Partial Least Squares Calibration Models Research Articles

Real-time on-line blend uniformity monitoring using near-infrared reflectance spectrometry: A noninvasive off-line calibration approach

A robust, noninvasive, real-time, on-line near-infrared (NIR) quantitative method is described for blend uniformity monitoring of a pharmaceutical solid dosage form containing 29.4% (w/w) drug load with three major excipients (crospovidone, lactose, and microcrystalline cellulose). A set of 21 off-line, static calibration samples were used to develop a multivariate partial least-squares (PLS) calibration model for on-line prediction of the API content during the blending process. The concentrations of the API and the three major excipients were varied randomly to minimize correlations between the components. A micro electrical-mechanical system (MEMS) based portable, battery operated NIR spectrometer was used for this study. To minimize spectral differences between the static and dynamic measurement modes, the acquired NIR spectra were preprocessed using standard normal variate (SNV) followed by second derivative Savitzky-Golay using 21 points. The performance of the off-line PLS calibration model were evaluated in real-time on 16 laboratory scale (30 L bin size) blend experiments conducted over 3 months. To challenge the robustness of the off-line calibration model, several blend experiments were conducted using a different bin size, faster revolution speed and variations in the potency of the API. Employing the PLS calibration model developed using the off-line calibration approach, the real-time API NIR (%) predictions for all experiments were all within 90–110%. These results were confirmed using the conventional thief sampling of the final blend followed by high performance liquid chromatography (HPLC) analysis. Further confirmation was established through content uniformity by HPLC of manufactured tablets. Finally, the optimized off-line PLS method was successfully transferred to a production site which involved using a secondary NIR instrument with a 15-fold scale-up in bin size from development.

On-Line Near-Infrared Spectrometer to Monitor Urea Removal in Real Time during Hemodialysis

The ex vivo removal of urea during hemodialysis treatments is monitored in real time with a noninvasive near-infrared spectrometer. The spectrometer uses a temperature-controlled acousto optical tunable filter (AOFT) in conjunction with a thermoelectrically cooled extended wavelength InGaAs detector to provide spectra with a 20 cm(-1) resolution over the combination region (4000-5000 cm(-1)) of the near-infrared spectrum. Spectra are signal averaged over 15 seconds to provide root mean square noise levels of 24 micro-absorbance units for 100% lines generated over the 4600-4500 cm(-1) spectral range. Combination spectra of the spent dialysate stream are collected in real-time as a portion of this stream passes through a sample holder constructed from a 1.1 mm inner diameter tube of Teflon. Real-time spectra are collected during 17 individual dialysis sessions over a period of 10 days. Reference samples were extracted periodically during each session to generate 87 unique samples with corresponding reference concentrations for urea, glucose, lactate, and creatinine. A series of calibration models are generated for urea by using the partial least squares (PLS) algorithm and each model is optimized in terms of number of factors and spectral range. The best calibration model gives a standard error of prediction (SEP) of 0.30 mM based on a random splitting of spectra generated from all 87 reference samples collected across the 17 dialysis sessions. PLS models were also developed by using spectra collected in early sessions to predict urea concentrations from spectra collected in subsequent sessions. SEP values for these prospective models range from 0.37 mM to 0.52 mM. Although higher than when spectra are pooled from all 17 sessions, these prospective SEP values are acceptable for monitoring the hemodialysis process. Selectivity for urea is demonstrated and the selectivity properties of the PLS calibration models are characterized with a pure component selectivity analysis.

An ensemble method based on a self-organizing map for near-infrared spectral calibration of complex beverage samples

Based on a so-called ensemble strategy, an algorithm is proposed for near-infrared (NIR) spectral calibration of complex beverage samples. This algorithm is a combination of a novel training set/test set sample-selection procedure based on a Kohonen self-organizing map (SOM) with a simple procedure to calculate an average partial least-squares (PLS) calibration model, which is therefore named SOMEPLS. In order to verify the proposed SOMEPLS, two NIR beverage datasets involving the determination of sugar content are considered, and three kinds of reference algorithm, i.e., conventional PLS (CPLS), the Kennard-Stone (KS) algorithm in combination with PLS (KSPLS), and sample set partitioning based on the joint x-y distance (SPXY) algorithm in combination with PLS (SPXYPLS), are used. Of these, both KS and SPXY are well-known representative sample-selection algorithms. By comparison, it was found that when there is a training set of appropriate size, SOMEPLS can achieve better prediction accuracy than the three reference algorithms, but without increasing the complexity of the corresponding calibration model for the future application, indicating that SOMEPLS can serve as a promising tool for NIR spectral calibration.

Nondestructive Application of Laser-Induced Fluorescence Spectroscopy for Quantitative Analyses of Phenolic Compounds in Strawberry Fruits (Fragaria x ananassa)

Laser-induced fluorescence spectroscopy (LIFS) was nondestructively applied on strawberries (EX = 337 nm, EM = 400-820 nm) to test the feasibility of quantitatively determining native phenolic compounds in strawberries. Eighteen phenolic compounds were identified in fruit skin by UV and MS spectroscopy and quantitatively determined by use of rp-HPLC for separation and diode-array or chemical reaction detection. Partial least-squares calibration models were built for single phenolic compounds by means of nondestructively recorded fluorescence spectra in the blue-green wavelength range using different data preprocessing methods. The direct orthogonal signal correction resulted in r (2) = 0.99 and rmsep < 8% for p-coumaroyl-glucose, and r (2) = 0.99 and rmsep < 24% for cinnamoyl-glucose. In comparison, the correction of the fluorescence spectral data with simultaneously recorded reflectance spectra did not further improve the calibration models. Results show the potential of LIFS for a rapid and nondestructive assessment of contents of p-coumaroyl-glucose and cinnamoyl-glucose in strawberry fruits.

Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: A process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms

Near-infrared calibration models were developed for the determination of content uniformity of pharmaceutical tablets containing 29.4% drug load for two dosage strengths (X and Y). Both dosage strengths have a circular geometry and the only difference is the size and weight. Strength X samples weigh approximately 425 mg with a diameter of 12 mm while strength Y samples, weigh approximately 1700 mg with a diameter of 20 mm. Data used in this study were acquired from five NIR instruments manufactured by two different vendors. One of these spectrometers is a dispersive-based NIR system while the other four were Fourier transform (FT) based. The transferability of the optimized partial least-squares (PLS) calibration models developed on the primary instrument (A) located in a research facility was evaluated using spectral data acquired from secondary instruments B, C, D and E. Instruments B and E were located in the same research facility as spectrometer A while instruments C and D were located in a production facility 35 miles away. The same set of tablet samples were used to acquire spectral data from all instruments. This scenario mimics the conventional pharmaceutical technology transfer from research and development to production. Direct cross-instrument prediction without standardization was performed between the primary and each secondary instrument to evaluate the robustness of the primary instrument calibration model. For the strength Y samples, this approach was successful for data acquired on instruments B, C, and D producing root mean square error of prediction (RMSEP) of 1.05, 1.05, and 1.22%, respectively. However for instrument E data, this approach was not successful producing an RMSEP value of 3.40%. A similar deterioration was observed for the strength X samples, with RMSEP values of 2.78, 5.54, 3.40, and 5.78% corresponding to spectral data acquired on instruments B, C, D, and E, respectively. To minimize the effect of instrument variability, calibration transfer techniques such as piecewise direct standardization (PDS) and wavelet hybrid direct standardization (WHDS) were used. The PDS approach, the RMSEP values for strength X samples were lowered to 1.22, 1.12, 1.19, and 1.08% for instruments B, C, D, and E, respectively. Similar improvements were obtained using the WHDS approach with RMSEP values of 1.36, 1.42, 1.36, and 0.98% corresponding to instruments B, C, D, and E, respectively.

A novel spectrophotometric method for determination of kinetic constants of aldehyde oxidase using multivariate calibration method

Although phenanthridine has been frequently used as a specific substrate for the assessment of aldehyde oxidase activity, the use of this method is questionable due to a lower limit of detection and its validity for kinetic studies. In the present study, a novel sensitive multivariate calibration method based on partial least squares (PLS) has been developed for the measurement of aldehyde oxidase activity using phenanthridine as a substrate. Phenanthridine and phenanthridinone binary mixtures were prepared in a dynamic linear range of 0.1–30.0 μM and the absorption spectra of the solutions were recorded in the range of 210–280 nm in Sorenson's phosphate buffer (pH 7.0) containing EDTA (0.1 mM). The optimized PLS calibration model was used to calculate the concentration of each chemical in the prediction set. Hepatic rat aldehyde oxidase was partially purified and the initial oxidation rates of different concentrations of phenanthridine were calculated using the PLS method. The values were used for calculating Michaelis–Menten constants from a Lineweaver–Burk double reciprocal plot of initial velocity against the substrate concentration. The limits of detection for phenanthridine and phenanthridinone were found to be 0.04 ± 0.01 and 0.03 ± 0.01 μM (mean ± SD, n = 5), respectively. Using this method, the K m value for the oxidation of phenanthridine was calculated as 1.72 ± 0.09 μM (mean ± SD, n = 3). Thus, this study describes a novel spectrophotometric method that provides a suitable, sensitive and easily applicable means of measuring the kinetics of phenanthridine oxidation by aldehyde oxidase without the need for expensive instrumentation.

Selectivity Assessment of Noninvasive Glucose Measurements Based on Analysis of Multivariate Calibration Vectors

Selectivity is paramount for the successful implementation of noninvasive spectroscopic sensing for the painless measurement of blood glucose concentrations in people with diabetes. Selectivity issues are explored for different multivariate calibration models based on noninvasive near-infrared spectra collected from an animal model. Noninvasive near-infrared spectra are collected through a fiber-optic interface attached to a thin fold of skin on the back of an anesthetized laboratory rat while glucose levels are varied in a controlled manner. Partial least-squares (PLS) calibration models are generated from noninvasive spectra collected during a single, 2-hour blood glucose transient. Calibration vectors are compared for optimized PLS calibration models created with correct and incorrect assignments of glucose concentrations to each noninvasive spectrum. Although both PLS models appear functional and seem capable of predicting glucose concentrations accurately during this transient, only the model generated from correct glucose assignments gives a credible calibration vector. When correct glucose assignments are used, the PLS calibration vector matches the corresponding net analyte signal calibration vector. No similarity in these calibration vectors is evident when incorrect glucose assignments are used. Glucose-specific spectral information is present within noninvasive near-infrared spectra collected from a rat model using a transmission geometry. Apparently functional, yet incorrect, calibration models can be generated, and the propensity to create such false PLS calibration models calls into question the validity of past reports. An analysis of calibration vectors can provide valuable insight into the chemical basis of selectivity for multivariate calibration models of complex systems.

Quantitative Analysis of Cotton (Gossypium hirsutum) Lint Trash by Fluorescence Spectroscopy

The presence of cotton plant botanical components, or trash, embedded in lint subsequent to harvesting and ginning is an important criterion in the classification of baled cotton by the U.S. Department of Agriculture Agricultural Marketing Service. The trash particles may be reduced in size to the point that specific trash types are not identifiable by image or gravimetric analysis, and it is desirable to quantify different trash types so that processing lines may be optimized for removal of the most problematic trash to enhance processing performance and cotton lint quality. Currently, there are no methods available to adequately quantify cotton lint trash based on botanical origin. The present work attempts to address this issue through the analysis by fluorescence spectroscopy of dimethyl sulfoxide extracts of mixtures of six botanical trash types. The fluorescence data are subsequently subjected to chemometric analysis. The resulting 6 partial least-squares calibration models obtained from 128 mixtures are demonstrated in the case of leaf and hull to be capable of predicting individual trash component concentrations with a high degree of confidence.

Effect of temperature variation on the visible and near infrared spectra of wine and the consequences on the partial least square calibrations developed to measure chemical composition

Many studies have reported the use of near infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition. However, little is known about the effect of variation in temperature on the NIR spectrum of wine and the subsequent effect on the performance of calibrations used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with temperature being one of the most important factors affecting the vibration intensity and frequency of molecular bonds. Wine is a complex mixture of chemical components (e.g. water, sugars, organic acids, and ethanol), and a simple ethanol and water model solution cannot be used to study the possible effects of temperature variations in the NIR spectrum of wine. Ten red and 10 white wines were scanned in triplicate at six different temperatures (25 °C, 30 °C, 35 °C, 40 °C, 45 °C and 50 °C) in the visible (vis) and NIR regions (400–2500 nm) in a monochromator instrument in transmission mode (1 mm path length). Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for alcohol, sugars (glucose + fructose) and pH at different temperatures. The results showed that differences in the spectra around 970 nm and 1400 nm, related to O H bonding were observed for both varieties. Additionally an effect of temperature on the vis region of red wine spectra was observed. The standard error of cross validation (SECV) achieved for the PLS calibration models tended to inverse as the temperature increased. The practical implication of this study it is recommended that the temperature of scanning for wine analysis using a 1 mm path length cuvette should be between 30 °C and 35 °C.

Fast detection of cyclopiazonic acid in cheese using Fourier transform mid-infrared ATR spectroscopy

A simple and rapid Fourier transform mid-infrared spectroscopic method with attenuated total reflection was developed for the detection of cyclopiazonic acid in ewe's cheese samples. A partial least-squares calibration model for the prediction of cyclopiazonic acid content in ewe medium-ripened cheese, based on spectra data, revealed to be the best approach. The PLS model was tested by cross-validation in order to minimize standard error of the model and the relevant RMSECV (root of mean-squared error in cross-validation) was calculated to be 0.05 μg/g. In parallel experiments, cyclopiazonic acid content was estimated using the developed SPME-HPLC/UV method and different cheese samples were analysed using both techniques. The method appears to be useful for a qualitative and semi-quantitative screening of a large number of cheese samples, important in food contaminants monitoring. Since no sample pretreatment is required at all, the analysis times are dramatically shortened in comparison with any chromatographic procedure.

Evaluation of Physical Properties of Human Hair by Diffuse Reflectance Near-Infrared Spectroscopy

The objective of the present study is to develop a novel nondestructive, simple, and quick method to evaluate the friction, twist, and gloss of human hair based on near-infrared diffuse reflectance (NIR-DR) spectroscopy and chemometrics. NIR-DR spectra were measured for human hair, which was collected from eleven Japanese women (age 5-44 years), by use of an optical fiber probe. Partial least squares (PLS) regression has been applied to the NIR-DR spectra of human hair after mean centering (MC), standard normal variate (SNV), and first derivative (1d) or second derivative (2d) analysis to develop calibration models that predict the friction, twist, and gloss of human hair. We identified the most suitable wavenumber region for the evaluation of each physical property. Correlation coefficients and standard errors of calibration of the PLS calibration models for the friction, twist, and gloss of hair were calculated to be 0.96 and 0.023, 0.81 and 3.27, and 0.90 and 0.36, respectively. Thus, the calibration models have high accuracy.

Comparison of multivariate calibration models for glucose, urea, and lactate from near-infrared and Raman spectra

Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction were 0.24, 0.11, and 0.14 mmol L(-1) for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L(-1) for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute. This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from near-IR spectra. This is consistent with the lower standard errors of prediction.

The prediction of soil carbon fractions using mid-infrared-partial least square analysis

This paper describes the application of mid-infrared (MIR) spectroscopy and partial least-squares (PLS) analysis to predict the concentration of organic carbon fractions present in soil. The PLS calibrations were derived from a standard set of soils that had been analysed for total organic carbon (TOC), particulate organic carbon (POC), and charcoal carbon (char-C) using physical and chemical means. PLS calibration models from this standard set of soils allowed the prediction of TOC, POC, and char-C fractions with a coefficient of determination (R2) of measured v. predicted data ranging between 0.97 and 0.73. For the POC fraction, the coefficient of determination could be improved (R2 = 0.94) through the use of local calibration sets. The capacity to estimate soil fractions such as char-C rapidly and inexpensively makes this approach highly attractive for studies where large numbers of analyses are required. Inclusion of a set of soils from Kenya demonstrated the robustness of the method for total organic carbon and charcoal carbon prediction.

Quantitative determination of ethanol in heated plumes by passive Fourier transform infrared remote sensing measurements

Quantitative calibration models are developed for passive Fourier transform infrared (FT-IR) remote sensing measurements of open-air-generated vapors of ethanol. These experiments serve as a feasibility study for the use of passive FT-IR measurements in quantitative determinations of industrial stack emissions. A controlled-temperature plume generator is used to produce plumes of known concentrations of pure ethanol and mixtures of ethanol and methanol. Analyte plumes are generated over the path-averaged concentration range of 20-300 ppm-m and stack temperatures of 125, 150, 175, and 200 degrees C. A novel experimental setup is employed in which an ambient temperature polyvinyl chloride backdrop is placed behind the emission stack and used as a target for the passive IR measurements. An emission FT-IR spectrometer with telescope entrance optics is then employed to view the generated plumes against the backdrop. Signal processing techniques based on signal averaging and bandpass digital filtering are applied to both interferogram and single-beam spectral data obtained from these measurements, and the resulting filtered signals are used as inputs into the generation of multivariate partial least-squares (PLS) calibration models. Successful calibration models are obtained with both interferogram and spectral data, and neither analysis requires the collection of separate IR background data. For a set of validation data collected on a different day from the calibration measurements, standard errors of prediction of 30.6 and 32.2 ppm-m ethanol are obtained for the PLS models based on interferogram and spectral data, respectively.

Bedside monitoring of subcutaneous interstitial glucose in healthy individuals using microdialysis and infrared spectrometry

An IR-spectroscopy-based bedside device, coupled to a subcutaneously implanted microdialysis probe, is developed for quasicontinuous glucose monitoring with intermittent readouts at 10-min intervals, avoiding any sensor recalibration under long-term operation. The simultaneous estimation of the microdialysis recovery rate is possible using an acetate containing perfusate and determining its losses across the dialysis membrane. Measurements are carried out on four subjects, with experiments lasting either 8 or 28 h, respectively. Using the spectral interval data either from 1180 to 950 or 1560 to 1000 cm(-1), standard errors of prediction (SEPs) between 0.13 and 0.28 mM are achieved using multivariate calibration with partial least-squares (PLS) or classical least-squares (CLS) calibration models, respectively. The transfer of a PLS calibration model using the spectral and reference concentration data of the dialysates from the three 8-h-long experiments to a 28-h monitoring episode with another healthy subject is tested. Including microdialysis recovery for the determination of the interstitial glucose concentrations, an SEP of 0.24 mM is obtained versus whole blood glucose values. The option to determine other metabolites such as urea or lactate offers the possibility to develop a calibration- and reagent-free point-of-care analyzer.

Effects of Spectral Resolution on the Determination of Glucose in a Simulated Biological Matrix by Fourier Transform near Infrared Spectrometry

The effects of spectral resolution on the performance of partial least-squares (PLS) calibration models were studied in the context of an analysis for physiological levels of glucose by Fourier transform near infrared (NIR) spectrometry in the combination region of 5000 to 4000 cm−1. Employing a simulated biological matrix of varying levels of triacetin and bovine serum albumin in phosphate buffer, two spectral data sets were evaluated. Single-sided interferograms consisting of 16,384 points collected over a 15,801 cm−1 bandwidth were Fourier processed to yield spectra with a nominal 2 cm−1 point spacing. These interferograms were then progressively truncated before Fourier processing to form sets of spectra with reduced resolution. The lower-resolution spectra were also computed with varying levels of zero-filling to evaluate the effects of spectral dimensionality on the PLS models. Across the levels of resolution and zero-filling, 21 parallel groups of spectra were computed with each of the two data sets. Calibration studies were then performed with spectra in absorbance units. Predictive models for glucose were optimised in terms of spectral range, the number of PLS latent variables and the parameters of a pre-processing Fourier filter. Models were evaluated for their performance in both short-term and long-term predictions. Through this research, it was found that models based on spectra with a nominal resolution of 16 cm−1 performed as well as those computed with higher-resolution spectra. Over the range of 1–20 mM glucose, these models achieved values of the standard error of prediction ( SEP) in the range of 0.5 mM for short-term predictions and 0.8 mM for predictions with spectra collected more than 200 days after the calibration data. These results are significant because they help to define the required instrumental characteristics of a potential dedicated NIR glucose sensor.

Partial least-squares near-infrared determination of hydrocarbons removed from polluted waters by using tanned solid wastes

A new procedure has been developed for the determination of hydrocarbons retained in solid tanned wastes from polluted waters. The method uses near-infrared (NIR) transmission spectra obtained from leachates of the hydrocarbons with CCl4 using a partial least-squares (PLS) calibration model based on the use of mixtures of n-hexane, isooctane, and toluene diluted with CCl4. This methodology was applied to the evaluation of the absorption capacity of chrome shavings in water samples polluted with hydrocarbons, resulting in a maximum absorption capacity of 6.30 g hydrocarbons per g solid waste.

Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples

A hyperspectral image in the near infrared contains thousands of position-referenced spectra. After imaging reference materials of known composition it is possible to build Partial Least Squares (PLS) regression models for predicting unknown compositions from new images or spectra. In this paper a comparison is made between spectra from a hyperspectral image and spectra from two spectrometers: a scanning grating instrument with rotating sample holders and an FT-NIR instrument utilizing a fiber-optic probe. The raw spectra and the quality of the PLS calibration models and predictions are compared. Two sample datasets consist of a set of 13 designed artificial mixtures of pure constituents and a selection of 13 sampled cheeses. The prediction error from the hyperspectral image spectra is between that of the two spectrometers. For a typical food sample, the average bias [and replicate standard deviation] was -0.6% [0.5%] for protein and -0.2% [1.3%] for fat. Comparable values for the best spectrometer were -0.2% bias for protein and -0.5% for fat. Some of the advantages of working with hyperspectral images are highlighted: the simultaneous exploration of representations of both spectral and spatial data, and the analysis of concentration profiles and concentration maps all contribute to better characterization of organic and biological materials.

Pulse glucometry: a new approach for noninvasive blood glucose measurement using instantaneous differential near-infrared spectrophotometry

We describe a new optical method for noninvasive blood glucose (BGL) measurement. Optical methods are confounded by basal optical properties of tissues, especially water and other biochemical species, and by the very small glucose signal. We address these problems by using fast spectrophotometric analysis in a finger, deriving 100 transmittance spectra per second, to resolve optical spectra (900 to 1700 nm) of blood volume pulsations throughout the cardiac cycle. Difference spectra are calculated from the pulsatile signals, thereby eliminating the effects of bone, other tissues, and nonpulsatile blood. A partial least squares (PLS) model is used with the measured spectral data to predict BGL levels. Using glucose tolerance tests in 27 healthy volunteers, periodic optical measurements were made simultaneously with collection of blood samples for in vitro glucose analysis. Altogether, 603 paired data sets were obtained in all subjects and two-thirds of the data or of the subjects randomly selected were used for the PLS calibration model and the rest for the prediction. Bland-Altman and error-grid analyses of the predicted and measured BGL levels indicated clinically acceptable accuracy. We conclude that the new method, named pulse glucometry, has adequate performance for safe, noninvasive estimation of BGL.

Partial Least Squares Processing of Near‐Infrared Spectra for Discrimination and Quantification of Adulterated Olive Oils

Abstract A new processing based on partial least squares (PLS) algorithm for the discrimination and determination of adulterants in pure olive oil using near‐infrared (NIR) spectroscopy has been introduced. The 280 adulterations of olive oil with corn oil (n=70), hazelnut oil (n=70), soya oil (n=70), and sunflower oil (n=70) were prepared, and their NIR spectra in the region 12,000–4550 cm−1 were collected. The 70 spectra of each adulteration of olive oil were divided into two sets, 50 spectra for a calibration set and 20 spectra for a prediction set. The spectra of a total calibration set (n=200) were separated into individual adulterant calibration sets (ni=50, i=corn, hazelnut, soya, sunflower) by using discriminant PLS (DPLS) analysis, and PLS calibration models for the quantification of adulterants with corn oil, hazelnut oil, soya oil, or sunflower oil were developed separately. A variety of wavelength ranges and data pretreatments were examined for obtaining optimal results for the discrimination and quantification objects. Four PLS models for differentiating the adulterant types were evaluated by classifying the NIR spectra of a total prediction set (n=80) into known adulterant types. Then, these known adulterant spectra were analyzed by the PLS calibration models developed for each type to determine the content of an adulterant in pure olive oil. The results of evaluation revealed that the processing reported in this article works excellently for the discrimination and quantification of the adulterations of olive oil.