Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover

We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.     

Assessing corn stover composition and sources of variability via NIRS

Corn stover, the above-ground, non-grain portion of the crop, is a large, currently available source of biomass that potentially could be collected as a biofuels feedstock. Biomass conversion process economics are directly affected by the overall biochemical conversion yield, which is assumed to be proportional to the carbohydrate content of the feedstock materials used in the process. Variability in the feedstock carbohydrate levels affects the maximum theoretical biofuels yield and may influence the optimum pretreatment or saccharification conditions. The aim of this study is to assess the extent to which commercial hybrid corn stover composition varies and begin to partition the variation among genetic, environmental, or annual influences. A rapid compositional analysis method using near-infrared spectroscopy/partial least squares multivariate modeling (NIR/PLS) was used to evaluate compositional variation among 508 commercial hybrid corn stover samples collected from 47 sites in eight Corn Belt states after the 2001, 2002, and 2003 harvests. The major components of the corn stover, reported as average (standard deviation) % dry weight, whole biomass basis, were glucan 31.9 (2.0), xylan 18.9 (1.3), solubles composite 17.9 (4.1), and lignin (corrected for protein) 13.3 (1.1). We observed wide variability in the major corn stover components. Much of the variation observed in the structural components (on a whole biomass basis) is due to the large variation found in the soluble components. Analysis of variance (ANOVA) showed that the harvest year had the strongest effect on corn stover compositional variation, followed by location and then variety. The NIR/PLS rapid analysis method used here is well suited to testing large numbers of samples, as tested in this study, and will support feedstock improvement and biofuels process research.     

Metabolomics of transgenic maize combining Fourier transform-ion cyclotron resonance-mass spectrometry,capillary electrophoresis-mass spectrometry and pressurized liquid extraction

In this work, the potential of combining capillary electrophoresis-time-of-flight-mass spectrometry (CE-TOF-MS) and Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) for metabolomics of genetically modified organisms (GMOs) is demonstrated. Thus, six different varieties of maize, three of them transgenic (PR33P66 Bt, Tietar Bt and Aristis Bt) and their corresponding isogenic lines (PR33P66, Tietar and Aristis) grown under the same field conditions, were analyzed. Based on the ultrahigh resolution and remarkable mass accuracy provided by the 12-T FT-ICR-MS it was possible to directly analyze a good number of metabolites whose identity could be proposed based on their specific isotopic pattern. For identification of metabolite isomers, CE-TOF-MS was also used combining the information on nominal mass with electrophoretic mobility corroborating in that way the identity of several new biomarkers. Furthermore, PLE extractions were evaluated in order to establish selective extraction as an additional criterion to obtain useful information in maize metabolomics. Differences in the metabolite levels were found between the three transgenic maize varieties compared with their wild isogenic lines in some specific metabolic pathways. To our knowledge, this is the first time that an approach as the one presented in this work (pressurized liquid extraction + FT-ICR-MS + CE-TOF-MS) is shown for a metabolomic study.     

常压下玉米秸秆多元醇液化的研究及其产物分析

利用合适的溶剂液化生物质不仅可以把木质纤维资源转化成液体燃料,还可以将得到的低分子降解产物制备成所需的化学品和化工原料。选用价格低廉的多羟基醇类液化剂进行液化,研究了二甘醇(diethylene glycol, DEG)混合1,2-丙二醇(1,2-propanediol, PG)、传统的乙二醇(ethylene glycol, EG)混合PG (均6∶1 ω/ω)分别作为液化剂对玉米秸杆液化得率和所得生物油产品性能的影响。并采用气质联用技术（GC-MS）、傅里叶红外光谱技术（FTIR）、热裂解气相色谱-质谱联用技术（Py-GC/MS）和X-射线衍射技术（XRD）对玉米秸秆、生物油及液化残渣的纤维特性进行了分析。结果表明,当DEG与PG混合液化时,玉米秸秆生物油的得率为98.57%;而EG混合PG时的液化得率为96.08%。GC-MS分析表明,玉米秸秆生物油的主要组成成分为醇类和有机酸类,总含量高达97%以上,而EG混合PG液化所得的生物油中含有有机酸将近60%,这是造成生物油具有酸性和腐蚀性的主要原因,不利于液化反应的进行;利用FTIR检测生物油中一些分子量较大的低聚物的相应官能团,以弥补GC-MS检测的局限性,结果表明了液化体系中生成了很多活泼化学键,提高了反应体系的活性,并且生物油中包含了大量的C-O和C=O官能团,有力地佐证了GC-MS的检测分析结果。对两种液化残渣进行表征,Py-GC/MS结果表明,液化残渣的成分比较复杂,含有一定量非常难降解的大分子物质。这些物质可能是反应后期裂解的小分子重新聚合生成的大分子物质;可能是玉米秸秆本身存在一些不能被液化降解的成分;还有可能是降解的小分子物质与液化剂之间相互反应生成的新的高分子化合物。通过FTIR表明,在液化过程中,液化残渣中纤维素、半纤维素和木质素的特征吸收峰都消失了,表明三大组分的基本结构单元都被破坏,三大组分都发生了液化,并且木质素降解程度最大。利用XRD对液化残渣进行表征,液化破坏了碳水化合物所构成的聚合物晶体结构,导致纤维素大分子被裂解,表明纤维素在液化作用下遭到降解,液化程度高。最终,该实验选取液化效果较好的DEG复配PG作为玉米秸秆液化时的溶剂,这也为玉米秸秆液化生产低成本、高品质的生物油提供了一种高效、环保的工艺流程。     

基于Sentinel-2A影像的玉米冠层叶绿素含量估算

农作物叶片中的叶绿素通过吸收光能参与光合作用产生化学能,及时、准确地估算叶绿素含量对于农作物长势、养分含量监测、品质评价和产量估算具有重要意义。Sentinel-2卫星的重访周期为5 d,空间分辨率为10 m,具有13个光谱波段,其中包括三个波宽仅为15 nm对叶绿素含量变化敏感的红边波段,是叶绿素含量估算的理想数据源。植被指数是基于农作物在不同波段的反射特性,通过不同波段组合方式刻画长势和叶绿素含量的差异,可用于大区域范围内的玉米冠层叶绿素含量快速、精确估算。以Sentinel-2A影像为数据源,开展基于多种植被指数的玉米冠层叶绿素含量估算方法研究。课题组于2016年8月6-11日在河北省保定市（115°29′-116°14′E,39°5′-39°35′N）进行玉米冠层叶绿素含量的实地测量,并在每个采样位置上采用中绘i80 智能RTK(real-time kinematic)测量系统进行定位。Sentinel-2A影像预处理工作包括几何校正、辐射定标和大气校正,其中大气校正使用Sen2Cor模型和SNAP模型。首先,基于预处理后的Sentinel-2A遥感影像,分别计算CI_green(green chlorophyll index), CI_red-edge(red-edge chlorophyll index), DVI(difference vegetation index), LCI(leaf chlorophyll index), MTCI(MERIS terrestrial chlorophyll index), NAVI(normalized area vegetation index), NDRE(normalized difference red-edge), NDVI(normalized difference vegetation index), RVI(ratio vegetation index), SIPI(structure insensitive pigment index)植被指数。然后,建立样方位置上实测叶绿素含量与各植被指数的统计关系,从而构建玉米冠层叶绿素含量估算模型,并以野外实测玉米冠层叶绿素含量为依据,对基于各植被指数的估算结果进行精度评价。最后,利用筛选出的最优叶绿素含量估算模型,估算研究区内的玉米冠层叶绿素含量。研究的目标为：（1）通过比较分析,构建合适的玉米冠层叶绿素含量估算模型,估算精度以决定系数R2、均方根误差RMSE以及相对误差RE作为评价指标;（2）确定最优波段组合方案：在红边波段中选择与可见光、近红外波段组合效果更优的波段组合方案;（3）确定参与植被指数计算的红边波段的最优数量。精度评价结果表明：（1）选用的植被指数与玉米冠层叶绿素含量呈多项式拟合关系,其中使用红边波段计算的植被指数的估算结果明显优于未使用红边波段的估算结果;红边波段引入后明显提高了可见光、近红外波段对叶绿素含量的拟合的精度,CI_{green(560, 705)}指数比CI_{green(560, 842)}的回归模型R2提高0.516,红边波段参与计算的DVI相对于RVI来说,估算结果更稳定。（2）对于不同的植被指数,参与运算的Sentinel-2A影像的两个红边波段,估算精度的提高程度不同。对于可见光波段参与计算的植被指数来说,在红边波段1（中心波长为705 nm）的估算精度较高,如LCI,CI_green,DVI和RVI等;对于近红外波段参与计算的植被指数来说,在红边波段2（中心波长为740 nm）的估算精度较高,如CI_red-edge,NDRE和NAVI等。（3）对于Sentinel-2A影像来说,两个红边波段共同参与叶绿素含量估算时能取得最高的的估算精度。选用的植被指数中,MTCI_{(665, 705, 740)}指数与玉米冠层叶绿素含量估算精度最高,回归模型拟合精度R2为0.803,模型验证R2为0.665,RMSE为3.185,相对误差RE为4.819%。MTCI_{(665, 705, 740)}指数计算中使用了两个红边波段,突出红边波段反射率差值变化,与玉米冠层叶绿素含量表现出很好的相关性。最后,利用优选出的基于MTCI指数的叶绿素含量估算模型,对研究区范围内的叶绿素含量进行估算并完成空间制图。     

基于空间谱的玉米叶片铜铅污染区分及程度监测

利用高光谱遥感技术监测并识别农作物受重金属污染信息是当今热点,研究设置了不同浓度铜离子(Cu2+)、铅离子(Pb2+)胁迫梯度的玉米盆栽实验,并测取了玉米叶片的光谱及叶片中重金属离子与叶绿素含量。基于获取的光谱数据,将光谱划分为紫谷、蓝边、绿峰、红谷、红边和红肩六个光谱特征区间,通过光谱的一阶微分和二维多重信号分类(2D-MUSIC)算法构造空间谱,对各光谱特征区间进行变换分析。实验结果表明：蓝边、绿峰和红边阵列信号的空间谱在Cu2+胁迫下为双高峰,在Pb2+胁迫下为单高峰,以此能够快速、直观地区分玉米叶片所受重金属污染的Cu2+和Pb2+元素类别。红谷和红肩阵列信号空间谱的方位角谱峰值与玉米叶片中Cu2+含量的相关系数分别达到-0.954 5和-0.964 8,说明用于监测Cu2+污染程度时效果理想;紫谷阵列信号空间谱的方位角谱峰值与玉米叶片中Pb2+含量的相关系数达到-0.999 8,说明用于监测Pb2+污染程度时效果理想。同时通过与常规重金属污染监测方法绿峰高度(GH)、红边位置(REP)、红边最大值(MR)、红边一阶微分包围面积(FAR)的应用结果进行比较分析,空间谱法的应用结果与玉米叶片中重金属离子含量的相关性较高,从而验证了空间谱应用于玉米重金属污染信息监测具有更好的有效性和优越性。     

近红外高光谱的活体玉米叶片水分成像研究

无损检测植物叶片水分对植物生理生化研究及灌溉管理和旱情监测等均具有重要意义。利用Gaia Sorter近红外高光谱仪(900～1 700 nm),以不同生育期的60个鲜活玉米叶片为试验材料,对叶肉不同区域的平均光谱及烘干称重法得到的水分含量分别用偏最小二乘法(PLS)及逐步多元线性回归(SMLR)进行建模分析。结果表明,验证集决定系数/标准偏差分别为0.975/1.18和0.980/1.02,均取得较好的预测效果,可实现单个玉米叶片平均含水量的测定;SMLR优选的特征波长(1 406和1 692 nm)建模预测结果表明,利用高通量近红外相机结合滤光片方法实现玉米叶片冠层或高空遥感测量的可行性。同时,进行了叶片不同区域水分含量的成像分析,结果表明,验证集中6个叶片的叶肉与主叶脉区域水分含量的参考均值和预测均值的相关系数均达到0.85以上,预测结果与实际情况相符合。     

基于支持向量机的玉米苗期田间杂草光谱识别

田间全面积均匀喷施除草剂不经济,还污染环境,精准喷施除草剂意义重大,其关键是正确识别杂草。用便携式野外光谱仪,在田间测量了玉米、马唐和稗草植株冠层在350～2 500 nm波长范围内的光谱数据,经过数据预处理,数据分析波长选为350～1 300和1 400～1 800 nm。数据处理采用支持向量机(SVM)模式识别方法。SVM具有可实现对小样本建模结构风险最小化、结果最优化、泛化能力强的优点。用线性、多项式、径向基和多层感知核函数对玉米和杂草建立二分类模型,结果表明,三阶多项式核函数SVM分类模型的正确识别率最高,达到80%以上,且支持向量比例较小。以二分类模型为基础,利用投票机制,建立了玉米、马唐和稗草的一对一多分类SVM模型,正确识别率达80%。田间光谱测量受光照、背景和仪器测量精度等条件的影响较大,但结果仍表明SVM结合光谱技术在田间杂草识别中应用潜力很大,此研究为田间杂草识别及传感器的建立提供了一种研究思路和应用基础。     

Millimeter wave investigation of dielectric cylinders absorption cross-section by resonant method

A new millimeter wave resonant method for determination of absorption cross-section of vegetation stalks is given. The method is developed for study of vegetation stalks absorption cross-section as a function of their moisture content, orkR parameter (k is a wave number, R is a stalk radius) in millimeter wavelengths band. ModesE _01p of a cylinder resonator are employed for such measurements. Dielectric permittivity and loss tangent of a sample can be found by measuring a frequency shift andQ- factor changes of the resonator. Absorption cross-section of a sample is then calculated using a formula obtained in our theoretical study. This formula can be written analytically as a result of rigorous solution of the related diffraction problem (scattering of a plane electromagnetic wave by cylinder with complex dielectric permittivity if stalk radius is less then 0.5 mm for the wavelength of 8 mm).Results of this investigation may be used in creating electrodynamical models of media containing cylindrical stalks of plants, in direct measurements of some canopy parameters, transmission properties measurements of vegetation canopies etc.     

SO2-catalyzed steam explosion of corn fiber for ethanol production

Corn fiber, a by-product of the corn wet-milling industry, represents a renewable resource that is readily available in significant quantities and could potentially serve as a low-cost feedstock for the production of fuel-grade alcohol. In this study, we used a batch reactor to steam explode corn fiber at various degrees of severity to evaluate the potential of using this feedstock in the bioconversion process. The results indicated that maximum sugar yields (soluble and following enzymatic hydrolysis) were recovered from corn fiber that was pretreated at 190°C for 5 min with 6% SO₂. Sequential SO₂-catalyzed steam explosion and enzymatic hydrolysis resulted in very high conversion (81%) of all polysaccharides in the corn fiber to monomeric sugars. Subsequently, Saccharomyces cerevisiae was able to convert the resultant corn fiber hydrolysates to ethanol very efficiently, yielding 90–96% of theoretical conversion during the fermentation process.