Simultaneous determination of four sesame lignans and conversion in Monascus aged vinegar using HPLC method.
A simple, accurate and specific high-performance liquid chromatography (HPLC) method has been developed and validated for simultaneous determination of sesamol, sesamin, asarinin and sesamolin in Monascus aged vinegar. The effects of acid hydrolysis and four heating treatments on the components content in Monascus aged vinegar were discussed. The results showed that the isomerisation of sesamin to asarinin, and decomposition of sesamolin to sesamol significantly increased, regardless of heating or acid hydrolysis. Thermal processes and acid hydrolysis increased the content of sesamol and asarinin, respectively, but severe thermal processes resulted in the loss of total sesame lignans. Sesamol and asarinin reached the highest (2.720 ± 0.202 μg/mL and 2.064 ± 0.075 μg/mL) for autoclaving (125 °C, 15 min) and acid hydrolysis (25 °C, 15 min, nature pH), respectively. Therefore, autoclaving and acid hydrolysis were considered as the optimal way to obtain higher content of sesamol and asarinin.
Validation of Cell-Based Assay for Quantification of Sesamol Uptake and Its Application for Measuring Target Exposure
The intracellular drug concentration is needed for determination of target exposure at the site of action regarding its pharmacological action and adverse effects. Sesamol is an antiproliferative molecule from Sesamum indicum with promising health benefits. We present a method for measuring the intracellular sesamol content using reverse-phase HPLC with a UV diode array in melanoma cells. Sesamol was completely resolved by isocratic elution (4.152 ± 0.008 min) with methanol/water (70%, v/v) through a 30 °C, 5-µm C-18 column and detection at 297 nm. The present assay offers high sensitivity, fast elution, and an accurate and linear nominal concentration range of 10–1000 ng/mL (R2 = 0.9972). The % accuracy of the sesamol quality control sample was −3.36% to 1.50% (bias) with a 0.84% to 5.28% relative standard deviation (RSD), representing high repeatability and high reproducibility. The % recovery was 94.80% to 99.29%, which determined that there was no loss of sesamol content during the sample preparation. The validated method was applied to monitor intracellular sesamol concentration after treatment from 5 min to 24 h. The remaining intracellular sesamol content was correlated with its antiproliferative effect (R2 = 0.9483). In conclusion, this assay demonstrated low manipulation, quick elution, and high sensitivity, precision, accuracy, and recovery, and it was successfully applied to the quantification of sesamol in target cells.
Linear Programming in the Allocation of Milk Resources for Cheese Making
Abstract To use linear programming, the cheese making process can be modeled as a resource conversion process. The relationship between input milk resources and output products can be analyzed using mass balance assumptions. The variables used in the linear programming model will represent the pounds of each milk resource chosen to formulate a vat of cheese milk. Additional variables will be used to represent the pounds of cream to be removed from a milk resource that has the potential to have cream removed. Three objectives that could be considered are 1) maximize net returns, 2) maximize cheese yield, or 3) minimize cost. A hypothetical example is used to illustrate how the cheese making process can be modeled. This linear programming model depends on formulas that relate ratio of casein to fat of milk to the fat on the dry basis of the cheese. Sensitivity analysis is performed to determine at what cost a milk resource not in the optimal formulation will replace a milk resource currently in the optimal formulation. The linear programming model can be an effective management tool in the allocation of milk resources for cheese making.