PLS Calibration Model Research Articles

NIR Spectroscopy for the Online Monitoring of Water and Olive Oil Content in Pomace during the Extraction Process

The main challenge of this scientific work was the implementation on an industrial olive oil extraction plant of an NIR device for the multispectral analysis of pomace to predict the percentage of humidity and oil contained in it. Subsequent to the implementation of the NIR device on the oil extraction line on the solid’s outlet from the decanter, NIRS interaction measurements in the 761–1081 nm region were used to probe the pomace. NIRS calibration models for the prediction of water and oil content in the pomace were obtained and successfully tested and validated. The correlations of calibration results for oil and water content were 0.700 and 0.829, while the correlations of validation were 0.773 and 0.676, respectively. Low values of root mean square error were found for both the prediction and validation set. The results highlight the good robustness of an NIR approach based on a PLS calibration model to monitor the industrial olive oil process. The results obtained are a first step toward the large-scale implementation of NIR devices for monitoring pomace in oil mills. The possibility of knowing the oil lost in the pomace, moment by moment, would open a new frontier towards system control and the sustainability of the olive oil extraction process.

Effects of wet granulation process variables on the quantitative assay model of transmission Raman spectroscopy for pharmaceutical tablets

Transmission Raman spectroscopy (TRS) is a process analytical technology tool for nondestructive analysis of drug content in tablets. Although wet granulation is the most used tablet manufacturing method, most TRS studies have focused on tablets manufactured via direct compression. The effects of upstream process parameter variations, such as granulation, on the prediction performance of TRS quantitative models are unknown. We evaluated the effects of process parameter variations during granulation on the prediction performance of the TRS quantitative model. Tablets with a drug concentration of 1%w/w were used. We developed PLS calibration models for the drug concentration range of 70–130% label claims. Subsequently, we predicted the drug content of the tablets with different granulation parameters. The results of our study demonstrate that the variation in the predicted recovery due to the variation in granulation parameters was practically acceptable. The calibration model showed a good prediction performance for tablets manufactured at different granulation scales and thicknesses. Therefore, we conclude that TRS quantitative models are robust to variations in upstream processes, such as granulation and downstream variations in tableting parameters. These results suggest that TRS is a versatile non-destructive quantitative analysis method that can be applied in tablet manufacturing.

The classification performance of multivariate curve resolution-discriminant analysis: A comparative study

Pattern recognition models establish the relationship between the selected variables and objects' belonging to different classes. Variations in measured variables, different object patterns, and relationship models explain the need to develop different algorithms. Partial least squares-discriminant analysis (PLS-DA) is based on the projection of object responses in the space of latent vectors of the PLS calibration model. Recently, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method has been introduced for discriminant analysis by our group. The possibility of incorporation of real physical and chemical information in the MCR decomposition of data leads to solutions that directly have meaning and simplifies the interpretation of results. Considering the new application of MCR in supervised pattern recognition, there is still a need to evaluate and compare the efficiency of MCR-DA with tested methods such as PLS-DA.Detection and discrimination of extra virgin olive oil from four other classes of pure edible oils and also their mixture with extra virgin olive oil, as adulteration was chosen just as a typical system to evaluate and compare the efficiency of MCR-DA and PLS-DA methods. Raman spectroscopy was used forresponse measurement of oil samples. Different classifications were made between extra virgin olive oil and diverse combinations of four other edible oils, and similar sets were used for the comparative evaluation of the two discrimination methods. Various criteria evaluated the performance of the two methods and in most cases the new MCR-DA method was comparable to PLS-DA. However, MCR-DA can assign pure Raman spectra resulting from decomposition to different classes, which can provide direct information about the components of the studied system.

Selection of reference samples for updating multivariate calibration models used in the analysis of pig faeces

Monitoring and updating calibration models are common tasks when analytical methods are based on near-infrared spectroscopy. This work describes a situation in which a PLS calibration model that is used routinely for the determination of phosphorus content in pig faeces in digestibility studies had to be updated in order to be used with the faeces collected in a new trial with phytases. An approach based on D-optimality is presented that selects a reduced number of the new samples to be analyzed with the reference analytical method so that the small set is used to confirm the need to update the model and validate it. The rest of the new samples that had not been selected by the algorithm were accurately predicted with the updated model. The updated model maintained its previous performance for the samples in the validation set (an RMSEP of 1.58 g kg−1 compared with an RMSEP of 1.54 g kg−1 before the update) and the prediction error for the new samples was RMSECV = 1.95 g kg−1, much lower than the RMSEP = 11.38 g kg−1 obtained before the model update. In addition, the predictive ability of the updated PLS model was significantly better than updated models selecting the reduced dataset using other sample selection methods such as Kennard-Stone, a leverage-based selection method and random selection.

Application of chemometric combination NIRS technology for analysis of mixed coconut oil levels in Aceh Patchouli Oil

Patchouli oil could be a sort of fundamental oil that’s delivered from the refining of the leaves, twigs, and stems of the patchouli plant. One of the problems that occur in determining the quality of patchouli oil is mixing with other oils, such as coconut oil, causing the quality of patchouli oil to be low and the selling price to be cheap. The reason of this inquire about is to construct a show for evaluating the levels of coconut oil mixed in Aceh patchouli oil using the chemometric method. This research uses patchouli oil (crude oil) and coconut oil. The NIRS tool used for spectrum acquisition is the FT-IR IPTEK T-1516. NIRS technology is combined with chemometric methods with two multivariate analyses, namely the PCR and PLS models. The comes about appeared that the PLS calibration show was superior than the PCR calibration model. The RPD value in the non pretreatment PLS calibration model was 3.96 and increased with the presence of SNV pretreatment to 4.78 so that it was included in the category of very good prediction model accuracy. While the PCR calibration model included in the prediction category is still rough, both non-pretreatment and with SNV pretreatment, which are 1.45 and 1.50, respectively.

Identification of the Elemental Composition of Granulated Blast Furnace Slag by FTIR-Spectroscopy and Chemometrics

Blast furnace slag is a key large-tonnage waste product of metallurgical production, which is considered to be a promising alternative material in construction. In order to determine the scope of potential use of slag as a marketable product, it is necessary to study its structure and composition, which is determined by means of modern analytical instrumental methods. This paper analyzes the application of Fourier transform infrared spectroscopy (FTIR) and chemometrics methods to develop calibration models for identifying pelletized slag by elemental composition. In a comparative analysis of FTIR-spectra of slag the characteristic frequencies of absorption bands responsible for the content of calcite, silicates and aluminosilicates in the composition of samples were determined. Multivariate regression methods (principal components regression, partial least squares regression) and data of elemental composition results by EDX method were used to develop calibration models for determining elemental composition of granulated blast furnace slag. Using the developed PLS models with high performance (R2 from 0.91 to 0.96 for different components), the prediction of the elemental composition (Ca, Si, O, Mg) of the test sample was carried out and a low deviation of the prediction in contrast to the EDX reference data was obtained. The use of PLS calibration models for rapid and nondestructive determination of the quantitative content of components of the composition of granulated blast furnace slag has been proposed.

Non-destructive follow-up of ‘Jintao’ kiwifruit ripening through VIS-NIR spectroscopy – individual vs. average calibration model’s predictions

Visible/near infrared spectroscopy (Vis-NIRS) was used to monitor the yellow-fleshed kiwifruit (Actinidia chinensis Planch ‘Jintao’) ripening on two selected orchards along 13 weeks, from pre-harvest to the late harvest. Calibration models for several Internal Quality Attibutes (IQA) were built from the spectral data of 375 individual kiwifruit. The analyzed IQA were L*, a* and b* from the CIELAB color space, hue angle, chroma, firmness, dry matter (DM), soluble solids content (SSC), juice pH and titratable acidity (TA). Different pre-processing methods were tested for the construction of PLS calibration models. SSC and Hue were the best performing models with a correlation coefficient of 0.81 and 0.88, and root mean square error of prediction (RMSEP) of 1.27% and 1.95∘, respectively. The interpretation of the models in terms of the known absorption bands and the impact of signal to noise ratio (SNR) in them is discussed. The calibration models were used to perform average predictions of the IQA on orchard subareas, for each day of the experiment. These average predictions were compared with the IQA’s average reference values on the same subareas and days. The model’s metrics improved significantly through the averaging procedure, with RMSEP = 0.26–0.36% and R2 = 0.99 for SSC; and RMSEP = 0.42∘ – 0.56∘ and R2 ≈ 1 for Hue. Since orchard management is done essentially through averages and not individual values, this result reinforces the applicability of the NIR technology for follow-up of fruit ripening in the tree.

Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics.

The aim of this study was to evaluate the ability of mid-infrared (MIR) spectroscopy combined with chemometrics to analyze unstimulated saliva as a method to predict satiety in healthy participants. This study also evaluated features in saliva that were related to individual perceptions of human–food interactions. The coefficient of determination (R2) and standard error in cross validation (SECV) for the prediction of satiety in all saliva samples were 0.62 and 225.7 satiety area under the curve (AUC), respectively. A correlation between saliva and satiety was found, however, the quantitative prediction of satiety using unstimulated saliva was not robust. Differences in the MIR spectra of saliva between low and high satiety groups, were observed in the following frequency ratios: 1542/2060 cm−1 (total protein), 1637/3097 cm−1 (α-amino acids), and 1637/616 (chlorides) cm−1. In addition, good to excellent models were obtained for the prediction of satiety groups defined as low or high satiety participants (R2 0.92 and SECV 0.10), demonstrating that this method could be used to identify low or high satiety perception types and to select participants for appetite studies. Although quantitative PLS calibration models were not achieved, a qualitative model for the prediction of low and high satiety perception types was obtained using PLS-DA. Furthermore, this study showed that it might be possible to evaluate human/food interactions using MIR spectroscopy as a rapid and cost-effective tool.

Intelligent assessment of the histamine level in mackerel (Scomber australasicus) using near-infrared spectroscopy coupled with a hybrid variable selection strategy

Determination of the histamine level in fish is essential not only because it is an indicator of fish freshness but also because this prevents the risk of histamine intoxication in consumers. This study used the strategy of near-infrared (NIR) spectroscopy coupled with a hybrid variable selection for rapid and nondestructive assessment of the histamine level in mackerel. To effectively identify the highly informative spectral variables, a three-step hybrid strategy, combining backward interval partial least squares, selectivity ratio and flower pollination algorithm, was developed. The optimized variables were fitted to the multivariate calibration models of partial least squares model (PLS), radial basis function neural network (RBFNN), and wavelet neural network (WNN). The best model was obtained by the optimized WNN model using the hybrid variable selection method, with R-squared (RP2) value and root mean squared error for prediction were, 0.79 and 70 mg/kg for flesh side dataset, and 0.76 and 75 mg/kg for skin side dataset. The obtained results for the skin side dataset significantly outperformed the PLS(RP2=0.58) and RBFNN (RP2=0.47) calibration models.

Spectro-chemometric determination of panthenol enantiomeric excess in pharmaceutical products

Abstract Antibiotic, soothing and healing properties of panthenol are exploited in various pharmaceutical and cosmetic products. Only D-panthenol is biologically active while its L-form might counteract the biological effectiveness of the D-enantiomer. Hydrating and moisturizing effects are exhibited by both enantiomers. Therefore, it is necessary to develop a rapid and cheap method for the determination of panthenol enantiomeric excess in pharmaceutical preparations. In this study, b-cyclodextrin was used as a chiral selector for the recognition of panthenol enantiomers. Inclusion complexes formed by b-cyclodextrin and the analyte showed small differences in NIR and UV/VIS spectra compared by chemometric assessment. Based on the figure of merit and model characteristics, PLS calibration model in the selected range of NIR spectra is preferred. UV/VIS spectrometry has the disadvantage of complicated sample preparation compared to NIR spectrometry. Results reached by both proposed methods were in good agreement. Statistical investigation of the results (by Student t-test and Fisher F-test) confirmed that the proposed methods are comparable and applicable to chiral analysis of panthenol pharmaceutical preparations.

The development and validation of a quality by design based process analytical tool for the inline quantification of Ramipril during hot-melt extrusion.

Continuous processing is superseding conventional batch processing as a means of manufacturing within the pharmaceutical research/industry. This paradigm shift has led to the implementation of Process Analytical Technology (PAT) as a semi-automatic, predictive tool offering real-time quality control that can be built into the production line. However, PAT tools have been mainly utilised to monitor a single process (e.g. powder blending, synthesis of biopharmaceuticals and small molecules) rather than a full continuous manufacturing process. In addition, there is a paucity of guidance documents that consider the continuous and dynamic conditions of real-time measurements for validation purposes. In this study, the feasibility of developing and validating a predictive and reliable Raman method based on quality by design (QbD) and PAT frameworks for the real-time quantification of Ramipril (RMP) during hot-melt extrusion (HME) were investigated. Through QbD, a design space elucidating the quality attributes of RMP stability was successfully identified based on offline HPLC measurements. Process temperature and powder feeding rate were the main quality attributes to affect the stability of RMP during HME. The optimum combination of process and formulation variables were extracted from the validated design space and used to extrude RMP at a concentration range of 2.5-12.5 %w/w. Three calibration models were established using PLS regression analysis. The developed PLS calibration models showed excellent linearity (R2=0.989, 0.995, 0.992), accuracy (RMSEcv=0.31, 0.26, 0.30%) and specificity (PC1=81, 85, 89%) for models 1, 2 and 3, respectively. Furthermore, the developed QbD-PAT model was able to predict the quantity of RMP at varied process feed rate (10, 35rpm) operating under long processing time (60min). The output of this study allows in-process optimisation of formulation and process variables to control the quality and quantity of RMP during HME. Furthermore, it allows the implementation of PAT tools as routine methods of analysis within the laboratory.

Prediction of drug dissolution from Toremifene 80 mg tablets by NIR spectroscopy

The aim of our study was to justify substitution of dissolution analysis for NIR measurement of Toremifene 80mg tablets. We studied implementation of a NIRS method by integrating the method development to discrimination power of the dissolution method. Hence, we analyzed 20 DoE tablet batches and studied which of the critical formulation factors affecting dissolution were statistically significant. To study if these factors can be detected by NIRS, PLS calibration models were developed. Finally, PLS model was built to correlate NIR data with the actual dissolution results to predict the released amount of toremifene in 30min. To obtain the data the tablet batches were measured by NIR using diffuse reflectance technique and multivariate analysis tool was used to calibrate the NIRS models. Correlations between the critical formulation factors and the NIR spectra of Toremifene 80mg tablet were shown and it was thus justified to develop a NIRS prediction model for dissolution. Variance (R2), standard error of estimate (SEE) and standard error of prediction (SEP) of the model were 90.0%, 4.3% and 5.9%, respectively. It was thus shown that multi-phased and time consuming dissolution procedure could be substituted for fast non-invasive NIRS method.

Detection and quantification of adulterants in honey by LIBS

The present work proposes methods for detection and quantification of honey adulterants using laser-induced breakdown spectroscopy (LIBS). The sample set consisted of 6 pure honey from different botanical sources, 2 sweetener syrups and 228 fortified samples. The spectra acquired using a spark discharge coupled to the LIBS system were used for the development of the PLS-DA (classification) and PLS (calibration) models. Several data preprocessing and variable selection methods were evaluated to obtain the best fit. The detection of adulterants was performed with 100% of accuracy. The quantification of adulterants was possible through a PLS model with the variables selected by iPLS. The PLS model was validated with external samples and presented good accuracy, selectivity, sensitivity, and linearity. The proposed methods highlighted the potential of the LIBS technique for honey authenticity certification, providing fast, simple, and clean determinations since no sample pretreatment was required.

Handheld near-infrared spectrometer for on-line monitoring of biodiesel production in a continuous process

In this work, the use of a handheld near-infrared spectrometer (MicroNIR) for on-line monitoring of biodiesel production in a continuous process is described. The process consists of a laboratory scale reactive distillation column where transesterification reactions between cottonseed oil and ethanol take place. This column can operate with different ethanol to oil molar ratios. In the present study, a severe condition using an excess of ethanol at the point monitored with NIRS was applied. This condition caused problems in modelling the content of the alkyl ester, particularly because of the broad and intense band related to the ethanol in the spectral region covered by MicroNIR. Therefore, for process monitoring and control, both an estimate of the alkyl ester as well as of ethanol contents were carried out. To deal with this, PLS calibration models were developed to estimate concentrations of ethanol and alkyl ester at the bottom of the column. In addition, in order to estimate the yield of the alkyl ester, a PLS model to assess glycerol content was also developed. In spite of the problematic conditions under which the column operated, mainly related to the excess of ethanol, the models presented satisfactory results for ethanol (RMSEP = 1.66 wt%), alkyl ester (RMSEP = 2.15 wt%) and glycerol (RMSEP = 1.08 wt%) quantifications and have demonstrated the feasibility of the handheld instrument to monitor biodiesel production in a continuous process.

Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling.

A two‐step chemometric procedure was developed on the attenuated total reflection‐Fourier transform infrared data of human breastmilk to detect adulteration by water or cow milk. The samples, collected from a Milk Bank, were analyzed before and after adulteration with whole, skimmed, semi‐skimmed cow milk and water. A preliminary clustering via principal component analysis distinguished three classes: pure milk, milk adulterated with water, and milk adulterated with cow milk. A first partial least square‐discriminant analysis (PLS‐DA) classification model was built and then applied on new samples to identify the specific adulterants. The external validation on this model reached 100% of the correct identification of pure milk and 90% of the type of adulterants. In the following step, four PLS calibration models were built to quantify the amount of the adulterant detected in the classification analysis. The prediction performance of these models on new samples showed satisfactory parameters with root mean square error of prediction and percentage relative error lower than 1.38% and 3.31%, respectively.

Spectrometric Determination of Content of Methyl Palmitate in Methyl Esters of Waste Cooking Oils

Abstract The second-generation liquid biofuels are fuels derived from non-food raw materials, i.e. waste cooking oils and animal fats. They are waste raw materials from the agri-food industry, hence their quantity is limited, and their quality depends, inter alia, on the place of their acquisition. Considering the fact that rheological properties of liquid biofuels are closely correlated with the quality of raw materials from which they are obtained, the industrial production of biofuels from waste fats requires development of new analytical methods, allowing for a quick assessment of the quality of the obtained products. The aim of the study was to confirm the possibility of using near infrared spectrometry to assess the content of methyl palmitate in biofuels produced from waste cooking oil. The calibration models were based on 41 absorbance spectra recorded in the range of 400-2170 nm for samples containing from 0 to 5 % of methyl palmitate. The obtained results confirmed that there is a possibility of effective detection of the concentration of this ester in biofuel using the spectrum in the range of 1644-1778 nm. The developed PLS calibration models are characterized by a determination co-efficient (R2) exceeding the value of 0.99.

Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films.

A nanostructured electrochemical bi-sensor system for the analysis of milks has been developed using the layer-by-layer technique. The non-enzymatic sensor [CHI+IL/CuPcS]2, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPcS) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity, and chitosan (CHI), that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPcS]2-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV–vis, and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal component analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using a PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components, such as fats, proteins, and acidity, can also be obtained. The method developed is simple, and the short response time permits its use in assaying milk samples online.

Advanced process control and monitoring of a continuous flow micro-reactor

This paper presents a real-time advanced process control and monitoring scheme for a continuous flow micro-reactor producing an ether compound. A PLS calibration model is designed to predict the ether product yield using inline spectral data. This yield is then controlled to the desired setpoint by the proposed MPC scheme. Through real-time results, it is shown that the MPC controller is able to deliver accurate setpoint tracking even in the face of substantial plant-model mismatch caused by diluted catalyst. Furthermore, it is shown that the designed PCA monitor can effectively detect process/reaction faults, such as irregular reaction chemistry.

Development of an in-line Raman spectroscopic method for continuous API quantification during twin-screw wet granulation

Raman spectroscopy was evaluated as a process analytical technology (PAT) tool for continuous API quantification during twin-screw wet granulation. Therefore, a Raman probe was implemented in front of the granulator barrel. This setup enabled the collection of Raman spectra upon a constant granule flow.To develop an in-line PLS calibration model, eight binary mixtures of the API and lactose monohydrate with API contents between 5 and 50% were pre-blended and granulated in a twin-screw granulator with a screw speed of 150 rpm and a powder feed rate of 40 g/min. Water was used as a granulation liquid with different liquid to solid ratios depending on the API content. Ibuprofen and diclofenac sodium were chosen as model drugs and separated PLS models were built for each API.The predictive performance of the developed PLS models was determined by granulating and monitoring new test samples containing different API concentrations. This evaluation showed that the models were able to predict the API concentration with an RMSEP of 0.59% for ibuprofen and 1.5% for diclofenac sodium.In a second part, the developed in-line Raman spectroscopic method was used to determine the API concentration during a split feeding process. Therefore, the API and lactose monohydrate were added by two independently adjustable feeders into the twin-screw granulator barrel. The in-line spectroscopy analysis which was verified by UV-analysis indicated that the mixing ability of the twin-screw granulator was good for the used settings and all adjusted API concentrations.

Detection of fresh palm oil adulteration with recycled cooking oil using fatty acid composition and FTIR spectral analysis

ABSTRACTThere is a growing concern over the food safety issue related to increased incidence of cooking oil adulteration with recycled cooking oil (RCO). The objective of this study was to detect fresh palm olein (FPO) adulteration with RCO using fatty acid composition (FAC) and Fourier-transform infrared spectroscopy (FTIR) spectral analyses combined with chemometrics. RCO prepared in the laboratory was mixed with FPO in the proportion ranged from 1% to 50% (v/v) to obtain the adulterated oil samples (AO). FACs for FPO, RCO, and AO were determined using gas chromatography equipped with a flame ionization detector (GC-FID). The compositions of most fatty acids in RCO lied within the normal ranges of Codex standard, except for C8:0, C10:0, C11:0, C15:0, trans C18:1, and polyunsaturated fatty acids (PUFAs), C20:5. PUFAs showed a consistent decreasing trend with increasing magnitude of change with respect to increasing adulteration level and thus might be a good indicator for detecting FPO adulteration with RCO. The evaluation parameters (coefficient of determination, root mean standard error) of the FTIR-partial least square (PLS) model of palm oil adulteration with recycled oil are R2 = 0.995 and 3.25, respectively. For FTIR spectral analysis, the distinct variations in spectral regions and aberrations in characteristic bands between FPO and RCO were observed. The optimized PLS calibration model developed from normal spectral of the combined region at 3602–3398, 3016–2642, and 1845–650 cm−1 overpredict the adulteration level. On the other hand, the discriminant analysis classification model was able to classify the FPO and AO into two distinct groups. Improvement of the principles of combined techniques in authenticating AO from fresh oil is beneficial as a guideline to detect adulteration in cooking oil.