity via a DPPH scavenging assay, confirming that the optimal conditions yielded samples within the range of maximum antioxidant activity in the studied experimental domain. [ABSTRACT FROM AUTHOR]

The development of fast, non-destructive, and green methods with adequate sensitivity for saffron authentication has important implications in the quality control of the entire production chain of this precious spice. In this context, the highly suitable sensitivity of a spectroscopic method coupled with chemometrics was verified. A total number of 334 samples were analyzed using attenuated-total-reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy; the collected spectra were processed by partial-least-squares discriminant analysis (PLS-DA) to evaluate the feasibility of this study for the discrimination between compliant saffron (fresh samples produced in 2020) and saffron samples adulterated with non-fresh stigmas produced in 2018 and 2016. PLS-DA was able to classify the saffron samples in accordance with the aging time and to discriminate fresh samples from the samples adulterated with non-fresh (legally expired) stigmas, achieving 100% of both sensitivity and specificity in external prediction. Moreover, PLS regression was able to predict the adulteration level with sufficient accuracy (the root-mean-square error of prediction was approximately 3–5%). In summary, ATR-FTIR and chemometrics can be employed to highlight the illegal blending of fresh saffron with unsold stocks of expired saffron, which may be a common fraudulent practice not yet considered in the scientific literature. [ABSTRACT FROM AUTHOR]

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms. [ABSTRACT FROM AUTHOR]

A multi-layer artificial neural network (ANN) was used to model the retention behavior of 16 o-phthalaldehyde derivatives of amino acids in reversed-phase liquid chromatography under application of various gradient elution modes. The retention data, taken from literature, were collected in acetonitrile–water eluents under application of linear organic modifier gradients (φ gradients), pH gradients, or double pH/φ gradients. At first, retention data collected in φ gradients and pH gradients were modeled separately, while these were successively combined in one dataset and fitted simultaneously. Specific ANN-based models were generated by combining the descriptors of the gradient profiles with 16 inputs representing the amino acids and providing the retention time of these solutes as the response. Categorical "bit-string" descriptors were adopted to identify the solutes, which allowed simultaneously modeling the retention times of all 16 target amino acids. The ANN-based models tested on external gradients provided mean errors for the predicted retention times of 1.1% (φ gradients), 1.4% (pH gradients), 2.5% (combined φ and pH gradients), and 2.5% (double pH/φ gradients). The accuracy of ANN prediction was better than that previously obtained by fitting of the same data with retention models based on the solution of the fundamental equation of gradient elution. [ABSTRACT FROM AUTHOR]

Ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD) was applied to improve separation and detection of mono- and bis-glucosyl esters of crocetin (crocins), the main red-colored constituents of saffron (Crocus sativus L.), and other polar components. Response surface methodology (RSM) was used to optimise the chromatographic resolution on the Kinetex C18 (Phenomenex) column taking into account of the combined effect of the column temperature, the eluent flow rate and the slope of a linear eluent concentration gradient. A three-level full-factorial design of experiments was adopted to identify suitable combinations of the above factors. The influence of the separation conditions on the resolutions of 22 adjacent peaks was simultaneously modelled by a multi-layer artificial neural network (ANN) in which a bit string representation was used to identify the target analytes. The chromatogram collected under the optimal separation conditions revealed a higher number of crocetin esters than those already characterised by means of mass-spectrometry data and usually detected by HPLC. Ultra-high performance liquid chromatography analyses carried out on the novel Luna Omega Polar C18 (Phenomenex) column confirmed the large number of crocetin derivatives. Further work is in progress to acquire mass-spectrometry data and to clarify the chemical structure to the newly found saffron components. [ABSTRACT FROM AUTHOR]