Partial Least-squares Calibration Research Articles

Forecasting wine phenolic composition from infrared spectra of grapes extracts and monitoring of fermentations with optimised time-specific prediction models

Incorporating monitoring and forecasting technologies has the potential to enhance the different aspects of winemaking. By monitoring certain chemical parameters, it is possible to evaluate the progress of the fermentation and phenolic extraction, while also detecting deviations from expected behaviour. This study aims to investigate the development of a rapid forecasting method and different strategies to improve the accuracy of process monitoring. The first part involves the introduction of partial least squares (PLS) calibration models able to forecast the trajectories of phenolic extraction during fermentation using grape extract spectra. Additionally, novel methods for reoptimizing calibration models are proposed, with the intention of increasing the prediction accuracy. Whilst this method is transferable to PLS models, principal component regression (PCR) models were used instead as a more simplistic way to test the new methods. First, dynamic PCR linear regression coefficients were adapted over time to capture the compound evolution in the wine matrix. Secondly, PCR calibration models were reoptimized using interpolated spectral and reference data. The forecasting PLS calibration models have shown promising results for total anthocyanin content (mg/L), colour density, polymeric pigments (mg/L), MCP tannins (mg/L), and total phenolic index with overall R2 values on the validation set above 0.6, and low RSMEV values. Using interpolated data to re-optimise models was found to be more effective than global models or those using the calculated regression coefficients methods for predicting the concentration of a particular compound at a certain time point. While R2 values are suboptimal for all considered PCR models, in 72% of the cases analysed, the interpolation method for re-optimisation showed RMSEV values between 6% and 83% lower than those reported for a more traditional PCR model and other re-optimisation strategies. Interpolated models have shown consistency in predicting values, along with good performance metrics, indicating that they could be applied to samples for which calibration models do not exist.

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.

Authentication analysis of milkfish oil treated with activated charcoal and bentonite using Fourier Transform Infrared Spectroscopy and Multivariate Data Analysis

Adulteration issues are important not only for fats and oils consumers but also for producers and regulatory bodies, therefore authentication analytical methods are continuously developed. The objective of this study was to highlight the employment of attenuated total Fourier transform infrared (FTIR) spectroscopy and chemometrics for the authentication of milkfish oil (MFO) from pumpkin seed oil (PSO). In this study, ATRFTIR spectra of MFO, PSO and its binary mixtures were scanned, and the information obtained was used for quantitative analysis of adulterants and classification between authentic MFO and MFO adulterated with PSO. Two multivariate calibrations of partial least square calibration (PLSR) and principal component regression (PCR) were compared for their performance during quantitative analysis and linear discriminant analysis was applied for discrimination between authentic MFO and MFO adulterated with PSO. The results revealed that PLSR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was preferred for simultaneous quantitative analysis of MFO-treated with activated charcoal, while PCR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was suitable for analysis of MFO-treated with bentonite providing the acceptable values of R2 , root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Linear discriminant Analysis using normal FTIR spectra at the whole wavenumber region was successful for the discrimination of MFO treated with activated charcoal and bentonite with accuracy levels of 100%. It can be concluded that the chemometrics approach for ATR-FTIR spectra could be a simple and effective tool for the authentication of MFO.

FTIR-based prediction of collagen content in hydrolyzed protein samples

Fourier transform infrared spectroscopy (FTIR) is a powerful analytical tool that has been used for protein and peptide characterization for decades. In the present study, the objective was to investigate if FTIR can be used to predict collagen content in hydrolyzed protein samples. All samples were obtained from enzymatic protein hydrolysis (EPH) of poultry by-products providing a span in collagen content from 0.3% to 37.9% (dry weight), and the FTIR analysis was performed using dry film FTIR. Since nonlinear effects were revealed by calibration using standard partial least squares (PLS) regression, Hierarchical Cluster-based PLS (HC-PLS) calibration models were constructed. The HC-PLS model provided a low prediction error when validated using an independent test set (RMSE = 3.3% collagen), while validation using real industrial samples also showed satisfying results (RMSE = 3.2%). The results corresponded well with previously published FTIR-based studies of collagen, and characteristic spectral features for collagen were well identified in the regression models. Covariance between collagen content and other EPH related processing parameters could also be ruled out in the regression models. To the authors' knowledge, this is the first time that collagen content has been systematically studied in solutions of hydrolysed proteins using FTIR. This is also one of few examples where FTIR is successfully used to quantify protein composition. The dry-film FTIR approach presented in the study is expected to be an important tool in the growing industrial segment that is based on sustainable utilization of collagen-rich biomass.

Determination of Chemical Content of Vanilla Pods (Vanilla planifolia) Non-destructively Using NIR Spectroscopy

This study aims to assess Near infrared (NIR) spectroscopy to predict vanillin and moisture content non-destructively. Twenty-four samples of vanilla pods (@55 grams/sample) from Sumatra and Sulawesi Island were tested. The reflectance spectra of vanilla pod were measured using a NIRFlex N-500 spectrometer at the wavelength of 1000-2500 nm. After that, the vanillin and moisture content were measured using the reference method. The reflectance spectra were transformed to absorbance spectra and several pre-treatment methods of NIR spectra were applied, and the results were calibrated with chemical data using principal component regression (PCR) and partial least square (PLS) methods. The best estimation for vanillin content using absorbance spectra with OSC pre-treatment at 3 PLS factors with the accuracy parameters of r = 0.91, SEC = 1.31%, SEP = 1.34%, CV = 45.60%, RPD = 2.30 with a consistency of 97.96%. The best prediction of vanillin content using PCR method was obtained with SNV pre-treated spectra at 6 PCR factors with the accuracy parameters of r = 0.89, SEC = 1.47%, SEP = 1.49%, CV = 43.21%, RPD = 2.09 and 101.40% consistency. The best estimation for water content using the PLS method is using the SNV pre-treatment spectra with a PLS factor of 5 with the accuracy parameters of r = 0.99, SEC = 2.11%, SEP = 2.13%, CV = 6.12%, RPD = 7.98 with consistency = 98.86%. While using the PCR method, the best estimation for moisture content was also obtained by SNV pre-treated spectra at 4 PCR factors with the accuracy values of r = 0.99, SEC = 2.33%, SEP = 2.25%, CV = 6.25%, RPD = 7.58 and consistency = 103.90%. The PLS calibration provide a better accuracy than the PCR. The NIR spectroscopy associated with the selected pre-treatments and factor numbers of PLS and PCR can be used for determination of chemical content of vanilla pods.

Rapid Determination of the Total Petroleum Hydrocarbon Content of Soils by Handheld Fourier Transform Near-Infrared Spectroscopy.

For successful soil remediation and hydrocarbon exploration operations, determining the total petroleum hydrocarbon (TPH) content of soils is an indispensable process step. This paper reports on the performance of a handheld Fourier transform near-infrared (FT-NIR) spectrometer for rapid and quantitative determination of TPH content of soils from two different sites by diffuse reflection measurements. For rapid decisions for exploration work or environmental site assessment projects, a quick─preferably on-site─determination of TPH content is valuable. Diffuse reflection NIR spectra were recorded from soil samples of two different sites with TPH reference values ranging from 350 to 30,000 ppm, as determined by capillary gas chromatography and flame ionization detection with hydrocarbon fingerprinting C1-C44. However, this paper not only addresses the development of site-specific partial-least squares (PLS) calibrations but also demonstrates the locally-weighted PLS (LW-PLS) technique, which can be used to develop global, site-independent PLS calibrations without significant penalty in calibration performance. As a first step, the diffuse reflection spectra were used to develop conservative, site-specific PLS calibration models with root-mean-square calibration/cross-validation errors (RMSEC/RMSECV) of 1043/1106 and 741/785 ppm TPH, respectively, and the average absolute prediction errors for samples not contained in the calibration set were 451 and 293 ppm for the two sites, respectively. In a further step, significant degradation of the RMSE values of a conservative PLS model based on the NIR spectra of both sites was then compared to the application of the LW-PLS method, with only a slight loss of the prediction accuracy relative to the site-independent models. This study confirms the ability of next-generation portable FT-NIR spectrometers to predict low TPH levels in various soil types through both─soil-specific and site-independent─calibrations, giving these spectrometers the potential to become rapid screening tools in the field.

Performance Comparison of Tungsten-Halogen Light and Phosphor-Converted NIR LED in Soluble Solid Content Estimation of Apple.

A Tungsten-Halogen (TH) lamp is the most popular light source in NIR spectroscopy and hyperspectral imaging, which requires a warm-up to reach very high temperatures of up to 250 °C and take a long time for radiation stabilization. Consequently, it has a large enough volume to enable heat dissipation to prevent the thermal runaway of the electric circuit and turn out its power efficiency very low. These are major barriers for miniaturizing spectral systems and hyperspectral imaging devices. However, TH lamps can be replaced by pc-NIR LEDs in order to avoid high temperature and large volume. We compared the spectral emission of the available commercial pc-NIR LEDs under the same condition. As a replacement for the TH lamp, the VIS + NIR LED module was developed to combine a warm-white LED and pc-NIR LEDs. In order to feature out the availability of the VIS + NIR LED module against the TH lamp, they were used as the light source for evaluating the Soluble Solid Content (SSC) of an apple through VIS-NIR spectroscopy. The results show a remarkable feasibility in the performance of the partial least square (PLS) model using the VIS + NIR LED module; during PLS calibration, the correlation coefficient (R) values are 0.664 and 0.701, and the Mean Square Error (MSE) values are 0.681 and 0.602 for the TH lamp and VIS + NIR LED module, respectively. In VIS-NIR spectroscopy, this study indicates that the TH lamp could be replaceable with a warm-white LED and pc-NIR LEDs.

Spectrophotometric quantification of paracetamol and tramadol hydrochloride by chemometric calibration methods.

The results of UV spectrophotometric analysis were analysed using partial least squares (PLS) and principal component regression (PCR) techniques to allow simultaneous evaluation of tramadol hydrochloride (TRA) and p-acetaminophen (PAR) in tablets. A calibration set of 16 mixtures, each containing PAR and TRA in various amounts, was created using a 24-full fractional design. The absorbance data set for the calibration set were obtained between 215-280 nm (Δλ = 0.1 nm). Subsequently, the concentration and absorbance sets were used to generate PCR and PLS calibrations. The ratio spectra- first derivative method was devised as a solution to the same problem to compare the outcomes of the chemometric methods used for the same experiment. After the proposed methods were shown to be accurate in the testing of validation samples, they were used in analysing commercial tablet samples. The analytical results showed that PCR and PLS methods can be used as alternative methods to high-performance liquid chromatography (HPLC). Correlation coefficients were determined for the working concentration range of 6-36 g.mL-1 for PAR and 4-22 g.mL-1 for TRA. The limits of detection and quantification were calculated as 0.9104 μg.mL-1 and 3.0347 μg.mL-1, respectively. The test results of the chemometric analyses and ratio spectra- first derivative method of the commercial tablet form are in agreement with the results of the one-way ANOVA with a confidence interval of 95%. This study shows that the ratio spectra- first derivative method, PCR and PLS models based on spectrophotometric measurements are very useful and straightforward techniques for the quantitative resolution of a two-component pharmaceutical preparation, requiring little sample preparation and little time for analysis.

Rapid quantitative analysis of rare earth elements Lu and Y in rare earth ores by laser induced breakdown spectroscopy combined with iPLS-VIP and partial least squares.

Rare earth ores are complex in composition and diverse in mineral composition, requiring high technical requirements for the selection of rare earth ores. It is of great significance to explore the on-site rapid detection and analysis methods of rare earth elements in rare earth ores. Laser induced breakdown spectroscopy (LIBS) is an important tool to detect rare earth ores, which can be used for in situ analyses without complicated sample preparation. In this study, a rapid quantitative analysis method for rare earth elements Lu and Y in rare earth ores was established by LIBS combined with an iPLS-VIP hybrid variable selection strategy and partial least squares (PLS) method. First, the LIBS spectra of 25 samples were studied using laser induced breakdown spectrometry. Second, taking the spectrum processed by wavelet transform (WT) as the input variables, PLS calibration models based on interval partial least squares (iPLS), variable importance projection (VIP) and iPLS-VIP hybrid variable selection were constructed to quantitatively analyze rare earth elements Lu and Y, respectively. The results show that the WT-iPLS-VIP-PLS calibration model has better prediction performance for rare earth elements Lu and Y, and the optimal coefficient of determination (R2) of Lu and Y were 0.9897 and 0.9833, the root mean square error (RMSE) were 0.8150 μg g-1 and 97.1047 μg g-1, and the mean relative error (MRE) were 0.0754 and 0.0766, respectively. It shows that LIBS technology combined with the iPLS-VIP and PLS calibration model provides a new method for in situ quantitative analysis of rare earth elements in rare earth ores.

Rapid Determination of the Peroxide Value of Edible Oil by Handheld NIR Spectroscopy in Combination with Wavelength Variables Selection and PLS Calibration

Detecting the peroxide value (PV) in oil is significant for people in everyday life, especially as a fast, convenient, and on-site method. To tackle this challenge, the near-infrared (NIR) spectra of oil were collected by a Viavi MicroNIR 1700 handheld NIR spectrometer and a liquid sample transmission accessory. Subsequently to the spectral pretreatment method of standard normal variate (SNV), the sensitive wavelength variables were optimized by the algorithms of competitive adaptive reweighted sampling (CARS), genetic algorithms (GA), and random frog (RF). The results showed that CARS was the best, and the selected variables were used to build the partial least squares (PLS) regression model. The root mean square error (RMSE) values for cross-validation (RMSECV) and prediction (RMSEP) were 0.984 mmol/ kg and 0.950 mmol/kg, respectively, and the corresponding R2cv and R2P were 0.875, and 0.867, respectively. Therefore, the PV of edible oil can be determined easily and quickly with a handheld NIR spectrometer.

Selecting relevant wavelength intervals for PLS calibration based on absorbance interquartile ranges

Wavelength selection is an important step in NIR data analysis as it yields more robust and accurate models for predicting products’ properties. This paper proposes a novel framework for wavelength selection in Partial Least Squares (PLS) models based on the absorbance interquartile range. The framework first divides samples into quartiles according to the response variable. The average quartile absorbance value is calculated for each wavelength; the distance in averages from samples belonging to the first and fourth quartiles is then used to generate a wavelength importance ranking, such that wavelengths with larger differences are ranked higher and expected to better explain variations in the response variable. The ranking guides the iterative inclusion of wavelength intervals into PLS models, starting with the interval presenting the largest average absorbance differences. The proposed framework was tested in three public datasets comprised of 10 response variables describing diesel, corn and soil properties, substantially reducing prediction error and the percentage of wavelengths included in the PLS models. When compared to four competing wavelength selection methods from the literature, the proposed framework yielded the best results in 7 out of 10 response variables in the training set, and in 6 out of 10 response variables in the testing set.

Estimation of Moisture and Protein Content in Pumpkin Seeds Using NIRS with Partial Least Square (PLS) Method

Most people do not use pumpkin seeds and only fruit for food production. Meanwhile, pumpkin seeds contain antioxidants, protein, carbohydrates, and other vitamins. Measurement of the chemical content of a material is usually carried out destructively (conventional methods) in the laboratory. This method is expensive, takes a long time to repair samples, and creates chemical waste. One method that is currently being developed to detect the chemical content of a material is the near-infrared spectroscopy (NIRS) method. Near-infrared spectroscopy (NIRS) is a non-destructive method used to quickly analyze and obtain information on a material's chemical content without using chemicals. The NIRS data calibration technique using partial least squares (PLS) is a step carried out to build a model that relates the response spectra of each sample at each wavelength to chemical concentrations known from laboratory analysis and protein content in pumpkin seeds with NIRS using PLS calibration. Spectrum data pre-treatment was carried out with GapDerivarive and Derivative Savitzky-Golay. The results showed that the best calibration model for moisture and protein content was obtained using DerivativeGap data processing with values of r = 0.92, R2 = 0.84, SEC = 0.11%, and RMSEC = 0.11%. RPD = 2.66 and using Latent Variable (LV) factor 2, while for protein content r = 0.95, R2 = 0.89, SEC = 0.71%, and RMSEC = 0.70%. RPD value = 3.33 and using Latent Variable (LV) factor 3.

Determination of total phenolic content and selected phenolic compounds in sweet wines by fluorescence spectroscopy and multivariate calibration

The aim of this work was to develop a simple, fast and reagent-free method for the prediction of total phenolic content (TPC) and selected phenolic compounds in Slovak botrytized Tokaj wines based on spectral data and partial least squares (PLS) regression. The significant variables for the prediction using PLS were obtained by evaluating the variable importance in projection (VIP). Folin-Ciocalteu and HPLC, respectively, were used as reference methods for the determination of TPC and selected phenolic compounds. Using UV–vis and excitation-emission matrix (EEM) fluorescence spectra recorded on the bulk and diluted samples, the calibration models for TPC were developed and compared. The best PLS model with relative predictive deviation (RPD) of 5.8, coefficient of determination (R2) of 0.972 and root mean squares error (RMSE) of 20.2 mg /L was based on the variables selected from unfolded EEM fluorescence spectra of diluted samples. Using unfolded EEM fluorescence spectra recorded on the diluted samples and VIP, the PLS calibration models for gallic, protocatechic, caffeic and p-coumaric acids and (+)-catechin were obtained with RPD > 4.0 and R2 > 0.9. The RMSE values were 0.7, 0.5, 0.3, 0.2 and 1.0 mg/L for gallic, protocatechic, caffeic and p-coumaric acids and (+)-catechin, respectively.

Evaluation of Vis-NIR preprocessing combined with PLS regression for estimation soil organic carbon, cation exchange capacity and clay from eastern Croatia

This study aimed to evaluate (i) the potential of visible-near-infrared (Vis-NIR) preprocessing techniques combined with a partial least square (PLS) regression to estimate soil organic carbon (SOC), the cation exchange capacity (CEC), and clay content and (ii) the effects of the wavelengths selection on the predictive capability of the PLS models. The study was conducted on a total of 132 topsoil samples (0–25 cm) collected on arable land in Eastern Croatia. Raw reflectance data and six commonly used spectral preprocessing, namely, multiplicative scatter correction (MSC), standard normal variate (SNV), de-trending (DT), continuum removal (CR) and Savitzky–Golay first and second derivative (SG1.der and SG2.der) were compared to identify the best for predicting selected soil properties. The PLS models calibrated with the SG1.der preprocessing achieved the best predictive performance for all soil properties compared to other methods used. The R2, RPD and RMSE of the SG1.der -PLS model in the validation mode for SOC, CEC and clay were 0.81, 2.12, 2.57 g C kg−1; 0.78, 1.95, 1.80 cmol(+) kg−1 and 0.83, 2.20, 37.2 g kg−1, respectively. The lowest predictability had PLS models that used soil raw reflectance data. These results indicate that the appropriate choice of Vis-NIR preprocessing can optimize the PLS calibration performance for accurate predicting SOC, CEC and clay in arable soils of the study area. The selection of informative wavelengths (IW) obtained using the PLS regression β-coefficient approach resulted in a highly reduced number of wavelengths in the IW-PLS models. Informative wavelengths in the IW-PLS model for SOC lay in the full Vis-NIR spectrum but mainly in the near-infrared (NIR), whereas for the CEC and clay in the NIR spectrum. In addition, IW-PLS models have a lower number of principal components and almost identical prediction capability relative to models based on full spectra (FS–PLS). Our results suggest that the Vis-NIR spectroscopy is a powerful tool to estimate soil properties rapidly, non-destructively and inexpensively applicable in some real situations e.g., monitoring and updating information on soil properties for precise soil management and soil quality assessment.

Evaluation of gasoline fuel quality using FTIR spectroscopy and multivariate technique: a case study in Aizawl city

Fuel quality control had become increasingly important due to the direct relationship between fuel quality and vehicle emissions. Variability of gasoline fuel from one refilling station to another is analyze using Fourier Transform Infrared spectroscopy (FTIR) and Principal Component Analysis (PCA). The result indicates that HC chain length, oxygenate, toluene and aromatic content are the major variables that cause sample variation and grouping. Determination of kerosene and Methyl tert-Butyl Ether (MTBE) concentration in gasoline fuel is done by using partial least squares (PLS) regression multivariate technique as applied to FTIR-ATR spectral data of the test samples. PLS calibration models were developed for the prediction of kerosene and MTBE and validated with independent set of known samples. Accordingly, among 179 tested samples, 103 samples contain kerosene (% v/v) less than 10%, 61 samples contain 11–20%, 11 samples contain 21–30% and 4 samples exceed 30% resulting that majority of gasoline fuel in the study area are adulterated. It is observed that 17 samples are MTBE blended with varying concentrations in the range of 1–7.34% v/v. This study provides an inexpensive and effective method to investigate gasoline quality, detection of adulterants and oxygenator in areas where adulteration of fuel is quite popular.

Optimisation of PLS Calibrations for Filtered and Untreated Samples towards In-Line Monitoring of Phenolic Extraction during Red-Wine Fermentations

Infrared spectroscopy provides an efficient, robust, and multivariate means to measure phenolic levels during red-wine fermentations. However, its use is currently limited to off-line sampling. In this study, partial least squares (PLS) regression was used to investigate the possibility of using spectral data from minimally pre-treated or untreated samples for the optimisation of prediction calibrations towards an in-line monitoring set-up. The evaluation of the model performance was conducted using a variety of metrics. Limits of detection and quantification of the PLS calibrations were used to assess the ability of the models to predict lower levels of phenolics from the start of fermentation. The calibrations were shown to be useful for the quantification of phenolic compounds and phenolic parameters with minimal or no sample pre-treatment during red-wine fermentation. Upon evaluation of performance, the calibrations built for attenuated-transmission Fourier-transform mid-infrared (ATR-FT-MIR) and diffuse-reflectance Fourier-transform near-infrared (DR-FT-NIR) were shown to be the most suitable spectroscopy techniques for eventual application in an automated and in-line system with values for limits of detection and quantification being suitable for the entire duration of fermentation.

Rapid determination of adulteration of clove essential oil with benzyl alcohol and ethyl acetate: Towards quality control analysis by FTIR with chemometrics

In this study, Fourier transform infrared (FTIR) spectroscopy in tandem with chemometrics was used for the discrimination and quantification of the adulterants, such as benzyl alcohol (B) and ethyl acetate (E) in clove essential oil (CO). Different multivariate models with various spectral derivatization methods were developed and their analysis abilities the adulterants were compared using statistical quality parameters. To discriminate the adulterations thanks to the FTIR data, 130 chemometric models were built by principal component analysis (PCA) algorithm. The statistical performances of the PCA models developed were evaluated by the number of samples outside of explained variance (95 %) and eigen value. To quantify the adulterant concentrations in CO samples, 117 partial least squares (PLS) regression models employing the FTIR data were developed. To find out the best PLS model, the root mean square error of prediction (RMSEP), and root mean square error of cross-validation (RMSECV) were mainly used. Root mean square error of calibration (RMSEC) and R-square were also evaluated. The best discrimination results were achieved using 1st derivative spectra in the region 3500-3100 cm−1 (SRB1) and 2nd derivative spectra in the region 1077-1008 cm-1 (SRE3) for the adulterants of B and E, respectively. The best PLS calibration results were obtained from the combinations of the normal spectra in the regions 3500-3100 cm−1, 1027-993 cm−1, and 756-569 cm−1 (SRBC) and SRE3 for the quantification of B and E, respectively. The results of the study indicated that FTIR with chemometrics could be used for simultaneously discrimination and quantification of the adulterants of B and E in COs without using any toxic chemicals or pretreatments.

Determination of Pork Adulteration in Roasted Beef Meatballs using Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

The pork adulteration in food is a matter of concern for Muslims worldwide. This study aimed to quantitatively analyze and classify pork and beef in roasted meatballs using the Fourier Transform Infrared (FTIR) spectroscopy method combined with chemometrics. To obtain the FTIR spectral data, pure beef meatballs and beef meatballs added with pork at various concentrations were analyzed in the mid-infrared region corresponding to wavenumbers ranging from 4000 to 400 cm-1. Samples of beef meatballs randomly selected from the market were analyzed in the optimum condition in terms of wavenumbers capable of providing best modeling. Qualitative and quantitative analyses were performed on the optimum wavenumbers of 1100-900 cm-1 using chemometrics of Partial Least Square (PLS) and Principal Component Analysis (PCA), respectively. The PLS calibration model for the relationship between actual values of pork (x-axis) and predicted FTIR values (y-axis) of roasted meatballs resulted in the equation of y = 0.99965x + 0.0221, with a coefficient of determination (R2-value) of 0.99965 and root mean square error of calibration (RMSEC) of 0.7137%. The PCA indicated differences in quadrants between pure beef and pork roasted meatballs. Most samples of marketed beef meatballs lied in the beef meatball quadrant. However, one sample of marketed beef meatballs was in the pork meatball quadrant; thus, it contained pork. To conclude, the FTIR spectroscopy method combined with chemometrics could qualitatively and quantitatively identify the ingredient of pork in roasted beef meatballs.

IR spectroscopy coupled with chemometrics used as a simple and rapid method to determine the caffeine content of tea products


 Introduction: Tea is a popular beverage that comes from Camellia sinensis. Tea is generally categorised into four types: black tea, oolong tea, green tea, and white tea. These four types are distinguished based on the presence or absence of a fermentation process during their processing. One of the compounds that play a role in providing freshness to tea is caffeine. 
 Aims: The purpose of this study was to determine the caffeine content in the tea samples that are on the market. 
 Methods: This was done using the near-infrared (NIR)-chemometric method and using the TLC-Densitometry method as a comparison. Infrared (IR) spectroscopy combined with chemometrics has been developed as a simple method to analyse the caffeine content in a tea sample. IR spectra of tea samples were correlated with caffeine content using chemometrics. 
 Results: In this study, the partial least squares (PLS) model of the NIR model that showed the best calibration with r-square was 0.958, and the root mean squared error of calibration (RMSEC) value was 0.070. The PLS calibration model of the NIR models was further used to predict the unknown caffeine content in commercial samples. The significance of the caffeine content that had been measured with NIR and TLC-Densitometry was evaluated using a two paired sample t-test. 
 Conclusion: The caffeine content measured with both methods gave no significant difference.

Raman Spectrometry as a Tool for an Online Control of a Phototrophic Biological Nutrient Removal Process

Real-time bioprocess monitoring is crucial for efficient operation and effective bioprocess control. Aiming to develop an online monitoring strategy for facilitating optimization, fault detection and decision-making during wastewater treatment in a photo-biological nutrient removal (photo-BNR) process, this study investigated the application of Raman spectroscopy for the quantification of total organic content (TOC), volatile fatty acids (VFAs), carbon dioxide (CO2), ammonia (NH3), nitrate (NO3), phosphate (PO4), total phosphorus (total P), polyhydroxyalkanoates (PHAs), total carbohydrates, total and volatile suspended solids (TSSs and VSSs, respectively). Specifically, partial least squares (PLS) regression models were developed to predict these parameters based on Raman spectra, and evaluated based on a full cross-validation. Through the optimization of spectral pre-processing, Raman shift regions and latent variables, 8 out of the 11 parameters that were investigated—namely TOC, VFAs, CO2, NO3, total P, PHAs, TSSs and VSSs—could be predicted with good quality by the respective Raman-based PLS calibration models, as shown by the high coefficient of determination (R2 > 90.0%) and residual prediction deviation (RPD > 5.0), and relatively low root mean square error of cross-validation. This study showed for the first time the high potential of Raman spectroscopy for the online monitoring of TOC, VFAs, CO2, NO3, total P, PHAs, TSSs and VSSs in a photo-BNR reactor.