PLS-2 Algorithm Research Articles

Using a Material Library to Understand the Change of Tabletability by High Shear Wet Granulation.

Understanding the tabletability change of materials after granulation is critical for the formulation and process design in tablet development. In this paper, a material library consisting of 30 pharmaceutical materials was used to summarize the pattern of change of tabletability during high shear wet granulation and tableting (HSWGT). Each powdered material and the corresponding granules were characterized by 19 physical properties and nine compression behavior classification system (CBCS) parameters. Principal component analysis (PCA) was used to compare the physical properties and compression behaviors of ungranulated powders and granules. A new index, namely the relative change of tabletability (CoTr), was proposed to quantify the tabletability change, and its advantages over the reworking potential were demonstrated. On the basis of CoTr values, the tabletability change classification system (TCCS) was established. It was found that approximately 40% of materials in the material library presented a loss of tabletability (i.e., Type I), 50% of materials had nearly unchanged tabletability (i.e., Type II), and 10% of materials suffered from increased tabletability (i.e., Type III). With the help of tensile strength (TS) vs. compression pressure curves implemented on both powders and granules, a data fusion method and the PLS2 algorithm were further applied to identify the differences in material properties requirements for direct compression (DC) and HSWGT. Results indicated that increasing the plasticity or porosity of the starting materials was beneficial to acquiring high TS of tablets made by HSWGT. In conclusion, the presented TCCS provided a means for the initial risk assessment of materials in tablet formulation design and the data modeling method helped to predict the impact of formulation ingredients on the strength of compacts.

An alternative point of view on PLS

PLS has been extensively studied in the past, but several issues about its theoretical foundation are still open. In this study, we draw an alternative formulation of PLS2 based on gradient descent method. A new algorithm to solve the least squares problem called “gradient descent” PLS2 is introduced. The “latent variable” language is not used in its formulation. Well-defined elements of linear algebra are used and the method is developed within the non-linear programming field, rather than statistical modelling. The new algorithm is equivalent to the standard “eigenvalue” PLS2 algorithm. The use of “gradient descent” PLS2 to solve linear regression problems is discussed and two data sets, one simulated and one real, are investigated to show the behaviour of the algorithm. • PLS2 is formulated within the framework of non-linear programming as a method to solve the least squares problem • The concept of latent variable is not used in its formulation • The objective function of PLS2 is formulated using the directional derivative instead of the covariance between scores • A new algorithm equivalent to standard “eigenvalue” PLS2 algorithm is introduced

Chemical composition and antioxidant capacity in strawberry cultivars (Fragaria x ananassa Duch.) by FT-MIR spectroscopy and chemometrics

ABSTRACTFT-MIR spectroscopy coupled to chemometrics can be an alternative technique to conventional methods to determine the chemical composition of berries such as strawberry. This study developed chemometric models based on FT-MIR spectroscopy to identify strawberry cultivars at seven stages of ripening and quantify the main attributes involved in sensory quality of strawberry fruits (total soluble solids, total acidity, reducing sugars, pH) and the main bioactive compounds involved in antioxidant capacities (ascorbic acid, phenolics, flavonoids, anthocyanins). PCR, PLS1 and PLS2 algorithms were used to develop the prediction models. PLS1 algorithm developed excellent predictions with Rc2 -greater than 0.90. SIMCA model identified the cultivars with 99% confidence. Unlike conventional methods, FT-MIR allowed us to analyze simultaneously the attributes of quality, bioactive compounds, and antioxidant capacity of strawberries fruits in a fast and reliable way without the use of reagents and/or solvents, which considerably reduces the time and cost of the analyzes.

Quantitative Analysis of Hexahydro-1,3,5-trinitro-1,3,5, Triazine/Pentaerythritol Tetranitrate (RDX-PETN) Mixtures by Terahertz Time Domain Spectroscopy.

Absorption spectra of explosives such as hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), pentaerythritol tetranitrate (PETN), and mixtures of both were measured by terahertz time domain spectroscopy (THz-TDS). Chemometrics was applied to quantitative analysis of terahertz absorbance spectra obtained in transmission mode to predict the relative amounts of RDX and PETN in samples containing pure components or their mixtures. This analysis was challenging because significant spectral overlap prevented identification of each product fingerprint. Partial least squares (PLS) regression models were thus applied to the terahertz spectra. A comparison between the so-called PLS1 and PLS2 algorithms was performed to predict the PETN concentrations in mixture samples. PLS2 demonstrated better predictive ability than PLS1 with RMSE value lower than 3.5 mg for 400 mg total weight pellets. Moreover, the influence of the highly overlapping spectral frequency band was investigated by reducing the original 0.2-3 THz (6-100 cm(-1)) spectral band to 1.8-3 THz (60-100 cm(-1)). The predictive ability was quite similar in both cases, highlighting the excellent ability of chemometrics to perform quantitative analysis when applied to THz-TDS data, even in the case of highly overlapping spectra.

Genetic algorithm based multi-objective least square support vector machine for simultaneous determination of multiple components by near infrared spectroscopy

The near infrared (NIR) spectrum contains a global signature of composition, and enables to predict different proper ties of the material. In the present paper, a genetic algorithm and an adaptive modeling technique were applied to build a multiobjective least square support vector machine (MLS-SVM), which was intended to simultaneously determine the concentrations of multiple components by NIR spectroscopy. Both the benchmark corn dataset and self-made Forsythia suspense dataset were used to test the proposed approach. Results show that a genetic algorithm combined with adaptive modeling allows to efficiently search the LS-SVM hyperparameter space. For the corn data, the performance of multi-objective LS-SVM was significantly better than models built with PLS1 and PLS2 algorithms. As for the Forsythia suspense data, the performance of multi-objective LS-SVM was equivalent to PLS1 and PLS2 models. In both datasets, the over-fitting phenomena were observed on RBFNN models. The single objective LS-SVM and MLS-SVM didn't show much difference, but the one-time modeling convenience al lows the potential application of MLS-SVM to multicomponent NIR analysis.

The use of hyperspectral imaging in the visible and near infrared region to discriminate between table grapes harvested at different times

Traditional analytical methods applied to the measurement of grape maturity and quality index in order to assess optimal harvest time have been proved to be slow and destructive. Therefore, non-destructive analytical techniques, including spectroscopy, can be a valid support for the choice of the best time to harvest. This study evaluated the feasibility of using a visible and near infrared spectral scanner (v. 1.4; DV Srl, Padova, Italy) with a detector in the region between 400-1000 nm to discriminate between grapes harvested at different times. Twelve clusters were harvested at 5 different times between October and December 2011. Spectra were acquired with a Spectral scanner on 3 intact berries from each bunch. These were randomly selected from top, medium and bottom zones, for a total of 180 spectra. Classification models were construed comparing 2 methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The SIMCA model was developed building individual principal component analysis (PCA) models for the spectra of each harvest time. Different pre-treatment methods were tested in order to enhance the power of the model, thus enhancing the score differences among samples from different harvest times. The transformation that allowed the best statistical separation among scores of grapes from different harvest times was the second derivate of Norris. Therefore, the PCA model obtained from the spectra subjected to this pre-treatment was used for SIMCA classification. The PLS-DA model were developed applying the PLS2 algorithm. In order to construct discriminant models to classify bunch spectra according to the 5 harvest times, spectral variations were correlated with the 5 categories established. No pretreatments were previously applied in this last case since they did not improve the final result. The SIMCA method was unable to correctly classify grapes from harvest time 2 (59% of correct classification) and was less efficient compared to the PLS-DA model. Using the PLS-DA model, all the grapes were correctly classified (100%) with the exception of those from harvest time 5 (94%). The overall results demonstrate that this method has excellent potential for discriminating grape quality.

The use of hyperspectral imaging in the visible and near infrared region to discriminate between table grapes harvested at different times

Traditional analytical methods applied to the measurement of grape maturity and quality index in order to assess optimal harvest time have been proved to be slow and destructive. Therefore, non-destructive analytical techniques, including spectroscopy, can be a valid support for the choice of the best time to harvest. This study evaluated the feasibility of using a visible and near infrared spectral scanner (v. 1.4; DV Srl, Padova, Italy) with a detector in the region between 400-1000 nm to discriminate between grapes harvested at different times. Twelve clusters were harvested at 5 different times between October and December 2011. Spectra were acquired with a Spectral scanner on 3 intact berries from each bunch. These were randomly selected from top, medium and bottom zones, for a total of 180 spectra. Classification models were construed comparing 2 methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The SIMCA model was developed building individual principal component analysis (PCA) models for the spectra of each harvest time. Different pre-treatment methods were tested in order to enhance the power of the model, thus enhancing the score differences among samples from different harvest times. The transformation that allowed the best statistical separation among scores of grapes from different harvest times was the second derivate of Norris. Therefore, the PCA model obtained from the spectra subjected to this pre-treatment was used for SIMCA classification. The PLS-DA model were developed applying the PLS2 algorithm. In order to construct discriminant models to classify bunch spectra according to the 5 harvest times, spectral variations were correlated with the 5 categories established. No pretreatments were previously applied in this last case since they did not improve the final result. The SIMCA method was unable to correctly classify grapes from harvest time 2 (59% of correct classification) and was less efficient compared to the PLS-DA model. Using the PLS-DA model, all the grapes were correctly classified (100%) with the exception of those from harvest time 5 (94%). The overall results demonstrate that this method has excellent potential for discriminating grape quality.

Using Partial Least-Squares Regression in Multivariate UV Spectroscopic Analysis of Mixtures of Imidazolium-Based Ionic Liquids and 1-Methylimidazole for Measurements of Liquid–Liquid Equilibria

Recent interest in the use of room-temperature ionic liquids in various industrial applications sets requirements for analytical techniques that could lead to an efficient determination of concentrations of ionic liquids and/or possible impurities contained in them. Catalytic processes are particularly sensitive to the amount of impurities in the reaction media. Finding suitable and efficient techniques of determining compositions of liquid mixtures appears to be of importance not only for the design and optimization of such catalytic processes but also in measurements of phase equilibria. Imidazolium-based ionic liquids are often contaminated by their precursors 1-chlorobutane and 1-methylimidazole. Therefore, in this work a calibration technique is proposed that makes use of partial least-squares regression in UV spectroscopic determination of concentrations of 1-butyl-3-methylimidazolium hexafluorophosphate and 1-methylimidazole. Spectra of these compounds show significant overlaps making their simultaneous quantitative analysis difficult. Partial least-squares regression using the PLS2 algorithm provides an effective resolution, decomposing complicated spectra and coping with component interferences, nonlinearities and collinearity. The calibration method for the chosen compounds was validated using test samples of known composition and by measuring liquid–liquid equilibria at 298.15 K in the ternary system 1-butyl-3-methylimidazolium hexafluorophosphate + 1-methylimidazole + 1-chlorobutane.

Mixture quantification using PLS in plastic scintillation measurements

This article reports the capability of plastic scintillation (PS) combined with multivariate calibration (Partial least squares; PLS) to detect and quantify alpha and beta emitters in mixtures. While several attempts have been made with this purpose in mind using liquid scintillation (LS), no attempt was done using PS that has the great advantage of not producing mixed waste after the measurements are performed. Following this objective, ternary mixtures of alpha and beta emitters ((241)Am, (137)Cs and (90)Sr/(90)Y) have been quantified. Procedure optimisation has evaluated the use of the net spectra or the sample spectra, the inclusion of different spectra obtained at different values of the Pulse Shape Analysis parameter and the application of the PLS1 or PLS2 algorithms. The conclusions show that the use of PS+PLS2 applied to the sample spectra, without the use of any pulse shape discrimination, allows quantification of the activities with relative errors less than 10% in most of the cases. This procedure not only allows quantification of mixtures but also reduces measurement time (no blanks are required) and the application of this procedure does not require detectors that include the pulse shape analysis parameter.

Simultaneous In-Line Monitoring of the Conversion and the Coating Thickness in UV-Cured Acrylate Coatings by Near-Infrared Reflection Spectroscopy

Near-infrared (NIR) reflection spectroscopy was used for in-line analysis of the conversion and the coating thickness (5-20 μm) of UV-cured clear and pigmented acrylate coatings. The quantitative evaluation of the recorded spectra was carried out by partial least-squares (PLS) regression, in particular with the PLS2 algorithm, which allows simultaneous prediction of both parameters. The efficiency of this method was investigated in roll coating experiments at line speeds up to 100 m min(-1). It was shown that the method is able to compensate for the effect of accidental variations of the coating thickness, which inevitably occur upon changes of the line speed, on the prediction of the conversion. Accordingly, the conversion could be determined with a precision of ±2...3%, whereas the error in the measurement of the thickness was found to be about 0.5-1 μm.

Development of a Partial Least-Squares Calibration Model for Simultaneous Determination of Elements by Inductively Coupled Plasma-Atomic Emission Spectrometry

A partial least-squares (PLS) calibration model was developed for simultaneous multicomponent elemental analysis with inductively coupled plasma-atomic emission spectrometry (ICP-AES) in the presence of spectral interference. The best calibration model was obtained using a PLS2 algorithm. Validation was performed with an artificial test set. Multivariate calibration models were constructed using 2 series of synthetic mixtures (Zn, Cu, Fe, and U, V). Accuracy of the method was evaluated with unknown synthetic and real samples.

Preliminary studies about thermal degradation of edible oils through attenuated total reflectance mid-infrared spectrometry

Degradation of edible oils during their heating process has been evaluated through the determination of cis-unsaturation and trans-fatty acids together with free fatty acids as a function of time and temperature heating. Two types of cooking oils, sunflower and seed (mixture of different seeds non-detailed), were heated at three different temperatures (147, 171 and 189 °C) during a total time of 1920 min (32 h) shared out in four sessions of 8 h each one, and samples were studied from their spectra obtained by Fourier transform infrared spectrometry using attenuated total reflectance measurements. A critical comparison between different multivariate calibration models built based on full spectra or selected wavenumbers, using both PLS1 and PLS2 algorithms, was carried out for a preliminary evaluation of the heating conditions (temperature and time) of oil samples.

Multivariate calibration applied to a highly interfering chemical system: The simultaneous spectrophotometric determination of aluminium and iron in plants using xylenol orange and partial least-squares regression

A partial least squares (PLS) calibration model was developed for the simultaneous spectrophotometric determination of Al and Fe in plant extracts by using an excess of xylenol orange as the cromogenic reagent. Experimental conditions were established to reduce interferences, decrease system complexity and produce a robust procedure that could be used for routine analysis. The spectra should be recorded from 2 to 4 h after mixing the reagents. Ethanolic solutions were used to improve the formation of the Al-XO complexes. The best calibration model was obtained using the PLS-2 algorithm after mean centering the data with five latent variables. Under the experimental conditions adopted, only Zn was found to be a potential interfering species with the plant extracts used. Validation of the proposed spectrophotometric method was performed by running nine plant samples from the 1997 International Plant-Analytical Exchange (IPE) which were also analyzed by ICP-AES for further correlation. The proposed procedure showed to be useful for prediction of Al and Fe values from 12.5 to 2500 mg kg −1 in plant dry weight basis.

Characterization of Cider Apple Fruits According to Their Degree of Ripening. A Chemometric Approach

A chemometric study was carried out in order to typify cider apples according to their degree of ripening. Several chemical variables (sugars, organic acids, amino acids, polyphenols, and pectins) were analyzed using HPLC and FIA methods. Univariate data treatment was not sufficient to allow the apple varieties to be differentiated according to their stage of ripening. Two linear combinations of original variables, ascertained by principal component analysis (PCA), provided an adequate data structurization. To classify apples by their degree of ripening, a mathematical decision rule was established with a prediction capacity of 85% using a LDA method; the most relevant variables in the canonical function ascertained by LDA were sugars, pectins, malic acid, glycine, serine, valine, and glutamic acid. The use of the PLS-2 algorithm demonstrated the influence of the ripening process on the chemical composition of the fruits (R 2 : 91.7%) and furthermore allowed authors to differentiate apple varieties according to their degree of ripening.

A robust PLS procedure

AbstractA robust partial least squares (PLS) regression algorithm is developed. This is achieved by substitution of the univariate regression steps in the iterative PLS2 algorithm by a robust alternative. The angle between loading vectors from both perturbed and unperturbed solutions is used as a measure of robustness. By means of a perturbation study on a structure‐activity data set, it is demonstrated that the stability of the robust method is superior to standard PLS.