Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I₂/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I₂/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.     

PLS在基于动态光谱的人体血液中性粒细胞无创测量中的应用

利用动态光谱指端透射法进行了人体血液中性粒细胞百分比无创测量的研究。对21名健康志愿者进行了在体测量,选用偏最小二乘法(PLS)对获取的动态光谱数据和中性粒细胞百分比实测值进行建模分析,建立的定标集的相关系数为0.922,最大相对误差为5.85%,平均相对误差为4.13%;对定标模型的预测能力进行了验证,其中预测集的相关系数为0.912,预测集的相对误差最大为6.74%,平均相对误差为5.07。结果表明：动态光谱法可以有效地克服测量位置及人体成分等对光谱测量的影响,较准确地进行人体血液中性粒细胞百分比的测量,是一种比较好的血液成分无创测量方法,具有较高的临床应用价值。     

Effect of pressure on the solubility of naphthalene in water at 25°C

Solubility of naphthalene in water was measured at 25°C and pressures up to 200 MPa. The solubility decreased with increasing pressure. From the pressure coefficient of the solubility, the volume change V accompanying the dissolution was estimated as 13.8±0.4 cm ³ -mol ^–1 . Further we estimated the volume change V _CH accompanying hydrophobic hydration as –0.1±0.6 cm ³ -mol ^–1 using the V value, the molar volume of crystalline naphthalene, and the partial molar volume of naphthalene in n-heptane. This V _CH is much larger (i.e., less negative) than that for hydrophobic hydration of alkyl-chain compounds and suggests that the hydration structure of naphthalene differs from that of alkyl-chain compounds.     

The effect of high pressure on the ion pair equilibrium constant of alkali metal fluorides: A spectrophotometric study

Bromophenol blue indicator was used in UV-visible spectrophotometric measurements to study ion association constants of alkali metal fluorides. The equilibrium constants for the ion pair formation of the alkali metal fluorides were determined as a function of ionic strength at one atmosphere pressure and 25°C. The effect of pressure on these association constants was measured at a constant total ionic strength of 1.0 mol-kg^–1 over a pressure range of 1 to 2000 atmospheres at 25°C. The pressure dependences of the stoichiometric association constants of the alkali metal fluorides are given by: lnK _LiF ^* =0.77–2.47×10^–4P–2.12×10^–8P²; lnK _NaF ^* =0.53–1.08×10^–4P–1.66×10^–8P²; lnK _KF ^* =0.24–4.41×10^–5P–7.15×10^–8P²; lnK _RbF ^* =–0.17–8.65×10^–5P–4.51×10^–8P²; and lnK _CsF ^* = –0.37–1.14×10^–4P–6.82×10^–8P², where P is the pressure in atmospheres. The stoichiometric molar volume and compressibility changes for ion pair formation of the alkali metal fluorides were evaluated from the pressure dependence of K _MF ^* data. The thermodynamic association constants were also calculated making use of activity coefficient data from the Pitzer equations. The partial molal volume and compressibility changes for ion pair formation of each alkali metal fluoride are reported.     

不可逆电极过程方波伏安法动力学研究 Ⅰ.平行催化体系

本文报道了在不可逆电极过程的强催化体系中，当采用快速方波扫描时，正、逆向电流将出现峰形。依据实验结果，提出了“迁出－进入”的等浓度面理论模型，推导了并讨论了电流方程式。结果表明，文中提出的理论能较好地说明实验结果。     

Determination of Partial Molar Volumes of EPA and DHA Ethyl Esters in Supercritical Carbon Dioxide

The use of supercritical fluids (SCFs) as solvents has been attracting widespread interest in the research and commercial applications. The study of partial molar volumes of solutes at infinite dilution in the near-critical region is of considerable fundamental importance, as these data reflects the interactions occurring between the solute and the solvent. Experimental measurements of partial molar volumes may be divided into two categories: direct and indirect methods. The direct methods…