What is the expected value of analyte recovery when RI is zero in an LLE experiment with a selectivity coefficient of 0.825 and an error of 4.82%?

In this study, two novel sample extraction methods for the analysis of bisphenol A (BPA) in edible oils were developed by using liquid–liquid extraction followed by a dispersive liquid–liquid microextraction (LLE-DLLME) and reversed-phase dispersive liquid–liquid microextraction (RP-DLLME). RP-DLLME showed a superior characteristic over LLE-DLLME and other previously reported procedures because of its easy operation, short extraction time, high sensitivity, low organic solvent consumption and waste generation. The optimized extraction conditions of RP-DLLME for 1.0g of edible oil diluted in 4mL of n-hexane were: extractant, 100μL 0.2M sodium hydroxide solution (80% methanol, v/v); extraction time, 1min; centrifugation, 3min. The determination of BPA was carried out by high performance liquid chromatography coupled with a DAD detector. The method offered excellent linearity over a range of 0.010–0.5μgg−1 with a correlation coefficient of r>0.997. Intra-day and inter-day repeatability values expressed as relative standard deviation were 1.9% and 5.9%, respectively. The quantitation limit and detection limit were 6.3 and 2.5ngg−1. The target analyte was detected in 5 out of 16 edible oil samples. The recovery rates in real samples ranged from 89.5 to 99.7%.

Two popular calibration strategies are classical least squares (CLS) and inverse least squares (ILS). Underlying CLS is that the net analyte signal used for quantitation is orthogonal to signal from other components (interferents). The CLS orthogonality avoids analyte prediction bias from modeled interferents. Although this orthogonality condition ensures full analyte selectivity, it may increase the mean squared error of prediction. Under certain circumstances, it can be beneficial to relax the CLS orthogonality requisite allowing a small interferent bias if, in return, there is a mean squared error of prediction reduction. The bias magnitude introduced by an interferent for a relaxed model depends on analyte and interferent concentrations in conjunction with analyte and interferent model sensitivities. Presented in this paper is relaxed CLS (rCLS) allowing flexibility in the CLS orthogonality constraints. While ILS models do not inherently maintain orthogonality, also presented is relaxed ILS. From development of rCLS, presented is a significant expansion of the univariate selectivity coefficient definition broadly used in analytical chemistry. The defined selectivity coefficient is applicable to univariate and multivariate CLS and ILS calibrations. As with the univariate selectivity coefficient, the multivariate expression characterizes the bias introduced in a particular sample prediction because of interferent concentrations relative to model sensitivities. Specifically, it answers the question of when can a prediction be made for a sample even though the analyte selectivity is poor? Also introduced are new component‐wise selectivity and sensitivity measures. Trends in several rCLS figures of merit are characterized for a near infrared data set.

In the present fundamental study, selectivity and efficiency of electromembrane extraction of 50 polar basic substances (-6.7<log P<+1.0) was systematically studied for ten different supported liquid membranes. For each model substance, 23 molecular descriptors were collected and these were investigated as potential parameters for understanding of extraction efficiency and selectivity by means of partial least squares regression. Overall, a highly aromatic deep eutectic solvent composed of coumarin and thymol with addition of 2% ionic carrier (di(2-ethylhexyl) phosphate) provided the highest extraction efficiency with an average extraction yield of 69% from pure water samples, 55% from plasma, and 62% from urine. With this solvent system, ionic, cation-π, and π-π interactions between the supported liquid membrane and analytes were dominant. Supported liquid membranes without aromaticity, however, operated primarily based on hydrogen-bonding interactions. This is the first time the relationship between analyte properties, solvent composition, and extraction yield has systematically been studied for polar bases in electromembrane extraction. This new knowledge represents a first step toward enabling future development and optimization of electromembrane extraction systems for polar bases based on rational design, rather than trial-and-error approaches.

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.

The presence of contaminants of emerging concern (CECs) in wastewater effluent and surface waters is an important field of research for analytical scientists. This study takes a suspect screening approach to wastewater and surface water analysis using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS). Two extraction procedures, traditional liquid-liquid extraction (LLE) and stir bar sorptive extraction (SBSE), were utilized and evaluated for their application to wastewater and surface water samples. Both techniques were evaluated regarding their recovery rates, range of compound classes extracted, and on their application to discovery of CECs. For the 14 surrogate compounds analyzed, LLE was able to extract all of them in each matrix with a recovery range of 19% to 159% and a median value of 74%. For SBSE, the recovery rates ranged from 19% to 117% with the median value at 66%, but only 8 of the compounds were able to be extracted because of the polarity bias for this extraction method. A new method of SBSE calibration was also developed using direct liquid injection of the internal standards before desorption of the stir bars. Initial findings indicate increased sensitivity and a greater range of unknown analyte recovery for SBSE, especially in the more dilute effluent and surface water samples. With the methods used in this study, SBSE has a concentration factor of approximately 416, improving that of LLE, which is 267. Suspect screening analysis was utilized to tentatively identify 32 CECs in the samples, the majority of which were pharmaceuticals and personal care products. More CECs were found using SBSE than LLE, especially in the surface water samples where 13 CECs were tentatively identified in the SBSE samples compared to 6 in the LLE samples.

A rapid and specific liquid chromatographic mass/mass (LC-MS/MS) method is described for the assessment of valsartan (VAL) in human plasma after protein precipitation (PPT) and liquid-liquid extraction (LLE) techniques. The column used in PPT method was Eclipse Plus column, (C18, 50 mm x 2.1 mm, 1.8 μm) with a mobile phase ratio of 0.1% formic acid: methanol (15:85, v/v) while the column used in LLE method was Agilent column, (C18, 50 mm x 4.6 mm, 5 μm) with a mobile phase ratio of 0.1 % formic acid: acetonitrile (20:80, v/v). The assay was found to be linear over the ranges of 20-10000 and 25-8000 ng/ mL VAL using the PPT and LLE, respectively. The developed method was validated following the united states food and drug administration (US - FDA) recommendations and directions from the European Medical Agency (EMA). The method was validated for sensitivity, selectivity, linearity, accuracy and precision, recovery, dilution integrity, matrix effect, carryover in the matrix, and analyte stability during both short- and long-term sample processing and storage.

The separation of hemicelluloses from thermomechanical pulping (TMP) process water is of particular interest because it yields a biopolymer suitable for various value-added biomaterials production and reduces the organic loading on the water treatment facility. However, during the TMP process, hemicellulose is released in the process water at relatively low concentrations that are difficult to recover by many methods efficiently. Liquid-liquid extraction (LLE) has been deemed as a potentially viable separation technique in modern industrial processes for valuable materials recovery and undesirable impurities removal. In this work, the extraction of hemicellulose from the process water and synthetic hydrolyzate using LLE was investigated. In particular, the effects of the major experimental variables (the type of solvent, hydrolyzate to solvent volume ratio, and pH) on extraction performance were explored. The tested solvents have shown varying affinity and selectivity for the recovery of hemicellulose. It was found that the hemicellulose extraction efficiency of n-hexane (71.03%) and tributyl phosphate (TBP) (72.34%) was higher than that of 1-butanol (62.36%), and toluene (67.03%) at a solvent: hydrolyzate volume ratio of 1:3. Although TBP showed a high degree of hemicellulose extraction (72.34), it was characterized by a low selectivity coefficient, while n-hexane achieved the highest selectivity. The average selectivity coefficients of n-hexane were 7.3, 5.1, and 8.7 followed by toluene 2.7, 2.7, and 2.9 for pH values of 9.5, 7, and 4.3, respectively. Changing the pH of the hydrolyzate has resulted in varying effects depending on the type of the solvent used. The optimum extraction pH, phase ratio, and extraction time were at 4.3, 1:3 mL/mL and 30 min, respectively.

&lt;p&gt;Levetiracetam (LEV), an antiepileptic drug (AED) with favorable pharmacokinetic profile, is increasingly being used in clinical practice, although information on its metabolism and disposition are still being generated. Therefore a simple, robust and fast liquid-liquid extraction (LLE) followed by high-performance liquid chromatography method is described that could be used for both pharmacokinetic and therapeutic drug monitoring (TDM) purposes. Moreover, recovery rates of LEV in plasma were compared among LLE, stir bar-sorptive extraction (SBSE), and solid-phase extraction (SPE). Solvent extraction with dichloromethane yielded a plasma residue free from usual interferences such as commonly co-prescribed AEDs, and recoveries around 90% (LLE), 60% (SPE) and 10% (SBSE). Separation was obtained using reverse phase Select B column with ultraviolet detection (235 nm). Mobile phase consisted of methanol:sodium acetate buffer 0.125 M pH 4.4 (20:80, v/v). The method was linear over a range of 2.8-220.0 µg mL&lt;sup&gt;-1&lt;/sup&gt;. The intra- and inter-assay precision and accuracy were studied at three concentrations; relative standard deviation was less than 10%. The limit of quantification was 2.8 µg mL&lt;sup&gt;-1&lt;/sup&gt;. This robust method was successfully applied to analyze plasma samples from patients with epilepsy and therefore might be used for pharmacokinetic and TDM purposes.&lt;/p&gt;

Dinotefuran is a compound belonging to the third generation of nicotinoid insecticides, and has been effective in combating pests that are resistant to conventional insecticides, such as organophosphates, carbamates, and pyrethroids. This molecule presents high-water solubility (39,830 mg L−1 at 25 °C) compared to other pesticides, which facilitates its drag and leaching to lower soil layers. Therefore, the present study aimed to optimize and validate liquid–liquid extraction with low temperature purification (LLE–LTP) to determine dinotefuran residues in water by high performance liquid chromatography with diode array detection (HPLC–DAD). The results revealed that the analyte recovery ranged from 85.44 to 89.72% with a relative standard deviation <5.8. LLE–LTP was selective, precise, accurate, and linear in the range from 10.0 to 210 µg L−1, and presented limits of detection and quantification of 5.00 and 10.00 µg L−1, respectively. The matrix effect was <14%. The stability study of dinotefuran in water revealed significant stability of this molecule in water in the absence of light (>130 days), and a half-life of 7 days in water with sunlight. LLE–LTP coupled to HPLC–DAD was a simple, easy, and efficient method for extracting and analyzing dinotefuran in water samples.

A sensitive and reproducible liquid chromatography–mass spectrometric (LC-MS) method has been developed and validated for the quantifi cation of doxycycline in human plasma, after liquid-liquid extraction (LLE). Best chromatographic resolution was achieved on a reverse-phase Phenomenex C18 column using the mobile phase acetonitrile–5mM ammonium acetate (80:20) in isocratic elution with a total run time of 3.5 min. The analyte, doxycycline was detected by using an electrospray ionization mass spectrometer in the selected ion monitoring (SIM) mode. Linear plot was obtained in the concentration range of 0.5–5.0 µg/ml (r2 = 0.998). Lower limit of quantifi cation (LLOQ) was found to be 100 ng/ml in 500µl of plasma. Average recovery of the analyte was found to range from 96.08 to 104.60% in plasma at the concentrations of 1.0, 3.0 and 5.0 µg/ml. Intra- and inter-day relative standard deviation was found to be 0.13-0.74 and 0.11-0.34 respectively. The present method was successfully applied in the pharmacokinetic study of doxycycline in human plasma.