Application of Quality-by-Design Approach in the Analytical Method Development for Quantification of Sugars in Sugarcane Honey by Reversed-Phase Liquid Chromatography

Abstract

Produced in Madeira Island from regional sugarcane cultivars through a traditional manufacturing and storage process, sugarcane honey (SCH) is a black syrup recognized by its excellent quality. Its economic value has led to the emergence of adulterated SCH, whereby the identification of molecular markers became an essential task in order to overcome the fraudulent activities, protect its authenticity, and guarantee the consumer safety. In the present study, an analytical strategy based on ultrasound-assisted liquidliquid extraction (USA-LLE) followed by reversed-phase liquid chromatography with a refractive index detector (LC-RI) was developed for the determination of sugars (glucose, fructose, sucrose, xylose, and mannose) in SCH samples from certified producers, supported on analytical quality-by-design (AQbD) approach, as a useful tool to establish its typicality. The application of AQbD was based on analytical risk assessment, multivariate statistics and quality control procedures for definition of the Method Operable Design Region (MODR). The optimal conditions into MODR were accomplished using BEH Amide column operating at a temperature of 80 °C and a flow rate of 300 μL min −1 , with a mobile phase composed by acetone and water (85:15, v v −1 ) at a flow rate of 1.0 mL min −1 . The robustness was determined by Monte Carlo simulation and capability analysis. The concentration-response function for all sugars was described by polynomial models. Accuracy was presented by recovery values between 98.2 and 119.5%. The analytical figures of merit validated the utility of AQbD in the systematic design of a LC-RI method with fine sensitivity for sugar analysis in SCH.

Analytical Quality by Design (AQbD) Approach to the Development of Analytical Procedures for Medicinal Plants

“…The method operable region and continuous improvement process provide robust analytics with regulatory flexibility [ 14 ]. Monte-Carlo probability [ 17 , 35 , 38 , 39 , 42 , 44 , 45 , 46 , 47 ], Bayesian probability [ 48 , 49 ], Desirability function [ 37 , 44 ], Capability analysis [ 38 , 46 ], and Overlay plot [ 36 , 40 ] have been utilized to establish MODR for the analytical procedure of medicinal plants.…”

“…Mass spectrometry was employed for the analysis of terpene lactones [ 41 ] and polyphenols [ 42 ], and supercritical fluid chromatography was utilized for the analysis of cannabinoids [ 43 ]. Various detection methods, such as the refractory index (RI) [ 44 ], evaporative light scattering detector (ELSD) [ 45 ] and sample preparation processes [ 46 , 47 ], were applied for the analysis of sugar and its derivatives because they are not easily detected by the UV/Vis spectrometer.…”

“…It can dissect the risk in various categories related, for example, to sample preparation, instrumentation, materials, personnel factors, and environmental conditions in order to determine the root causes of the problem. For the most of chromatographic methods, risk assessment is performed using the Ishikawa diagram with 6M branches (Mother nature, Machine, Materials, Method, Measurement, and Man) [ 34 , 37 , 41 , 42 , 43 , 44 , 46 ]. This method allows us to visualize and simultaneously show various parameters that have the potential to influence the analysis results, however it is not able to determine the association between parameters.…”

“…The asymmetrical designs, such as D-optimal design, are applied when it is necessary to evaluate an asymmetrical experimental area [ 53 , 62 ]. To optimize the analytical procedure for natural products, 3 3 -level FFD [ 40 , 43 , 44 , 46 ], CCD [ 34 , 35 , 36 , 39 ], BBD [ 37 , 41 , 45 , 47 , 49 ], and Doehlert design [ 42 , 48 ] are mainly conducted. The characteristics of the optimization DoE models are summarized in Table 3 , and four commonly used optimization models are described in detail.…”

“…The number of experiments in 3-level FFD is 3 factors (level factors ), i.e., the experiment with two factors requires nine runs (3 2 ) and the experiment with three factors requires 27 runs (3 3 ) [ 62 ]. The matrix of 3-level FFD for three factors is summarized in Table 7 and Figure 4 a. Silva et al [ 44 ] designed 3 3 -FFD to evaluate three CAPAs (area, resolution, and asymmetry of the objective peaks) for the separation, identification, and quantification of sugar from sugarcane honey using HPLC-RI and three CAPPs (flow rate, column temperature, and ratio of mobile solvent) were selected from the risk assessment. Parab Gaonkar et al [ 40 ] applied 2 2 -FFD without testing CP to optimize concentration of orthophosphoric acid in the mobile phase and the ratio of mobile phases for the quantification of curcuminoids in Curcuma longa using HPLC-UV.…”