iPLS Model Research Articles

Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm

The accurate determination of sugar content in tangerines plays a pivotal role in assessing their quality, nutritional value, and marketability. Traditional methods for sugar quantification often involve time-consuming and resource-intensive processes. In this paper, we introduce a novel approach for sugar determination in tangerines utilizing fluorescence spectroscopy in conjunction with an improved Partial Least Squares (iPLS) algorithm. A robust testing model was developed, incorporating a diverse dataset of tangerine samples with known sugar concentrations. Fluorescence spectra were acquired for 80 samples, of which 37 were used to build the iPLS model and were considered as the training dataset. The remaining 43 samples served as the validation dataset and were used to show the model’s efficacy. The training dataset was evaluated using cross-validation, and F-values were computed to determine how many main components should be utilized to build the model. The result approved validation dataset’s R-square and root-mean-square error were 0.9777 and 0.002992, respectively. These findings open the door to broader applications in the citrus industry and beyond, with the potential for automating the analysis process and improving overall quality control.

Non-Destructive Measurement of Egg's Haugh Unit by Vis-NIR with iPLS-Lasso Selection.

Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg's freshness by semi-transmittance spectral acquisition. A fiber spectrometer with a spectral range of 550-985 nm was embedded in the developed spectral scanner, which was designed with rich light irradiation mode from another two reflective surfaces. The semi-transmittance spectra were collected from the waist of eggs and monitored every two days. Haugh unit (HU) is a key indicator of egg's freshness, and ranged 56-91 in 14 days after delivery. The profile of spectra was analyzed the relation to the changes of egg's freshness. A series of iPLS models were constructed on the basis of spectral intervals at different divisions of the spectral region to predict the egg's HU, and then the least absolute shrinkage and selection operator (Lasso) was used to sparse the number of iPLS member models acting as a role of model selection and fusion regression. By optimization of the number of spectral intervals in the range of 1 to 40, the 26th fusion model obtained the best performance with the minimum root mean of squared error of prediction (RMSEP) of 5.161, and performed the best among the general PLS model and other intervals-combined PLS models. This study provided a new, rapid, and reliable method for the non-destructive and in-site determination of egg's freshness.

Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee

Food systems are negatively affected by food frauds with food recalls challenging the system's sustainability and consumer confidence in food safety. Coffee, an economically important commodity is frequently adulterated for economic gains, thereby requiring fast and reliable detection techniques. Of the various tracing strategies, spectroscopic techniques have seen considerable commercial success but rely heavily on human-engineered features. Thus, this study aims to evaluate feasibility of deep chemometrics (i.e., convolutional neural network, CNN) for coffee adulterant quantification in comparison to standard chemometrics approaches (i.e., partial least squares, PLS; and interval-PLS, iPLS). Commercial ‘espresso’ coffee was admixed with chicory, barley, and maize (0–25%, w/w) and subjected to Fourier Transformed-Near Infrared (FT-NIR) spectral analysis. The results confirmed the feasibility of CNN algorithm for adulterant quantification from FT-NIR spectra with excellent performances (R 2 > 0.98). Furthermore, CNN with Data augmentation (DA) with either autoscaling (AS) and/or standard normal variate (SNV) pre-treatment showed better prediction performances with RMSEP (0.76–0.82%) and BIASP (−1.00 × 10 −2 to −1.00 × 10 −1 %) that were better to comparable to those of PLS and/or iPLS models (0.72 < % RMSEP <3.045; −7.98 × 10 −2 < % BIASP <8.63 × 10 −2 ) for the adulterants tested. The study showed that deep learning algorithms can be potential alternatives to standard methods with little to no human interference for feature extraction during real-time applications of spectroscopic tools targeted to overcome food fraud crisis. • Deep chemometric algorithm tested for coffee adulteration prediction. • Convolutional neural network led to better performance than chemometrics. • Outlier removal and data augmentation benefitted the CNN algorithm. • CNN can be a feasible alternative for FT-NIR data analysis.

Improvement of NIR prediction ability by dual model optimization in fusion of NSIA and SA methods

Feature selection and sample partitioning are both important to establish a quantitative analytical model for near-infrared (NIR) spectroscopy. The classical interval partial least squares (iPLS) model for waveband selection can be improved in combination of the simulated annealing (SA) algorithm. The sample set partitioning based on a joint x-y distance (SPXY) method for sample partitioning is based on the distances of both the x- and y- dimensions; it is expected to be optimized using the non-dominant sorting strategies (NS) combined with the immune algorithm (IA). In this study, we investigated the dual model optimization mode for simultaneous selection of feature waveband and sample partitioning, and proposed a novel method defined as SA-iPLS & SPXY-NSIA. The method explores a population evolution process, and takes the candidate individual as the link for the fusion optimization of SA-iPLS and SPXY-NSIA. The method screens feature wavebands and observes a good partition of the modeling samples, to construct a combined optimization strategy for fusion optimization of the target waveband and suitable sets of sample partitioning. The performance of the SA-iPLS & SPXY-NSIA method was tested using a soil sample dataset. To prove model enhancement, the proposed method was compared to the two traditional methods of Kennard-Stone (KS) and SPXY in combination with SA-iPLS. Experimental results show that the fusion model established by SA-iPLS & SPXY-NSIA performed better than the KS-SA-iPLS and SPXY-SA-iPLS models. The best testing results of the fusion model is with RMSET, RPDT and RT observed as 0.0107, 1.7233 and 0.9097, respectively. The proposed method is prospectively able to effectively improve the predictive ability of the NIR analytical model.

Near-infrared spectroscopy and machine learning-based classification and calibration methods in detection and measurement of anionic surfactant in milk

The monitoring of milk and milk-based products are crucial in the dairy industry because of their global consumption, fraudulent practice applied to mask the quality parameters for gaining more profit. Non-destructive near-infrared (NIR) spectroscopy and machine learning (ML) methods like interval partial least squares (iPLS), principal components analysis (PCA), and soft independent modeling of class analogy (SIMCA) were used to classify and discriminate pure milk samples from adulterated with an anionic surfactant. The surfactant’s model was sodium dodecylbenzene sulfate (SDBS) in different concentrations (1.94–19.4 gkg−1). ML models were assessed by sensitivity, specificity and efficiency rates as classification figures of merit. As a result, iPLS regression method based on standard normal variate (SNV) pre-processed spectra provided better performance compared to partial least squares (PLS) regression with a full-spectrum dataset. The root mean square error of cross-validation (RMSECV) was 0.123 and 0.135 for the iPLS and full-spectrum models, respectively. PCA as a qualitative grouping analysis demonstrated separate clusters of samples in terms of their concentration level of SDBS. For the iPLS dataset in SIMCA model, all classification figures of merit were recorded 100 % for pure milk samples. In contrast, the average sensitivity, specificity and efficiency rates were 97.6 %, 93 %, and 94.9 % for adulterated samples.

Whey protein supplement adulteration with rice flour quantification: A simple method using ATR-FT-MIR and iPLS

In this work, a method using ATR-FT-MIR and iPLS was developed to quantify whey protein supplement adulteration with rice flour. The original vanilla flavor commercial whey protein samples were adulterated with commercial rice flour with concentrations between 11.49% to 29.14% (w/w). After the adulteration, the ATR-FT-MIR spectra were obtained with no additional preparation procedure. The iPLS model analysis was performed using RStudio software with the mdatools package. The RMSEC was 1.26, the R2= 0.954 and the cross-validation error (RMSECV) was 3.31. The prediction error (RMSEP) for the validation set was equal 3.48 and the validation R2 was 0.610. These parameters, associated with the fact that the method does not require sample preparation, demonstrate the procedure viability as a tool to quantify adulterations of whey protein with rice flour.

In Vitro to ex Vivo/In Vivo Correlation (IVIVC) of dissolution kinetics from inhaled particulate solutes using air/blood barrier models: Relation between in vitro design, lung physiology and kinetic output of models

Correlating data from in vitro dissolution testing of inhaled particulate pharmaceuticals or air pollutants with the dissolution and distribution to the blood circulation of the same aerosols following inhalation in vivo is a major challenge. Better methods could benefit both inhaled drug development and the risk assessment of air pollutants. Five properties known to influence the results produced by in vitro methods are the particle deposition pattern, barrier thickness, surfactant distribution, perfusion strategy and distribution volume of dissolution. The purpose with this manuscript was to compare the rates of dissolution- and absorption of two particulate solutes, nicotine and Fluticasone Propionate (FP), in the Isolated, ventilated, and Perfused Lung of the rat (IPL) with that of non-biological test barriers simulating the same process in vitro. Three published test model barriers were compared; the DissolvIt with a mucous gel simulant and data for two other models, utilizing particle deposition on glass fiber filters; one Paddle over Disk model, and one Modified Transwell model. The more water-soluble nicotine, only tested in the ex vivo IPL and the in vitro DissolvIt model, had a very rapid first absorption half-time in both the IPL and the DissolvIt of respectively 1.9 and 0.55 min. For more lipophilic FP (Flixotide, MDI) the correlation of dissolution- and absorption data was compared for the inhaler formulation between the ex vivo rat IPL and all three in vitro test models. The results varied substantially between the in vitro models regarding simulated appearance in the blood circulation and, thus, regarding correlation with the ex vivo IPL model. The first half-time of in vitro dissolution- and absorption of FP to perfusate when normalized to the 8.6 h half-time measured for FP in the ex vivo IPL ranked as; 2.9, 0.021, and 0.17 for respectively the DissolvIt, the Paddle over Disk model, and the Modified Transwell model. Despite similar physicochemical properties of the solvents involved in all three in vitro models, it seems like resemblance of the models with the in vivo physiology has a profound influence on the dissolution- and absorption data produced.

Near-infrared spectroscopy for the prediction of rare earth elements in soils from the largest uranium-phosphate deposit in Brazil using PLS, iPLS, and iSPA-PLS models.

The largest uranium-phosphate deposit in Brazil also contains considerable levels of rare earth elements (REEs), which allows for the co-mining of these three ores. The most common methods for REE determination are time-consuming and demand complex sample preparation and use of hazardous reagents. Thus, the development of a safer and faster method to predict REEs in soil could aid in the assessment of these elements. We investigated the efficiency of near-infrared (NIR) spectroscopy to predict REEs in the soil of the uranium-phosphate deposit of Itataia, Brazil. We collected 50 composite topsoil samples in a well-distributed sampling grid along the deposit. The NIR measures in the soils ranged from 750 to 2500 nm. Three partial least squares regressions (PLSR) were selected to calibrate the spectra: full-spectrum partial least squares (PLS), interval partial least squares (iPLS), and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The concentrations of REEs were measured by inductively coupled plasma optical emission spectroscopy (ICP-OES). In addition to raw spectral data, we also used spectral pretreatments to investigate the effects on prediction results: multiplicative scatter correction (MSC), Savitzky-Golay derivatives (SG), and standard normal variate transformation (SNV). Positive results were obtained in PLS for La and ΣLREE using MSC pretreatment and in iSPA-PLS for Nd and Ce using raw data. The accuracy of the measurements was related to the REE concentration in soil; i.e., elements with higher concentrations tended to present more accurate results. The results obtained here aim to contribute to the development of NIR spectroscopy techniques as a tool for mapping the concentrations of REEs in topsoil.

Multivariate calibration: Identification of phenolic compounds in PROPOLIS using FT‐NIR

AbstractThe chemical composition of propolis is complex and varies according to location, seasonality, and vegetation where the hives are located. Raw and macerated propolis produced in different regions of Brazil were assessed and produced iPLS models from FTNIR combined to multivariate calibration to determine total phenolic content (TPC). The spectra of raw propolis were obtained analyzing samples directly, without treatment or processing, while macerated propolis was submitted to the milling process using liquid nitrogen. Concerning the results, the TPC ranged from 3.61 to 90.4 mg GAE g‐1, which indicates a significant difference among samples. It was possible to conclude that macerated propolis presented the best multivariate calibration obtained from the second derivative and SNV pre‐processing. The error values RMSECV and RMSEP were 3.79 and 6.17 mg GAE g‐1 respectively, while the determination coefficients in the calibration, cross‐validation, and external validation were 0.97, 0.87, and 0.82, respectively.

Estimating chlorophyll content at leaf scale in viroid-inoculated oil palm seedlings (Elaeis guineensis Jacq.) using reflectance spectra (400 nm–1050 nm)

ABSTRACTOrange Spotting (OS) is an emerging disease in oil palm (Elaeis guineensis Jacq) that has been associated with Coconut cadang-cadang viroid (CCCVd; Cocadviroid, Pospiviroidae). This study was aimed at selecting efficient wavebands in the Visible/near infrared (VNIR) spectrum (400–1050 nm) for estimation of chlorophyll content in CCCVd-inoculated oil palm seedlings. The VNIR spectrum is most sensitive to chlorophyll stress and able to differentiate between healthy and diseased plants. Therefore, a greenhouse experiment was conducted to remotely sense the chlorophyll content using a hyperspectral hand-held spectroradiometer (Analytical Spectral Device (ASD) FieldSpec–II) and a portable chlorophyll meter (Minolta Soil-Plant Analysis Development (SPAD)−502). FieldSpec–II provides reflectance spectra with narrow and contiguous wavebands while SPAD-502 churns out SPAD readings corresponding to chlorophyll content. Interval Partial Least Squares (iPLS) model was used to assess the relationship between independent variable (spectra) and dependent variable (SPAD readings). Five different spectral datasets (i.e. raw spectra, first-order derivatives, Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and standard normal transformation) were investigated. Results showed that the MSC dataset yielded the most accurate estimation with a root mean square error of prediction of 3.70% and a correlation coefficient for prediction of 0.72. A total of thirty wavebands within a range between 601 nm and 630 nm was selected. This work showed that VNIR spectra has the potential to diagnose OS disease in oil palm using the iPLS regression method. This is completely a new application of hyperspectral remote sensing for the oil palm industry, particularly in the domain of plant protection.

Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

As important components of air pollutant, volatile organic compounds (VOCs) can cause great harm to environment and human body. The concentration change of VOCs should be focused on in real-time environment monitoring system. In order to solve the problem of wavelength redundancy in full spectrum partial least squares (PLS) modeling for VOCs concentration analysis, a new method based on improved interval PLS (iPLS) integrated with Monte-Carlo sampling, called iPLS-MC method, was proposed to select optimal characteristic wavelengths of VOCs spectra. This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling. The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum. Different wavelength selection methods were built, respectively, on Fourier transform infrared (FTIR) spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory. When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times, the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10, which occupies only 0.22% of the full spectrum wavelengths. While the RMSECV and correlation coefficient (Rc) for ethylene are 0.2977 and 0.9999[Formula: see text]ppm, and those for ethanol gas are 0.2977 ppm and 0.9999. The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively, and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

Ropivacaine inhibits pressure-induced lung endothelial hyperpermeability in models of acute hypertension

AimsIncreases in hydrostatic pressure results in endothelial hyperpermeability via eNOS-dependent pathways. Ropivacaine is known to inhibit eNOS activation and to attenuate lung injury. Herein, we sought to determine if ropivacaine regulates pressure-induced lung endothelial hyperpermeability. Main methodsThe effects of ropivacaine on lung permeability were assessed in two models of acute hypertension (AH): the isolated perfused lung preparation where acute increases in left atrial pressure model the hemodynamic changes of severe hypertension, and an animal model of AH induced by norepinephrine. In the IPL model, whole lung filtration coefficient (Kf) was used as the index of lung permeability; pulmonary artery pressure (Ppa), pulmonary capillary pressures (Ppc), and zonal characteristics (ZC) were measured to assess the effects of ropivacaine on hemodynamics and their relationship to Kf2/Kf1. In vivo, ropivacaine effects were investigated on indices of pulmonary edema (changes in PaO2, lung wet-to-dry ratio), changes in plasma volume and nitric oxide (NO) production. Key findingsRopivacaine provided robust protection from pressure-dependent barrier failure; it inhibited pressure-induced increases in Kf without affecting Ppa, Ppc or ZC. In vivo, ropivacaine prevented pressure-induced lung edema and associated hyperpermeability as evidence by maintaining PaO2, lung wet-to-dry ratio and plasma volume in levels similar to sham rats. Ropivacaine inhibited pressure-induced NO production as evidenced by decreased lung nitro-tyrosine content when compared to hypertensive lungs. SignificanceCollectively these data show that ropivacaine inhibits pressure-induced lung endothelial hyperpermeability and suggest that ropivacaine may be a clinically useful agent to prevent endothelial hyperpermeability when pulmonary pressure is acutely increased.

Evaluation of moisture content in processed apple chips using NIRS and wavelength selection techniques

Rapid determination of moisture content during processing period of apple chips is of great significance for their real-time quality control. In this study, near infrared spectroscopy (NIRS) combined with chemometric techniques were used to measure moisture content of apple chips during processing period. Interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and genetic algorithm (GA) were utilized to select efficient wavelength regions, respectively, and partial least squares regression (PLSR) was applied to develop prediction model. The performance of models was evaluated by three types of parameters, including the root mean square error (RMSE), correlation coefficient (R) and the residual prediction deviation (RPD). The results demonstrated that, in contrast to classical PLS, iPLS and GA-PLS model, siPLS model for the prediction of moisture content had superior performance with RMSEC = 0.0877, Rc = 0.9540, RMSEP = 0.0627, Rp = 0.9747, and RPD = 4.37. Therefore, the application of NIRS and wavelength selection techniques in this study provides a feasibility of a non-destructive and rapid way to enhance the efficiency of quality assurance and control investment.

Quantitative optical analysis of component content in multivariate mixture solution

The partial least square method (PLS) was introduced to quantitatively analyze component content of multivariate mixture by using ultraviolet-visible spectroscopy. Two models were built to establish component content of multivariate mixture based on the transmittance spectra of multivariate mixture, which is the full band PLS model and the feature band iPLS model, respectively. The comparative study of the models was carried out with ethanol solution as the research object and the samples were divided into two groups, including the calibration sets and the prediction sets. This study indicates that there is a certain relationship between the spectra information of the multivariate mixture and its component content, and the above two models are feasible for solving the component content. Compared with the full band PLS model, the feature band iPLS model has higher accuracy and the model is simpler. Therefore, the feature band iPLS model, which is an efficient method, can be used to analyze the component content in multivariate mixture solution based on the ultraviolet-visible spectrum.

Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

ABSTRACTTextile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

The Quantification of Adulteration in Arabica Coffee using UV-Visible Spectroscopy in Combination with Two Different PLS Regressions

Arabica coffee is being considered to be of better quality than robusta because of their superior taste and aroma. Adulteration of arabica with other cheaper coffee like robusta coffee has become a great problem related to authentication of food products. Ground coffee samples are most challenging to be discriminated each other and visual inspection by our eyes or even machine vision method becomes very difficult. For this reason, in this research we propose a new analytical method based on UV-visible spectroscopy for quantification of adulteration in arabica-robusta coffee blend. The proposed method is easy to use, low cost with affordable spectrometer and safe for environment with free-chemical analysis. A number of 100 samples was used as samples with different degree of adulteration (10-60% of robusta concentration in arabica-robusta coffee blend). Spectral data of aqueous samples was obtained using a UV-visible spectroscopy in the range 200-400 nm . The result shows that calibration model using selected subintervals (iPLS model) can improve the prediction performance of calibration model using full spectrum (FS-PLS). It is noted that iPLS model removed wavelengths that is not related to the quantification of adulteration in arabica-robusta coffee blend. This study has shown a potential application of using UV-visible spectroscopy for simple and low cost tool to detect the authentication of arabica coffees

Using PLS, iPLS and siPLS linear regressions to determine the composition of LDPE/HDPE blends: A comparison between confocal Raman and ATR-FTIR spectroscopies

Polyethylenes can be distinguished from each other by branching degree and molar mass, which cause significant changes in their properties, rheological and thermal behaviors. However, the distinction of these polyolefins by a highly reliable method is still a challenge for their mechanical recycling. In this contribution, confocal Raman and ATR-FTIR spectroscopies with modified algorithms of Partial Least Squares (PLS) linear regression (Interval PLS and Synergy PLS regressions) have been successfully applied to quantify the concentration of LDPE/HDPE reprocessed blends. The effects of the number of subintervals (10–50) and interval combination (2–4) on the prediction performance of the regression methods were evaluated. The iPLS-based models, which are built with the spectra data subdivided in 20 and 30 intervals, present best predictive performance than siPLS-based models for confocal Raman and ATR-FTIR data. Even with the presence of antioxidants, the proposed test method is suitable for the whole blend composition range (0–100wt% of HDPE) since the PLS, iPLS and siPLS-based models presented root mean square error of prediction in the range of 2.68–6.94wt% and 2.84–6.49wt% using Raman and FTIR measurements, respectively.

Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS

Olive oil adulteration with waste cooking oil was detected and quantified by combining optical Raman scattering spectroscopy and chemometrics. Spectra of 96 olive oil samples with waste cooking oil (2.5%, 5%, 10%, 20%, 30% and 50%) were collected by the portable Raman spectroscopy system. iPLS and SiPLS quantitative analysis models were established. The results revealed that spectral data after SNV processing are the best for synergy interval partial least square (SiPLS) modeling and forecast. The root mean squared error of calibration (RMSEC) is 0.0503 and the root mean squared error of validation (RMSEV) is 0.0485. The lower limit of application (LLA) of the proposed method is c[WCO]=0.5%. According to linear regression calculation, the theoretical limit of detection (LOD) of the proposed method is about c[WCO]=0.475%. The established model could make effective quantitative analysis on adulteration of waste cooking oil. It provides a quick accurate method for adulteration detection of waste cooking oil in olive oil.

Simultaneous determination of aspartame, cyclamate, saccharin and acesulfame-K in powder tabletop sweeteners by FT-Raman spectroscopy associated with the multivariate calibration: PLS, iPLS and siPLS models were compared

For the first time, a procedure for simultaneous determination of the main artificial sweeteners, aspartame (ASP), cyclamate (CYC), saccharin (SAC), and acesulfame-K (ACSK) by a spectroscopic method associated with the multivariate calibration is proposed. These analytes were quantified in tabletop sweeteners samples using FT-Raman spectroscopy. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used as reference method. Partial least squares (PLS), interval PLS (iPLS), and synergism PLS (siPLS) methods were evaluated in a comparative study where the selected interval models presented better results. Multivariate regression models, such as PLS, iPLS and siPLS were built and the lower root mean square errors for prediction (RMSEP) found were 0.027–0.031% w/w, 0.316–0.363% w/w, 0.082–0.184% w/w, and 0.040–0.049% w/w to ASP, CYC, SAC, and ACSK, respectively. The coefficient of determination for prediction (R2p) varied between 0.978 and 0.979, 0.969–0.977, 0.952–0.994, and 0.959–0.965 for ASP, CYC, SAC and ACSK, respectively. The analysis of model's residues was made by bias and permutation tests to evaluate systematic and trend errors. The selected intervals by iPLS and siPLS were evaluated and the bands related to the vibrational modes of the analytes were assigned with the aid of density functional theory calculations (DFT).