Development of non-destructive methods for the assessment of authenticity of sports whey protein supplements.

In the category of sports supplements, whey protein powder is one of the popular supplements for muscle building applications. Therefore, verification of the sport supplements as authentic products has become a universal concern. This work aimed to propose vibrational spectroscopy including near infrared (NIR) and infrared (IR) as rapid and non-destructive testing tools for the detection and quantification of maltodextrin, milk powder and milk whey powder in whey protein supplements. Initially, principal component analysis was applied to data for pattern recognition and the results displayed a fine pattern of discrimination. Partial least square discrimination analysis (PLS-DA) and K-nearest neighbours (KNN) were exploited as supervised method modelling classification. This process was done in order to respond to two vital questions whether the sample is adulterated or not and what is the kind of adulteration. PLS-DA showed better classification results rather than KNN according to the figure of merits of the model. Partial least square regression (PLSR) was employed on pre-treated spectra to quantify the amount of adulteration in sport whey supplements. Eventually, it seems vibrational spectroscopy could be implemented as a simple, and low-cost analysis method for the detection and quantification of mentioned adulterants in whey protein supplements.

Oral delivery of posaconazole-loaded phospholipid-based nanoformulation: Preparation and optimization using design of experiments, machine learning, and TOPSIS.

Phospholipid-based nanosystems show promising potentials for oral administration of hydrophobic drugs. The study introduced a novel approach to optimize posaconazole-loaded phospholipid-based nanoformulation using the design of experiments, machine learning, and Technique for Order of Preference by Similarity to the Ideal Solution. These approaches were used to investigate the impact of various variables on the encapsulation efficiency (EE), particle size, and polydispersity index (PDI). The optimized formulation, with %EE of âˆ¼ 74 %, demonstrated a particle size and PDI of 107.7 nm and 0.174, respectively. The oral pharmacokinetic profiles of the posaconazole suspension, empty nanoformulation + drug suspension, and drug-loaded nanoformulation were evaluated. The nanoformulation significantly increased maximum plasma concentration and the area under the drug plasma concentration-time curve (∼3.9- and 6.2-fold, respectively) and could be administered without regard to meals. MTT and histopathological examinations were carried out to evaluate the safety of the nanoformulation and results exhibited no significant toxicity. Lymphatic transport was found to be the main mechanism of oral delivery. Caco-2 cell studies demonstrated that the mechanism of delivery was not based on an increase in cellular uptake. Our study represents a promising strategy for the development of phospholipid-based nanoformulations as efficient and safe oral delivery systems.

Applications and opportunities of experimental design for the dispersive liquid-liquid microextraction method - A review.

Nowadays, the trend to simplify and miniaturize sample preparation methods has resulted in the development of effective and low-cost microextraction techniques that utilize a very small volume of the extracting phase. Among them, the liquid-liquid microextraction (LLME) method is a simple and effective sample pre-treatment technique applicable to numerous analytical methods. A related miniaturized and environmentally friendly extraction technique, dispersive liquid-liquid microextraction (DLLME), has been developed within the last decade and shows a very high enrichment factor and very low solvent consumption compared to other liquid- or even solid-phase extraction methods. The inclusion of several effective parameters in DLLME and its variants has increased the need for optimization to obtain the best possible extraction results. In fact, experimental design and optimization of performance conditions are the most important applications of chemometrics in analytical chemistry. Thus, design of experiments (DoE) helps us to determine the best model of the relationship between variables, as well as the optimal experimental conditions. Here, a comprehensive review of recent advancements in the use of DoE methodologies including full factorial, fractional factorial, Plackett-Burman, orthogonal array, central composite, Box-Behnken, Doehlert, and D-optimal designs to optimize DLLME applications is provided. In addition, the preponderance and drawbacks of each optimal method are discussed. The overall purpose of this review is to present a general overview of the different DoEs that are currently used to optimize DLLME for various matrices and analytes.