Calibration Correlation Research Articles

Quantitative Analysis of Mixtures Using Terahertz Time-Domain Spectroscopy and Different PLS Algorithms

This paper attempted the feasibility to determine component concentrations in multicomponent mixtures with terahertz time-domain spectroscopy (THz-TDS) combined with different partial least-squares regression (PLS) algorithms. First, THz absorbance spectra for 75 ternary mixtures of anhydrous theophylline, lactose monohydrate and magnesium stearate were investigated using THz-TDS in the frequency range from 0.1 to 3.0 THz, then four different PLS methods, including interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS) and moving window PLS (mwPLS), were employed to perform quantitative analysis of anhydrous theophylline concentrations in ternary mixtures. The performance of mwPLS model is the best in contrast to other PLS models and full spectrum PLS. The optimal model was achieved with higher correlation coefficient for calibration (RC) of 0.9842, higher correlation coefficient for prediction (RP) of 0.9851, lower root mean square error of cross-validation (RMSECV) of 3.8241, and lower root mean square error of prediction (RMSEP) of 4.1540. Experimental results demonstrate that THz spectroscopy combined with PLS algorithms could be successfully applied as an effective nondestructive tool for the quantitative analysis of component concentrations in multicomponent mixtures, and mwPLS is an ideal method for reducing the complexity and improving the performance of the model.

Nondestructive determination of paper properties of Populus × euramericana with near infrared spectroscopy

Five Populus × euramericana wood samples representing three different sites were selected and near infrared (NIR) spectra were obtained. For these sections, basis weight, brightness and three mechanical properties (tensile index, tearing index and bursting index) were determined by standard analytical methods. Calibrations were developed for each paper property using the NIR spectra, data on paper properties, using partial least squares (PLS) regression. The results show that the coefficients of correlation of calibration and validation for basis weight were 0.8824 and 0.8299, respectively; the standard error of calibration (SEC) and prediction (SEP) were 1.150 and 1.170, respectively. In testing for brightness, the correlation coefficient of calibration was 0.9621 and for validation 0.9612, while the SEC and SEP were 0.997 and 1.300, respectively; paper brightness and NIR spectroscopy were highly correlated. NIR spectroscopy can be used to predict tensile, tearing and bursting indices of paper samples rapidly. We found that the paper properties fitted by NIR and GB methods were highly correlated. The coefficients of correlation of calibration and validation for basis weight exceeded 0.8000, while the SEC and SEP were very small. These results reveal that the five paper properties of Populus × euramericana and those predicted by the NIR model were highly correlated. We conclude that the NIR models can be used for the prediction of paper properties.

Determination of the Biodiesel Content in Petrodiesel/Biodiesel Blends: A Method Based on Uv-Visible Spectroscopy and Chemometrics Tools

In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentrations between 0-100 wt% were prepared. The spectral fingerprints that were obtained from the analysis of the samples by UV-visible spectroscopy were used to build predictive model using PLS regression. The predictive ability of the models was evaluated through statistical parameters: the standard error of calibration (SEC), the standard error of validation (SEV), the correlation coefficient of calibration (r Cal) and validation (r Val), the ratio (SEC/SEV), the coefficient of determination R2, the paired data Student’s t-test, cross-validation and external validation. The results indicate that the PLS model predicts the concentration of biodiesel from African palm with high precision in mixtures with petrodiesel. The method developed in this study can be applied to determine the concentration of biodiesel African palm in mixtures of petrodiesel in a more rapid and economical way. Moreover, this method has less analytical errors and is more environmentally friendly than the conventional methods.

The Analysis and Detection Research of Different Coal Samples Based on near Infrared Spectroscopy

The present study is concerning qualitative and quantitative detection of different coal samples based on near infrared spectroscopy. Firstly, near infrared spectroscopy combined with discriminant analysis (DA) was used for the coal cinder characteristics in different spectral pre-processing methods, at the same time it will establish the quantitative of the total moisture. The result indicated that 97.78% recognition ratio for calibration and 93.33% recognition ratio for validation were achieved by DA for the cinder characteristics. The PLS quantitative model of the total moisture of the establishment of the original spectral was the best, the correlation coefficients of calibration and prediction respectively were 0.981 and 0.641, RMSEC, RMSEP and RMSECV respectively were 0.859, 2.51 and 3.44.

Prediction of pork marbling scores using pattern analysis techniques

Marbling is an important technical quality attribute of pork. Its assessment usually corresponds to a subjective score being given by trained panelists based on the marbling standards charts. The purpose of this study was to investigate objective determination of pork marbling using pattern analysis techniques. A line pattern recognition technique called the wide line detector (WLD) and a texture extraction technique based on an improved grey-level co-occurrence matrix (GLCM) were employed and compared. Fifty three fresh pork loin chops from the longissimus dorsi (LD) muscle were collected and their marbling scores were assessed in a plant. Red-Green-Blue (RGB) images of these chops were acquired using a digital camera. The loin eye area was selected as the region of interest (ROI) of the pork images. Marbling was extracted from the ROI by either GLCM or WLD. Proportion of marbling (PM) obtained from WLD or image texture measurement from GLCM (GI) was formulated as indices of the marbling score. Linear regressions based on the PM and GI were carried out at the red, green, and blue channels as well as the combined RGB channels. The results of WLD and GLCM based models showed the effectiveness of pattern analysis techniques for pork marbling assessment. The comparison indicated that the WLD based models had stronger predictive ability for pork marbling score determination than GLCM. The green channel was demonstrated to have the best explanatory for pork marbling assessment no matter which pattern analysis technique used. High correlation coefficients of calibration and validation (Rc = 0.94, Rv = 0.94) of the WLD based linear model at the green channel strongly indicated the great potential of pattern analysis techniques especially the line pattern recognition methods for the accurate and real-time evaluation of pork marbling.

Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network

The potential of a combination of a low cost visible and shortwave near infrared (VIS–SWNIR) spectrometer and an artificial neural network in the non-invasive soluble solids content assessment of pineapple was evaluated. Four data sets (i.e. VIS–SWNIR spectra and soluble solids content reference) of pineapple samples from different days were acquired and independently processed. Baseline shift effect in the reflectance spectra was removed using a first order derivative coupled with a first order Savitzky–Golay smoothing filter. The dispersion of the spectral data was reduced by applying robust principal component analysis. Potential outliers were identified via an externally studentised residual approach. An artificial neural network was trained using one of the four data sets and validated using the other three data sets. From interpolation analysis, the root mean square error of calibration (RMSEC), correlation coefficient of calibration (rc), root mean square error of prediction (RMSEP) and correlation coefficient of prediction (rp) of the artificial neural network with the first two robust principal components were 0.84 °Brix, 0.85, 0.87 °Brix and 0.68, respectively. The predicted results by using three data sets from different days suggest that the use of a low cost VIS–SWNIR spectrometer is promising for the non-invasive soluble solids content assessment of pineapple.

Rapid Measurement of Diesel Engine Oil Quality By Near Infrared Spectroscopy (NIRS)

A near infrared spectroscopy (NIRS) rapid measurement was applied to diesel engine oil quality. 100� kinematic viscosity change rate and flash point of CD 15W lubricant were built by the means of interval and combination partial least (icPLS). The calibration correlation coefficient (R 2 validation) and least prediction standard error (SEP) of these models were 0.8954, 2.24% and 0.7729, 2°C respectively. A t-test (� =0.05) showed that the NIR model had an accuracy equivalent to that of the standard method in the analysis for both 100� kinematic viscosity and flash point. It was indicated that NIRS method was a new analysis means, which could take place of traditional method, for rapid measurement of diesel engine oil quality.

Near infrared spectroscopy for deoxynivalenol content estimation in intact wheat grain

Non-invasive determination of deoxynivalenol (DON) still presents a challenging problem. Therefore, the present study was aimed at a rapid determination of DON in whole wheat grain by means of FT-NIR spectroscopy, with a wide range of concentrations for potential applications in breeding programs and common systems of quality management using partial least square calibration (PLS) and discriminant analysis technique (DA). Using a set of artificially infected wheat samples with a known content of DON, four PLS models with different concentration range were created. The broadest model predicting DON in the concentration range of 0–90 mg/kg possessed the highest correlation coefficients of calibration and cross validation (0.94 and 0.88); but also possessed the highest prediction errors (SEP = 6.23 mg/kg). Thus the subsequent combination of DA as the wide range predictive model and the low-range PLS model was used. This technique gave more precise results in the samples with lower DON concentrations – below 30 mg/kg (SEP = 2.35 mg/kg), when compared to the most wide-range PLS model (SEP = 5.95 mg/kg).<br />Such technique enables to estimate DON content in collections of artificially infected wheat plants in Fusarium resistance breeding experiments.  

Non-destructive prediction of sweetness of intact mango using near infrared spectroscopy

Agriculture industries are continuously in search of new user friendly technologies to evaluate the intrinsic properties of fruits before it is put in the market for the consumer. In the current study the potential of near-infrared (NIR) spectroscopy in the wavelength range of 1200–2200nm was evaluated to determine total soluble solids and pH for seven major cultivars of mangoes from seven states of India. NIR models were developed based on multiple-linear regression (MLR) and partial least square (PLS) regression employing preprocessing technologies (baseline correction, smoothening, multiplicative scatter correction (MSC) and second order derivatisation). The multiple correlation coefficients for calibration and validation were found to be 0.782 and 0.762 for total soluble solids and 0.715 and 0.703 for pH respectively. The standard errors of calibration, prediction, biases and differences in them were low which indicated the NIRS potential to predict internal quality parameters (TSS and pH) of mango non-destructively for both models.

A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics

The feasibility of rapid analysis of glucose and fructose in lotus root powder by Fourier transform near-infrared (FT-NIR) spectroscopy was studied. Diffuse reflectance spectra were collected between 4000 and 12,432cm(-1). Calibration models established by partial least-squares regression (PLSR), interval PLS of forward (FiPLS) and backward (BiPLS), back propagation-artificial neural networks (BP-ANN) and least squares-support vector machine (LS-SVM) were compared. The optimal models for glucose and fructose were obtained by LS-SVM with the first 10 latent variables (LVs) as input. For fructose the correlation coefficients of calibration (rc) and prediction (rp), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.9827, 0.9765, 0.107%, 0.115% and 4.599, respectively. For glucose the indexes were 0.9243, 0.8286, 0.543%, 0.812% and 1.785. The results indicate that NIR spectroscopy technique with LS-SVM offers effective quantitative capability for glucose and fructose in lotus root powder.

Advantage of Applying OSC to 1H NMR-Based Metabonomic Data of Celiac Disease

BackgroundCeliac disease (CD) is a disorder associated with body reaction to gluten. After the gluten intake, an immune reaction against the protein occurs and damages villi of small intestine in celiac patients gradually.ObjectivesThe OSC, a filtering method for minimization of inter- and intra-spectrometer variations that influence on data acquisition, was applied to biofluid NMR data of CD patients.Patients and MethodsIn this study, metabolites of total 56 serum samples from 12 CD patients, 15 CD patients taking gluten-free diet (GFD), and 29 healthy cases were analyzed using nuclear magnetic resonance (NMR) and associated theoretical analysis. Employing ProMetab (version ProMetab_v3_3) software, data obtained from NMR spectra were reduced and orthogonal signal correction (OSC) effect on celiac disease metabonomics before and after the separation by principle component analysis (PCA) was investigated.ResultsThe three groups were separated by OSC and findings were analyzed by partial least squares discriminant analysis (PLS-DA) method. Root mean square error of calibration (RMSEc) and correlation coefficient of calibration (Rc) for PLS-DA referred to an efficient group separation filtered by OSC.ConclusionsThe applied leave-one-out cross-validation to PLS-DA method performed along with OSC confirmed validation of data analysis. Finally four metabolites are introduced as CD biomarkers.

Non-destructive prediction of quality of intact banana using spectroscopy

Potential of spectroscopy in the wavelength range of 299–1100nm for nondestructive prediction of dry matter (DM) content, pH, sweetness (total soluble solids, TSS), and acid–Brix ratio (ABR) for banana was studied to have proper estimation of their maturity and ripening stage. Partial least square (PLS) and multiple linear regression (MLR) employing pre-processing techniques were carried out. PLS models were found to be the best with multiple correlation coefficients for calibration and validation were found to be 0.88 and 0.81°Brix for TSS, 0.85 and 0.78 for ABR, 0.88 and 0.83 for pH and 0.90 and 0.87% for DM, respectively. The standard errors of calibration, prediction, biases and differences in them were low, which indicated that NIRS has potential to predict maturity and ripening parameters of banana nondestructively.

Extracting Coal Ash Content from Laser-Induced Breakdown Spectroscopy (LIBS) Spectra by Multivariate Analysis

Laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) analysis has been applied for the quantitative analysis of the ash content of coal in this paper. The multivariate analysis method was employed to extract coal ash content information from LIBS spectra rather than from the concentrations of the main ash-forming elements. In order to construct a rigorous partial least squares regression model and reduce the calculation time, different spectral range data were used to construct partial least squares regression models, and then the performances of these models were compared in terms of the correlation coefficients of calibration and validation and the root mean square errors of calibration and cross-validation. Afterwards, the prediction accuracy, reproducibility, and the limit of detection of the partial least squares regression model were validated with independent laser-induced breakdown spectroscopy measurements of four unknown samples. The results show that a good agreement is observed between the ash content provided by thermo-gravimetric analyzer and the LIBS measurements coupled to the PLS regression model for the unknown samples. The feasibility of extracting coal ash content from LIBS spectra is approved. It is also confirmed that this technique has good potential for quantitative analysis of the ash content of coal.

In-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz based on qualitative and quantitative uses of near-infrared spectroscopy

The application of near-infrared (NIR) spectroscopy for in-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz was investigated. For NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm pathlength flow cell were utilized to collect spectra in real-time. High performance liquid chromatography (HPLC) was used as a reference method to determine scutellarein in extract solution. Partial least squares regression (PLSR) calibration model of Savitzky–Golay smoothing NIR spectra in the 5450–10,000 cm −1 region gave satisfactory predictive results for scutellarein. The results showed that the correlation coefficients of calibration and cross validation were 0.9967 and 0.9811, respectively, and the root mean square error of calibration and cross validation were 0.044 and 0.105, respectively. Furthermore, both the moving block standard deviation (MBSD) method and conformity test were used to identify the end point of extraction process, providing real-time data and instant feedback about the extraction course. The results obtained in this study indicated that the NIR spectroscopy technique provides an efficient and environmentally friendly approach for fast determination of scutellarein and end point control of extraction process.

Prediction of the Crystallinity of White Pine Using near Infrared Spectroscopy

The crystallinity of wood is an important property of wood materials, it has an important effect on the physical, mechanical and chemical properties of cellulose fibers such as MOR, density, hardness increase, alpha-cellulose content, dimensional stability, moisture regain and dye sorption, chemical reactivity etc. The aims of this study were to investigate the ability of near infrared spectroscopy (NIR) to predict the crystallinity of white pine wood and the effect of spectra pretreatment on the prediction of crystallinity using NIR. Spectra were collected from wood powder a slowly rotating turntable and the crystallinity of wood was determined by X-ray diffractmeter (XRD) in this experiment. The results showed that NIR coupled with partial least square (PLS) method could be correlated with the crystallinity of white pine wood, and the ability of NIR prediction based on first derivative spectra was better than based on raw spectra or second derivative pretreated spectra. There was a significant correlation between NIR spectra and XRD determined crystallinity. The correlation coefficient for calibration (RC) was 0.932; the mean square error of calibration (RMSEC) was 0.022; the correlation coefficient for validation (RV) was 0.911; the mean square error of calibration (RMSEV) was 0.023. It was proved that NIR can rapidly and accurately predict white pine wood crystallinity.

Prediction of Egg's Freshness Using Backward Propagation Neural Network

Chemical, biological, and physical changes occur in eggs with the increase in the number of storage days and the storage condition. One hundred twenty eggs laid on the same day ranging in color from light brown to dark brown and of different size were bought from the local market. The samples were stored in the lab room ambient temperature and relative humidity. The spectral data from 300 to 1100 nm of 20 sample eggs on each alternative days were obtained until the 12th day of storage. Predictive model for Haugh Unit (HU) and thick albumen height was developed using backward propagation neural network (BPN) with Savitzky Golay (SG) third pass filter and Multiplicative Scatter Correction (MSC) pre-processing method. BPN predicted HU with correlation coefficient (R) and Root Mean Square Error of Calibration (RMSEC) of 0.82 and 8.11, respectively. Similarly the BPN predicted the thick albumen height with R and RMSEC of 0.83 and 0.88 mm. The device used in the research for spectral collection is simple and portable and the results obtained are satisfactory, this method can be used to evaluate the freshness of eggs in terms of HU and thick albumen height in the local market.

Prediction of titratable acidity, malic acid, and citric acid in bayberry fruit by near-infrared spectroscopy

An experiment was conducted to simultaneously measure titratable acidity, malic acid, and citric acid of bayberry fruit in a nondestructive manner using near-infrared (NIR) transmittance spectroscopy and chemometrics. The sampling set included different cultivars that were obtainable from different areas in China. Calibration models using partial least squares (PLS) regression were developed based on GB 12293-90 of China and with high-performance liquid chromatography (HPLC) as reference methods. Different preprocessing methods and different wave bands were applied. The correlation coefficient of calibration (rc), root–mean–square error of calibration (RMSEC), and root–mean–square error of prediction (RMSEP) of the best model for titratable acidity was 0.8959, 2.24, and 2.89g/L, respectively, with the range of 10,000–5405cm−1. Rc, RMSEC, and RMSEP values for malic acid and citric acid were 0.6689, 0.32, 0.47 and 0.8970, 1.51, 2.12g/L, respectively. The prediction accuracies could not be improved by using first and second derivative pretreatment methods. Due to the short time consumption and low monitoring cost, NIR spectroscopic technique has its potential for the rapid and nondestructive prediction of titratable acidity and citric acid in bayberry fruit in a temperature-controlled room, although the accuracy was not high.

Non-destructive prediction of quality of intact apple using near infrared spectroscopy

Potential of near infrared spectroscopy (NIRS) in the wavelength range of 900-1700 nm for determination of sweetness (total soluble solids, TSS); sourness (acidity) and their ratio for 5 cultivars of apple was studied. Partial least square and multiple linear regression (MLR) employing pre-processing techniques were carried out. MLR models were found to be the best for prediction after treating the spectral data with multiple scatter correction technique. The multiple correlation coefficients for calibration and validation were found to be 0.887, 0.745 °Brix for TSS, 0.890, 0.752 % for acidity and 0.893, 0.751 for acidity/TSS ratio, respectively. The standard errors of calibration, prediction, biases and differences in them were low, which indicated that NIRS has potential to predict internal quality of apple non-destructively.

Implementation of an FTIR calibration curve for fast and objective determination of changes in protein secondary structure during formulation development

The aim of this study was to develop a quick and objective method for the determination of changes in protein secondary structure by Fourier transform infrared spectroscopy (FTIR). Structural shifts from native regions (α-helix, intramolecular β-sheet) to aggregated strands (intermolecular β-sheet) were used to evaluate protein damage. FTIR spectra of 16 different proteins were recorded and quantified by peak fitting of the non-deconvolved and baseline corrected amide I bands. The resulting percentile secondary structures were correlated with the shape and intensity of the area normalized amide I bands using an interval partial least squares algorithm (iPLS). Structural elements were focused on the following regions: α-helix 1660–1650 cm −1, intramolecular β-sheet 1695–1683 cm −1 and 1644–1620 cm −1, intermolecular β-sheet 1620–1595 cm −1. Three calibration curves were created from the data sets. Calculated α-helix content ranged from 0% to 79.59%, intramolecular β-sheet from 10.64% to 63.89% and intermolecular β-sheet from 0.23% to 9.70%. The linear relationship between actual values (as determined by peak fitting) and calculated values was evaluated by correlation coefficient and root mean square error of calibration while cross-validation was performed to detect possible outliers. Results were verified by including two proteins as validation standards and comparing the calculated values to peak fitting and X-ray data. Structural changes of human serum albumin (HSA) due to elevated temperatures and the fibrillation of glucagon were quantified by calibration curve analysis. Performance and reliability of the iPLS algorithm were evaluated by comparing calculated secondary structure elements with results from peak fitting and circular dichroism. Different methods for the determination of secondary structure gave slightly different results but overall tendencies concurred. Additionally, formation of HSA aggregates could be linked to increasing β-sheet content by comparing SEC–HPLC and turbidity analysis with results from the FTIR calibration curves. In summary, quantification of the α-helix to β-sheet transition by iPLS analysis proves to be a feasible and objective way for the determination of damage to protein secondary structure.

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R(c))=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.