Partial Least Squares Algorithm Research Articles

Non-destructive analysis of germination percentage, germination energy and simple vigour index on wheat seeds during storage by Vis/NIR and SWIR hyperspectral imaging.

Two hyperspectral imaging (HSI) systems, visible/near infrared (Vis/NIR, 304-1082nm) and short wave infrared (SWIR, 930-2548nm), were used for the first time to comprehensively predict the changes in quality of wheat seeds based on three vigour parameters: germination percentage (GP, reflecting the number of germinated seedling), germination energy (GE, reflecting the speed and uniformity of seedling emergence), and simple vigour index (SVI, reflecting germination percentage and seedling weight). Each sample contained a small number of wheat seeds, which were obtained by high temperature and humidity-accelerated aging (0, 2, and 3days) to simulate storage. The spectra of these samples were collected using HSI systems. After collection, each seed sample underwent a standard germination test to determine their GP, GE, and SVI. Then, several pretreatment methods and the partial least-squares regression algorithm (PLS-R) were used to establish quantitative models. The models for the Vis/NIR region obtained excellent performance, and most effective wavelengths (EWs) were selected in the Vis/NIR region by the successive projections algorithm (SPA) and regression coefficients (RC). Subsequently, PLS-R-RC models using selected wavebands (sixteen wavebands for GP, 14 wavebands for GE, and 16 wavebands for SVI) exhibited similar performance to the PLS-R models based on the full wavebands. The best R2 results obtained in the simplified models' prediction sets were 0.921, 0.907, and 0.886, with RMSE values of 4.113%, 5.137%, and 0.024, for GP, GE, and SVI, respectively. Distribution maps of GP, GE, and SVI were produced by applying these simplified PLS models. By interpreting the EWs and building prediction models, soluble protein and sugar content were demonstrated to have a relationship with spectral information. In summary, the present results lay a foundation towards the development of a significantly simpler, more comprehensive, and non-destructive hyperspectral-based sorting system for determining the vigour of wheat seeds.

Black tea withering moisture detection method based on convolution neural network confidence

AbstractDeep learning method was applied to rapidly and nondestructively predict the moisture content in withered leaves. In this study, a withering moisture detection method based on confidence of convolution neural network (CNN) was proposed. The method used data augmentation to preprocess the original image. The prediction results obtained by the CNN model were compared with the results of traditional partial least squares (PLS) and support vector machine regression (SVR) models. The results clarified that the quantitative prediction model of the moisture content in withering leaves based on the confidence of convolutional neural network has the best prediction performance. The performance parameters of the optimal prediction model: correlation coefficient (Rp), root‐mean‐square error of external verification set (RMSEP) and relative standard deviation (RPD) are 0.9957, 0.0059, and 9.5781, respectively. Compared with traditional linear PLS and nonlinear SVR algorithms, deep learning method can better characterize the correlation between images and moisture. The moisture‐related information in the image can be extracted to a greater degree by the convolution kernel of the convolutional neural network. The model has better generalization, which can rapidly and nondestructively predict the moisture content in withered leaves.Practical applicationsCNN is increasingly used in food technology. This study solves the problem that the withered leaves moisture content cannot be quantitatively predicted based on the confidence of the proposed CNN. Compared with traditional machine vision methods, our proposed CNN model can retain more original information in addition to the color and texture features of withered leaves. And it can quickly and accurately judge the moisture content without destroying the tissue components of the withered leaves. This study is of great significance to the intelligence of black tea processing equipment. Simultaneously, the proposed model based on deep learning provides a new idea for the intelligent detection of black tea withering process.

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model’s coefficients of determination (R2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha−1 and 786.5 kg ha−1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion.

Application of chemometric methods for the voltammetric determination of tryptophan in the presence of unexpected interference in serum samples

An analytical methodology based on differential pulse voltammetry (DPV) on an Au nanoparticles modified carbon paste electrode (AuNPs/CPE) and the partial least-squares (PLS) algorithm for the determination of tryptophan (Trp) in the presence of unexpected electroactive interference, tyrosine (Tyr) with overlapped peaks, in serum samples was described. At first, the synthesized Au nanoparticles were characterized by scanning electron microscopy (SEM) and the suitability of AuNPs/CPE for determination of Trp in the presence of Tyr was checked using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Then experimental conditions were optimized with central composite rotatable design (CCRD) and response surface methodology (RSM), involving several chemical and instrumental variables. After optimization, the resolution of the two multicomponent mixtures has been accomplished by PLS for voltammmetric studies of Trp. Finally, the proposed method was found to be applicable for the analysis of the Trp in the presence of unexpected interference in serum samples.

Rapid Determination of Total Polar Compounds in Frying Oil Using ATR‐FTIR Combined with Extended Partial Least Squares Regression

AbstractA method of rapidly determining the total polar compounds (TPCs) in frying oils using attenuated total reflectance‐Fourier transform infrared spectroscopy combined with partial least squares (PLS) regression is developed. Oils of various types and geographic origins are used to ensure that the proposed model is robust. The first derivative spectrum is selected as the spectral processing method. The interval PLS, forward interval PLS, and backward interval PLS algorithms are compared in terms of their performance. A correlation coefficient (R2) of 0.9942, a root mean square error of calibration (RMSEC) of 1.1, a root mean square error of prediction (RMSEP) of 2.30, a residual predictive deviation (RPD) of 4.1, and a limit of detection (LOD) of 1.65% are obtained by the fiPLS33 model with fewer latent variables and a lower spectral interval number. In addition, sub‐models using a single type of oil showed higher performance (R2 0.9957–0.9998, RMSEC 0.12–0.92, RMSEP 0.79–1.58, RPD 4.79–9.64, LOD 0.66–1.26%) than the general model. The TPC models developed are accurate, stable, and adaptable, and they can be used to analyze general frying oil samples quickly, regardless of the oil type, and to analyze samples of specific oil types accurately.Practical applications: The content of TPCs is an important indicator of whether the oil has been overused and whether it will be harmful during the frying process. However, traditional chemical methods are time‐consuming, and they have not been used to determine large‐sized samples. In addition, due to a lack of regional optimization, most studies on determining TPCs with FTIR give unsatisfactory model performance. A general TPC model that incorporates several oil types and regional optimization is expected to improve prediction performance. Therefore, the proposed method represents a rapid and accurate tool for measuring TPCs in edible fats and oils.

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model’s prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.

Nonlinear sparse partial least squares: an investigation of the effect of nonlinearity and sparsity on the decoding of intracranial data

Objective. Partial Least Squares (PLS) regression is a suitable linear decoder model for correlated and high dimensional neural data. This algorithm has been widely used in the application of brain–computer interface (BCI) for the decoding of motor parameters. PLS does not consider nonlinear relations between brain signal features. The nonlinear version of PLS that considers a nonlinear relationship between the latent variables has not been proposed for the decoding of intracranial data. This nonlinear model may cause overfitting in some cases due to a larger number of free parameters. In this paper, we develop a new version of nonlinear PLS, namely nonlinear sparse PLS (NLS PLS) and test it in BCI applications. Approach. In motor related BCI systems, improving the decoding accuracy of both kinetic and kinematic parameters of movement is crucial. To do this, two BCI datasets were chosen to decode the force amplitude and position of hand trajectory using the nonlinear and sparse versions of PLS algorithm. In our new NLS PLS method, we considered a polynomial relationship between the latent variables and used the lasso penalization in the latent space to avoid overfitting and to improve the decoding accuracy. Main results. Some linear and nonlinear based PLS models and our new proposed method, NLS PLS, were applied to the two datasets. According to our results, significant improvement from the NLS PLS method is confirmed over other methods. Our results show that nonlinear PLS outperforms generic PLS in the force decoding but it has lower accuracy in the hand trajectory decoding because of high dimensional feature space. By using lasso penalization, we presented a sparse nonlinear PLS-based model that outperforms generic PLS in both datasets and improves the coefficient of determination, 34% in the force decoding and 10% in the hand trajectory decoding. Significance. We constructed a simple PLS-based model that considers a nonlinear relationship between features and it is also robust to overfitting because of using the lasso penalty in the latent space. This model is suitable for a high dimensional and correlated datasets, like intracranial data and can improve the accuracy of estimation.

Semi-supervised partial least squares

Traditional supervised dimensionality reduction methods can establish a better model often under the premise of a large number of samples. However, in real-world applications where labeled data are scarce, traditional methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method by using partial least squares (PLS) which we call semi-supervised partial least squares (S2PLS). To combine the labeled and unlabeled samples into a S2PLS model, we first apply the PLS algorithm to unsupervised dimensionality reduction. Then, the final S2PLS model is established by ensembling the supervised PLS model and the unsupervised PLS model which using the basic idea of principal model analysis (PMA) method. Compared with unsupervised or supervised dimensionality reduction algorithms, S2PLS not only can improve the prediction accuracy of the samples but also enhance the generalization ability of the model. Meanwhile, it can obtain better results even there are only a few or no labeled samples. Experimental results on five UCI data sets also confirmed the above properties of S2PLS algorithm.

Knowledge based recursive non-linear partial least squares (RNPLS)

Soft sensors driven by data are very common in industrial plants to perform indirect measurements of difficult to measure critical variables by using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the colinearity of the predictor variables, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed in many applications to model the linear relations between process variables, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and time-varying characteristics of the processes. In this work, a new knowledge-based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is set up by carrying out the PLS regression over the augmented input matrix, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the system’s nature, and takes into account the available knowledge about the process, which is provided by expert knowledge or emulated using software tools. Then, the recursive exponential weighted PLS is used to modify and adapt the model according to the process changes. This RNPLS algorithm has been tested using two case studies according to the available knowledge, a real industrial evaporation station of the sugar industry, where the expert knowledge about the process permits the formulation of the relationships, and a simulated wastewater treatment plant, where the necessary knowledge about the process is obtained by a software tool. The results show that the methodology involving knowledge regarding the process is able to adjust the process changes, providing highly accurate predictions.

Development of a Facile and Convenient Method for Sugar Determination in Low Moisture Confectioneries and Honeys Using Fourier Transform Infrared Attenuated Total Reflectance Spectroscopy and Chemometrics

A method using Fourier transform infrared with attenuated total reflectance spectroscopy and partial least squares (PLS) chemometric analysis to simultaneously quantify the mass percent concentrations of glucose, fructose, and sucrose, commonly found in honey and low moisture confectionery food products, was developed. A three-component full factorial design was generated as a training set (n = 64) to predict the sugar concentrations in a Box-Behnken testing set (n = 16). Analysis of the entire spectral region from 649.99 to 3996.38 cm−1 using Savitzky–Golay signal processing technique and PLS algorithm garnered optimal errors of prediction and better linearity between predicted and measured concentrations in the test set compared to analysis using a specific spectral region from 800 to 1500 cm−1 and using a wide array of signal processing techniques (first derivative, second derivative, moving average, binning, and standard normal variate). The model further detected these sugar concentrations in 3 standard honeys, 12 commercial honey samples, 8 honey adulterants, 13 low moisture confectionery food samples, and 15 unknown honey samples from Louisiana apiaries. Applying principal component and clustering analysis also enabled the discrimination of these honey samples based on their fructose:glucose ratios. Thus, the developed method represents a potentially novel, simple, accurate, nondestructive, and rapid means of quantifying concentrations of glucose, fructose, and sucrose in honey and low-moisture confectioneries in 1–2 minutes.

Development of Detection System with Low Predictive Errors for Determining Vitamin C Content of Indian Jujube

Herein, we developed a nondestructive detection system with low prediction errors for determining the vitamin C content in Indian jujube. This system comprises a Ge photodetector, a halogen lamp and five near-infrared (NIR) bandpass filters. The detection of vitamin C is enabled by the absorption of its OH and CH2 bonds in the NIR region. The light beams of our system were parallel-polarized and designed to be incident on the fruit at the Brewster angle (θB), which reduces reflectance noise from the fruit’s skin and enhances the OH and CH2 absorption signals of the fruit’s flesh. After the reflectance signal was analyzed by the partial least squares (PLS) algorithm to obtain the predicted vitamin C content of each fruit, the coefficient of prediction ( r p 2 ) and root-mean-square error of prediction (RMSEP) were calculated. When wavelengths of 1200, 1400, 1450, 1500 and 1550 nm were used for probing, r p 2 and RMSEP of the system detecting vitamin C were 0.84 and 1.65 mg/100 g, respectively. In summary, the vitamin C content of Indian jujube was predicted using a low-cost NIR detection system having a high r p 2 and low RMSEP; further, it comprises five parallel-polarized NIR beams and the PLS algorithm.

Chemical Composition of Hexene‐Based Linear Low‐Density Polyethylene by Infrared Spectroscopy and Chemometrics

AbstractMid and near infrared (MIR and NIR) spectroscopy associated with the partial least squares (PLS) method makes it possible to rapidly characterize the composition of linear low‐density polyethylene (LLDPE) in a large range of 1‐hexene content from 0 to 21 mol%. LLDPEs are produced using zirconocene catalysts activated with methylaluminoxane. PLS regression methods for MIR and NIR are constructed from this series of LLDPEs to quantify the 1‐hexene content in unknown copolymers. In this case, the PLS regression method aims to correlate the 1‐hexene content in the copolymers with their IR spectra. Multivariate calibration models are constructed by the PLS algorithm on pretreated data of MIR and NIR analyses. They are tested and validated by comparing results obtained by nuclear magnetic resonance and the PLS analyses for four unknown ethylene‐1‐hexene copolymers.

Waste crime in Italian Regions: A Structural Equation Approach

The main aim of this paper is to analyze the determinants of waste crime in Italy by employing a Structural Equation Approach estimated by the Partial Least Square algorithm (PLS-SEM). By treating waste crime as a latent variable that is directly related to its causes and effects, the PLS-SEM methodology allows us to derive a Waste Crime Index (WCI) at the regional level over the period from 2004 to 2016. This analysis decomposes the overall effects of each cause of WCI from the direct and indirect components. Accordingly, we provide some hints to design more effective policies to prevent waste crime.

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process.

Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.

Simultaneous estimation of amlodipine and atorvastatin by micelle-augmented first derivative synchronous spectrofluorimetry and multivariate analysis.

Five Selective, rapid and sensitive spectrofluorimetric methods were performed in this study for the simultaneous estimation of amlodipine besylate (AML) and atorvastatin (ATR) in their binary mixtures and combination polypills that are used for management of cardiovascular conditions. The first method depends on micelle-enhanced first derivative synchronous fluorimetric analysis (method I) and the other four methods are multivariate analysis techniques based on the use of factor-based calibration prediction methods comprising partial least squares (PLS), Principal Component Regression (PCR), genetic algorithm PLS (GA-PLS) and genetic algorithm PCR (GA-PCR). The synchronous fluorescence spectra of the solutions were measured at a constant wavelength difference; Δλ = 100 nm. The magnitudes of the peaks of the first derivative spectra (1D) were measured at 292 nm and 387 nm for ATR, and AML correspondingly. The multivariate models were constructed utilizing fifteen mixtures as a calibration set and ten mixtures as a validation set. The linearity of all the methods was in the concentration ranges of (0.1-4.0 μg mL-1, 0.4-10.0 μg mL-1) for AML and ATR, correspondingly. Statistical analysis revealed no significant difference between the proposed methods and the reference method. The validity of the proposed methods allows their suitability for quality control work. All the analysis settings were optimized and all the suggested procedures were applied productively for the determination of both drugs in synthetic mixtures, validation set, and combination polypills.

Output relevant slow feature extraction using partial least squares

Slow feature analysis (SFA) is an approach that extracts features from a time series dataset in an unsupervised manner based on the temporal slowness principle. These slow features contain relevant information about the dynamics of the process and hence are useful for developing models. For supervised learning objectives, these slow features need to be relevant to the outputs. Partial least squares (PLS) is a method used to perform supervised feature extraction and build models. For time series datasets this approach cannot be used directly as it assumes each sample to be sequentially independent of each other. We propose an approach to perform feature extraction which combines the temporal slowness element of SFA and the output relevance element of PLS. The proposed approach extracts temporally slow features that are relevant to the outputs, which is an essential aspect of supervised learning. The proposed formulations can be solved using the existing PLS algorithms like NIPALS and SIMPLS with proposed modifications. The proposed methods are applied to three case studies: simulated, industrial and experimental case studies to demonstrate their efficacy.

RETRACTED ARTICLE: Application of PLS algorithm in discriminant analysis in multidimensional data mining

Data mining technology emerges as the times require. Through the integrated learning of data, the original data is transformed into a form suitable for operation, useful data is extracted for mining, and finally, various strategies of data mining are applied to generate useful patterns and rules. Multiplicative regression has the potential of small computation, stable algorithm, easy to understand results, and maximum potential for mining data, which can be widely used in small-sample data mining. Aiming at the high-dimensional and low-sample problem, this paper constructs a small-sample mining algorithm using partial least squares (PLS) model and realizes dimension reduction and classification learning under the unified framework of PLS and in the classification of gene expression spectrum (colon) cancer data. To realize the mining and visualization of small-sample data by PLS. Compared with the classical algorithm SVMs, the results verify the validity and reliability of the PLS algorithm for high-dimensional and low-sample data mining problems.

Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS-PLS Algorithm.

This work applied the FT-NIR spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R2) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.

Flow monitoring of underground aquifers by interfacial tensiometry

The aim of this study is online specification of water productivity of crude oil wells based on the multidimensional analysis of the dynamic surface tension (DST) of produced water from underground sub–layers, assuming the impacts of asphaltenes emulsification. The novelties are the discriminant analysis of produced water streams from one aquifer but via different sub–layers, enhancement of tensiometery characteristics to improve the validity and reliability of algorithm, and smoothing the uncertainties and error function or sources. The experimental tests were conducted by the pendant–drop tensiometer via computation of water–air DST of samples obtained from distinct wells exposed to individual sub–layers. DST–partial least squares (PLS) and elasticity–PLS algorithms was computed and the results revealed that the production ratio of L1–L4 sub–layers of aquifer were set in the range of 1–53% (for DST–PLS) and 10–50% (for elasticity–PLS) while the conventional method had reported an equal 25% productivity for each layer. The algorithm of elasticity–PLS was developed to determine the production ratio of four sub–layers as that of DST–PLS; however the validity of this algorithm can be improved to higher frequencies more than 5.0 Hz. Both models of DST–PLS and elasticity–PLS were applied successfully for accurate and fast determination of layers productivity in an oil well exposed to multi layers of aquifer and error limit of DST–PLS was determined to be in the range of 1.0–3.2%.

Study on the Processing Technology of Calamine Calcination by Near-Infrared Spectroscopy

Near-infrared spectroscopy has been widely used in qualitative and quantitative analysis and online monitoring in the production process of traditional Chinese medicines. The aim was to establish a fast determination model of zinc oxide (ZnO) content in calcined calamine and to explore methods through judging the end point of calamine calcination. Eight batches of calamine samples sourced from hydrozincite with different sizes and textures were calcined at different temperatures. During the calcination process, ZnO contents, X-ray diffraction (XRD) patterns, and near-infrared spectra of the samples were used to analyze their changes rules. The model of determining ZnO content of calcined calamine was established to use near-infrared spectroscopy based on the partial least squares (PLS) regression algorithm. In addition, this paper summarized the change rules of calamine in calcination according to XRD patterns, using the “K value” quantitative method to define the characteristic T value. When the T value was equal to 1.00 (100%), that is to say, the calamine sample was completely calcined. Then, matching the near-infrared spectroscopy data with the T value and establishing the T value analysis model using the PLS algorithm were performed. Through cross and independent validation and evaluation, it was proved that the two models were very effective and had strong predictive abilities. Finally, the purpose of the online monitoring of the calcination process and controlling the quality of the calcined calamine was achieved.