无效变量消除法在油菜籽芥酸近红外无损速测中的应用

探索改善油菜籽芥酸近红外预测模型准确度与精密度的方法,利用无效变量消除法（UVE）,对135个油菜籽样品近红外光谱信号进行筛选,并利用筛选后的光谱对油菜籽芥酸含量进行偏最小二乘法交叉验证。结果表明,UVE法筛选变量后建立的芥酸校正模型对未知样品预测结果的准确度和速度显著优于全波长参与建立的芥酸校正模型。散射校正加一阶导数对光谱预处理,UVE法筛选变量,偏最小二乘法交叉验证建立的校正模型效果最好,其预测值与标准值的相关系数R达到0.92,交叉验证预测均方差为2.2。因此,用UVE进行波长选择后建立的近红外模型,能准确快速地对油菜籽芥酸含量进行定量分析。     

Prediction of NaCl,nitrate and nitrite contents in minced meat by using a voltammetric electronic tongue and an impedimetric sensor

A method for predicting levels of sodium chloride, sodium nitrite and potassium nitrate in minced meat by using a combination of two different electrochemical methods; namely an electronic tongue (ET) based on pulse voltammetry and electrochemical impedance spectroscopy measurements is proposed here. The measurements with the voltammetric ET were carried out on both saline solutions (brines) and minced meat, whereas the impedimetric sensor was used only in minced meat. The addition of the salts was performed following an experimental design in which a system of three compounds/three levels was established. Multivariate analysis including Cross validation and Partial Least Square (PLS) techniques were applied for data management and prediction models building. A very good prediction of the concentration of chloride was achieved, whereas the prediction for the concentration of nitrate and nitrite can be considered as moderate.     

递推PLS方法及其在过程监控中的应用

针对处理批量数据时传统的偏最小二乘(PLS)模型无法在线更新的问题,提出了结合遗忘因子法的递推PLS算法,进而更新Qa控制限,有效地克服了传统PLS中Qa无法反映系统时变性的缺点。并对异丁烯酸甲酯(MMA)聚合反应过程中的数据进行了仿真分析。研究结果表明,递推PLS方法大大降低了监控过程中的误报率和漏报率,提高了监控系统的性能。     

基于iPLS原理最优化信息区间的桃糖度组合权重PLS模型研究

采用反向区间偏最小二乘法和组合区间偏最小二乘法优化桃糖度可见/近红外光谱的信息区间组合,在选择的信息区间的基础上建立了一种线性组合权重PLS模型.对近红外光谱进行二阶导数处理、卷积平滑校正后,发现在区间分割数为15时筛选结果最优,BiPLS所选择的信息区间为742~770nm和 862~920nm,SiPLS所选择的信息区间为742~770nm、832~860nm和892~920nm.直接组合信息区间BiPLS和SiPLS模型的RMSEP值分别为0.386和0.308,线性组合权重PLS模型的RMSEP值分别为0.351和0.364.结果说明在近红外定量分析中线性组合权重模型的建立克服了复杂样品各信息区间对PLS建模贡献率不一样的问题.     

Dynamic nonlinear soft sensor modelling method using linear slow feature analysis and least squares support vector regression for batch processes

Data-driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low-volume and high value-added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data-driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.     

Comparative analyses of apple aroma by a tin-oxide gas sensor array device and GC/MS

The apple cultivars “fuji”, “jina” and “huaniu” aroma volatiles were collected and analyzed using a tin-oxide gas sensor array device and the gas chromatography combined with mass spectrometry (GC-MS). Twenty two of the most abundant volatile compounds were taken into account for further study. Eight compounds were found in every cultivar. The principal components analysis (PCA), partial least squares (PLS) and back-propagation feed-forward artificial neural network (BP-ANN) were used to analyze the sensor array and SPME-GC-MS measurements. From the plots of the first two PCs by PCA, different apple cultivars could be clearly distinguished by SPME-GC-MS measurements, while there was slight overlap by sensor array measurements. BP-ANN was used to distinguish different cultivars based on gas sensor array responses, and the accuracy was 87%. Due to the composition of gas sensors in the array, results of PLS models showed that the correlation between fourteen gas sensor array responses and the two PCs of twenty-two compounds were better than the correlation between those and each volatile compound. Furthermore, an ANN was used to build the relationship between the two predicted PCs by PLS model and the three cultivars. The recognition probability was increased to 97%.     

Nondestructive determination of soluble solids content and pH in tomato juice using NIR transmittance spectroscopy

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The r _c, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices.     

Distributed process monitoring framework based on decomposed modified partial least squares

With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large‐scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large‐scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality‐based distributed process monitoring scheme is proposed. Firstly, the Girvan‐Newman (GN) algorithm in complex network divides process variables into multiple sub‐blocks. Secondly, greedy algorithm‐based high‐dimensional mutual information (HDMI) is used to extract quality‐related variables in each sub‐block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality‐related or not in each sub‐block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub‐blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme.     

Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy

Total volatile basic nitrogen (TVB-N) content is one of important index of pork’s freshness, and Warner–Bratzler shear force (WBSF) is seen as the important index of pork’s tenderness. This paper attempted the feasibility to determine TVB-N content and WBSF in pork by Fourier transform near infrared (FT-NIR) spectroscopy. Synergy interval partial least square (SI-PLS) algorithm was performed to calibrate regression model. The number of PLS factors and the number of intervals were optimised simultaneously by cross-validation. The performance of the model was evaluated according to two correlation coefficients (R) in calibration and prediction sets. Experimental results showed that the correlations coefficients in the calibration set (R_c) and prediction set (R_p) were achieved as follows: R_c = 0.8398 and R_p = 0.8084 for TVB-N content model; R_c = 0.7533 and R_p = 0.7041 for WBSF model. The overall results demonstrated that NIR spectroscopy combined with SI-PLS could be utilised to determinate TVB-N content and WBSF in pork.     

The analysis of sensory and chemical–physical variables via multivariate additive PLS splines

When the information given by a panel of judges on wine sensory characteristics is resumed in a three-way table, Y, and the objective is to extract knowledge from a chemical data-set, X, which has a predictive power for wine sensorial variables, the study of the relationships between chemical–physical variables and sensorial data sets is really complex. In this paper after resuming the information in Y, we propose to consider multivariate additive partial least squares via splines, recently presented in literature, to predict Y by X.