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。     

高能湿法球磨制备鱼胶原肽螯合钙及其理化特征

为了拓展胶原肽螯合钙的制备工艺,以鱼胶原肽和CaCl2为原料,以高能湿法球磨的方法制备了鱼胶原肽螯合钙,并采用FT-IR红外图谱、X-衍射、EDS能谱等技术手段对其理化特性和结构进行分析。结果显示,随着球磨时间从15 min延长到240 min,鱼胶原肽的平均粒径逐渐降低,ZETA电位绝对值、分子量和pH值逐渐增加,鱼胶原肽对钙的螯合率逐渐从74.21%增加到85.42%。鱼胶原肽螯合钙与鱼胶原肽的主体化学结构相似,但是球磨后鱼胶原肽的-COO-1 VAS吸收峰(1 643.89 cm~(-1))向短波数方向移动到1 540～1 555 cm~(-1),而-NH2的特征吸收峰(3 313.9 cm~(-1))向长波数方向移动到3 337.12～3 380.59 cm~(-1)。球磨后鱼胶原在2θ=20°处的特征宽吸收峰消失,而在2θ=26、28、48和58°处出现多个尖峰和弥散峰,表明其从无定形结构向兼具结晶和无定形结构转变。鱼胶原肽螯合钙中钙元素比例逐渐增加,证实鱼胶原肽对钙的螯合率增加。研究表明,高能湿法球磨可应用于鱼胶原肽螯合钙的制备,该方法具有加工工艺简单、无污染和螯合率高等优点。     

4种植物油热氧化同步荧光光谱分析

以亚麻籽油、大豆油、菜籽油与葵花籽油为原料,在不同温度条件下氧化并测定其不同时间的同步荧光光谱,分析同步荧光光谱及荧光物质变化情况。结果表明,4种植物油不同温度下的同步荧光光谱峰变化主要集中在300~415 nm内,且随着加热时间的延长存在波动现象;在50℃和150℃加热氧化条件下,300,330,375,415 nm这4个峰都存在明显的波动现象;二维相关分析表明,这4个峰的荧光强度变化速率不同,不同油变化差异较大;同种油在不同温度下,4个峰的波动情况、相互关系、变化速率均有明显差异。这些峰的变化与油脂氧化过程中荧光物质和油脂氧化程度的变化有关,可以利用同步荧光光谱的变化特性作为监控油脂氧化的依据。     

Changes in wettability of wood surface using electron beam irradiation

ABSTRACT

This study deals with the influence of electron beam irradiation (EBI) on the wettability of Norway spruce surface samples. To evaluate this possible effect in detail, the changes in chemistry and wood structure were analysed using the methods of IR spectroscopy and Scanning Electron Microscopy (SEM). The surface wettability was determined by the contact angle measurement method. The results of infrared spectra show decreased relative numbers of available hydrophilic groups (e.g. hydroxyl and carbonyl groups) in the wood surfaces due to the electron beam irradiation. This behaviour is consistent with the values from the surface wettability measurements. Structural differences of the wood surfaces after electron beam irradiation were not detectable. Based on these findings pre-treatment of wood surfaces for industrial usages can be a potential field of application of this technology.     

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

Maize haploid breeding technology is able to identify haploid seeds non‐destructively, rapidly and at low cost with the help of Near‐infrared (NIR) spectral analysis. However, due to the hybridization of numerous parents and the low production rate of haploid, the haploid data collection becomes a burden for engineering this technology. Biologically, there are considerable similarities between the progeny of the same female parent and different male parents. Based on this advantage, similar spectral data can be transferred when the NIR technology is employed. A revised method of Transfer adaptive boost (TrAdaBoost) is proposed to improve identifying for the backpropagation neural network (BPNN) classifier. To avoid the negative transfer, a screening thresh is used to select out similar data, and the amount of these data are limited to join current training. The results show that the identification performances are improved significantly when the data amount is small. This method shows a high ability to make the seed identification more convenient for engineering maize haploid breeding.     

不同生育时期冬小麦叶片相对含水量高光谱监测

为实现冬小麦不同生育时期叶片水分含量的快速监测，以冬小麦冠层高光谱数据和红外热成像数据为基础，计算得到5种光谱参数，通过对不同生育时期叶片相对含水量与光谱参数拟合状况进行分析和筛选，分别构建了基于光谱参数的叶片相对含水量反演模型，并对模型进行检验。结果表明，不同生育时期叶片相对含水量与比值指数（RVI）、归一化差值植被指数（NDVI）、比值/归一化植被指数（R/ND）、优化土壤调整植被指数（OSAVI）、冠气温差（TDc-a）均呈极显著相关（P＜0.01）；拔节期、抽穗期、开花期、灌浆前期和灌浆后期叶片相对含水量分别与NDVI、OSAVI、R/ND、TDc-a和TDc-a拟合效果较好，决定系数分别为0.842、0.884、0.831、0.864和0.945；预测模型的均方根误差分别为0.019、0.016、0.027、0.032和0.024，相对误差分别为2.16%、1.80%、3.30%、3.81%和3.53%。因此，在拔节期、抽穗期、开花期、灌浆前期和灌浆后期，可以分别利用NDVI、OSAVI、R/ND、TDc-a和TDc-a估测冬小麦叶片相对含水量。     

剔除土壤背景的棉花水分胁迫无人机热红外遥感诊断

剔除无人机热红外影像中的土壤背景是提高作物水分诊断精度的有效途径,但也是热红外图像处理的难点问题。本文以不同水分处理的花铃期棉花为研究对象,分别在09:00、13:00和17:00等3个时刻,连续5 d采集无人机高分辨率热红外影像,并采用二值化Ostu算法和Canny边缘检测算法对热红外图像进行掩膜处理,实现对土壤背景的剔除,然后分别计算二值化Ostu算法、Canny边缘检测算法和包含土壤背景下的3种棉花水分胁迫指数(Crop water stress index,CWSI),最后建立不同时刻下3种CWSI与棉花叶片气孔导度Gs的关系模型。研究结果表明,应用Canny边缘检测算法可有效剔除热红外影像中的土壤背景,剔除土壤背景后的温度直方图呈单峰的偏态分布;3种处理方法获得的作物水分胁迫指数CWSI中,Canny边缘检测算法的CWSI最小,二值化Ostu算法的CWSI较高,包含土壤背景的CWSI最大;采用Canny边缘检测算法剔除土壤背景后的CWSI与棉花叶片气孔导度Gs的决定系数R2达到0.84,Ostu算法的结果次之,包含土壤背景的最差。本研究可为无人机热红外遥感监测作物水分状况提供参考。     

Impact of sampling and storage technique,and duration of storage,on the composition of fresh grass when analysed using near‐infrared reflectance spectroscopy

The objective of this study was to examine the effect of sampling technique (pluck or cut), storage duration (immediate analysis, 24‐h or 48‐h), storage temperature (ambient or chilled) and storage conditions (air present, air excluded or breathable) on the composition of fresh grass sampled from a sward managed to simulate grazing. Treatments were repeated across four sampling dates, with grass samples stored in grip seal bags prior to analysis using near‐infrared reflectance spectroscopy. Grass sampled by ‘pluck’ had a higher crude protein and ME content, and a lower acid detergent fibre (ADF) content, compared to that sampled by ‘cut’. Grass stored for 48 h had a lower water soluble carbohydrate (WSC) and ME content and a higher ADF content than for immediate analysis. Samples stored for 24 h did not differ from immediate analysis. Grass stored at ambient temperature had a lower WSC and ME content compared to immediate analysis. Grass stored under ‘breathable’ conditions had a lower ME content and higher ADF content than immediate analysis or samples stored with air present or air excluded. It is recommended that grass for analysis should be sampled by cutting, stored chilled (4°C) in a sealed bag to minimize exposure to oxygen and analysed within 24 h of harvest.     

Comparison of multivariate models and variable selection algorithms for rapid analysis of the chemical composition of field crops

This study evaluates the use of visible and near-infrared spectroscopy for rapid prediction of total carbon, total nitrogen, and total phosphorus concentrations in field crop samples. Two multivariate models (partial least squares regression and support vector machine regression) were compared. In addition, four spectral variable selection algorithms (competitive adaptive reweighted sampling, genetic algorithm, uninformative variable elimination, and variable importance for projection) were applied with support vector machine regression to determine the most accurate predictions. The results showed that support vector machine regression performed better than partial least squares regression for predicting the three chemical compositions. The combination of competitive adaptive reweighted sampling and support vector machine regression outperformed the other models for the predictions of total carbon and total nitrogen with high coefficients of determination of 0.91 and 0.90, respectively. For the determination of total phosphorus, the prediction accuracy of competitive adaptive reweighted sampling was comparable with the best result obtained from genetic algorithm with the coefficients of determination of 0.73 and 0.77, respectively. In conclusion, the support vector machine regression combined with competitive adaptive reweighted sampling has great potential to accurately determine the chemical composition of field crops using the visible and near-infrared spectroscopy.