Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression

Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments.     

Standardization of Raman spectra for transfer of spectral libraries across different instruments

In this paper we evaluate methods for standardization of Raman spectra that are required to improve spectral correlation computations between spectra measured on different instruments. Five commercially-available 785 nm Raman spectrometers from different vendors were included in the study. These spectrometers have diverse specifications and performance levels and range in size from laboratory-based instruments to field-deployable portable and handheld platforms. Since each Raman spectrometer has different characteristics, spectra obtained on one instrument cannot readily be compared to a library acquired on a different instrument without performing various types of spectral corrections (standardization). We outline a procedure that combines previously established Raman shift and intensity correction protocols with a resolution matching step to facilitate the comparison of a centralized master library with spectra acquired on different geographically distributed Raman spectrometers. The standardization procedure is effective in reducing the inherent instrument-to-instrument variability so that spectra from different spectrometers can be compared and reliable results obtained using library-based spectral correlation methods. The findings have important implications for the ability to transfer Raman spectral libraries between instruments.     

A procedure for calibration transfer between near-infrared instruments--a worked example using a transmittance single tablet assay for piroxicam in intact tablets

A procedure was developed for different modes of calibration transfer in near-infrared (NIR) spectroscopy, which included a method for the selection of a subset of samples appropriate for transfer. As a worked example, these guidelines were applied to the transfer of a multivariate calibration model, representing a validated NIR single tablet assay for the active within an intact pharmaceutical product, between three equivalent dispersive NIR transmission instruments. Transfer was first evaluated between two instruments, representing the situation where both were available during calibration development. A spectral correction method alone, applied to the transfer instrument, was not sufficient to facilitate transfer, with further optimisation of the calibration model using a novel wavelength selection algorithm necessary to remove regions of the spectral range that resulted in skewed predictions on the second instrument. Through this approach, a single calibration model was found to be equally accurate and precise on the two instruments. A procedure, using the Kennard-Stone algorithm, is described for determining a reduced number of samples as a transfer set using only the spectral information from the original instrument. The purpose of the subset was to permit transfer to a new instrument where that instrument was not available until after calibration development or where it was undesirable to re-measure the full sample set (i.e. due to excessive reference chemistry). Utilising the transfer set, transfer to a third instrument was evaluated. The calibration model, optimised between the first two instruments, was not directly applicable for the third instrument, with further wavelength selection required to remove a small region of spectral data. On completion, using a full statistical evaluation, a single calibration model was found to be equally accurate and precise on all three instruments.     

基于小波变换动态时间规整的近红外光谱模型传递方法

为解决因测量环境及仪器差异而导致的近红外光谱模型通用性较差的不足,提出一种基于小波变换动态时间规整算法的模型传递方法(Wavelet transform combined with dynamic time warping,WDTW),从而实现不同仪器之间模型的共享。首先,该方法将光谱进行小波变换预处理,然后利用动态时间规整算法(Dynamic time warping,DTW)找到近红外光谱波长点之间最优的对应关系并建立回归方程。使用近红外药品光谱数据集和汽油数据集建立传递模型,验证了基于小波变换动态时间规整模型传递方法的有效性。汽油光谱数据集C7、C8、C9和C10成分的预测标准偏差(SEP)分别为0.414 4、0.801 1、1.090 4和1.290 8;药品光谱数据集活性、硬度和重量的SEP分别为2.585 6、0.434 5和2.270 3,均小于传统方法。上述实验结果表明,所建立的模型传递方法能有效消除源机光谱和目标机光谱之间的差异,提高模型的稳定性和准确性,实现模型传递的效果。     

Near-infrared calibration transfer based on spectral regression

A calibration transfer method for near-infrared (NIR) spectra based on spectral regression is proposed. Spectral regression method can reveal low dimensional manifold structure in high dimensional spectroscopic data and is suitable to transfer the NIR spectra of different instruments. A comparative study of the proposed method and piecewise direct standardization (PDS) for standardization on two benchmark NIR data sets is presented. Experimental results show that spectral regression method outperforms PDS and is quite competitive with PDS with background correction. When the standardization subset has sufficient samples, spectral regression method exhibits excellent performance.     

The transfer between instruments of a reflectance near-infrared assay for paracetamol in intact tablets

This study compares several correction methods to facilitate the transfer of a validated near-infrared (NIR) assay for paracetamol in intact tablets between two reflectance NIR instruments of the same type. Transfer was defined as the ability to accurately predict the true assay value of a sample measured on a NIR system using an assay developed on a different system, and was assessed using a comprehensive set of statistical tests. Direct electronic transfer of the calibration models, representing the NIR assay, was not possible as a result of a definite residual spectrum between instruments. The use of a correction method based on the standardisation of the material used to record the reference spectrum also proved ineffective. Two methods investigated did succeed, the first employed a response surface calculated between the reflectance values of a set of six certified photometric standards measured on both instruments, with all full range partial least square (PLS) regression models subsequently transferred. The next was correction of the spectra from the second instrument utilising the residual spectrum between the mean sample of the validation set measured on both instruments. Through this approach all PLS regression models and also a single multiple linear regression (MLR) model were transferred. As an outcome of this study guidelines are suggested for the transfer of NIR assays along with the criteria deemed necessary to conclusively prove transfer and justify any correction method utilised. The significant criteria were determined to be the paired t-test with both the UV reference assay data and the original NIR assay data, and comparison of the coefficient of multiple determinations.     

Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

The application of mobile near-infrared (NIR) spectrometers in field measurements is growing. Calibration transfer techniques offer simple solutions for enabling models constructed on benchtop instruments for use on mobile spectrometers. Since different types of spectrometers with different components, scanning ranges and resolutions cause great differences in the spectral response, calibration transfer is difficult to apply. In this paper, we focus on calibration transfer among benchtop, portable and handheld spectrometers by a method of calibration transfer based on canonical correlation analysis (CTCCA). Its capability was illustrated by the example of a group of NIR spectra dataset for predicting reducing sugars, total sugar, and nicotine contents in tobacco leaves. The experimental results showed that the transferability of CTCCA was superior to other conventional calibration transfer methods, including piecewise direct standardization, spectral space transformation, calibration transfer based on independent component analysis, and calibration transfer based on the weight matrix. Moreover, the best transfer results were obtained in the three cases by canonical correlation analysis method executing transfer while the spectra were not interpolated, which shows that this approach has the advantage of easy implementation for calibration transfer. Therefore, CTCCA without interpolation calculation offers a new and simple solution for transferring the spectra acquired by mobile spectrometers to the optimized spectral models built on benchtop devices to improve the accuracy of the results. Additionally, the results show that the benchtop spectrometer is more suitable as the master instrument for calibration transfer with more accurate prediction than using a portable device as the master.     

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.     

Evaluation of protocols for reproducible electrospray in-source collisionally induced dissociation on various liquid chromatography/mass spectrometry instruments and the development of spectral libraries

Mass spectral libraries provide a tool for identifying unknown compounds using both molecular weight and fragmentation information. Mass spectrometers with electrospray ionisation (ESI) and atmospheric chemical ionisation (ApCI) sources have the capability to produce data of this type using in-source collisionally induced dissociation (CID), and in-source CID libraries can be created. Due to the variation in electrospray source design from different instrument manufacturers, the production of reproducible in-source CID spectra that can be used in libraries for all instrument types is not a trivial task. To date, the evaluation of the production of in-source CID libraries has tended to focus on similar instruments from one manufacturer. The studies have also tended to focus on specific compound classes, with a limited molecular weight range.This report describes the findings from the investigation of protocols for the creation of mass spectral libraries using ESI in-source CID on six instruments from four different manufacturers. The overall goal was to create a spectral library for the identification of unknowns. The library could then be applied across all manufacturers' electrospray instruments.Two different experimental protocols were attempted. The first used a tuning compound to establish standard ESI source conditions, with fixed fragmentation potentials. The second involved the attenuation of the [M + H](+) ion to a known degree. A diverse range of compounds (pharmaceutical, photographic, pesticides) was tested to establish the reproducibility of the spectra on the six instruments. Both protocols produced spectra on the various instruments that in many cases were very similar. In other examples, the spectra differed not only in their relative ion abundances, but also in terms of the spectral content. Important observations regarding the effect of ion source design are also reported.The degree of spectral reproducibility was calculated off-line by comparing the five most abundant ions (20% for each ion that matches) from each spectrum on each instrument. This approach was adopted, as we do not possess a software package that met our requirements for spectral comparison. Match factors (% fit) were calculated by comparing each spectrum against the spectra recorded for the same compound and then for all other compounds, on each instrument. The % fit values derived by the off-line approach gave a clear view of the spectral reproducibility from instrument to instrument and also discriminated the spectra of the various compounds from each other. The applicability of this approach was tested using a blind trial in which several compounds were presented as unknowns, their in-source CID spectra recorded and the five-ion approach used for identification.     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R2cv)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.     

Multivariate curve resolution based chromatographic peak alignment combined with parallel factor analysis to exploit second-order advantage in complex chromatographic measurements

In the present contribution, a new combination of multivariate curve resolution-correlation optimized warping (MCR-COW) with trilinear parallel factor analysis (PARAFAC) is developed to exploit second-order advantage in complex chromatographic measurements. In MCR-COW, the complexity of the chromatographic data is reduced by arranging the data in a column-wise augmented matrix, analyzing using MCR bilinear model and aligning the resolved elution profiles using COW in a component-wise manner. The aligned chromatographic data is then decomposed using trilinear model of PARAFAC in order to exploit pure chromatographic and spectroscopic information. The performance of this strategy is evaluated using simulated and real high-performance liquid chromatography-diode array detection (HPLC-DAD) datasets. The obtained results showed that the MCR-COW can efficiently correct elution time shifts of target compounds that are completely overlapped by coeluted interferences in complex chromatographic data. In addition, the PARAFAC analysis of aligned chromatographic data has the advantage of unique decomposition of overlapped chromatographic peaks to identify and quantify the target compounds in the presence of interferences. Finally, to confirm the reliability of the proposed strategy, the performance of the MCR-COW-PARAFAC is compared with the frequently used methods of PARAFAC, COW-PARAFAC, multivariate curve resolution-alternating least squares (MCR-ALS), and MCR-COW-MCR. In general, in most of the cases the MCR-COW-PARAFAC showed an improvement in terms of lack of fit (LOF), relative error (RE) and spectral correlation coefficients in comparison to the PARAFAC, COW-PARAFAC, MCR-ALS and MCR-COW-MCR results.     

Control production of polyester resins by NIR spectroscopy

The control of the esterification reaction for production of polyester saturated resins is followed usually by determination of the acid value (AV) and hydroxyl value (OHV).These parameters are determined by titrimetry, but these methods are slow, intensity working and produce waste. In this paper an alternative methodology is proposed, based in the construction of multivariate models on NIR spectroscopic data and different models are constructed in order to apply to different steps of the production process. The ensuing methodology provides models of good predictive ability and constitute an advantageous alternative to existing titrimetric reference methods as regards expeditiousness and environmentally compatible. The multivariate calibration models established were also used with a different instrument; to this end, the spectra recorded with the original equipment were subjected to Piecewise Direct Standardization (PDS) in order to make them equivalent to those provided by the new equipment. Also, PLS calibration was reproduced by using the same samples, spectral treatment, wavenumber range and number of factors as in the original model, and the AV and OHV results thus obtained were similarly good.     

Filter design for molecular factor computing using wavelet functions

Molecular factor computing (MFC) is a new strategy that employs chemometric methods in an optical instrument to obtain analytical results directly using an appropriate filter without data processing. In the present contribution, a method for designing an MFC filter using wavelet functions was proposed for spectroscopic analysis. In this method, the MFC filter is designed as a linear combination of a set of wavelet functions. A multiple linear regression model relating the concentration to the wavelet coefficients is constructed, so that the wavelet coefficients are obtained by projecting the spectra onto the selected wavelet functions. These wavelet functions are selected by optimizing the model using a genetic algorithm (GA). Once the MFC filter is obtained, the concentration of a sample can be calculated directly by projecting the spectrum onto the filter. With three NIR datasets of corn, wheat and blood, it was shown that the performance of the designed filter is better than that of the optimized partial least squares models, and commonly used signal processing methods, such as background correction and variable selection, were not needed. More importantly, the designed filter can be used as an MFC filter in designing MFC-based instruments.     

SIMPLISMA applied to two-dimensional wavelet compressed ion mobility spectrometry data

A modified SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) algorithm, referred to as real-time (RT) SIMPLISMA has been combined with two-dimensional (2D) wavelet compression (WC²). This tool was evaluated with datasets of drugs and bacteria that were acquired from two different ion mobility spectrometers and published reference data that comprised Raman, FTIR microscopy, near-infrared (NIR) and mass spectral data. RTSIMPLISMA is amenable for real-time modeling and is able to determine the number of components automatically. The 2D wavelet compression, which compresses both acquisition and drift time dimensions of measurement, was applied to the datasets prior to RTSIMPLISMA modeling. RTSIMPLISMA models obtained from the compressed data were wavelet transformed back to the uncompressed representation. The effects of wavelet filter types and compression levels were investigated. The relative root-mean-square errors (RRMSE) of reconstruction, which calculate the relative difference between the extracted models with and without 2D compressions, were used to evaluate the effects of compression on self-modeling. The results showed that satisfactory models could be obtained when a data was compressed to 1/256 of its size.     

三线性分解算法对液相色谱-质谱联用仪多样本测定数据分辨适用性的比较

从三线性分解算法对液相色谱-质谱联用仪(LC-MS)多样本测定数据分辨的适用性角度入手,探讨了双线性和三线性分解算法的实际应用效果、存在的问题及其解决方案。本文选择含有低丰度肽段和高干扰背景信号的代表性测定数据进行测试。结果表明,双线性方法不具有分辨唯一性,不能分离LC-MS多样品测定数据存在的背景干扰,从而不适用于低丰度肽段问题的分析。常规的三线性分解算法难以满足质谱信号具有的数学特性--稀疏性,分辨结果并不完全可靠。本文提出了具有非负约束的交替三线性分解(non-negative alternating trilinear decomposition, NNATLD)新算法用于LC-MS多样本测定数据分辨及数学分离,能够很好地适应质谱的数学特性,且具有计算资源节约和收敛速度快等特点。     

A multimodal spectrometer for Raman scattering and near-infrared absorption measurement

Raman and infrared (IR) spectroscopy are complementary spectroscopic techniques. However, measurement of Raman and IR spectra are commonly carried out on separate instruments. A dispersive system that enables both Raman spectroscopy and NIR spectroscopy was designed, built, and tested. The prototype system measures spectral ranges of 2600–300 cm⁻¹ and 752–987 nm for Raman and NIR channels, respectively. A wavelength accuracy better than 0.6 nm and spectral resolution better than 1 nm (14.4 cm⁻¹ for Raman channel) could be achieved with our configuration. The linearity of spectral response was better than 99.8%. The intensity stability of the instrument was found to be 0.7% and 0.4% for Raman and NIR channels, respectively. The performance of the instrument was evaluated using binary aqueous solutions of ethanol and ovalbumin. It was found that ethanol concentrations (2–10%) could be predicted with a root mean squared error of prediction (RMSEP) of 0.45% using Raman peak height at 882.2 cm⁻¹. Quantification of ovalbumin concentration (8–16 g/L) in aqueous solutions and in denatured states yielded RMSEP values of 1.05 g/L and 0.74 g/L, respectively. Using concentration as external perturbation in two-dimensional correlation spectroscopy (2DCOS), heterospectral correlation analysis revealed the relationship between NIR and Raman spectra.     

Development and Certification of NIST Standard Reference Materials for Relative Raman Intensity Calibration

In analytical Raman spectroscopy it becomes increasingly important to employ a procedure for the correction of the relative intensity of Raman spectra. The determination of the intensity response function of a Raman instrument traditionally has been carried out through a white light source that has been calibrated for its relative spectral irradiance. While this method will furnish a correction curve to yield spectra corrected to relative Raman intensity, it is often cumbersome and fraught with experimental difficulties that can profoundly affect the reliability of the correction procedure. An alternate methodology that permits a simplified calibration of the Raman instrument response function is based on the use of luminescent glass standards that transfer a white light calibration onto the Raman measurement system. In this procedure, a measurement of the luminescence of an intensity standard, whose relative irradiance has been determined, provides a means to establish the instrument response function. Correction of measured spectra by this function furnishes spectra that are free of instrumental intensity artifacts. Based on this approach, NIST is developing a series of Standard Reference Materials (SRMs) for the calibration of Raman intensity. This process, and the results obtained thereby, is described for Raman spectroscopy measurements employing 785nm excitation. The procedure is valid for both macro-sampling and micro-sampling Raman work.     

Static TOF‐SIMS — a VAMAS interlaboratory study. Part I. Repeatability and reproducibility of spectra

An interlaboratory study involving 32 Time‐of‐Flight Static SIMS instruments from 12 countries has been conducted. Analysts were supplied, by NPL, with a protocol for analysis together with three reference materials; a thin layer of polycarbonate (PC) on a silicon wafer, a thin layer of polystyrene (PS) oligomers on etched silver and poly(tetrafluoroethylene) (PTFE). The study involved static SIMS analysis of each reference material for both positive and negative polarity secondary ions. The option to test instrument suitability for G‐SIMS was also provided. The results of this study show that over 84% of instruments have excellent repeatabilities of better than 1.9%. Repeatabilities can be as good as 0.4%. A relative instrument spectral response (RISR) is calculated for each instrument for each reference material and ion polarity. The RISR is used to evaluate variations in spectral response between different generic types of SIMS instruments. Use of the RISR allows the identification of contamination, charge stabilisation problems and incorrectly functioning ion detectors. The high quality of the data presented here allows the RISR to reveal differences in individual operation of each instrument such as the use of apertures to remove metastables from the spectra and the use of different post‐acceleration voltages for ion detection. Spectral reproducibility can be measured, here, by the equivalence of RISRs between materials and ion polarities. It is found that reproducibilities are on average 10% but can be as good as 4% for the best instruments. This figure shows the consistency between instruments in measuring spectra from different samples. This study sets out the basic framework to develop static secondary ion mass spectrometry (SSIMS) as a reliable measurement method. © Crown Copyright 2005. Reproduced with the permission of Her Majestry's Stationery Office. Published by John Wiley & Sons, Ltd.     

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction – the boxcar signal transfer (BST) – was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.