芝麻籽粒植酸含量高通量检测方法的建立与低植酸种质的筛选

Establishment of High-Throughput Detection Method for Phytic Acid Content in Sesame Seeds and Screening of Low Phytic Acid Germplasms

【目的】植酸是芝麻中主要的抗营养成分之一。探究芝麻籽粒植酸高效提取最优条件,建立植酸含量高通量检测方法,并应用于芝麻群体材料植酸含量变异检测和低植酸种质资源筛选,推动芝麻植酸基础研究和低植酸品种选育。【方法】以0.4 mol·L-1 HCl为提取溶剂,进行种子量、捣碎时间、捣碎频率、提取液体积等单因素试验,对芝麻籽粒植酸进行提取,结合微量化铁沉淀法高通量测定植酸含量。在单因素试验的基础上,进行Box-Behnken四因素三水平的响应面试验,建立植酸得率为响应值的二次多项式回归方程模型,并绘制响应曲面图和等高线图,分析影响植酸得率的主要因素及各因素间的交互作用,确定植酸含量检测的最优提取条件。利用该参数条件对2个环境下种植的200份芝麻种质资源进行植酸含量测定,筛选低植酸种质。【结果】方差分析显示,建立的回归模型具有高度显著性(P<0.0001),失拟项不显著(P>0.05),方程对试验拟合较好,可用此回归方程对芝麻籽粒植酸提取试验进行优化。4个因素的一次项对植酸得率的影响均达到极显著水平,对植酸得率的影响大小依次为种子量>捣碎时间>捣碎频率>提取液体积。响应面分析图表明,种子量和捣碎时间、种子量和捣碎频率、捣碎时间和捣碎频率、捣碎时间和提取液体积、捣碎频率和提取液体积之间具有一定的交互作用。经过单因素条件及响应面设计优化的植酸最佳提取条件参数为种子量30 mg、捣碎时间6.5 min、捣碎频率50 Hz、提取液体积435μL,在此条件下的植酸得率预测值为14.03 mg·g^(-1),实际植酸平均得率为14.14 mg·g^(-1),模型预测值与试验实际值十分接近。从200份种质资源中筛选出3份稳定的低植酸芝麻种质,平均含量为11.63 mg·g^(-1)。【结论】建立了芝麻植酸高效提取检测技术,可以显著减少试验时长,减少样品使用量,检测结果重复性好,准确性高。

【Objective】Phytic acid is one of the main anti-nutritional components in sesame.To explore the optimal conditions for efficient extraction of phytic acid from sesame seeds,establish a high-throughput detection method for phytic acid content,and apply it to the detection of phytic acid content variation in sesame population materials and the screening of low phytic acid germplasm resources,so as to promote the basic research of phytic acid in sesame and the breeding of sesame varieties with low phytic acid content.【Method】Using 0.4 mol·L-1 HCl as the extraction solvent,single factor tests such as seed weight,crusher time,crusher frequency and extraction solution volume were carried out to extract phytic acid from sesame seeds,and the content of phytic acid was determined in a high-throughput manner using modified iron precipitation method.On the basis of the single factor test,the response surface experiment of Box-Behnken four factors and three levels was carried out.The quadratic polynomial regression equation model with phytic acid yield as the response value was established,and the response surface plot and contour plot were drawn.The main factors affecting phytic acid yield and the interaction between the factors were analyzed to determine the optimal extraction conditions for the detection of phytic acid content.Using this parameter condition,the phytic acid content of 200 sesame germplasm resources planted in two environments was determined to screen low phytic acid germplasm.【Result】Analysis of variance(ANOVA)showed that the established regression model was highly significant(P<0.0001),the lack of fit was not significant(P>0.05),the equation fitted the test well,and this regression equation could be used to optimize the extraction of phytic acid from sesame seeds.The linear term of the four factors had a very significant effect on phytic acid yield,and the order of influence on the yield of phytic acid was seed weight>crusher time>crusher frequency>extract solution volume.The response surface analysis diagram showed that there were some interactions between seed weight and crusher time,seed weight and crusher frequency,crusher time and crusher frequency,crusher time and extraction solution volume,and crusher frequency and extraction solution volume.The optimal phytic acid extraction conditions optimized by single factor test and response surface design were as follows:seed weight of 30 mg,crusher time of 6.5 min,crusher frequency of 50 Hz,extraction solution volume of 435μL.The predicted phytic acid yield under this condition was 14.03 mg·g^(-1),and the actual average phytic acid yield was 14.14 mg·g^(-1),the model prediction was very close to the actual value of the test.Three stable low phytic acid sesame germplasms were screened out from 200 germplasm resources,with an average content of 11.63 mg·g^(-1).【Conclusion】An efficient phytic acid extraction and detection technology for sesame seeds was established,which could significantly reduce the experimental time and the amount of samples used,and provided a feasible method for the high-throughput detection of phytic acid content in sesame seeds with good reproducibility and high accuracy.