Evaluation of Transmittance and Reflectance Pulse Oximetry in a Canine Model of Hypotension and Desaturation

Ten, anesthetized dogs were instrumented with three pulse oximeter probes; two lingual transmittance probes and one rectal reflective probe. Arterial oxygen desaturation was produced by decreasing the inspired oxygen concentration. Hypotension was produced with an infusion of nitroprusside. Simultaneous pulse oximeter readings (SpO₂) were compared to co-oximeter measured arterial saturation (SaO₂) collected over a range of SaO₂ (50–100%) and mean arterial pressures (40–100mmHg). Each of the monitors and means of evaluating SpO₂ studied provided accurate SpO₂ measurements over a range of mean arterial pressure from 40–100mmHg. All of the monitors tested tended to overestimate the SaO₂ when the arterial saturation was less than 70%.     

Prediction of chemical composition and peroxide value in unground pet foods by near‐infrared spectroscopy

The massive development of the pet food industry in recent years has lead to the formulation of hundreds of canine and feline complete extruded foods with the objective of meeting both the needs of the animals and numerous demands from pet owners. In the meantime, highly variable raw material compositions and the industry's new production techniques oblige manufacturers to monitor all phases of the extrusion process closely in order to ensure the targeted composition and quality of the products. This study aimed at evaluating the potential of infrared technology (visible and near‐infrared spectrophotometer; 570–1842 nm) in predicting the chemical composition and peroxide value (PV) of unground commercial extruded dog foods. Six hundred and forty‐nine commercial extruded dog foods were collected. For each product, an unground aliquot was analysed by infrared instrument while a second aliquot was sent to a laboratory for proximate analysis and PV quantification. The wide range of extruded dog food typologies included in the study was responsible for the wide variability observed within each nutritional trait, especially crude fibre and ash. The mean value of the 208 pet foods sampled for PV quantification was 17.49 mEq O₂/kg fat (min 2.2 and max 94.10 mEq O₂/kg fat). The coefficients of determination in cross‐validation of NIRS prediction models were 0.77, 0.97, 0.83, 0.86, 0.78 and 0.94 for moisture, crude protein, crude fat, crude fibre, ash and nitrogen‐free extract (NFE) respectively. PV prediction was less precise, as demonstrated by the coefficient of determination in cross‐validation (0.66). The results demonstrated the potential of NIRS in predicting chemical composition in unground samples, with lower accuracy for moisture and ash, while PV prediction models suggest use for screening purposes only.     

Pedotransfer functions to assess adsorbed phosphate using iron oxide content and magnetic susceptibility in an Oxisol

Adsorbed phosphate in soils can be chemically extracted; however, this process is both time‐consuming and not cost‐effective if large numbers of samples have to be analysed. Indirect assessment of adsorbed phosphate by pedotransfer functions (PTFs) can help optimize fertilizer strategies. This study aimed to evaluate the spatial variability of adsorbed phosphate (P_ads), iron oxides and magnetic susceptibility (MS) in oxisols and to calibrate PTFs to predict P_ads. A total of 308 soil samples were collected from Hapludox and Eutrudox soils formed from sandstone in Brazil. The contents of clay (196–607 g/kg), iron oxides (40–165 g/kg), MS (1.2–29 × 10^?6 m³/kg) and P_ads (327–842 mg/kg) were in the range of typical values for these highly weathered soils. This study showed that the attributes studied were spatially dependent. Geomorphic surfaces enabled understanding of spatial variability and helped to develop a more efficient sampling scheme to calibrate PTFs. Moreover, the adsorbed phosphate in these oxisols could be predicted by a PTF using iron oxides and MS as predictors. The MS attribute enabled the most accurate prediction (concordance coefficient = 0.95, root‐mean‐square error = 46 mg/kg and relative improvement in root‐mean‐square error = ?4.12) of spatial variability through PTF compared to other predictors.     

基于机器学习算法的土壤有机质 质量比估算

为快速高效地估测干旱、半干旱地区土壤有机质(soil organic matter, SOM)质量比,提出了一种结合竞争适应重加权法(CARS)和随机森林(RF)的估测模型.以内陆干旱区艾比湖流域为研究区,测定土壤高光谱反射率和SOM质量比,经预处理后,利用CARS对原始光谱(R)、一阶导数(R′)、吸光度(log(1/R))及吸光度一阶导数［log(1/R)］′4种光谱变量的可见-近红外光谱进行筛选,并结合RF算法,建立全谱段RF模型与CARS-RF模型.结果表明,基于CARS方法对光谱进行变量筛选后,得出4种光谱变量的优选变量集个数分别为35,26,34和121;在4种光谱变量中,R′和［log(1/R)］′的SOM估测模型精度较高,以［log(1/R)］′为基础数据获得的模型精度最高;CARS-RF模型精度优于全谱段RF模型,模型验证集决定系数(R²)、均方根误差(RMSE)、相对分析误差(RPD)分别为0.881,6.438 g/kg和2.177.该研究在预处理的基础上通过变量优选,应用较少的变量个数获得较高的估测精度,为干旱、半干旱区SOM高光谱估测提供了适宜高效的方法.     

水盐交互作用对河套灌区土壤光谱特征的影响

为探究土壤水盐交互作用对Sentinel-2卫星光谱特征的影响，该研究以内蒙古河套灌区沙壕渠灌域为研究区域，分别在2018年和2019年的4-5月共采集280个裸土期表层土壤样本，测定其土壤含水率和含盐量，并获取同步的Sentinel-2卫星遥感数据，构建基于土壤水盐-反射率原理的土壤光谱特征理论模型，在此基础上结合土壤水盐交互作用构建水盐交互模型，并比较2种模型对土壤光谱的模拟效果，分析土壤水盐交互作用对土壤光谱估算的影响。结果表明：1）土壤水盐交互作用对光谱的影响因波段类型和水盐含量的不同而有所不同。在可见光范围上影响相对较弱，其作用范围为-0.11～0.29；在近红外和短波红外范围上影响相对较强，其作用范围为-0.35～0.61；当水分或盐分中某个含量较高时对光谱影响较弱，其主要集中在-0.1～0.23；，在水盐含量程度相似时影响较强，其作用范围为0.3～0.6。2）与土壤光谱特征理论模型相比，水盐交互模型能明显地改善土壤光谱的模拟效果，能将模拟相关系数由0.14～0.44提升到0.29～0.70，均方根误差由0.032～0.082降低到0.029～0.068。该研究结果揭示了盐分和水分对光谱特征的干扰过程，为土壤盐分的估算提供策略与方法，对实现区域尺度上土壤盐分的精准监测具有一定的意义。     

东京野茉莉花色成分的初步研究

对东京野茉莉花色成分进行了初步的探究。结果表明:通过特征颜色反应和光谱扫描说明了其花瓣细胞内有异黄酮、儿茶精类,还有叶绿素a、b,可能含有胡萝卜素、黄酮类化合物,以及无色花色素,可能不含查耳酮或橙酮。     

Estimation of litchi (Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra

The estimation of crop nitrogen status in fresh vegetation leaf using field spectroscopy is challenging due to the weak responses on leaf/canopy reflectance and the overlapping with the absorption features of other compounds. Although the spectral indices were proposed in the literature to predict leaf nitrogen content (LNC), the performance of selected spectral indices to estimate the LNC is often inconsistent. Moreover, the models for nitrogen content estimation changed with the growth stage. The goal of this study was to evaluate the performance of published indices, ratio of data difference index (RDDI) and ratio of data index (RDI) developed by band iterative-optimization algorithm in LNC estimation. The correlation analysis, linear regression and cross validation were used to analyze the relationship between spectral data and LNC and construct the best performed estimation model. The study was conducted by the data of five growing seasons of litchi from the orchards in Guangdong Province of China. Results showed that the relationship between chlorophyll (Chl) related spectral indices and LNC varied with the growth stage. Even in flower bud morphological differentiation stage and autumn shoot maturation stage, there were not significant correlations between the proposed spectral indices and LNC. Besides it is difficult to estimate the LNC by the general model across the growth stages due to the integrated effects of cultivar, biochemical, canopy structure, etc. The band iterative-optimization algorithm can improve the sensitivity of spectral data to LNC to some extent. The optimal RDDI performed better than other indices for the synthetic dataset and the dataset in each growth stage. And the sensitive bands selected in the optimal indices at each growth stage are not consistent, which are not only related to the Chl absorption but also other biochemical components, such as starch, lignin, cellulose, protein, etc. In general, the LNC can be estimated by the optimized CR-based RDDI indices in autumn shoot maturation stage, flower spike stage, fruit maturation stage, and flowering stage with the R² > 0.50 and RMSE < 0.14. Although there were the significant relationship between RDIs and RDDIs in flower bud morphological differentiation stage, the highest R²s of the model developed by RDDIs and RDIs were less than 0.50 in cross validation. This study indicated that the applicability of canopy reflectance to estimate litchi LNC was closely related to the growth stage of litchi. Growth stage-specific models will be preferred for estimating litchi LNC estimation.     

Influence of Soil Particle Size on the Measurement of Sodium by Near-Infrared Reflectance Spectroscopy

This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0–10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100–2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R²) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg^–1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg^–1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R² and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg^–1) and 0.80 (RMSECV: 0.03 mg kg^–1) for SPS-0.053 and SPS-0.212 samples, respectively.     

黄连及黄连须、黄连茎叶体外抗氧化活性的谱效关系研究

测定黄连与黄连须、黄连茎叶主要生物碱含量并建立高效液相(HPLC)指纹图谱,分析其抗氧化活性的谱效关系。采用HPLC分析比较黄连及黄连须、黄连茎叶中小檗碱、表小檗碱、药根碱、黄连碱、巴马汀含量差异并建立指纹图谱,通过羟自由基(·OH)、1,1-二苯基-2-三硝基苯肼(DPPH)自由基、2,2-联氮基双(3-乙基苯并噻唑啉-6-磺酸)(ABTS)自由基体外清除试验与总还原力试验评价其体外抗氧化活性差异,应用Pearson相关系数法分析HPLC指纹图谱与抗氧化活性的谱效关系。结果发现小檗碱、巴马汀含量为黄连>黄连茎叶>黄连须;药根碱、黄连碱和表小檗碱含量为黄连>黄连须>黄连茎叶;总还原力为黄连>黄连茎叶>黄连须,·OH、DPPH、ABTS·+自由基清除力为黄连茎叶>黄连>黄连须;总还原力与各峰均有较强相关性,·OH、DPPH、ABTS·+自由基清除力与4号峰相关性较强。说明黄连茎叶体外抗氧化活性较强,黄连须与黄连茎叶均有一定药用开发价值。     

基于近红外光谱法的紫花苜蓿品质综合评价

探索近红外光谱法测定紫花苜蓿品质的可行性,采用近红外光谱法与常规方法对8个紫花苜蓿品种(系)的粗蛋白、粗脂肪、粗灰分、酸性洗涤纤维、中性洗涤纤维、钙、磷和干物质等8项指标进行测定。利用配对T检验、多重比较和回归分析等方法检验近红外光谱法测定结果的可行性,运用灰色关联度分析对苜蓿品质进行综合评价。结果表明,粗蛋白、粗灰分、中性洗涤纤维、酸性洗涤纤维、磷和干物质等6项指标用近红外光谱法测定可信度较高。紫花苜蓿品质评价指标可简化为粗蛋白、粗灰分、中性洗涤纤维、酸性洗涤纤维和干物质等5项。灰色关联度分析综合表现较好的苜蓿品种(系)有WL358、WL440HQ、WL656HQ和WL366HQ。