Saccharification of corn fiber using enzymes fromAureobasidium sp. strain NRRL Y-2311-1

Crude enzyme preparations fromAureobasidium sp. strain NRRL Y-2311-1 were characterized and tested for the capacity to saccharify corn fiber. Cultures grown on xylan, corn fiber, and alkaline hydrogen peroxide (AHP)-pretreated corn fiber produced specific levels of endoxylanase, amylase, protease, cellulase, and other activities. Using equal units of endoxylanase activity, crude enzymes from AHP-pretreated corn fiber cultures were most effective in saccharification. Multiple enzyme activities were implicated in this process. Pretreatment of corn fiber with AHP nearly doubled the susceptibility of hemicellulose to enzymatic digestion. Up to 138 mg xylose, 125 mg arabinose, and 490 mg glucose were obtained per g pretreated corn fiber under conditions tested. The use of brand or trade names may be necessary to report factually on available data. The USDA neither guarantees nor warrants the standard of the product, and the use of the name by USDA implies no approval of the product to the exclusion of others that may also be suitable. All programs and services of the USDA are offered on a non-discriminatory basis without regard to race, color, national origin, religion, sex, age, marital status, or handicap.     

玉米麦根酸测定方法研究

禾本科作物麦根酸类物质(MAs)分泌量的准确、快速测定,对于铁营养高效育种及缺铁耐性的生理研究具有重要价值。以玉米自交系为材料,根据麦根酸类物质的溶铁特性,建立了ICP-AES间接测定麦根酸类物质的方法,改良了脱氧麦根酸(DMA)的UV-HPLC直接测定法,并与传统的比色法测定结果进行比较。结果表明,采用ICP-AES法,以Fe2O3粉末作为活化对象,与采用Fe(OH)3的传统方法测定结果相比,结果稳定,t(ICP-AES)=4.080>t0.05(15)=2.131重复性好,RSD(n=3,%)=3.3,说明ICP-AES法是评价玉米及其它禾谷类作物麦根酸类物质分泌量的一种简单、快速的方法。改良的UV-HPLC法也具有准确、高效的特点,可应用于禾谷类作物DMA分泌量的直接测定。     

Thermal Effects on Hydrothermal Biomass Liquefaction

Batch pressure vessels commonly used for hydrothermal liquefaction have typical heating times in the range of 30 to 60 min. Thermodynamically, the complex set of reactions are path dependent, so that the heating rate can possibly affect yields and the composition of the resultant liquid products. It is postulated that the mode of heat transfer becomes an uncontrolled variable in kinetic studies and can seriously impact scale-up. To confirm this hypothesis and minimize these heat-transfer-related artifacts, we designed a batch pressure vessel equipped with an induction heating system, which allows the reduction of heat-up times by about two orders of magnitude to several seconds, compared to tens of minutes with standard pressure reactors. This system was used to study the direct liquefaction of corn stover and aspen wood with a pretreatment. The heating rate was found to have no significant effect on the composition of the liquid products. However, the liquid yields are dependent on the heating rate. Varying the cooling rate does not show obvious effects. The results confirm that the heating rate, as governed by the mode of heat transfer, is an important factor that needs to be considered during scale-up.     

秸秆稀酸水解液的气相色谱／质谱法研究

在秸秆两步稀酸水解工艺中,用气相色谱/质谱(GC/MS)法对其水解液中的单糖成分进行测定,采用2%硼氢化钠的氨溶液将稀酸水解液中的单糖还原成糖醇,然后在甲基咪唑催化剂的作用下和乙酸酐在水相中直接反应生成乙酰化的糖醇,用二氯甲烷萃取后进行GC/MS测定.研究结果表明:秸秆稀酸水解液中有五种单糖,主要是木糖和葡萄糖,其次是阿拉伯糖、半乳糖和少量的甘露糖;利用此方法测定了一批秸秆稀酸水解液,得到了该秸秆稀酸水解过程的最佳的反应时间.该方法可快速、准确测定秸秆稀酸水解液中单糖的浓度,为水解工艺的研究提供一种有效的分析方法.     

High speed measurement of corn seed viability using hyperspectral imaging

Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.     

Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging

Genetic algorithm (GA) has a significant effect in the band optimization selection of Partial Least Squares (PLS) correction model. Application of genetic algorithm in selection of characteristic bands can achieve the optimal solution more rapidly, effectively improve measurement accuracy and reduce variables used for modeling. In this study, genetic algorithm as a module conducted band selection for the application of hyperspectral imaging in nondestructive testing of corn seedling leaves, and GA-PLS model was established. In addition, PLS quantitative model of full spectrum and experienced-spectrum region were established in order to suggest the feasibility of genetic algorithm optimizing wave bands, and model robustness was evaluated. There were 12 characteristic bands selected by genetic algorithm. With reflectance values of corn seedling component information at spectral characteristic wavelengths corresponding to 12 characteristic bands as variables, a model about SPAD values of corn leaves acquired was established by PLS, and modeling results showed r = 0.7825. The model results were better than those of PLS model established in full spectrum and experience-based selected bands. The results suggested that genetic algorithm can be used for data optimization and screening before establishing the corn seedling component information model by PLS method and effectively increase measurement accuracy and greatly reduce variables used for modeling.     

催化加氢不同预处理玉米秸秆制取多元醇的研究

以玉米秸秆为研究对象,采用5%硫酸和5%氢氧化钠对其进行预处理,通过对解聚前后玉米秸秆官能团表征和成分分析,发现酸、碱处理后秸秆中大部分半纤维素和木质素被脱除,其中先碱后酸、先酸后碱处理后秸秆的木质素含量由28.04%分别下降至11.54%和12.14%,而纤维素相对含量由42.02%分别增加到75.12%和77.68%。通过浸渍法制备的非贵金属5%Ni-15%W/MCM-41催化剂用于催化转化预处理秸秆制取多元醇,结果表明:与未经处理的玉米秸秆多元醇得率21.00%相比,先碱后酸、先酸后碱处理的玉米秸秆催化转化多元醇得率分别达到60.72%和61.40%。α-葡萄糖和β-葡萄糖的加氢结果显示乙二醇(EG)和1,2-丙二醇(1,2-PG)得率均相近,说明葡萄糖的旋光构型对催化加氢没有影响。与C6糖加氢产物分布比较,C5糖的产物中除了有EG和1,2-PG,同时还有丙三醇的生成,提出了糖加氢制取多元醇的可能机理。     

Determination of Herbicides in Corn Flour by Novel Extraction and Gas Chromatography–Mass Spectrometry

A simple and efficient procedure was developed to determine eight herbicides in corn flour by gas chromatography–mass spectrometry in selected ion-monitoring mode. Samples were prepared with a modified, quick, easy, rapid, effective, rugged, and safe procedure. The type and volume of extraction solvent, type and amount of adsorbent, and time of sonication were optimized. The protocol method was rigorously verified. The mean recoveries were from 85 to 108% at various fortification levels with relative standard deviations below 15% and limits of quantification from 4 to 48?ng g^?1. The method was used to determine herbicides in corn flour.     

基于红边位置提取验证成像与非成像高光谱数据的一致性

定量遥感是当前遥感发展的前沿,作物组分信息解析是农业定量遥感的研究热点,而成像高光谱技术为解决微观尺度的作物组分信息探测研究提供了强有力的手段.利用成像光谱仪( pushbroom imaging spectrometer,PIS)与地物光谱仪(FieldSpec ProFR2500,ASD)同步收集冬小麦、玉米不同生...     

基于贝叶斯优化的SVM玉米品种鉴别研究

为了快速检测玉米品种类型,基于支持向量机(SVM)和近红外光谱联合建立玉米品种的分类模型。以郑单958、先玉335、京科968、登海605和德美亚等五个品种共计293个样本为研究对象,对采集的近红外光谱进行标准正态变量变换(SNV)处理后使用主成分分析法(PCA)对光谱数据进行降维处理。按照6∶1比例,随机选取251个样本为训练集,42个样本作为测试集,探讨贝叶斯优化算法(BO)对SVM模型性能的影响。分别使用网格搜索(GS)、遗传算法(GA)和BO算法等三种方法对SVM模型的两个重要参数惩罚因子C和径向基核函数参数γ进行寻优。选择各模型十折交叉验证识别准确率最高时对应的惩罚因子和核参数作为建模参数,建立SVM分类模型。将使用BO算法建立的SVM分类模型与使用GS和GA进行参数寻优后建立的模型性能进行比对。实验发现,使用BO优化的SVM分类模型相比于其他两种优化算法得到的SVM模型性能具有显著优势,测试集的识别准确率可达到100%。说明使用BO算法寻优的SVM模型参数是全局最优参数,其他两种优化算法寻优的参数可能陷入了局部最优,从而导致模型性能表现不佳。在进行PCA降维前后的光谱数据上分别建立BO-SVM模型,结果表明,BO算法对于高维数据优化效果不佳,更适用于低维数据。对于不同样本类别间数量不均衡导致模型性能表现不佳的问题,通过剔除郑丹958和先玉335两类数量较少的样本,使用剩余三个类别,共计248个样本重新建立SVM模型,实验发现,剔除两类小样本之后,各个模型在测试集上的性能均有提升,说明对于类间样本数量不均衡问题,某类样本数量越多,对于模型参数的修正就越细腻,模型对该类的拟合效果就越好。研究结果可用于玉米品种的快速鉴别,也可为基于近红外光谱的其他农产品分类和产地鉴别提供参考。