Establishment And Evaluation Of The Analysis And Detection Methods For Main Quality Indexes Of Flax Breeding Materials

Flaxseed is a very potential functional food,it is rich in nutrients,and it has many health benefits for,have become the potential of health food.But because of its contain anti-nutritional factors,especially cyanogenic glycosides,making its development and utilization of the value of greatly reduced.In this experiment,the flax varieties cultivated in Jingtai County of Gansu Province in 2014 were tested.The method of detecting cyanogenic glycosides in flaxseed was established.The method was based on the response surface design The optimum extraction conditions of hydrocyanic acid in flaxseed were determined.At the same time,the nutrition indexes(oil content,fatty acid composition,protein)of flaxseed were determined,Near-infrared spectroscopy(NIRS)was used to scan flaxseed,and a near-infrared spectroscopy(NIR).The aim of this study was to provide a theoretical basis for screening high quality flax varieties with high nutritional value and low antinutritional factors.The main findings are as follows:1 Using Longya No.10 linseed as material,the single factor and Box-Behnken experimental design were used to study the main factors affecting the extraction rate of hydrocyanic acid.The results showed that the concentration of sodium hydroxide solution had a significant effect on the extraction rate of hydrocyanic acid(p <0.01),while the extraction time and liquid-solid ratio had no significant effect on the extraction rate of hydrocyanic acid(p> 0.05).The optimal extraction conditions were as follows: the ratio of liquid to solid was 50: 1(mL / g),the extraction time was 45 min and the concentration of absorbing liquid was 10 g / L.The average recoveries ranged from 90.35% to 97.37%,and the relative standard deviations were less than 5% by adding 1.0,2.0 and 5.0 ?g of standard addition experiments.This method can be used to detect the content of hydrocyanic acid in flaxseed,and the result is true and reliable.2 The 78 quality indexes of flaxseed varieties were tested by the optimized method.Results: The high-quality varieties of crude fat were screened out: 6,6,11,J · W · S,Longya 5,Vimgnar,Ningya 7,Baya 10,CDC.Sorre,4,Yannong 1,22.09140,MOROCCO,wheel selection 1,outside the 10,TAMMES TYPE11,Long Ya Za 1,Zhang 1,dam Asia 1,Dingya 5,Jinya 1,OTTAWA WHITE FLOWER,Ningya 15,vimy,(HAURRA OLA)LEN.Ningxia 6,Longya 6,Dingya 17,Jiuquan 078,Longya 1,Ningya.7,Longya 7,Dingya 15,Ningya 6,Longya 1,KENYAC.I.T09,Linxia Ga flax,Ningya 2,Jinya 4,Dingyahong 1,Lunxuan 1,66-39-7-1,22.09140.The quality of cyanogenic glucoside varieties: Tian Ya 5,Wai 11,Linxia Ga flax,98036,Jingningxuan 2,Dingya 17,Damia 1,TAMMES TYPE11,Wai 10,Kangle-2,Ningya 2,95005,Yannong 1.The crude fat content and cyanogenic glycoside content of Wai 10,Wai 11,Yan Nong 1,Baya 1,TAMMES TYPE11 were double-superior;The crude fat content and oleic acid content of Jinya 1,Lunxuan 1,Longyaza 1,22.09140,Ningxia 1,Ningya 7 were the best.The content of oleic acid and the content of cyanoglycoside in Linxiaga flax,Dingya 17 and Ningya 2 were double-superior.78 varieties of flax,failed to screen out three superior and above varieties.3 The flaxseed resources were scanned by near infrared spectroscopy,using the protein content of the test results to establish protein near infrared analysis model,the PSL1 method was applied to analyze the scanning spectra and chemical analysis results.and several kinds of samples were selected as the forecasting sample set to carry on the forecasting work,after the forecast the result carries on the test to verify the model the forecast ability.The results show that the protein model and the established calibration equation can be used in actual prediction,but there are some errors in prediction.Mainly due to the protein concentration of the sample is closer,covering a smaller range,and the determination of the chemical value of the use of semi-automatic Kjeldahl instrument,titration still manual operation,the error is large.The need to expand the scope of the sample set of protein coverage,while improving the chemical value detection methods.