红外相机技术在河北雾灵山国家级自然保护区中的应用

为了充实河北雾灵山自然保护区的野生动物资源本底资料,提高监测能力和监测水平,雾灵山保护区自2015年开始将红外相机技术应用到野生动植物的资源调查和管护中。目前应用红外相机技术发现兽类5目10科14种,鸟类4目6科10种,掌握了区内主要物种猪獾(Arctonyx collaris)、狍(Capreolus)和勺鸡(Pucrasia macrolopha)的日活动节律,补充了保护区的本底调查资料,为保护区进一步开展金钱豹(Panthera pardus)、斑羚(Naemorhedus goral)和鸟类多样性等科学研究奠定了基础,为保护区制定关于野生动物资源的相关管护政策提供了科学依据。     

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。     

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。     

甜菜种子活力的测定——基于近红外高光谱技术

为保证田间甜菜种子的出苗率和生产潜力,需要在种植前对种子进行活力检测,挑选出活力高的甜菜种子进行推广种植。利用高温处理法对甜菜种子进行老化处理,再基于标准正态变换(SNV)、去趋势校正(DET)、Savitzky-Golay平滑处理(SG)、一阶差分(1D)和二阶差分(2D)5种近红外高光谱预处理方法,构建种子活力智能检测模型得出预测的种子发芽率,再利用老化处理后的甜菜种子进行发芽实验得出实际的发芽率。研究发现,甜菜种子经过一阶差分预处理建立的智能检测模型预测性能最好,其预测准确率达到91.92%。将未处理的甜菜种子的实际发芽率与一阶差分预处理法建立的智能模型预测结果进行对比,发现模型的预测准确率为88.2%。近红外高光谱技术是一种快速、无损的作物种子活力测定的新方法。     

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.     

龙须菜细胞壁多糖对硼的吸附性能研究

为研究龙须菜(Gracilaria lemaneiformis)细胞壁多糖对硼的吸附作用机制及官能团之间的交互作用,本试验分别采用电感耦合等离子发射光谱(ICP-OES)和傅里叶变换红外光谱(FTIR)技术对其细胞壁多糖组分在吸附试验中进行硼的测定和表征。结果表明,细胞壁去琼胶后,硼吸附量减少50.13%,半纤维素去除后,硼吸附量减少21.20%,故可得纤维素吸附量占细胞壁总硼吸附量的28.67%。同时,通过细胞壁不同组分脱硼及硼胁迫后的红外光谱表征结果可知,龙须菜细胞壁及多糖在吸附硼的过程中,羟基、羧基、多糖碳链C-C为硼离子的主要结合位点,其中,琼胶、半纤维素中起主要作用的官能团为羟基、羧基,纤维素中起主要作用的官能团为多糖碳链C-C。本研究结果为进一步探究龙须菜多糖组分与硼的内在结合机制提供了基础依据。     

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.     

Using vibrational molecular spectroscopy to detect moist heating induced carbohydrates structure changes in cool-climate adapted barley grain

There is limited research to study how moist heating affects internal structure of barley grain on a molecular basis. The objectives of this study were to use vibrational molecular spectroscopy: 1) to determine the moist heating induced changes of barley carbohydrate (CHO) structure on a molecular basis, 2) to study the effects of moist heating on CHO chemical profiles, Cornell Net Carbohydrate and Protein System (CNCPS) subfractions, in situ rumen degradation, and predicted intestinal carbohydrate supply of barley grain; and 3) to reveal the association between molecular structure spectral features and CHO related metabolic characteristics. Barley samples (CDC cowboy) were collected from Kernen Crop Research Farm (Saskatoon, Canada) during two consecutive years. Half of each sample was kept as raw barley and the other half underwent moist heating (autoclaving at 120 °C for 60 min). The molecular spectroscopy (attenuated total reflectance-fourier transform infrared, ATR-FTIR) was used to detect the barley CHO related molecular structure spectral features. Moist heating did not affect carbohydrate related chemical profiles and CNCPS subfractions but it decreased rumen degradable carbohydrate. Rumen undegradable and intestinal digestion of CHO subfractions were not affected by moist heating. The advanced vibrational molecular spectroscopy can be used to detect carbohydrate molecular spectral features. Nutrient utilization prediction using molecular spectral characteristics is warranted and further investigation is encouraged.