不同合成树脂工艺对改性材性能影响研究

为研究树脂对改性材性能的影响，采用2种不同工艺合成三聚氰胺-脲醛树脂（MUF），测试了树脂的相关性能。结果表明，不同合成工艺路线下制备的 MUF 树脂在固体含量、粘度、固化时间、游离甲醛含量间存在显著差异。最终树脂的分子结构类型相似性极高，但相同结构组分在不同树脂中所占比例各有差异。羟甲基基团在MUF2中所占比例高，而亚甲基桥键及醚键在MUF1中含量高。MUF1改性材的增重率（weight percent gain，WPG）值更大，但MUF2改性材的抗溶胀性（anti-swelling efficiency，ASE）和体积膨胀率（bulking rates，B）更高，MUF2改性材的尺寸稳定性更好。     

不同氮效率油菜种质苗期氮吸收转运与利用差异研究

氮效率研究是油菜营养性状遗传改良的前提,为探究不同氮效率油菜种质苗期氮吸收、转运和利用的异同,以2个氮效率差异油菜种质H6（氮高效）和L18（氮低效）为供试材料,利用水培营养液设置正常氮（CK）和低氮（LN）2个氮浓度处理,培养14d后检测植株氮含量,计算氮累积量、氮转运系数和氮利用效率。结果显示,不同氮浓度处理对油菜苗期氮的吸收、转运和利用效率影响的差异达到极显著水平（P＜0.01）。与CK相比,LN处理的油菜生物量、氮含量和氮累积量均显著降低,而氮利用效率和根冠比显著提高;氮高效油菜种质H6的生物量、氮累积量、氮转运系数和氮生理利用效率均显著大于氮低效种质L18,分别为L18的2.07、1.42、3.23和1.56倍。从氮的吸收、转运和利用3个方面探讨了低氮胁迫处理下油菜种质苗期氮效率差异的原因,对深入开展油菜氮高效机理研究及油菜品种氮效率改良具有一定的指导意义。     

玉米粉淀粉含量近红外模型建立与优化

以230份玉米自交系为样本，采用旋光法与一阶导数及去一条直线的光谱预处理法，构建玉米粉样淀粉含量的近红外分析（NIRS）模型。研究标明，该模型可显著提高子粒淀粉含量预测的准确性。该模型的定标标准偏差（RMSEE）、交叉验证标准偏差（RMSECV）、外部验证标准偏差（RMSEP）、定标相关系数（R_cal²）、交叉验证相关系数（R_cv²）、外部验证相关系数（R_cv²）分别为0.609、0.722、0.738、0.909、0.864和0.854。建立的玉米粉样NIRS模型可将预测值与化学值偏差控制在1.7%内，能够准确定量分析玉米子粒淀粉含量，应用于育种材料早期筛选及群体水平粗淀粉分析。     

不同有机肥氮替代化肥氮比例对马铃薯根系吸收能力和形态的影响

针对甘肃定西地区马铃薯生产长期大量施用化肥造成马铃薯产量降低、品质下降以及环境污染等问题,通过大田试验,研究了不同有机肥氮替代化肥氮比例处理对旱地覆膜马铃薯根系吸收能力和形态的影响。结果表明: 不同施用量有机肥氮替代化肥氮处理显著增加了马铃薯根系活力、活跃吸收面积和总吸收面积,且随着替代比例的增加逐渐增加,在有机肥氮替代化肥氮比例达到30%时达到最大值,而后降低;不同施用量有机肥氮替代化肥氮处理对马铃薯根系总根长、根体积、总根尖数和主根直径有显著影响,特别是增加了0~0.50、0.51~1.00和1.01~1.50mm直径范围内马铃薯根系的总根长;施用有机肥的处理显著增加了马铃薯块茎产量,与纯施化肥相比分别提高了11.32%、16.04%、23.53%和12.69%。综上所述,在定西地区,施氮量为纯氮180kg/hm²,氮、磷、钾配比为4:3:3（N:P₂O₅:K₂O）,有机肥氮替代化肥氮比例为30%是最佳施肥模式。     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.     

Feasibility analysis of NIR for detecting sweet corn seeds vigor

This paper explores the feasibility of particle-based detection and grading of seed vigor based on a self-built seed single-granulation device using near infrared spectroscopy (NIRS). Sweet corn with uniform kernel size was used for this study. The seed samples were divided into three types, they were normal seeds, artificially aged seeds and heat-damaged seeds. A 2-part spectral acquisition of each seed were performed, one for the collection of seeds that fall into the detection zone within the separation pipe, another was on the static platform, whose collection was performed on 5 faces of each seed. Partial least squares discriminant analysis (PLS-DA) was used to classify the original data of the seeds. In the 2 parts, the discriminant results of the unprocessed normal seeds and the artificial accelerated aging seeds, the untreated normal seeds and the heat-damaged seeds showed that classification accuracy was higher than 98%. The research indicates that the spectral data of different positions of seeds can reflect their activity information, and it is feasible to detect and classify seeds in real time in the detection area of the separation pipeline.     

FT-Raman and FT-IR studies of the gluten structure as a result of model dough supplementation with chosen oil pomaces

The pomaces obtained after cold pressing oil production from oil seeds can be regarded as a source of many valuable nutrients such as unsaturated fatty acids, dietary fibre and antioxidants. The aim of the research was to determine the effect of five oil pomaces from black seed, hemp, pumpkin, milk thistle and primrose on the structure of gluten proteins. The model dough was supplemented with 3%, 6%, and 9% of the pomaces. Structural changes in gluten proteins were studied using Fourier transform Raman spectroscopy (FT-Raman) and Fourier transform infrared spectroscopy (FT-IR). Additionally, chemical composition of the pomaces was determined. Changes observed in the gluten structure resulting from the model dough supplementation with oil pomaces allow the pomace to be divided into two groups depending on the type and amount of fatty acids present in the pomaces. If the pomace contains a low number of fatty acids (black seed and pumpkin), pseudo-β-sheets are formed from β-turns and antiparallel-β-sheets in the gluten network. If the pomace contains a high number of fatty acids, non-aggregated β-structures are observed. Although the pomaces contain considerable amount of dietary fibre and polyphenols, comparison of the observed structural changes in gluten network with pomaces’ chemical composition indicates that the changes are connected with presence of fatty acids.     

不同波长选择方法在土壤有机质含量检测中对比研究

由于近红外光谱数据的多重共线性，特征波长选择一直是近红外光谱分析技术的重要研究内容。以108个土壤样本光谱数据和土壤有机质(SOM)含量为研究对象，以连续投影算法（SPA）、间隔偏最小二乘法（IPLS）、竞争自适应重加权采样法（CARS）三种典型的特征波长选择算法进行近红外光谱波长选择和土壤有机质含量建模。研究结果表明，基于上述三种方法提取的特征波长所建立的模型预测能力均优于全谱模型。其中，基于SPA算法的MLR预测模型精度最优，预测集相关系数（Rp）和均方根误差（RMSEP）分别为0970 2和1.214 4，模型参数只有6个。因此，SPA-MLR可以有效地应用近红外光谱的建模，并且简化模型的复杂度，提高模型的计算效率。     

宁夏玉米营养成分近红外分析模型建立与应用

研究采集宁夏区内各市县(固原、海原、同心、中卫、中宁、永宁、平罗)抽取玉米样品120批,用近红外分析仪扫描定标样品集获得玉米近红外光谱图,利用偏最小二乘法建立模型,并分别经过无预处理、均值中心化、标准正态变量转换、一阶导数、标准正态变量转换结合去趋势校正(SNV+D)预处理光谱,获得水分、粗蛋白、粗脂肪、粗纤维预测模型,分别是经过MC、SNV+D、SNV+D、SNV+D光谱预处理后效果最好。预测决定系数R² _p分别为0.951、0.976、0.728、0.897,相对分析误差RPD除粗脂肪为2.62外,其他均大于3的近红外预测模型,通过预测模型验证集验证后,并对模型预测值与实测值进行U检验,结果为差异不显著(P>0.05)。该近红外预测模型对玉米中水分、粗蛋白、粗纤维具有较佳预测效果,粗脂肪的预测精度有待进一步提高。