Analyzing seed weight,fatty acid composition,oil, and protein contents in <Emphasis Type="Italic">Vernonia galamensis</Emphasis> germplasm by near-infrared reflectance spectroscopy

Vernonia galamensis is a potential new industrial oilseed crop from the Asteraceae family. The interest in this species is due to the presence of a high vernolic acid content of its seed oil, which is useful in the oleochemical industry for paints and coatings. The development of a rapid, precise, robust, nondestructive, and economical method to evaluate quality components is of major interest to growers, processors, and breeders. NIR reflectance spectroscopy (NIRS) is routinely used for the prediction of quality traits in many crops. This study was conducted to establish a rapid analytical method for determining the quality of intact seeds of V. galamensis. A total of 114 Vernonia accessions were scanned to determine seed weight, FA composition, oil, and protein contents using NIRS. Conventional chemical analysis for FA composition, total oil, and protein contents were performed by GC, Soxhlet extraction, and the Dumas combustion method, respectively. Calibration equations were developed and tested through cross-validation. The coefficient of determination in cross-validation for FA ranged from 0.47 (linoleic acid) to 0.55 (vernolic acid), and for oil, protein, and seed weight from 0.71 (oil) to 0.86 (seed protein). It was concluded that NIRS calibration equations developed for seed weight and seed quality traits can be satisfactorily used as early screening methods in V. galamensis breeding programs.     

Vibrational Spectroscopy in Assessment of Early Osteoarthritis—A Narrative Review

Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.     

The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar

A novel methodology is proposed to reconstruct 3D tree architectures from terrestrial LiDAR (TLiDAR) scans. The methodology is robust and relatively insensitive to wind- and occlusion-induced artefacts in the 3D TLiDAR point clouds. A quantitative evaluation of structural attributes, like the vertical foliage and wood area profiles, as well as the shoot orientation distribution, was performed. Due to the difficulties of acquiring reliable and accurate estimates of these parameters in the field, an original evaluation approach was chosen that reproduces the TLiDAR scanning and subsequent tree reconstruction process in a virtual environment. In a second step the reconstructed tree models were ingested in a validated 3D radiative transfer model to simulate both their reflectance signatures (observable by space borne instruments) and directional transmission properties (measurable during field campaigns) under various spectral, illumination and tree density scenarios. The results of these evaluations confirm the appropriateness of the proposed tree reconstruction model for the generation of structurally and radiatively faithful copies of existing plant and canopy architectures.     

Mapping the total phosphorus concentration of biosolid amended surface soils using LANDSAT TM data

Conventional methods for soil sampling and analysis for soil variability in chemical characteristics are too time-consuming and expensive for multi-seasonal monitoring over large-scale areas. Hence, the objectives of this study are: 1) to determine changes in chemical concentrations of soils that are amended with treated sewage sludge; and 2) to determine if LANDSAT TM data can be used to map surface chemical characteristics of such amended soils. For this study, we selected two fields in NW Ohio, designated as F34 and F11, that had been applied with 34 and 11 ton acre^− 1 of biosolids, respectively. Soil samples from a total of 70 sampling locations across the two fields were collected one day prior to LANDSAT 5 overpass and were analyzed for several elemental concentrations. The accumulation of Ba, Cd, Cu, S and P were found to be significantly higher in the surface soils of field F34, compared to field F11. Regression equations were established to search for algorithms that could map these five elemental concentrations in the surface soils using six, dark-object-subtracted (DOS) LANDSAT TM bands and the 15 non-reciprocal spectral ratios derived from these six bands for the May 20, 2005, LANDSAT 5 TM image. Phosphorus (P) had the highest R² adjusted value (67.9%) among all five elements considered, and the resulting algorithm employed only spectral ratios. This model was successfully tested for robustness by applying it to another LANDSAT TM image obtained on June 5, 2005. Our results enabled us to conclude that LANDSAT TM imagery of bare-soil fields can be used to quantify and map the spatial variation of total phosphorous concentration in surface soils. This research has significant implications for identification and mapping of areas with high P, which is important for implementing and monitoring the best phosphorous management practices across the region.     

一种新型的高性能近红外平行光管的研究

采用长焦距单透镜方案,研制了一种新型的高性能近红外平行光管。以单模光纤导出的近红外激光(λ=1053nm)为光源,设计了满足光强均匀性要求的准直物镜焦距,使用新型的近红外消光涂料消除了杂散光的影响,并运用双平板剪切干涉技术完成了平行光管的不可见光束调校。采用自制的1053nm近红外干涉系统测试了平行光管的光学质量,结果表明,准直后的出射光束波面峰谷值(PV)达到0.045λ,光强的均匀性达79.2%。     

二维近红外光谱定量分析内燃机油粘度指数性能的研究

针对常规近红外光谱技术测试内燃机油性能指标的不足,提出了以电压为外扰方式,获取内燃机油二维近红外光谱的技术。对内燃机油样品进行二维相关光谱分析可以定性分析内燃机油粘度等级。使用来自不同厂家、不同质量等级的35个内燃机油样品建立多维偏最小二乘（N-PLS）模型对内燃机油粘度指数进行了预测研究。结果表明，内燃机油的二维近红外光谱中含有与粘度指数相关的信息，使用N-PLS模型能够实现对内燃机油粘度指数的定量分析。该方法的提出为研究复杂对象性能指标的快速检测提供了一种新的技术手段。     

近红外光谱分析技术在蛋鸡配合饲料质量监测上的应用

本文采用常用饲料原料参照通用配方,按蛋白、水分、脂肪、纤维的梯度分布配制成58个蛋鸡配合饲料样品。用该配制样品的化学成分计算值建立近红外反射光谱(NIRS)定标方程。并用从哈市饲料研究所试验厂采集的30个原料相同、配方近似的蛋鸡配合饲料样品检验上述定标方程。蛋鸡配合饲料的七项指标的NIRS测定值均达到了与常规化学方法分析值(ME为计算值)相接近的水平。证明了将NIRS技术应用于饲料工厂配合饲料生产线上的产品质量监测是快速可靠的。     

准噶尔盆地地热场特征与油气

本文根据准噶尔盆地760个测温点资料,结合盆地构造演化过程对盆地地热场特征及其与油气的关系进行了探讨。认为准噶尔盆地是一个较冷的盆地,地热场偏低,而且烃源岩偏老,以致生油气门限深度偏大,给勘探工作造成一定困难。     

煤岩及泥炭成熟度热模拟研究--镜质组反射率演化特征及其地质意义

通过高温高压模拟实验，在20℃／h和2℃／h两种升温速率条件下，对沁水盆地山西组和太原组煤岩以及现代泥炭模拟固体产物镜质组反射率Ro演化特征进行了分析和研究．结果表明：1)在336．8～600℃的模拟温度区间，2℃／h和20℃／h两种升温速率条件下，模拟固体产物Ro值太原组煤岩分别为1．07％～4．50％和0．83％～3．97％，山西组煤岩分别为0．98％～4．51％和0．80％～4．05％，泥炭分别为0．96％～4．19％和0．72％～3．72％．2)随着实验温度的增高，煤岩以及泥炭的热模拟固体产物累积Ro值都呈直线增加，并且在实验温度区间范围内，2C／h升温速率条件下的Ro值增长值都要高于20℃／h升温速率条件下的Ro值增长值，说明升温速率是影响有机质热成熟演化过程的重要因素．3)在2种升温速率条件下，煤岩以及泥炭固体产物的Ro值随温度的相对波动较为明显，这主要反映了Ro值热演化的阶段性特征，总体来说，煤岩与泥炭的固体产物镜质组反射率在演化阶段上具有较多的相似性．但是太原组煤岩固体产物的Ro值变化相对幅度较大，而山西组煤岩和泥炭则较小．4)在相同的升温速率条件下，同一温度不同样品之间模拟固体产物的累积Ro值不同，大部分温度点处太原组煤岩Ro值最高，山西组煤岩次之，泥炭最低，并且各个样品之间Ro值的相对变化显，反映了样品性质对模拟固体产物Ro值演化的影响．结合实验所获得的结果和认识，对其地质意义进行了探讨．     

Image-based atmospheric correction of QuickBird imagery of Minnesota cropland

High spatial resolution QuickBird satellite data have provided new opportunities for remote sensing applications in agriculture. In this study, image-based algorithms for atmospheric correction were evaluated on QuickBird imagery for retrieving surface reflectance (ρ_λ) of corn and potato canopies in Minnesota. The algorithms included the dark object subtraction technique (DOS), the cosine approximation model (COST), and the apparent reflectance model (AR). The comparison with ground-based measurements of canopy reflectance during a 3-year field campaign indicated that the AR model generally overestimated ρ_λ in the visible bands, but underestimated ρ_λ in the near infrared (NIR) band. The DOS-COST model was most effective for the visible bands and produced ρ_λ with the root mean square errors (RMSE) of less than 0.01. However, retrieved ρ_λ in the NIR band were more than 20% (mean relative difference or MRD) lower than ground measurements and the RMSE was as high as 0.16. The evaluation of the COST model showed that atmospheric transmittance (T_λ^θ) was substantially overestimated on humid days, particularly for the NIR band because of the undercorrection of water vapor absorption. Alternatively, a contour map was developed to interpolate appropriate T_λ^θ for the NIR band for clear days under average atmospheric aerosol conditions and as a function of precipitable water content and solar zenith angle or satellite view angle. With the interpolated T_λ^θ, the accuracy of NIR band ρ_λ was significantly improved where the RMSE and MRD were 0.06 and 0.03%, respectively, and the overall accuracy of ρ_λ was acceptable for agricultural applications.