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Optimization of alkylresorcinols extraction from triticale bran using response surface methodology

机译:响应面法优化黑小麦麸皮中烷基间苯二酚的提取

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Optimization conditions for alkylresorcinols (ARs) extraction from triticale bran were determined using response surface methodology. A central composite design was used to determine the effects of extraction temperature (degrees Celsius) and solid-to-solvent ratio (weight per volume) on yield of saturated, unsaturated and total ARs. Extraction of ARs was affected significantly (p value ≤ 0.05) by temperature and solid-to-solvent ratio on the yield of saturated, unsaturated and total ARs. The highest quantity of total ARs from triticale bran was extracted at 24 °C between 16 and 24 h at a solid-to-solvent ratio (weight per volume) of 1:40 and ranged from 278 to 308 mg/100 g, while saturated and unsaturated ARs were 163 to 225 mg/100 g and 22 to 29 mg/100 g, respectively. The ARs identified by high-performance liquid chromatography included: C15:0, C17:0, C19:0, C21:0, C23:0 and C25:0. Also some unsaturated analogues (12.4% to 14.0%) were found including C17:1, C19:1, C21:1 and C23:1. Analysis of variance (p value = 0.05) indicated that the response surface methodology (RSM) developed for saturated, unsaturated and total ARs were adequate and explained most of the variability (85% to 89%) with high coefficient of multiple determination (R 2 = 0.89). The main effect of the response variable was quadratic rather than a linear function. Our results showed that RSM is a tool that is useful to optimise experimental conditions for the extraction of ARs.
机译:使用响应面方法确定了从黑小麦麸皮中提取烷基间苯二酚(ARs)的优化条件。中心复合设计用于确定萃取温度(摄氏度)和固溶比(每体积重量)对饱和,不饱和和总AR收率的影响。温度,固溶比和饱和,不饱和和总ARs的收率对ARs的提取有显着影响(p值≤0.05)。从小黑麦麸中提取的最高总AR量是在24°C下16至24 h之间以固溶比(每体积重量)为1:40提取的,范围为278至308 mg / 100 g(饱和时)和不饱和AR分别为163至225 mg / 100 g和22至29 mg / 100 g。通过高效液相色谱鉴定的AR包括:C15:0,C17:0,C19:0,C21:0,C23:0和C25:0。还发现了一些不饱和类似物(12.4%至14.0%),包括C17:1,C19:1,C21:1和C23:1。方差分析(p值= 0.05)表明,针对饱和,不饱和和总ARs开发的响应面方法(RSM)足够,并解释了大多数变异性(85%至89%)和高多次测定系数(R 2) = 0.89)。响应变量的主要影响是二次函数,而不是线性函数。我们的结果表明,RSM是一种工具,可用于优化提取AR的实验条件。

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