Backward Interval Partial Least Squares Research Articles

Quantitative determination of oxides in cement raw meal based on near-infrared spectroscopy and hybrid feature selection strategy

Determining the content of components in cement raw meal is vital in Portland cement manufacturing. Near-infrared spectroscopy (NIRS)enables rapid, safe, and accurate quantitative analysis of the primary constituents of cement raw meal (CaO, SiO2, Al2O3, and Fe2O3), thereby assisting the cement industry in reducing the time delay in raw meal proportioning control and enhancing the quality of the raw meal. This study employed a hybrid feature selection strategy to identify effective feature points in the NIRS of raw meal samples. Effective feature points from the raw meal were integrated with partial least squares (PLS) to develop a regression model. Initially, NIRS data from raw meal samples were obtained using a Fourier NIR spectrometer. Subsequently, sample set partitioning based on joint X-Y distance (SPXY) was utilized to divide the samples into a calibration set (70 samples) and a validation set (22 samples). Next, A backward interval partial least squares (BiPLS) approach, along with various preprocessing algorithms, was then employed to initially screen the optimal waveband combinations of the NIRS. Within these combinations, effective feature points were further identified using competitive adaptive reweighted sampling (CARS), variable combined population analysis (VCPA) and weighted fusion variable space shrinkage approach (WFVSSA). Finally, we developed and compared PLS models based on the full spectrum and three hybrid feature selection methods. The results demonstrated that.WFVSSA offers a comprehensive assessment of wavelength importance, effectively preserving feature points while eliminating redundant information. BiPLS-WFVSSA-PLS outperformed BiPLS-CARS-PLS and BiPLS-VCPAPLS on the validation set, with R2, RMSEP, and RPD values of 0.8676, 0.1431, and 2.8129 for CaO; 0.9212, 0.1216, and 3.6461 for SiO2; 0.9198, 0.0607, and 3.5618 for Al2O3; and 0.8717, 0.0166, and 2.8614 for Fe2O3, respectively. These findings underscore the superior accuracy and stability of BiPLS-WFVSSA-PLS in determining cement raw meal component contents.

Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge

Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid–liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.

Classification of wheat powdery mildew based on hyperspectral: From leaves to canopy

Powdery mildew is considered to be one of the important diseases that threaten wheat production. It can cause serious economic losses and damage food security. Therefore, early detection of powdery mildew infection is essential to limit its amplification in reproduction. The purpose of this study is to use the hyperspectral image (HSI) to detect wheat powdery mildew (WPM) in an objective and accurate way. A method is proposed to classify the infection degree of WPM by combining cascade interval selection sensitive band with support vector machine (SVM). Firstly, HSI of leaf and canopy scale are obtained, and the image was segmented based on HSV (hue, saturation, value) color space and OTSU algorithm (HSV + OTSU). The spectral reflectance data were extracted after removing the background region of interest (ROI), and then the multiple scattering correction (MSC) was performed. The spectral data after MSC was called RAW, and then the first derivative (D1) and second derivative (D2) operations were performed respectively. Subsequently, the method of backward interval partial least squares (BiPLS) was used to screen out the characteristic bands of the sensitive interval. Finally, the classification models of WPM disease degree at the leaf scale and the canopy scale (RAW-BiPLS-SVM, D1-BiPLS-SVM and D2-BiPLS-SVM) were established respectively. The results showed that the spectral data after derivative operation was enhanced, and the classification effect of D1-BiPLS-SVM model had advantages at both leaf and canopy scales, in which the overall accuracy (OA) at the leaf scale was 95.2 %, and the OA at canopy scale was 74.4 %. In order to increase the difference between diseases, the canopy-scale disease severity levels were integrated. The correct rates of health and disease were 96.9 % and 99.2 %, respectively, and the OA of disease grade recombination was 87.9 %. Therefore, the method proposed in this paper can not only accurately identified healthy and infected wheat, but also realized the classification of different degrees of WPM, and provide technical support for the prevention and control of WPM.

Rapid determination of rice protein content using near-infrared spectroscopy coupled with feature wavelength selection

Protein content is a key indicator for rice nutritional value, and its accurate determination has become a necessary tool for rice quality evaluation and breeding. To solve the shortcomings of slow and costly testing by traditional methods, a rapid detection model of rice protein content was constructed based on near-infrared spectroscopy coupled with feature wavelength selection. The backward interval partial least squares (BiPLS) was combined with the genetic simulated annealing algorithm (GSA) and the simulated annealing binary particle swarm optimization algorithm (SABPSO), respectively, to construct BiPLS-GSA and BiPLS-SABPSO for selecting protein feature wavelengths, thereby establishing a corresponding partial least squares quantitative calibration model. Among them, the regression model established using feature wavelengths selected by BiPLS-SABPSO had the best performance. The determination coefficient of the model for the validation set and the independent test set were 0.949 and 0.956, the root mean square error were 0.174% and 0.214%, the relative root mean square error were 2.621% and 3.118%, and the residual predictive deviation being 4.235 and 4.500, respectively, which could meet the requirement for rapid and accurate determination of rice protein content. The combination of BiPLS-SABPSO and near-infrared spectroscopy has developed into a new method to achieve fast and reliable determination of rice protein content, providing an alternative strategy for rapid qualitative detection of related agricultural products.

A simple and green method for simultaneously determining the geographical origin and glycogen content of oysters using ATR–FTIR and chemometrics

Oysters are a marine bivalve extensively used as a food product and in medicinal drugs. However, the geographical origin of oysters greatly affects their economic value and quality. In this study, a simple and eco-friendly method is proposed for geographically tracing oysters and determining their bioactive glycogen content using attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Three classification algorithms, including partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA), and least squares support vector machine (LS-SVM), were applied to determine the geographical origin of oysters. Simultaneously, three types of PLS algorithms, backward interval PLS (BI-PLS), synergy interval PLS (SI-PLS), and competitive adaptive reweighted sampling PLS (CARS-PLS), were used to evaluate the feasibility of determining the glycogen content of oysters. In addition, five signal preprocessing methods were compared to enhance the prediction performance of the qualitative and quantitative models. For qualitative analysis, 100% classification accuracy was achieved using the PLS-DA, OPLS-DA, and LS-SVM. For quantitative analysis, the SI-PLS model showed the best predicted results (correlation coefficient of prediction (RP) = 0.96, relative analysis error of prediction (RPDP) = 3.38), indicating its stable and high predictive performance as a new analytical technique for the traceability supervision and quality evaluation of oysters.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model.

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.

Identification of wheat kernel varieties based on hyperspectral imaging technology and grouped convolutional neural network with feature intervals

Variety identification of seeds is essential for evaluating seed purity and ensuring crop yields. Hyperspectral imaging is a mainstream nondestructive testing method for seed purity. The hyperspectral data contains spectral and spatial information, which is large and redundant. According to the characteristics of hyperspectral data, this study used spectral feature intervals coupled with a grouped convolutional network to construct a seed variety identification model. Firstly, three spectral wavelength interval selection methods are used to constrain the spectrum, which are interval continuum removal (iCR), backward interval partial least squares (BiPLS), and interval random frog (iRF). Since spectral feature intervals in the hyperspectral image represent different physical and chemical characteristics, it is more effective to extract features of each interval independently by group convolution (GC). Then, the features are integrated into the network structure. The results showed that the combination of feature wavelength and grouping convolution enabled classification models effectively to obtain better classification accuracy and improve the inference speed of the model. The iCR-GC CNN model had the highest classification accuracy with a 4.4% increase and the fastest inference speed with a 44.21% reduction by comparing with the reference CNN classification model.

Fast identification of soybean varieties using Raman spectroscopy

Fast and accurate identification of soybean varieties is important for determining seed quality, protecting the interests of soybean growers and ensuring food safety. A combination of Raman spectroscopy with chemometric methods has been widely applied in the identification of agricultural products such as grains, fruit, edible oils, and honey; however, the identification methods of soybean varieties mainly focuses on infrared spectroscopy and hyperspectral imagery. Therefore, a partial least squares (PLS) identification model was established by Raman spectroscopy combined with characteristic wavenumber extraction method to rapidly identify four high-protein soybean varieties (Heinong 88, Heinong 98, Suinong 71, and Suinong 76) that are extensively planted in Heilongjiang Province, China. Owing to the high-dimensional and high correlation data obtained from Raman spectroscopy, a full-spectrum prediction mode will lead to high complexity and poor stability. Therefore, first, the optimal combinations of spectral subintervals were selected by backward interval PLS (BIPLS) and synergy interval PLS (SIPLS); however, redundant variables still existed in the selected continuous band and strong collinearity was observed between adjacent wavenumbers. Consequently, ElasticNet was used to re-filter the characteristic spectral regions of BIPLS and SIPLS, which effectively reduced the collinearity between the selected wavenumbers. The performance of different algorithms, i.e., full-spectrum and five characteristic wavenumber selection algorithms—BIPLS, SIPLS, ElasticNet, BIPLS-ElasticNet, and SIPLS-ElasticNet —were compared. In the BIPLS-ElasticNet algorithm, 1113 effective wavenumbers were extracted, which accounted for approximately 35% of the total wavenumbers. Moreover, the values of root mean square error, determination coefficient, and identification accuracy of the prediction set were determined to be 0.2298,0.9568 and 100%, respectively. Therefore, the proposed BIPLS-ElasticNet demonstrated the best performance among the PLS models and can be used for the fast and accurate identification of soybean varieties; moreover, it can also provide a reference for the rapid identification of other crop seeds.

Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection.

Excessive illegal addition of talc in flour has always been a serious food safety issue. To achieve rapid detection of the talc content in flour (TCF) by near-infrared spectroscopy (NIRS), this study used a Fourier transform near-infrared spectrometer technique. The identification of efficient spectral feature wavelength selection (FWS), such as backward interval partial-least-square (BiPLS), competitive adaptive reweighted sampling (CARS), hybrid genetic algorithm (HGA), and BiPLS combined with CARS; BiPLS combined with HGA; and CARS combined with HGA, was also discussed in this paper, and the corresponding partial-least-square regression models were established. Comparing with whole spectrum modeling, the accuracy and efficiency of regressive models were effectively improved using feature wavelengths of TCF selected by the above algorithms. The BiPLS, combined with HGA, had the best modeling performance; the determination coefficient, root-mean-squared error (RMSE), and residual predictive deviation of the validation set were 0.929, 1.097, and 3.795, respectively. BiPLS combined with CARS had the best dimensionality reduction effect. Through the FWS by BiPLS combined with CARS, the number of modeling wavelengths decreased to 72 from 1845, and the RMSE of the validation set was reduced by 11.6% compared with the whole spectra model. The results showed that the FWS method proposed in this paper could effectively improve detection accuracy and reduce modeling wavelength variables of quantitative analysis of TCF by NIRS. This provides theoretical support for TCF rapid detection research and development in real-time.

Identification and quantitative analysis of salt-adulterated honeysuckle using infrared spectroscopy coupled with multi-chemometrics

The quality of honeysuckle is typically degraded seriously by unscrupulous mixing with adulterating substances, especially salt. This study proposes a model based on fourier-transform mid-infrared (Mid-IR) spectroscopy and diode-array near-infrared (NIR) spectroscopy in combination with chemometric techniques for the identification and quantification of salt-adulterated honeysuckle. For model training, Mid-IR and NIR spectra of 72 sample batches (including 32 batches of salt-free samples and 40 batches of salt-adulterated samples) were collected. After that, honeysuckle sample authentication was performed using the partial least squares discriminant analysis (PLS-DA) model based on NIR spectroscopy data. This model was optimized after processing by 28 types of pretreatment schemes, possessed of 100% correct authentication of salt-free and salt-adulterated sample at 0.5%-20% (W/W) levels. Subsequently, PLS regression models were built for quantify salt adulteration levels. These models were optimized by the synergy interval PLS (SiPLS) and backward interval PLS (BiPLS) algorithms. The SiPLS-optimized mid-IR-based PLS model demonstrated the lowest values of two error metrics, namely, a root-mean-square error of estimation (RMSEE) of 2.38%, and a root-mean-square error of prediction (RMSEP) of 3.14%. Then, optimal PLS models were obtained through applying several variable selection methods, including variable importance for projection (VIP), competitive adaptive reweighted sampling (CARS), the successive projection algorithm (SPA), and uninformative variable elimination (UVE). The results indicated that the SiPLS-VIP multi-dimensional analysis methods showed a maximum RMSEP reduction of 1.02% in comparison with models constructed using the BiPLS or SiPLS algorithms alone. In general, the proposed multi-dimensional analysis methods show a great potential to complement existing methodologies for rapid and effective salt adulteration detection in honeysuckle samples.

Chlorophyll content estimation based on cascade spectral optimizations of interval and wavelength characteristics

Spectroscopy is an efficient way to estimate the chlorophyll content of crop canopy in the visible and near-infrared region to indicate the photosynthesis capacity and growth status. Although hyperspectral data of crop canopy provide a lot of information, a critical issue is that the subsequent redundant and interference wavelengths could reduce the accuracy and robustness of the estimated results in the field. Therefore, a cascade method of interval-wavelength screening was proposed to reduce the data dimension and improve the crop estimation accuracy and robustness for the chlorophyll content diagnosis of maize crops. In the experiments, spectral reflectance data of maize canopy within 325–1075 nm were obtained, and chlorophyll contents were measured under the conditions of six nitrogen application rates and three growth periods following the early flaring, flaring, and tasseling stages. First, the backward interval partial least squares (BiPLS) method was used to optimize the spectrum interval. Then, competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were used for secondary wavelength screening, and the partial least squares regression (Bi-CARS-PLSR and Bi-GA-PLSR) model was established with the chlorophyll content. Compared with the full-spectrum modeling (Full-PLSR) results and the GA and CARS screening wavelength modeling (Full-CARS-PLSR and Full-GA-PLSR) results in the full-spectrum range, the number of wavelengths in the Full-PLSR model was 751. The accuracy of the Full-PLSR model with the coefficient of determination was 0.88 for the calibration set (Rc2) and 0.66 for the verification set (Rv2). The root mean square error (RMSE) was 2.05 mg/L. The wavelength numbers of Full-CARS-PLSR and Full-GA-PLSR were 125 and 99, and their Rc2 and Rv2 were 0.88 and 0.67, respectively. The RMSE values of Full-CARS-PLSR and Full-GA-PLSR were 2.04 and 1.89 mg/L, respectively. By using the proposed cascade method, the wavelength numbers of the Bi-CARS-PLSR model and Bi-GA-PLSR model after interval optimization were 60 and 49, respectively. Their accuracy values for the calibration set (Rc2) were both 0.87, while those for the verification set (Rv2) were 0.7 and 0.78, respectively. The RMSE values of both models were 1.97 and 1.86 mg/L, respectively. The results showed that the proposed cascaded interval-wavelength screening method could eliminate redundant and collinearity variables and improve model performances. At the same time, the Bi-GA-PLSR modeling result was used for field prediction to establish a chlorophyll distribution map, which had a high consistency with the real chlorophyll distribution, and showed the potential to detect chlorophyll content of maize in the field.

Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)

In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples including, amongst others, total phenolic content, total flavanoid content and total antioxidant capacity. The developed models were assessed using coefficients of determination for the calibration (R2) and prediction (r2); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The R2 for calibration and r2 for prediction varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.

Real-coded GA coupled to PLS for rapid detection and quantification of tartrazine in tea using FT-IR spectroscopy

In this study, Fourier-transform infrared (FT-IR) spectral data was combined with variable selection methods to measure tartrazine adulteration in tea powder. Partial least square (PLS) regression and its variants such as backward interval PLS (BiPLS), genetic algorithm PLS (GA-PLS), and competitive adaptive reweighted sampling PLS (CARS-PLS) for variable selection were established as calibration models for the quantitative prediction of tartrazine. A simple and efficient real-coded GA (RCGA) was also implemented as a variant of GA-PLS regression. The performance of these models was adjudged based on root mean square errors (RMSE) for both cross-validation (RMSECV) and prediction (RMSEP) along with their respective correlation coefficients (RC and RP). The developed RCGA-PLS was observed to be a robust technique to achieve a model with low RMSECV and RMSEP values of 0.8331 and 0.923, respectively. This model uses 30 selection variables (1.19% of full variable count) to predict tartrazine in the range of 0–30 mg/g with a correlation coefficient of 0.987. This study demonstrated that FT-IR spectroscopy, combined with the developed RCGA-PLS procedure for variable selection could be a robust technique for the rapid detection of tartrazine in tea samples.

Development of chemometric models using Vis-NIR and Raman spectral data fusion for assessment of infant formula storage temperature and time

This study evaluated the potential of Vis-NIR and Raman spectral data fusion combined with PLS and SVM chemometric models developed using a large dataset (n = 1700) of commercial infant formula (IF) samples to (i) discriminate between different IF storage temperature (20, 37 °C) and (ii) predict IF storage time (0–12 months). Three interval-based PLS variable selection methods (forward interval PLS (FiPLS), backward interval PLS (BiPLS) and synergy interval PLS (SiPLS)) and SVM-recursive feature elimination (SVM-RFE) methods were compared for model development. The best IF storage temperature discrimination model was developed using SVM classification (SVMC) and Vis-NIR spectra (400–2498 nm) (AccuracyCV = 99.82%, AccuracyP = 100%). SVM regression (SVMR) models developed using medium level data fusion (features selected by SVM-RFE) had the lowest root mean square error (RMSE) values for IF samples stored at either temperature, 20 °C or 37 °C (RMSECV = 0.7–0.8, RMSEP = 0.6–0.9). Industrial relevanceSpectroscopic technologies, including Vis-NIR and Raman spectroscopy have been widely applied for process analysis and increasingly for on-line process monitoring in areas of chemicals, food processing, agriculture and pharmaceuticals, etc. Due to their rapid measurement and minimal or no sample preparation, they are highly suitable for in-line process monitoring. This study demonstrates that Vis-NIR and Raman process analytical tools either individually or combined may be employed for quality assessment and process control of IF manufacture.

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

ABSTRACT Laboratory visible and near-infrared (Vis-NIR) spectroscopy with partial least squares (PLS) regression can be used to determine the soil carbon (C) content, and the waveband selection procedures can refine the predictive ability. However, individually selected wavebands are not always the same depending on the location, scale, and approach. To simplify the variable selection issue, some methods for selecting wavelength regions instead of individual wavebands have been proposed. In this study, we explore relevant wavelength regions for predicting the total carbon (TC) content of lowland and upland soils in Madagascar from Vis-NIR spectroscopy using a dynamic version of backward interval PLS (biPLS) regression. The predictive ability of dynamic biPLS was compared with that of standard full-spectrum PLS (FS-PLS) using the cross-validated coefficient of determination (R 2), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). The biPLS models using reflectance (R 2 = 0.877, RMSE = 0.690) and first derivative reflectance (FDR) (R 2 = 0.940, RMSE = 0.494) data sets showed better predictive accuracy than the FS-PLS models using reflectance (R 2 = 0.826, RMSE = 0.809) and FDR (R 2 = 0.933, RMSE = 0.518) data sets, the spectral efficiency was improved. By using biPLS to predict soil TC, the model was simplified by using only four selected wavelength regions in the reflectance (400–490, 1402–1440, 1846–1980 and 2151–2283 nm) and FDR (652–687, 1322–1443, 1856–1985, and 2290–2400 nm) data sets, which yielded reliable (RPIQ > 2.5) predictions.

Detection of Three-component Adulteration in Camellia Oleifera Abel Seed Oil by Near Infrared Spectroscopy

In this paper, A combination of near-infrared microscopy and chemometrics were employed to identify the adulteration in Camellia oleifera Abel seed oil. The qualitative and quantitative detection methods for three-component adulteration in Camellia oleifera Abel seed oil was studied, and the models for the authenticity identification and content determination of Camellia oleifera Abel seed oil were established. Multivariate scattering correction (MSC), standard normal variate (SNV) and combination of standard normal variate and de-trend (SNV-DT) were separately used for the preprocessing of original spectra. Backward interval partial least squares (BiPLS) method was used to optimize feature variables, and partial least squares (PLS) method was employed to extract the spectrum from the characteristic wavelength. The parameters were optimized through comparative interactive verification (CV) and genetic algorithm (GA). With the establishment of qualitative and quantitative models in the full wavelength and characteristic wavelength ranges, the qualitative and quantitative identification paths were optimized. The results show that the identification and quantitative detection of Camellia oleifera Abel seed oil adulterated with three dopants could be realized by NIRS. The accuracy of as-established MSC-BiPLS-GA-SVC model was up to 100%. The correlation coefficient R of the MSC-BiPLS-GA-SVC model reached 99.7254%, and the MSE value was 0.0234.

Development of Robust Partial Least Squares Regression Model for Spectroscopic Determination of Diclofenac Sodium in Environmental Samples

Background: Diclofenac (DCF) is an important widely used non-steroidal antiinflammatory drug. Disposal of expired formulation, excretion from administered dose, the poor performance of sewage treatment process, contributes to its frequent detection in environment. Analysis of DCF in environmental sample requires time consuming pretreatment, extraction steps. Though, UV absorption analysis of DCF is simple but spectral interference of soil organic matter is a problem. The aim of this paper is to establish appropriate partial least square chemometric model for DCF quantitation through variable selection, and validation of analytical method through multivariate figure of merit analysis. Methods: Spectral data of DCF spiked soil solution is recorded and variants of partial least squares (PLS) regression viz., backward-interval PLS (biPLS), synergy-interval PLS (siPLS) and genetic algorithm (GA) based PLS models (GA-PLS) are developed from autoscaled and 2nd order differential spectrum. Prediction fidelity of the selected models was evaluated from a blind-folded semi-synthetic spectral data. The method was validated through figures of merit estimates, such as selectivity, analytical sensitivity, limits of detection and quantitation. Results: The siPLS model developed offered the minimum root mean square error of crossvalidation (RMSECV) of 0.1896 mg/l and 0.1910 mg/l for autoscaled data (9 variables) and derivative spectra (12 variables), respectively. Refinement of the derivative spectrum with GA offered a simplified model (RMSECV:0.1712, 10 variable). Conclusion: The GA based variable selection for PLS regression analysis offers robust analytical tool for DCF in environmental samples. Further research is warranted to model variable interference in spectral data unknown to analyst in priori.

Nondestructive determination of lignin content in Korla fragrant pear based on near-infrared spectroscopy

The lignin content is a key index affecting the quality of Korla fragrant pear. This study applied a method of near-infrared diffuse reflectance spectroscopy combined with chemometric method to establish a predictive model of lignin content. Among them, moving average, baseline, standard normal variate transformations and multiplicative scatter correction were used to preprocess the collected near-infrared spectra. then, the backward interval partial least-squares, synergy interval partial least squares and uninformative variable elimination algorithms were used to select characteristic wavelengths from the whole spectral range to establish a partial least-squares prediction model of lignin content. The partial least-squares model based on uninformative variable elimination selected characteristic wavelengths was simplified, and the prediction of lignin content in Korla pear was more accurate. Multiple determination coefficient values, standard prediction errors and residual prediction errors for prediction were 0.87, 1.36 and 2.03%, respectively. Compared with partial least squares based on the whole spectral range and partial least squares based on Uninformative variable elimination selection, the number of characteristic wavelength variables was reduced from 289 to 51, thereby improving the prediction accuracy of the model. The experimental results showed that the uninformative variable elimination by partial least-squares model was the best model. The combination of near-infrared spectroscopy and uninformative variable elimination optimization can effectively determine the lignin content in Korla fragrant pear. The near-infrared diffuse reflectance spectroscopy proved to be a good tool for nondestructive determination of lignin content in Korla fragrant pear. The selection of characteristic wavelengths and appropriate pretreatment methods can improve the accuracy of near-infrared spectroscopy in actual detection.

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.