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.     

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.     

干旱胁迫对3种冷季型草坪草光谱反射率及生理特征的影响

为探讨高光谱分析技术在草坪草水分监测上的应用,以草地早熟禾、高羊茅、多年生黑麦草为试验材料,测定了干旱胁迫下3种冷季型草坪草生理指标及光谱反射率。结果表明:随着干旱胁迫程度的加深,供试草坪草叶片相对含水量和叶绿素含量显著下降(P0.05),丙二醛含量、脯氨酸含量及敏感波段的(550和760 nm)的光谱反射率显著上升(P0.05),3种草坪草抗旱性能的强弱顺序为:高羊茅多年生黑麦草草地早熟禾。3种草坪草敏感波段光谱反射率(R_(550 nm),R_(760 nm))及高光谱参数(Rg,R0,GNDVI)与生理指标间呈显著或极显著相关,以R_(550 nm)与生理指标间的相关系数绝对值最大。同时R_(550 nm)构建了供试草坪草生理指标的估测模型,经检验,模型估测值与实测值相关性达显著或极显著水平。     

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer ...     

近红外反射技术建立合肥地区精米直链淀粉含量测定模型

以合肥地区种植的203份水稻材料为检测对象,用近红外反射技术采集光谱,常规化学方法测定精米直链淀粉含量。结果表明,定标样品的直链淀粉含量分布范围为3.439%～28.046%,代表性和连续性良好。采用多种计量数学处理方法和偏最小二乘法(PLS),优化建立了精米直链淀粉含量的定量分析预测模型。定标集(C-Set)样品数132个,相关系数(Rc)0.9278,定标标准差(SEC)1.6582;验证集(V-Set)样品数67个,相关系数(Rv)0.8736,预测标准差(SEP)1.9083,并证实所建立的模型在测定精米直链淀粉含量上具有很好的准确性和实用性,对合肥地区水稻品质育种及种质资源相关研究具有实用价值。     

开都河流域下游绿洲土壤盐渍化特征及其光谱分析

以新疆开都河流域下游绿洲为研究区,通过土壤样品化学分析及其光谱反射率的测量,分析了土壤盐渍化特征以及土壤反射光谱曲线与土壤盐渍程度之间的关系;基于统计分析的方法,建立了表征土壤盐渍化特征的因子与其高光谱数据的多元回归模型。研究结果表明:研究区土壤pH值的平均值大于8.0,属于弱碱性土壤;土壤盐分含量呈T型分布;盐渍化土壤的光谱反射率曲线特征在形态上相似,变化平缓;在可见光-近红外波段,盐渍化土壤的光谱反射率曲线形状基本上由五个折线段和四个吸收带组成。     

RAPID DIAGNOSIS OF NUTRIENT ELEMENTS IN FINGERED CITRON LEAF USING NEAR INFRARED REFLECTANCE SPECTROSCOPY

In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQ_CAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g^?1), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g^?1) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.