Simultaneous Determination of Adulterants in Dietary Food Supplements Using Multivariate Data Analysis after Preconcentration with Novel Nanosorbents and Chromatographic Measurement.

BACKGROUND: The increasing popularity of dietary supplements and, consequently, related adulteration emphasizes the rising need to examine the association of food supplements with fraud. Intentional or unintentional fraud in food supplements by hazardous chemicals compounds is a problem that many countries are struggling with. Much effort have been made to effectively and reliably control the quality of food supplements. OBJECTIVE: Due to the importance of the subject, an analytical method for the simultaneous and reliable detection and quantitative determination of three key adulterants in dietary food supplements was developed. The proposed method benefits from analytical methods and multivariate calibration methods to progress the determination of adulterants in a complex matrix. METHODS: HPLC assisted by multivariate curve resolution-alternating least square (MCR-ALS) analysis was used to detect adulterants in real samples after separation and preconcentration using novel mesoporous carbon nanoparticles. Solid-phase extraction (SPE) optimization was accomplished by central composite design (CCD). In order to obtain the best results, the MCR-ALS model was compared with the parallel factor analysis 2 (PARAFAC2) model and validated by estimation of linearity, detection limits, and recovery. RESULTS: The detection limits and linear dynamics were calculated as 1.5, 4.27, and 4.77 µg/mL, and 1-50, 5-20, and 5-20 µg/mL for caffeine, ephedrine, and fluoxetine, respectively. Mean recovery for determination of caffeine, ephedrine, and fluoxetine using the developed method was reported as 101.75, 91.7, and 92.36, respectively. CONCLUSION: The results showed that to avoid negative health outcomes associated with the excessive consumption of adulterated food supplements releasing such products should be carefully regulated. The developed method was validated using statistical factors and showed acceptable and reliable results. HIGHLIGHTS: (1) The application of MCR-ALS coupled with HPLC-Diode-Array Detection data sets allowed the simultaneous identification and quantification of three key adulterants (caffeine, ephedrine, and fluoxetine) in dietary food supplements. (2) A small amount of the novel adsorbent was successfully used to preconcentrate the trace amounts of adulterants in samples. (3) This method benefits from the chemometrics tools and experimental design to significantly reduce the use of toxic solvents and complicated instruments to propose a less time-consuming method for quantification of multicomponents in the presence of uncalibrated interferents.

Orthogonality constrained inverse regression to improve model selectivity and analyte predictions from vibrational spectroscopic measurements.

In analytical chemistry spectroscopy is attractive for high-throughput quantification, which often relies on inverse regression, like partial least squares regression. Due to a multivariate nature of spectroscopic measurements an analyte can be quantified in presence of interferences. However, if the model is not fully selective against interferences, analyte predictions may be biased. The degree of model selectivity against an interferent is defined by the inner relation between the regression vector and the pure interfering signal. If the regression vector is orthogonal to the signal, this inner relation equals zero and the model is fully selective. The degree of model selectivity largely depends on calibration data quality. Strong correlations may deteriorate calibration data resulting in poorly selective models. We show this using a fructose-maltose model system. Furthermore, we modify the NIPALS algorithm to improve model selectivity when calibration data are deteriorated. This modification is done by incorporating a projection matrix into the algorithm, which constrains regression vector estimation to the null-space of known interfering signals. This way known interfering signals are handled, while unknown signals are accounted for by latent variables. We test the modified algorithm and compare it to the conventional NIPALS algorithm using both simulated and industrial process data. The industrial process data consist of mid-infrared measurements obtained on mixtures of beta-lactoglobulin (analyte of interest), and alpha-lactalbumin and caseinoglycomacropeptide (interfering species). The root mean squared error of beta-lactoglobulin (% w/w) predictions of a test set was 0.92 and 0.33 when applying the conventional and the modified NIPALS algorithm, respectively. Our modification of the algorithm returns simpler models with improved selectivity and analyte predictions. This paper shows how known interfering signals may be utilized in a direct fashion, while benefitting from a latent variable approach. The modified algorithm can be viewed as a fusion between ordinary least squares regression and partial least squares regression and may be very useful when knowledge of some (but not all) interfering species is available.