Partial Least Squares Factors Research Articles

Analytical methodology to elemental quantification of weathered terrestrial analogues to meteorites using a portable Laser-Induced Breakdown Spectroscopy (LIBS) instrument and Partial Least Squares (PLS) as multivariate calibration technique

The present work is focused on the in situ quantitative analysis of Si, Al, Mg, Ca, Ba, Na, and Fe, present in weathered terrestrial analogues to meteorites (black steel slag and impact glasses), using a portable Laser Induced Breakdown Spectroscopy (LIBS) instrument. For that purpose, several standards pellets of known elemental concentrations were manufactured. The elemental and molecular homogeneity of the pellets was studied by means of Scanning Electron Microscopy coupled to Energy Dispersive X-ray spectroscopy (SEM-EDS) and Raman spectroscopy. This check was always made before the LIBS analysis. Univariate and multivariate (Partial Least Squares (PLS) regression) calibration approaches on LIBS spectra were selected as initial calibration models. After a comparison between both approaches, the former was discarded due to the poor linearity of the calibration curves, and PLS regression was chosen to treat the LIBS spectra as the multivariate calibration approach (in the ultraviolet (UV) and infrared (IR) spectral ranges). Predictive capabilities of each calibration model were evaluated by calculating regression coefficient (r), number of PLS factors (rank), relative errors of cross validation (RMSECV), residual predictive deviation (RPD) and the Bias value. At the end, the simultaneous use of both ranges of wavelengths was demonstrated to be more fruitful rather than using the individual ones, probably due to the higher number of emission lines, number of spectral variables and the PLS latent variables for each element. Moreover, a Reference Material was used as external validation, obtaining satisfactory results in the determination of elements. The predictive ability of the PLS models was evaluated on samples of Darwin Glasses (Australia), Libyan Desert Glasses (Western Desert of Egypt) and black steel slag residues (steelworks of Basque Country). The obtained results were in concordance with the range of composition measured also by X-ray Fluorescence Spectrometer (ED-XRF). Our methodology is a good, rapid, simple and cost-effective alternative for in situ analysis of these terrestrial analogues over other techniques.

An enantioselective electrochemical sensor for simultaneous determination of mandelic acid enantiomers using dexamethasone-based chiral nanocomposite coupled with chemometrics method

A nanocomposite-based sensor that responds selectively to mandelic acid (MA) enantiomers was constructed with Dexamethasone (DEX) as a chiral recognition element. The DEX-modified electrode was selective towards MA enantiomers compared with the glassy carbon electrode (GCE) modified by graphene (GR) nanosheets or bare GCE. The entrapment of DEX within the overoxidised polypyrrole (OPPy) film occurred on the surface of the GR-modified electrode using a two-step electrodeposition approach. The deposited film was characterized by scanning electron microscopy (SEM), differential pulse voltammetry (DPV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The OPPy-DEX/GR/GCE sensor for sensing of MA enantiomers required ∼0.3V less overpotential than the GC electrode. Upon exposure to (S)- and (R)-MA, the sensor showed the current signals with different ratios, which were differentiated at the voltages of 1.38 and 1.43V (vs. Ag/AgCl), respectively. Under optimal conditions, the chiral sensor exhibited a good linear relationship with MA enantiomers concentrations ranging from 1 to 25mM with a detection limit of 0.25mM. Due to the highly overlapping signals, partial least squares (PLS) regression was applied to distinguish the MA enantiomers in synthetic mixtures. In addition, the genetic algorithm-based potential selection procedure was applied to optimize the number of PLS factors used for the building of the PLS calibration models, and also its predictive ability was studied.

Insight into the structural requirements of thiophene-3-carbonitriles-based MurF inhibitors by 3D-QSAR, molecular docking and molecular dynamics study

The discovery of clinically relevant inhibitors against MurF enzyme has proven to be a challenging task. In order to get further insight into the structural features required for the MurF inhibitory activity, we performed pharmacophore and atom-based three-dimensional quantitative structure–activity relationship studies for novel thiophene-3-carbonitriles based MurF inhibitors. The five-feature pharmacophore model was generated using 48 inhibitors having IC50 values ranging from 0.18 to 663 μm. The best-fitted model showed a higher coefficient of determination (R2 = 0.978), cross-validation coefficient (Q2 = 0.8835) and Pearson coefficient (0.9406) at four component partial least-squares factor. The model was validated with external data set and enrichment study. The effectiveness of the docking protocol was validated by docking the co-crystallized ligand into the catalytic pocket of MurF enzyme. Further, binding free energy calculated by the molecular mechanics generalized Born surface area approach showed that van der Waals and non-polar solvation energy terms are the main contributors to ligand binding in the active site of MurF enzyme. A 10-ns molecular dynamic simulation was performed to confirm the stability of the 3ZM6-ligand complex. Four new molecules are also designed as potent MurF inhibitors. These results provide insights regarding the development of novel MurF inhibitors with better binding affinity.

Interrelationships of Flour, Dough, and Bread Properties Under Reduced Salt Level Conditions

The interrelationships between flour quality and the variability in the dough physical properties and bread loaf characteristics were investigated under reduced salt conditions using partial least squares (PLS) regression analysis. Seventy‐two percent of the variability in dough physical properties was explained by the flour quality using a three‐factor PLS model. Damaged starch content (DS), protein content, and farinograph dough development time (DDT) explained the variability of dough creep‐recovery behavior along PLS‐1. Farinograph absorption (FAB), located along PLS‐2, was strongly related to dough adhesiveness, in which adhesiveness was highly correlated to dough stickiness (r = 0.91). Eighty‐nine percent of the variability in bread loaf characteristics was explained by the flour quality using a four‐factor PLS model; the first two PLS factors explained 66% of the variability. The loaf volume was related to a high number of loaf cells, whose expansion resulted in a greater loaf height. The relation between loaf volume and loaf height was expressed more in PLS‐3 than PLS‐1 and PLS‐2. Mean cell wall thickness and mean cell diameter were closely related negatively along PLS‐1, for which DS and farinograph dough stability explained much of the variability in these loaf characteristics. Along the third PLS factor, FAB explained the variability in loaf weight.

Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.

High-throughput NIR-chemometric methods for chemical and pharmaceutical characterization of sustained release tablets

The aim of this study was the development and validation of methods based on near-infrared spectroscopy (NIRS) and chemometry, useful for characterization of sustained release (SR) tablets with indapamide, in terms of tablet composition (API and two excipients), in vitro drug release mechanism (k and n Peppas) and crushing strength. A calibration set consisting of 25 different tablets formulations containing API, HPMC and lactose at five different content levels in the range 100±20% relative to a targeted tablet composition, were manufactured by direct compression in order to develop the methods for prediction of tablet composition, and in vitro drug release mechanism. On the other hand, a 15 batches calibration set prepared at five different compression forces was used for development of methods for prediction of crushing strength. Moreover, independent batches were manufactured for validation of all methods Intact tablets were analyzed by transmission mode with NIRS, the spectra were pre-processed, and partial least square (PLS) regression was used to build prediction models. Cross-validation was carried out in order to select the optimal number of PLS factors for all models, and the best model was chosen based on their RMSECV and bias. All developed methods were validated in terms of trueness, precision and accuracy. Based on the validation results, the methods proposed in this work can successfully be applied for routine determination of indapamide, HPMC and lactose content of sustained release tablets, as well as for prediction of their in vitro drug release mechanism (k and n Peppas) and crushing strength.

Study of an adaptable calibration model of near-infrared spectra based on KF-PLS

The key to the quantitative analysis of near-infrared spectra is the establishment of an accurate and adaptable model. In this paper, a calibration method of a dynamic evolutionary model combined with Kalman filter (KF) and partial least squares (PLS) algorithms is proposed. First, an initial quantitative calibration model is built by applying the method of PLS regression to calculate the spectral data samples and their chemical constituent concentrations. Second, inspired by the dynamic evolution characteristics of the KF, the main PLS factor coefficient iterative method is set up based on the KF, by which the calibration model is dynamically corrected. Finally, to prove the validity of the proposed method, a near-infrared spectral dataset from a real corn sample is tested, and the standard PLS method and KF-PLS method are compared. For the 40 external samples (2015), the root mean square error of prediction (RMSEP) of the PLS model was 0.7046% (moisture) and 0.9352% (protein), which is obviously worse than the effects of the KF-PLS model (moisture, 0.3726%, and protein, 0.5451%). Moreover, the R2val (validation decision coefficient) of the KF-PLS model was 0.9035 (moisture) and 0.8565 (protein), which is better than the result of the PLS model (moisture, 0.5659, and protein, 0.6654). Regarding the ability to adapt to equipment (disturbance factor equal to 15%), KF–PLS (RMSEP: moisture, 0.1942%, and protein, 0.3299%; R2val: moisture, 0.9221, and protein, 0.8936) had less prediction error than PLS (RMSEP: moisture, 0.8546%, and protein, 1.0881%; R2val: moisture, 0.5077, and protein, 0.4946). The proposed method has satisfactory accuracy in tracking the evolution of the near-infrared spectrometry measurement state. The novel algorithm can effectively detect the moisture and protein concentration in corn and is also more compatible with outdated equipment, environmental changes and external samples than the PLS algorithm. Thus, this study explores a new approach towards a highly adaptive near-infrared spectrum calibration model.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study.

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.

Near-infrared Detection of Straw Cellulose by Orthogonal Signal Correction and Partial Least Squares

Orthogonal signal correction (OSC)and partial least squares(PLS)were used during the pretreatment of straw to reduce environmental noise and prediction models were established for near-infrared detection of straw cellulose. Tests were run with soybean stalk as the object of study. Test results indicated that compared to a model established using a traditional denoising method, the determination coefficients for calibration set models established by second derivative+smoothing and OSC were 0.9318595 and 0.9328905 respectively while the root mean square error for calibration (RMSEC) were 0.6762902 and 0.6696454. For an OSC-PLS regression model with a factor of 8, the relative standard deviation of a prediction model was less than 5%. In the OSC denoising process, the root mean square error fluctuated with the increasing number of PLS factors. Compared to the second derivative-smoothing denoising, OSC-PLS denoising removed the non-correlated variation from spectra and improved interpretation ability of variation while the analysis and convergence were expedited. It was therefore concluded that OSC-PLS denoising could be used to realize the rapid and accurate near-infrared detection of straw cellulose.

Exploration of attenuated total reflectance mid-infrared spectroscopy and multivariate calibration to measure immunoglobulin G in human sera

Immunoglobulin G (IgG) is crucial for the protection of the host from invasive pathogens. Due to its importance for human health, tools that enable the monitoring of IgG levels are highly desired. Consequently there is a need for methods to determine the IgG concentration that are simple, rapid, and inexpensive. This work explored the potential of attenuated total reflectance (ATR) infrared spectroscopy as a method to determine IgG concentrations in human serum samples. Venous blood samples were collected from adults and children, and from the umbilical cord of newborns. The serum was harvested and tested using ATR infrared spectroscopy. Partial least squares (PLS) regression provided the basis to develop the new analytical methods. Three PLS calibrations were determined: one for the combined set of the venous and umbilical cord serum samples, the second for only the umbilical cord samples, and the third for only the venous samples. The number of PLS factors was chosen by critical evaluation of Monte Carlo-based cross validation results. The predictive performance for each PLS calibration was evaluated using the Pearson correlation coefficient, scatter plot and Bland–Altman plot, and percent deviations for independent prediction sets. The repeatability was evaluated by standard deviation and relative standard deviation. The results showed that ATR infrared spectroscopy is potentially a simple, quick, and inexpensive method to measure IgG concentrations in human serum samples. The results also showed that it is possible to build a united calibration curve for the umbilical cord and the venous samples.

Predicting cytotoxicity of complex mixtures in high cancer incidence regions of the Huai River Basin based on GC–MS spectrum with partial least squares regression

Complex mixture exposures, such as those associated with water sources, are an important issue in health risk assessment. This study assessed the cytotoxicity of chemical mixtures extracted from water sources in regions of the Huai River Basin with high cancer incidences and built statistical models of cytotoxicity based on pollution profiles that were measured with gas chromatography–mass spectrometry (GC–MS). Both surface and ground waters were collected from rural water sources of Shenqiu County, Henan Province of China from 2008 to 2011 and extracted with XAD-2 resigns. Cytotoxicity was evaluated with Chinese hamster ovary K1 (CHO-K1) cells and compared against the pollution profiles of the extracts. IC50 of water samples ranged from 0.023 to 0.338L-eq/mL. The pollutants in waters determined by GC–MS are complex and some of the compounds that contributed to cytotoxicity lack toxicity data. A partial least squares (PLS) regression model of cytotoxicity was built based on linear aggregation of predictor variables (i.e., peaks for single compounds in the gas chromatograms). The PLS model contains 2 PLS factors extracted from 141 variables. The model was validated internally with training data permutation and externally with a test sample. The model explained 92% of the cytotoxicity in the training samples and 40% in the test sample. This approach provides a general, rapid method for relating water toxicity to GC–MS chromatograms and for predicting the compounds that contribute most to toxicity.

Application of Near Infrared Spectroscopy Coupled with Fluidized Bed Enrichment and Chemometrics to Detect Low Concentration of β-Naphthalenesulfonic Acid.

A fluidized bed enrichment technique was developed to improve sensitivity of near infrared (NIR) spectroscopy with features of rapidness and large volume solution. D301 resin was used as an adsorption material to preconcentrate β-naphthalenesulfonic acid in solutions in a concentration range of 2.0-100.0 μg/mL, and NIR spectra were measured directly relative to the β-naphthalenesulfonic acid adsorbed on the material. An improved partial least squares (PLS) model was attained with the aid of multiplicative scatter correction pretreatment and stability competitive adaptive reweighted sampling wavenumber selection method. The root mean square error of cross validation was 1.87 μg/mL at PLS factor of 7. An independent test set was used to assess the model, with the relative error (RE) in an acceptable range of 0.46 to 10.03% and mean RE of 3.72%. This study confirmed the viability of the proposed method for the measurement of a low content of β-naphthalenesulfonic acid in water.

Determination of omega-3 fatty acids in fish oil supplements using vibrational spectroscopy and chemometric methods.

The potential of Fourier transform infrared (FT-IR), near-infrared (NIR), and Raman spectroscopic techniques combined with partial least squares (PLS) regression (PLSR) to predict concentrations of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and total omega-3 fatty acids (n-3 FAs) in fish oil supplements was investigated. FT-IR spectroscopy predicted EPA (coefficient of determination (R(2)) of 0.994, standard error of cross-validation (SECV) of 2.90%, and standard error of prediction (SEP) of 2.49%) and DHA (R(2) = 0.983, SECV = 2.89%, and SEP = 2.55%) with six to seven PLS factors, whereas a simpler PLS model with two factors was obtained for total n-3 FAs (R(2) = 0.985, SECV = 2.73%, and SEP = 2.75%). Selected regions in the NIR spectra gave models with good performances and predicted EPA (R(2) = 0.979, SECV = 2.43%, and SEP = 3.11%) and DHA (R(2) = 0.972, SECV = 2.34%, and SEP = 2.60%) with four to six PLS factors. Both the whole and selected NIR regions gave simple models (two PLS factors) with similar results (R(2) = 0.997, SECV = 2.18%, and SEP = 1.60%) for total n-3 FAs. The whole and selected regions of Raman spectra provided models with comparable results and predicted EPA (R(2) = 0.977, SECV = 3.18%, and SEP = 2.73%) and DHA (R(2) = 0.966, SECV = 3.31%, and SEP = 2.56%) with seven to eight PLS factors, whereas a simpler model (three PLS factors) with R(2) = 0.993, SECV = 2.82%, and SEP = 3.27% was obtained for total n-3 FAs. The results demonstrated that FT-IR, NIR, and Raman spectroscopy combined with PLSR can be used as simple, fast, and nondestructive methods for quantitative analysis of EPA, DHA, and total n-3 FAs. FT-IR and NIR spectroscopy, in particular, have the potential to be applied in process industries during production of fish oil supplements.

Application of FT-NIR Spectroscopy to Apple Wine for Rapid Simultaneous Determination of Soluble Solids Content, pH, Total Acidity, and Total Ester Content

The feasibility of rapid simultaneous analysis of soluble solid content (SSC), PH, total acidity, and total ester content in apple wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least squares (PLS) models was studied in this work. A set of 380 apple wine samples were scanned in a quartz cuvette with a 1-mm path length in the NIR region (4,000–12,000 cm−1) in transmission mode. Calibration models were established by PLS with full cross-validation. The NIR spectra were pretreated with different methods, and the effective wave number range together with the number of PLS factors used in the model calibration was optimized. The optimal models yielded the determination coefficients of 0.91, 0.93, 0.98, and 0.92 and the root mean standard error of prediction (RMSEP) of 0.601, 0.077, 0.021, and 0.096 for SSC, pH, total acidity, and total ester content, respectively. This work demonstrated that NIR spectroscopy was a rapid and convenient technique for analyzing and predicting SSC, pH, total acidity, and total ester content simultaneously during apple wine fermentation process.

Application of near-infrared spectroscopy for the rapid analysis ofLonicerae Japonicae Flossolution extracted by water

A rapid quantitative analytical method for three components of Lonicerae Japonicae Flos solution (Lonicera Japonica Thumb.) extracted by water was developed using near-infrared (NIR) spectroscopy and the partial least-squares (PLS) method. The NIR spectra of 81 samples collected from a production line were obtained. The concentrations of secologanic acid, chlorogenic acid and galuteolin were determined by using high-performance liquid chromatography-diode array detection as the reference method. Several pretreatment methods for the NIR spectra were used during PLS calibration. The most appropriate latent variable number of the PLS factor was selected based on the standard error of cross-validation (SECV). The performance of the final PLS models was evaluated according to SECV, standard error of prediction (SEP) and determination coefficient (R2). The compounds secologanic acid, chlorogenic acid and galuteolin had SEP values of 0.030, 0.061 and 1.668 μg/mL, respectively and R2values over 0.85. This work shows that NIR spectroscopy is a rapid and convenient method for the analysis of Lonicerae Japonicae Flos solution extracted by water. The proposed method can help in the application of process analytical technology in the pharmaceutical industry, particularly in traditional Chinese medicine injections.

Quantifying silica in filter-deposited mine dusts using infrared spectra and partial least squares regression.

The feasibility of measuring airborne crystalline silica (α-quartz) in noncoal mine dusts using a direct-on-filter method of analysis is demonstrated. Respirable α-quartz was quantified by applying a partial least squares (PLS) regression to the infrared transmission spectra of mine-dust samples deposited on porous polymeric filters. This direct-on-filter method deviates from the current regulatory determination of respirable α-quartz by refraining from ashing the sampling filter and redepositing the analyte prior to quantification using either infrared spectrometry for coal mines or x-ray diffraction (XRD) from noncoal mines. Since XRD is not field portable, this study evaluated the efficacy of Fourier transform infrared spectrometry for silica determination in noncoal mine dusts. PLS regressions were performed using select regions of the spectra from nonashed samples with important wavenumbers selected using a novel modification to the Monte Carlo unimportant variable elimination procedure. Wavenumber selection helped to improve PLS prediction, reduce the number of required PLS factors, and identify additional silica bands distinct from those currently used in regulatory enforcement. PLS regression appeared robust against the influence of residual filter and extraneous mineral absorptions while outperforming ordinary least squares calibration. These results support the quantification of respirable silica in noncoal mines using field-portable infrared spectrometers.

Immunoglobulin G Measurement in Blood Plasma Using Infrared Spectroscopy

A rapid, simple, and inexpensive method to measure the immunoglobulin G (IgG) concentrations in blood samples in human and veterinary medicine is highly desired. Infrared spectroscopy (coupled with chemometric manipulation of spectral data) has the advantages of simple sample preparation, rapid implementation of analysis, and low cost. Here a method that exploits infrared spectroscopy as the basis to measure IgG concentration in animal plasma samples is reported, with radial immunodiffusion (RID) used as the reference test method for partial least squares (PLS) calibration model development. Smoothed non-derivative and the second-order derivative spectra were used to develop calibration models. Various additional spectral preprocessing steps were evaluated to optimize the calibration models, and the possible benefits of using an internal standard (potassium thiocyanate [KSCN]) were investigated. Monte Carlo cross-validation was used to determine the optimal number of PLS factors, and an independent prediction set was used to test the predictive performances of provisional models. The effects of various preprocessing options (spectral smoothing, derivation, normalization, region selection, mean-centering, and standard deviation scaling) on quantification accuracy were investigated. The root mean squared error of prediction (RMSEP) for different combinations of spectra preprocessing steps was 394 ± 36 mg/dL for the non-derivative spectra and 427 ± 101 mg/dL for the second-order derivative spectra. Immunoglobulin G concentrations produced by the optimized PLS model for the non-derivative spectra (RMSEP = 352 mg/dL) were found to be stable with respect to different splits of the samples among the calibration, validation, and prediction sets. The precision of the Fourier transform infrared (FT-IR) method is found to be slightly superior to that of the RID method. The results of this work indicate that infrared spectroscopy is a promising technique for economically and rapidly determining the IgG concentrations of plasma and plasma-derived samples.

Simultaneous determination of propanil and monalide by modified glassy carbon electrode with nickel oxide nanoparticles, using partial least squares modified by orthogonal signal correction and wavelet packet transform

In this study a Glassy carbon electrode (GCE) modified with nickel oxide nanoparticles was developed. This electrode has a good sensitivity and electrocatalytic activity toward the oxidation of propanil and monalide over a wide concentration range. The propanil and monalide oxidation mechanism on the electrode surface was investigated, and the overall number of electrons involved in the electrocatalytic oxidation was found to be 3. The detection limit of this electrode for determination of propanil and monalide was 0.05 and 0.21μM and its relative standard deviation for 10 independent measurements was 3 and 3.7%, respectively. Finally the proposed electrode exhibited several advantageous features, including simple preparation, fast response time, and good repeatability. The electrode was successfully applied for simultaneous determination of propanil and monalide based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processing tools with overlapping peaks. In this case, by optimization, the kind of wavelet function, the decomposition level, the number of OSC components and number of PLS factors for the OSC-WPT-PLS method were selected as Daubechies 3, 1, 1 and 4, respectively. The relative standard errors (R.S.E) of prediction obtained for two components using OSC-WPT-PLS, WPT-PLS and PLS were compared. Experimental results demonstrated that the OSC-WPT-PLS method produced the best results among the three methods.

Use of Fourier‐Transform Infrared Spectroscopy to Quantify Immunoglobulin G Concentrations in Alpaca Serum

BackgroundRapid, economical, and quantitative assays for measurement of camelid serum immunoglobulin G (IgG) are limited. In camelids, failure of transfer of maternal immunoglobulins has a reported prevalence of up to 20.5%. An accurate method for quantifying serum IgG concentrations is required.ObjectiveTo develop an infrared spectroscopy‐based assay for measurement of alpaca serum IgG and compare its performance to the reference standard radial immunodiffusion (RID) assay.AnimalsOne hundred and seventy‐five privately owned, healthy alpacas.MethodsEighty‐two serum samples were collected as convenience samples during routine herd visits whereas 93 samples were recruited from a separate study. Serum IgG concentrations were determined by RID assays and midinfrared spectra were collected for each sample. Fifty samples were set aside as the test set and the remaining 125 training samples were employed to build a calibration model using partial least squares (PLS) regression with Monte Carlo cross validation to determine the optimum number of PLS factors. The predictive performance of the calibration model was evaluated by the test set.ResultsCorrelation coefficients for the IR‐based assay were 0.93 and 0.87, respectively, for the entire data set and test set. Sensitivity in the diagnosis of failure of transfer of passive immunity (FTPI) ([IgG] <1,000 mg/dL) was 71.4% and specificity was 100% for the IR‐based method (test set) as gauged relative to the RID reference method assay.Conclusions and Clinical ImportanceThis study indicated that infrared spectroscopy, in combination with chemometrics, is an effective method for measurement of IgG in alpaca serum.

Evaluation of the Salt Content of Canned Sardines in Tomato Ketchup by Diffuse Reflection near Infrared Spectroscopy

This research aimed to determine the salt content of canned sardines in tomato sauce using NIR spectroscopy compared with traditional methods employing an auto-titrator, which are more consuming in terms of time and chemicals used. Scanning of NIR radiation was performed using an FT-NIR spectrometer with a wavenumber range of 12,500–4000 cm−1 (800–2500 nm) with the diffuse reflection mode. Model development was achieved using partial least-squares regression from three groups of samples, which were the sample group that included salt-adjusted samples, the sample group that did not include salt-adjusted samples and the sample group that included only salt-adjusted samples plus five non-salt-adjusted samples. The model from the sample group that did not include salt-adjusted samples used the first derivative + straight-line subtraction spectra between 9403.8 cm−1 and 4242.9 cm−1 with nine partial least-squares factors providing the lowest root mean square error of prediction ( RMSEP), with a coefficient of determination of calibration ( R2) of 0.90, root mean square error of estimation of 0.039%, coefficient of determination for prediction ( r2) of 0.85, RMSEP of 0.046%, average error of prediction (bias) of −0.002% and ratio of standard error of validation to the standard deviation of 2.58. The sample group that included salt-adjusted samples had a skewed distribution. The model returned an RMSEP of 0.109% on test samples, but the performance-to-interquartile range index indicated that this was a poor calibration. For the smaller salt-adjusted samples plus five non-salt-adjusted samples, cross-validation indicated the potential to evaluate salt in canned sardines (root mean square error of cross-validation = 0.158%). It was found that the vibration of water had an effect on the prediction accuracy as well as the vibration of starch and protein. In addition, the CONH2 and CO2H groups, which are common amino acid functional groups, also showed an effect. These findings represent novel findings concerning the use of NIR spectroscopy to determine salt that has no signal of its own in the NIR region.