Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018
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paper_title: Modeling the characteristics and quantification of adulterants in gasoline using FTIR spectroscopy and chemometric calibrations

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AbstractThe criminal act of fuel (gasoline) adulteration still remains a global worry due to its environmental, health and economic effect. Current methods for the detection of fuel adulteration ha...
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paper_title: Analytical Methods for the Evaluation of Melamine Contamination

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Abstract There is an urgent need for the analysis of melamine in the global pharmaceutical supply chain to detect economically motivated adulteration or unintentional contamination using a simple, nondestructive analytical technique that confirms the extent of adulteration in a shorter time period. In this work, different analytical techniques (thermal analysis, X‐ray diffraction, Fourier transform infrared (FT‐IR), FT‐Raman, and near‐infrared (NIR) spectroscopy) were evaluated for their ability to detect a range of melamine levels in gelatin. While FT‐IR and FT‐Raman provided qualitative assessment of melamine contamination or adulteration, powder X‐ray diffraction and NIR were able to detect and quantify the presence of melamine at levels as low as 1.0% w/w . Multivariate analysis of the NIR data yielded the most accurate model when three principal components were used. Data were pretreated using standard normal variate transformation to remove multiplicative interferences of scatter and particle size. The model had a root‐mean‐square error of calibration of 2.4 ( R 2 = 0.99) and root‐mean square error of prediction of 2.5 ( R 2 = 0.96). The value of the paired t test for actual and predicted samples (1%–50% w/w ) was 0.448 ( p
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paper_title: Linear support vector regression and partial least squares chemometric models for determination of Hydrochlorothiazide and Benazepril hydrochloride in presence of related impurities: A comparative study

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Partial least squares regression (PLSR) and support vector regression (SVR) are two popular chemometric models that are being subjected to a comparative study in the presented work. The comparison shows their characteristics via applying them to analyze Hydrochlorothiazide (HCZ) and Benazepril hydrochloride (BZ) in presence of HCZ impurities; Chlorothiazide (CT) and Salamide (DSA) as a case study. The analysis results prove to be valid for analysis of the two active ingredients in raw materials and pharmaceutical dosage form through handling UV spectral data in range (220-350 nm). For proper analysis a 4 factor 4 level experimental design was established resulting in a training set consisting of 16 mixtures containing different ratios of interfering species. An independent test set consisting of 8 mixtures was used to validate the prediction ability of the suggested models. The results presented indicate the ability of mentioned multivariate calibration models to analyze HCZ and BZ in presence of HCZ impurities CT and DSA with high selectivity and accuracy of mean percentage recoveries of (101.01±0.80) and (100.01±0.87) for HCZ and BZ respectively using PLSR model and of (99.78±0.80) and (99.85±1.08) for HCZ and BZ respectively using SVR model. The analysis results of the dosage form were statistically compared to the reference HPLC method with no significant differences regarding accuracy and precision. SVR model gives more accurate results compared to PLSR model and show high generalization ability, however, PLSR still keeps the advantage of being fast to optimize and implement.
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paper_title: Integration of Commercial Screen-printed Electrodes into a Voltammetric Electronic Tongue for the Analysis of Aminothiols

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A voltammetric sensor array (or electronic tongue) is developed for the simultaneous quantification of cysteine, glutathione and homocysteine without need of previous separation. It is based on the integration of three commercial screen-printed electrodes (gold curated at high and low temperature and carbon modified with carbon nanotubes). Linear sweep voltammograms measured simultaneously by all three sensors are processed by Partial Least Squares (PLS) regression and different variables selection algorithms such as Genetic Algorithm and interval-Partial Least Squares. The method was applied to synthetic mixtures and successfully validated, with correlation coefficients of prediction (Rp2) of 0.9542, 0.9429 and 0.9589 for cysteine, glutathione, and homocysteine respectively.
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paper_title: Quantification of active ingredients in pharmaceutical suspensions by FT Raman spectroscopy

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Abstract A method for quantitative determination of nifuroxazide, acetaminophen, ibuprofen and naproxen in commercial oral suspensions containing active compounds in the 1.7–4.0% (w/w) range is described. Partial least squares regression and counter-propagation artificial neural network models were built and validated. The lowest relative standard error of prediction values was found for the strongest scattering nifuroxazide suspensions. Commercial suspensions were quantified with recovery in the 99.3–101.2% and the 98.2–100.2% range, applying partial least squares and artificial neural network modeling techniques, respectively.
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paper_title: Effective identification of clinical bacterial pathogens by fourier transform near-infrared spectroscopy

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The Escherichia coli (E. coli, ATCC 25922), Staphylococcus aureus (S. aureus, ATCC 29213), and Salmonella (SE, ATCC 14028) are three common bacterial pathogens of BSIs (Bloodstream infection). Accurately identifying these three bacterial pathogens will greatly help doctors to reduce the number of days to cure the patients. In this study, the identification models for bloodstream infection are studied. Firstly, Fourier transform near-infrared spectroscopy (FT-NIR) and multivariate calibration method are applied to detect and discriminate these bacterial suspension samples. Four preprocessing methods (multiplicative scatter correction (MSC), standard normal variate correction, Savitzky-Golay first-derivative, and Savitzky-Golay second-derivative are adopted to modify the raw spectral data. Then, three discriminant models are built to distinguish unknown bacterial samples, they are the model based on the principal component analysis and mahalanobis distance discriminant (PCA-MDD), the model based on the partial least squares-discriminant analysis (PLS-DA), and the back propagation neural network model. Finally, the effectiveness of the pre-processing methods and models are discussed and the comparison results are given. The results indicate that MSC is the most useful pre-processing method for the bloodstream infection spectra. By using MSC, the PLS-DA model and the BP model obtain higher accuracy than PCA-MDD. Moreover, although both the prediction accuracy of the PLS-DA model and the BP model are 100%, the difference between the predicted values and the real value are the minimal in the BP model. Thus BP model has better performance. Hence, FT-NIR spectroscopy combined with chemometrics techniques can be a useful, rapid, and nondestructive tool to discriminate clinical bacterial pathogens.
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paper_title: Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations

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An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.
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paper_title: A rapid quantification method for the screening indicator for β-thalassemia with near-infrared spectroscopy.

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Near-infrared (NIR) spectroscopy combined with chemometrics was applied to rapidly analyse haemoglobin A2 (HbA2) for β-thalassemia screening in human haemolysate samples. The relative content indicator HbA2 was indirectly quantified by simultaneous analysis of two absolute content indicators (Hb and Hb∙HbA2). According to the comprehensive prediction effect of the multiple partitioning of calibration and prediction sets, the parameters were optimized to achieve modelling stability, and the preferred models were validated using the samples not involved in modelling. Savitzky-Golay smoothing was firstly used for the spectral pretreatment. The absorbance optimization partial least squares (AO-PLS) was used to eliminate high-absorption wave-bands appropriately. The equidistant combination PLS (EC-PLS) was further used to optimize wavelength models. The selected optimal models were I=856nm, N=16, G=1 and F=6 for Hb and I=988nm, N=12, G=2 and F=5 for Hb∙HbA2. Through independent validation, the root-mean-square errors and correlation coefficients for prediction (RMSEP, RP) were 3.50gL-1 and 0.977 for Hb and 0.38gL-1 and 0.917 for Hb∙HbA2, respectively. The predicted values of relative percentage HbA2 were further calculated, and the calculated RMSEP and RP were 0.31% and 0.965, respectively. The sensitivity and specificity for β-thalassemia both reached 100%. Therefore, the prediction of HbA2 achieved high accuracy for distinguishing β-thalassemia. The local optimal models for single parameter and the optimal equivalent model sets were proposed, providing more models to match possible constraints in practical applications. The NIR analysis method for the screening indicator of β-thalassemia was successfully established. The proposed method was rapid, simple and promising for thalassemia screening in a large population.
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paper_title: A process analytical technology approach for the production of fixed-dose combination tablets of zidovudine and lamivudine using near infrared spectroscopy and chemical images ☆

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Abstract The production of solid drugs generally consists of several steps, which are monitored using quality control assays. In this work, rapid and non-destructive methods, which conform to the principles of the process analytical technology (PAT) framework, were developed for monitoring the production of fixed-dose combination tablets of zidovudine (AZT) and lamivudine (3TC). NIR spectroscopy was employed to determine the content of AZT and 3TC at three different steps of production: in the granulate material, in the core and in the manufactured tablet. NIR chemical images (NIR-CI) were used as a complementary study to evaluate the chemical distribution on the uncoated tablet surfaces, and its internal correlation structures. NIR methods for monitoring the moisture content in the granulate material and the hardness of the tablets before the coating step were also developed. A comparison between two multivariate calibration techniques: PLS and MLR was carried out. The models showed accurate results and prediction errors below those allowed by the regulatory agencies. Through the concentration maps associated with multivariate analysis, the internal chemical structure of API distribution and the excipient increased the understanding of the mixing process, allowing for further improvements.
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paper_title: High-throughput identification of monoclonal antibodies after compounding by UV spectroscopy coupled to chemometrics analysis

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Monoclonal antibodies (mAbs) compounded into the hospital pharmacy are widely used nowadays. Their fast identification after compounding and just before administration to the patient is of paramount importance for quality control at the hospital. This remains challenging due to the high similarity of the structure between mAbs. Analysis of the ultraviolet spectral data of four monoclonal antibodies (cetuximab, rituximab, bevacizumab, and trastuzumab) using unsupervised principal component analysis led us to focus exclusively on the second-derivative spectra. Partial least squares-discriminant analysis (PLS-DA) applied to these data allowed us to build models for predicting which monoclonal antibody was present in a given infusion bag. The calibration of the models was obtained from a k-fold validation. A prediction set from another batch was used to demonstrate the ability of the models to predict well. PLS-DA models performed on the spectra of the region of aromatic amino acid residues presented high ability to predict mAb identity. The region corresponding to the tyrosine residue reached the highest score of good classification with 89 %. To improve the score, standard normal variate (SNV) preprocessing was applied to the spectral data. The quality of the optimized PLS-DA models was enhanced and the region from the tyrosine/tryptophan residues allowed us excellent classification (100 %) of the four mAbs according to the matrix of confusion. The sensitivity and specificity performance parameters assessed this excellent classification. The usefulness of the combination of UV second-derivative spectroscopy to multivariate analysis with SNV preprocessing demonstrated the unambiguous identification of commercially available monoclonal antibodies. Graphical abstract PLS-DA models on the spectra of the region of aromatic amino acid residues allows mAb identification with high prediction.
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paper_title: Matrix Effects in Quantitative Assessment of Pharmaceutical Tablets Using Transmission Raman and Near-Infrared (NIR) Spectroscopy

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Raman spectroscopy can be an alternative to near-infrared spectroscopy (NIR) for nondestructive quantitative analysis of solid pharmaceutical formulations. Compared with NIR spectra, Raman spectra have much better selectivity, but subsampling was always an issue for quantitative assessment. Raman spectroscopy in transmission mode has reduced this issue, since a large volume of the sample is measured in transmission mode. The sample matrix, such as particle size of the drug substance in a tablet, may affect the Raman signal. In this work, matrix effects in transmission NIR and Raman spectroscopy were systematically investigated for a solid pharmaceutical formulation. Tablets were manufactured according to an experimental design, varying the factors particle size of the drug substance (DS), particle size of the filler, compression force, and content of drug substance. All factors were varied at two levels plus a center point, except the drug substance content, which was varied at five levels. Six tablets from each experimental point were measured with transmission NIR and Raman spectroscopy, and their concentration of DS was determined for a third of those tablets. Principal component analysis of NIR and Raman spectra showed that the drug substance content and particle size, the particle size of the filler, and the compression force affected both NIR and Raman spectra. For quantitative assessment, orthogonal partial least squares regression was applied. All factors varied in the experimental design influenced the prediction of the DS content to some extent, both for NIR and Raman spectroscopy, the particle size of the filler having the largest effect. When all matrix variations were included in the multivariate calibrations, however, good predictions of all types of tablets were obtained, both for NIR and Raman spectroscopy. The prediction error using transmission Raman spectroscopy was about 30% lower than that obtained with transmission NIR spectroscopy.
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paper_title: Fast Discrimination of Milk Contaminated with Salmonella sp. Via Near-Infrared Spectroscopy

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Conventional methods to detect Salmonella in milk generally take a long time and are significantly laborious besides requiring the use of different media and reagents. Thus, this research aimed to offer an alternative methodology based on near-infrared spectroscopy (NIRS) to discriminate the presence of Salmonella in milk. For this purpose, different milk samples, contaminated and free of Salmonella, were prepared to be measured by NIRS and evaluated by chemometric method of partial least squares with discriminant analysis. The model to discriminate skimmed milk samples used four latent variables presenting root mean square error of calibration (RMSEC) 0.1639 and prediction (RMSEP) of 0.0971, while the model to the whole milk discrimination six latent variables were needed, presenting RMSEC 0.1351 and RMSEP 0.0928. The validation parameters, sensitivity, and specificity shows that all samples were correctly classified. The results suggest that the methodology could be used to discriminate the presence/absence of Salmonella in milk samples using NIR as a screening method.
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paper_title: Optimization of NIR spectroscopy based PLSR models for critical properties of vegetable oils used in biodiesel production

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Abstract Near infrared spectroscopy associated with multivariate analysis has been applied to monitor vegetable oils quality. In this work, twelve physical and chemical parameters of vegetable oils, relevant for quality monitoring of biodiesel raw materials, are quantified using NIRS: iodine value, water content, acid number, phosphorous content, saponification, flash point and fatty acids (myristic acid, palmitic acid, stearic acid, oleic acid, linoleic acid and α-linolenic acid). Two different procedures to select the most adequate spectral range for each property were proposed and applied to optimize partial least squares regression (PLSR) models on NIRS data. Models were developed resourcing to cross-validation error ( RMSECV ) and tested on an independent prediction set ( RMSEP ). Variable Window Size Method (VWSM) is based on the sequential screening of all possible single contiguous spectral regions. The Multiple Window Combination Method (MWCM) consists of testing every combination of defined spectral regions. Both approaches produced approximately similar quality models for most modelled properties in terms of cross-validation indicators, despite MWCM in general yielded lower errors when applied to an unseen dataset. Optimal models produced RMSEP of 0.03 ppm, 0.51 g I 2 100 g −1 and 0.11% (w/w), respectively for water content, iodine value and α-linolenic acid. These last two parameters are especially relevant, given the need to comply with the EN 14214 and the first one because transesterification process yield. Application of VWSM and MCWM yielded superior models in terms of accuracy when compared with literature reported models for the same parameters, while increasing models interpretability.
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paper_title: Near-Infrared (NIR) Spectroscopy for Rapid Measurement of Antioxidant Properties and Discrimination of Sudanese Honeys from Different Botanical Origin

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The extraction methods used in wet chemical analysis of honey prior to antioxidant constituent analysis may decrease their activities due to decomposition. Therefore, near-infrared (NIR) spectroscopy technology has been used to measure these compounds. Besides, partial least squares regression (PLSR) was also applied to establish the calibration models with 105 samples and 45 unknown honeys to affirm the robustness of the established model. The outcomes of calibration models excellently affirmed the quantification of the concentrations of phenolics, flavonoids, carotenoids, ferric-reducing antioxidant power (FRAP), and 1,1-diphenyl-2-picrylhydrazy (DPPH) with high correlation coefficients (0.95–0.96), and acceptable standard error of cross validation (RMSECV) ranged from 0.92 to 13.60. The ratio performance deviation (RPD) for phenolics, flavonoids, carotenoids, FRAP, and DPPH ranged from 3.45 to 3.89, hence indicating that the NIR equations established were appropriate for unknown samples. Moreover, two discrimination modeling techniques including least squares support vector machine (LS-SVM) and linear discriminant analysis (LDA) were applied for discrimination of honey varieties. Cross validation was applied to optimize the performance of the models. The results revealed that LDA model was superior to LS-SVM model with discrimination rate of 100 % in both the training and prediction sets, whereas LS-SVM model had 99.05 and 93.33 % for the training and prediction sets, respectively. In general, the findings indicated that NIR coupled with chemometrics and linear/non-linear classification algorithms could be applied successfully to determine the antioxidant properties and discriminate honeys of different floral sources.
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paper_title: Near and mid infrared spectroscopy and multivariate data analysis in studies of oxidation of edible oils

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Abstract Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60 °C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR – based on NIR ( R 2 = 0.890), MIR ( R 2 = 0.707) and combined NIR and MIR ( R 2 = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR ( R 2 = 0.701, RPD = 1.7), NIR ( R 2 = 0.970, RPD = 5.3), and combined NIR and MIR data ( R 2 = 0.954, RPD = 3.1).
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paper_title: A simple approach for rapid detection and quantification of adulterants in stingless bees (Heterotrigona itama) honey.

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In this study, we propose an easy approach by combining the Fourier transform infrared and attenuated total reflectance (FTIR-ATR) spectroscopy together with chemometrics analysis for rapid detection and accurate quantification of five adulterants such as fructose, glucose, sucrose, corn syrup and cane sugar in stingless bees (Heterotrigona itama) honey harvested in Malaysia. Adulterants were classified using principal component analysis and soft independent modeling class analogy, where the first derivative of the spectra in the wavenumber range of 1180-750cm-1 was utilized. The protocol could satisfactorily discriminate the stingless bees honey samples that were adulterated with the concentrations of corn syrup above 8% (w/w) and cane sugar over 2% (w/w). Feasibility of integrating FTIR-ATR with chemometrics for precise quantification of the five adulterants was affirmed using partial least square regression (PLSR) analysis. The study found that optimal PLSR analysis achieved standard error of calibrations and standard error of predictions within an acceptable range of 0.686-1.087% and 0.581-1.489%, respectively, indicating good predictive capability. Hence, the method developed here for detecting and quantifying adulteration in H. itama honey samples is accurate and rapid, requiring only 7-8min to complete as compared to 3h for the standard method, AOAC method 998.12.
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paper_title: Quantitative and qualitative analysis of polycyclic aromatic hydrocarbons in urine samples using a non-separative method based on mass spectrometry.

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Abstract In this work, a method for the quantitative and qualitative analysis of 11 polycyclic aromatic hydrocarbons (PAHs) in urine samples is reported. The method is based on the coupling of a programmed temperature vaporizer (PTV) with a quadrupole mass spectrometer (qMS), via a deactivated fused silica tubing. Before the PTV-qMS analysis, the samples were subjected to a liquid-liquid extraction (LLE). The method was rapid since no chromatographic separation was performed. The samples were introduced directly into the PTV, and the analytes were trapped in the Tenax-TA® packed liner while the solvent was purged. After that, all the compounds reached the mass spectrometer, obtaining the fingerprint of the analysed samples. Urine samples free of PAHs and the same samples spiked with the compounds were analysed. The resulting profile signals were used to quantify the analytes using multivariate calibration, and to classify the samples according to the presence or absence of the PAHs. In the latter task, non-supervised and supervised pattern recognition techniques were employed. The calibration models worked satisfactorily and errors lower or equal to 15% were obtained, in most cases, when an external validation set was analysed. Regarding the classification of the samples, most of the supervised pattern recognition techniques provided excellent results (100% success), where all of the samples were classified correctly.
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paper_title: Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds

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Abstract A near-infrared (NIR) hyperspectral imaging (HSI) system was used to predict viability and vigor (in term of germination periods) in muskmelon seeds. Hyperspectral images of muskmelon seeds were acquired using a NIR push-broom HSI system covering the spectral range of 948–2494 nm. After NIR spectra collection, all seeds underwent a germination test to confirm their viability and vigor. The spectra from seeds with 3 and 5 germination days and nongerminated seeds were further used for development of a classification model of partial least-squares discriminant analysis (PLSDA). Most effective wavelengths were selected using three model-based variable selection methods, i.e., variable important in projection (VIP), selectivity ratio (SR), and significance multivariate correlation (sMC), which selected 23, 18, and 19 optimal variables, respectively, from full set of 208 variables. The selected variables from different waveband selection methods were found genuine and significant for interpreting the prediction results of seed viability and vigor. Subsequently, the PLS-DA model was constructed using individual VIP-, SR-, or sMC-selected variables. The results demonstrated that the PLSDA model developed with the selected optimal variables from the different methods provided comparable results for the calibration set; however, the PLSDA-SR method afforded the highest classification accuracy (94.6%) for a validation set used to determine the viability and vigor of muskmelon seeds. The wavelengths selected by the different methods represents chemical components of the seed and the attribute of germination ability was chosen most often.
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paper_title: Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes.

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Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%).
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paper_title: Measurement of the concentration of nutrients in grapevine petioles by attenuated total reflectance Fourier transform infrared spectroscopy and chemometrics

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Background and Aims ::: ::: We determined the effectiveness of attenuated total reflectance Fourier transform infrared spectroscopy as a rapid and low-cost method for measuring the concentration of nutrients in grapevine petioles. ::: ::: ::: ::: Methods and Results ::: ::: Attenuated total reflectance Fourier transform infrared spectra were recorded for 392 dried and ground petiole samples collected from Chardonnay, Semillon and Shiraz vineyards in the Riverina between 2003 and 2010. Predictive models of nutrient concentration obtained with standard reference methods were developed using partial least squares regression. Good predictive models were produced for all macronutrients, with R2 values for nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and sulfur (S) of 0.945, 0.915, 0.951, 0.961, 0.940 and 0.849, respectively. For sodium (Na) and the micronutrients iron (Fe), manganese (Mn), boron (B), copper (Cu) and zinc (Zn), R2 values were 0.773, 0.750, 0.743, 0.630, 0.612 and 0.835, respectively. When ranked according to the residual predictive deviation, expressed as the ratio of the standard error of performance to the calibration set standard deviation, values of 5.4, 3.8, 3.1, 3.8 and 3.5 were obtained for N, P, K, Mg and Ca, respectively; intermediate values of 2.4, 2.0 and 1.8 were obtained for S, Zn and Na, while Fe, Mn, B, Cu had a value of 1.3 or below. ::: ::: ::: ::: Conclusion ::: ::: All macronutrients in grapevine petiole tissue can be determined by attenuated total reflectance Fourier transform infrared spectroscopy with sufficient accuracy to assess grapevine nutritional status against standard interpretive ranges. Certain micronutrients can also be determined, although the predictive models for these elements were not as robust as models for the macronutrients. ::: ::: ::: ::: Significance of Study ::: ::: The study presents a rapid, low-cost and easy-to-implement technique suited to the widespread adoption and routine analysis of plant tissue for the management of grapevine nutrition in the Australian winegrape vineyards.
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paper_title: Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations.

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To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower±longan±buckwheat±rape) model were achieved as follow: RMSEP=0.0235 and R=0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.
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paper_title: Application of Fourier Transform Infrared (FT-IR) Spectroscopy Combined with Chemometrics for Analysis of Rapeseed Oil Adulterated with Refining and Purificating Waste Cooking Oil

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Currently, the detection of rapeseed oil (RSO) adulterated with refining and purificating waste cooking oil (WCO) is an attractive work for researchers in accordance with food safety all over the world particularly in China. There is a rapid increasing requirement for more accurate and sensitive detection method to cope the more subtle adulterations in edible oils. For this reason, Fourier transform infrared (FT-IR) spectroscopy in the combination with chemometrics was developed for detection and quantification of WCO as adulterants in RSO. The quantification of WCO was carried out by using multivariate calibrations of partial least squares (PLS) and principle component regression (PCR). PLS with first derivative FT-IR spectra was more suitable compared with PCR for quantification purposes with coefficient of determination (R2) higher than 0.998 and root mean square error of calibration (RMSEC) of 0.494 % (v/v). The PLS model was further used to predict the levels of WCO in independent samples for validation/prediction purpose. The proposed methodology is a useful tool to authenticate RSO adulterated with used WCO.
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paper_title: A simplified FTIR chemometric method for simultaneous determination of four oxidation parameters of frying canola oil.

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Transmission Fourier transform infrared (FTIR) spectroscopic method using 100 μm KCl cell was applied for the determination of total polar compounds (TPC), carbonyl value (CV), conjugated diene (CD) and conjugated triene (CT) in canola oil (CLO) during potato chips frying at 180 °C. The calibration models were developed for TPC, CV, CD and CT using partial least square (PLS) chemometric technique. Excellent regression coefficients (R(2)) and root mean square error of prediction values for TPC, CV, CD and CT were found to be 0.999, 0.992, 0.998 and 0.999 and 0.809, 0.690, 1.26 and 0.735, respectively. The developed calibration models were applied on samples of canola oil drawn during potato chips frying process. A linear relationship was obtained between CD and TPC with a good correlation of coefficient (R(2)=0.9816). Results of the study clearly indicated that transmission FTIR-PLS method could be used for quick and precise evaluation of oxidative changes during the frying process without using any organic solvent.
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paper_title: Determination of Total Sugar Content in Soy-Based Drinks Using Infrared Spectroscopy and Chemometrics

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Partial least square (PLS) regression models were developed and compared in order to determine the total sugar content in soy-based drinks using an infrared spectroscopy technique known as attenuated total reflectance Fourier transform infrared (ATR-FTIR). On a spectrophotometer set for analyzing on the middle infrared region, spectral band of 1900 to 900 cm−1, commercial samples of soy beverage were analyzed, as well as samples with crescent water additions of 5, 10, and 20% v/v. Reference data for total sugars were obtained using the Lane-Eynon method. To construct regression models, algorithms of interval partial least square (iPLS) and synergy of interval partial least square (siPLS) were applied using iToolbox package on Matlab 8.1 environment. Kennard-Stone algorithm was used to the selection of calibration and prediction sets. Two models have been the best obtained: the first was an iPLS with seven latent variables, which selected the spectral band of 1399–900 cm−1 and presented root mean square error of cross-validation (RMSECV) = 0.1678% (w/w). The second best model was siPLS with six latent variables, which selected spectral bands of 1025–1150 and 1151–1476 cm−1 and presented RMSECV = 0.1963% (w/w). The proposed method presents advantages such as a small-required amount of sample for spectrum achievement, no sample destruction, and a high analytical frequency.
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paper_title: Determination of Saturates, Aromatics, and Polars in Crude Oil by 13C NMR and Support Vector Regression with Variable Selection by Genetic Algorithm

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The contents of saturates, aromatics, and polars in crude oil were determined using carbon-13 nuclear magnetic resonance spectroscopy (13C NMR) associated with support vector regression (SVR) and a genetic algorithm (GA) for the simultaneous selection of spectral variables and SVR model parameters. The developed models presented prediction sample errors of 4.4% (w/w) for saturates, 4.3% (w/w) for aromatics (w/w), and 3.7% (w/w) for polars. These results are acceptable for the petroleum industry, considering that the error obtained by the standard methodology is 5% (w/w), which is the maximum value of variation allowed in SARA analysis. The proposed methodology made these determinations using small amounts of samples (approximately 2 mL) in a relatively short time (approximately 2 h).
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paper_title: Modeling the characteristics and quantification of adulterants in gasoline using FTIR spectroscopy and chemometric calibrations

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AbstractThe criminal act of fuel (gasoline) adulteration still remains a global worry due to its environmental, health and economic effect. Current methods for the detection of fuel adulteration ha...
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paper_title: Detection and estimation of Super premium 95 gasoline adulteration with Premium 91 gasoline using new NIR spectroscopy combined with multivariate methods

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Abstract Super premium 95 octane gasoline is a special blend of petrol with a higher octane rating that can produce higher engine power, as well as knock-free performance for cars with a high-octane requirement. Super premium grade gasoline 95 is often adulterated with cheaper Premium grade 91 that lowers the octane number of the Super premium gasoline. In the present study a new Near Infrared (NIR) spectroscopy combined with multivariate analysis was developed to detect as well as to quantify the level of Premium 91 gasoline adulteration in Super premium 95 octane gasolines. In this study standard samples of Premium 91 and Super premium 95 octane gasoline were collected from Oman Oil Refineries and Petroleum Industries Company SAOC (ORPIC) and were investigated. Super premium 95 samples were then adulterated with eighteen different percentage levels: 0%, 1%, 3%, 5%, 7%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, and 75% of Premium 91 gasoline. All samples were measured using NIR spectroscopy in absorption mode in the wavelength range from 700 to 2500 nm. The multivariate methods like PCA, PLSDA and PLS regression were applied for statistical analysis of the obtained NIR spectral data. Partial least-squares discriminant analysis (PLSDA) was used to check the discrimination between the pure and adulterated gasoline samples. For PLSDA model the R-square value obtained was 0.99 with 0.012 RMSE. Furthermore, PLS regression model was also built to quantify the levels of Premium 91 adulterant in Super Premium 95 gasoline samples. The PLS regression model was obtained with the R-square 0.99 and with 1.33 RMSECV value having good prediction with RMSEP value 1.35 and correlation of 0.99. This newly developed method is having lower limit of detection less than 1.5% level for Premium 91 adulteration. It was desirable to have simple, rapid and sensitive methods to detect the presence of one petroleum product in another.
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paper_title: High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.

paper_content:

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.
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paper_title: Characterization of Gasoline by Raman Spectroscopy with Chemometric Analysis

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ABSTRACTA rapid Raman spectroscopy protocol is reported to classify gasoline according to its distributor and to identify and quantify common adulterants. Gasoline from three distributors was collected from 19 stations in Sao Paulo, Brazil. Principal component analysis (PCA) showed specific clusters for each distributor, and partial least squares discriminant analysis (PLS-DA) correctly identified the origin of the samples. To evaluate the technique for the identification and quantification of the adulterants, authentic samples from each distributor were fortified at levels from 2.5 up to 25.0% (v/v) using ethanol, methanol, toluene, and turpentine to obtain 120 altered samples. PCA showed clear separation among the samples with the adulterants and PLS-DA precisely identified the adulterants (478 in 480 predictions by cross-validation), irrespective of the distributor and the concentration. One classification model was used to characterize all distributors. To quantify the adulterants, 36 multivariate cal...
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paper_title: Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study

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Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort has been dedicated to combating this cancer, in particular by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM, n = 42) and squamous intraepithelial lesion (SIL, n = 34). The crude lipid extract was directly analyzed with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM), linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA results, achieving sensitivity and specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions.
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paper_title: Sweet and nonsweet taste discrimination of nectarines using visible and near-infrared spectroscopy

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Abstract The feasibility of using visible and near-infrared spectroscopy technology combined with multivariate analysis to discriminate cv. ‘Big Top’ and cv. ‘Diamond Ray’ nectarines has been studied. These varieties are very difficult to differentiate visually on the production line but show important differences in taste that affects the acceptance by final consumers. The relationship between the diffuse reflectance spectra and the two nectarine varieties was established. Five hundred nectarine samples (250 of each variety) were used for the study. Tests were performed by using a spectrometer capable of measuring in two different spectral ranges (600–1100 nm and 900–1700 nm). These spectral ranges were used to develop two accurate classification models based on linear discriminate analysis (LDA) and partial least squares discriminate analysis (PLS-DA). Later, selection techniques were applied to select the most effective wavelengths. The results showed that the PLS-DA model achieved better accuracy and less latent variables than LDA model, and specifically, good results with 100% classification accuracy were obtained using only the 600–1100 nm spectral range for the two models and eight selected wavelengths. These results places visible and near-infrared spectrocopy as an accurate classification tool for nectarine varieties with a very similar appearance but different tastes that could be potentially used in an automated inspection system.
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paper_title: Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B 1 contaminated maize kernels

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Abstract A short-wave infrared (SWIR) hyperspectral imaging system (1000–2500 nm) combined with chemometric data analysis was used to detect aflatoxin B 1 (AFB 1 ) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB 1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB 1 contamination. PCA results revealed partial separation of control kernels from AFB 1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB 1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB 1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB 1 . The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB 1 contamination.
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paper_title: Quantitative and qualitative analysis of polycyclic aromatic hydrocarbons in urine samples using a non-separative method based on mass spectrometry.

paper_content:

Abstract In this work, a method for the quantitative and qualitative analysis of 11 polycyclic aromatic hydrocarbons (PAHs) in urine samples is reported. The method is based on the coupling of a programmed temperature vaporizer (PTV) with a quadrupole mass spectrometer (qMS), via a deactivated fused silica tubing. Before the PTV-qMS analysis, the samples were subjected to a liquid-liquid extraction (LLE). The method was rapid since no chromatographic separation was performed. The samples were introduced directly into the PTV, and the analytes were trapped in the Tenax-TA® packed liner while the solvent was purged. After that, all the compounds reached the mass spectrometer, obtaining the fingerprint of the analysed samples. Urine samples free of PAHs and the same samples spiked with the compounds were analysed. The resulting profile signals were used to quantify the analytes using multivariate calibration, and to classify the samples according to the presence or absence of the PAHs. In the latter task, non-supervised and supervised pattern recognition techniques were employed. The calibration models worked satisfactorily and errors lower or equal to 15% were obtained, in most cases, when an external validation set was analysed. Regarding the classification of the samples, most of the supervised pattern recognition techniques provided excellent results (100% success), where all of the samples were classified correctly.
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paper_title: Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis

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Abstract Near-infrared (NIR) hyperspectral imaging was explored as a rapid and non-destructive method of investigating seed quality parameters such as seed viability and variation in tomato seed lots. The seed lots differed with year of production and variety. Four tomato varieties: Cal J, Monprecus, NCL and Chiuri from 2013, 2014 and 2015 were used in the study. The extracted NIR hyperspectral data from 975 to 2500 nm were analysed by principal component analysis (PCA) and partial least squares- discriminant analysis (PLS-DA). No distinct patterns of separation between viable and non-viable tomato seeds were revealed by the PCA. Our findings showed a pattern of separation in the tomato seed lots due to production years and varieties. The PLS-DA showed the ability to predict with ∼100 percent accuracy for varietal class membership when only the seeds of a single harvest year were included in the model. The accuracy from PLS-DA on pooled samples (all seeds from all varieties) predicted varietal class membership in the range from 34 to 88 percent. High variation in the seed lots could have caused high variation in the predicted varietal class membership. The NIR regions with chemical information from C H, N H and O H had influence on the PCA and PLS-DA models. The study presents the prospects of using NIR hyperspectral imaging in varietal identification studies of tomato seeds though we recommend a thorough validation of models.
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paper_title: Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis.

paper_content:

Male factor infertility is involved in almost half of all infertile couples. Lack of the ejaculated sperm owing to testicular malfunction has been reported in 6-10% of infertile men, a condition named nonobstructive azoospermia (NOA). In this study, we investigated untargeted metabolomic profiling of the seminal plasma in NOA men using gas chromatography-mass spectrometry and advance chemometrics. In this regard, the seminal plasma fluids of 11 NOA men with TESE-negative, nine NOA men with TESE-positive and 10 fertile healthy men (as a control group) were collected. Quadratic discriminate analysis (QDA) technique was implemented on total ion chromatograms (TICs) for identification of discriminatory retention times. We developed multivariate classification models using the QDA technique. Our results revealed that the developed QDA models could predict the classes of samples using their TIC data. The receiver operating characteristic curves for these models were >0.88. After recognition of discriminatory retention time's asymmetric penalized least square, evolving factor analysis, correlation optimized warping and alternating least squares strategies were applied for preprocessing and deconvolution of the overlapped chromatographic peaks. We could identify 36 discriminatory metabolites. These metabolites may be considered discriminatory biomarkers for different groups in NOA.
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paper_title: Authentication and inference of seal stamps on Chinese traditional painting by using multivariate classification and near-infrared spectroscopy

paper_content:

Abstract Chinese traditional paintings occupy an important position in Chinese cultural heritage and it is very important for archeologist and artist to identify their authenticity, which is difficult to be realized. Near-infrared spectroscopy (NIRS) coupled with multivariate models was used for authenticating stamps of 12 seals on a Chinese traditional painting in this work. The robustness of linear and nonlinear multivariate models, i.e. partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM), were evaluated by adding 5 different levels of noise (from 1% to 5%) into 3 original NIR spectra of each the stamps. These spectral data with noise added were fused together with original spectra to establish identification models and then to evaluate the abilities of the two models to tolerate noise disturbance. Accuracies of 92.6% and 100% were yielded by linear PLS-DA and nonlinear SVM methods respectively. The results demonstrate the feasibility of multivariate approaches in authenticating stamps of seals on the Chinese traditional painting. It is also important and necessary to infer the approximate eras of seal stamps on Chinese traditional painting in archeological study. By comparing the Mahalanobis distances between the 12 stamps on the painting, hierarchical cluster analysis (HCA) was adopted to assist the inference of eras for those unknown seal stamps on the Chinese traditional painting. This work demonstrates that NIR spectroscopy combined with multivariate models can be utilized as a non-destructive approach for authentication of stamps on Chinese traditional painting. HCA can also provide useful information to speculate the time period of the stamps of unknown seals on the Chinese traditional painting.
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paper_title: Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy

paper_content:

Abstract The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed.
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paper_title: Classification of gasoline as with or without dispersant and detergent additives using infrared spectroscopy and multivariate classification

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Abstract Gasoline may contain additives which can minimize the amount of pollutants emitted to the atmosphere. Detergents and dispersants added to gasoline can reduce gas emissions towards atmosphere and the formation of deposits in engines. The Brazilian Agency of Petroleum, Natural Gas and Biofuel (ANP) has established that Brazilian commercial gasoline must contain detergent and dispersant additives, thus requiring the development of methods for their identification in commercial gasoline. This work proposes a methodology which uses infrared spectra in the medium and near region (MIR and NIR) of the residue of distillation for classification of gasoline samples into two groups: with or without detergent/dispersant additives. The performances of three types of classification methods were compared: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and Support Vector Machines (SVM). Different algorithms for selection of spectral variables for LDA were evaluated: stepwise (SW), genetic algorithm (GA) and successive projections algorithm (SPA). The best results were obtained using LDA/GA or SPA/LDA for MIR region.
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paper_title: Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system

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The fermentation process plays an important role in black tea's quality control and determination. This article presented a portable artificial olfaction system for real-time monitoring of black tea. Herein, a colorimetric sensor array, which was fabricated by printing 12 chemical dyes, including 9 porphyrins/metalloporphyrins and 3 pH indicators on a C2 reverse silica-get flat plate, was utilized to develop the artificial olfaction system for data acquisition. With specific colorific fingerprint of volatile compounds from black tea, fermentation time can be achieved with efficient chemometrics. Additionally, a novel classification algorithm, which combined K-Nearest Neighbor and adaptive boosting, namely KNN-AdaBoost, was proposed with a comparison of two classical classification algorithms (e.g., KNN and BP-ANN). Results demonstrated that the optimum KNN-AdaBoost model was superior to others with discrimination rate of 100% in both the training and prediction set. The overall results sufficiently reveal that the colorimetric sensor has promising applications in real-time fermentation monitoring. ::: ::: Practical applications ::: The fermentation process plays an important role in black tea's quality control and determination. However, traditional analytical chemical methods (i.e., mass spectrometry, liquid chromatography gas, and gas chromatography-mass spectrometry) can monitor the fermentation process and have some drawbacks, such as complicated operation, high costs of implementation, and lengthy analysis time. This research notably proposed a portable artificial olfaction system for real-time monitoring black tea; and examined the feasibility, accuracy and effectiveness of this sensor. The overall results obtained from this study sufficiently reveals that the colorimetric sensor has promising applications in real-time fermentation monitoring.
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paper_title: How do artificial neural networks (ANNs) compare to partial least squares (PLS) for spectral interference correction in optical emission spectrometry?

paper_content:

Spectral interference from overlaps of spectral lines is a well-documented problem optical emission spectrometry. Spectral interference is encountered even when spectrometers with medium to high resolution are used (e.g., with a focal length of 0.75 m 1 m). The adverse effects of spectral interference are more pronounced when portable spectrometers with low resolution are used (e.g., with focal lengths of about 12.5 cm). Portable spectrometers are suited for “taking part of the lab to the sample” types of applications. We used Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) to address spectral interference correction. And our efforts using these methods are described.
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paper_title: A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

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Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.
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paper_title: Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms.

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BACKGROUND ::: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. ::: ::: ::: RESULTS ::: The ANN method was able to create more accurate models (R2 = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type. ::: ::: ::: CONCLUSION ::: The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry.
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paper_title: Classification of Orange Growing Locations Based on the Near-infrared Spectroscopy Using Data Mining

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AbstractThe classification of growing locations is very important for quality control in the orange industries, which is also challenging work, because of its complex chemical composition and varies of taste and sizes. The traditional ways to classify them by human’s sense are time consuming and at high cost. In this paper, a new general classification framework based on the Near-Infrared Reflection (NIR) spectroscopy using data mining technology was proposed. First, the raw NIR spectra data were reduced by the principal components analysis (PCA), and then an attribution selection method was applied to find the best feature subset. An evolution process was also introduced to test the performance of five classifiers (Decision Tree, KNN, Naive Bayesian, SVM and ANN) used in this paper. The proposed classification framework was verified on three NIR spectra datasets, which were collected from the different part of oranges (including two parts of fruit surface and juice) from 15 different places in china. The...
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paper_title: Verification of the performance of Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction in optical emission spectrometry

paper_content:

Interference from overlaps of spectral lines is a key concern in optical emission spectrometry even when a spectrometer ::: with relatively high resolution and a long focal length (e.g., 1 m) is used. The problem becomes more complex when a ::: portable spectrometer (e.g., with a focal length of 12.5 cm) with low resolution is used. Such a spectrometer is better ::: suited for “taking part of the lab to the sample” types of applications. We used Artificial Neural Networks (ANNs) and ::: Partial Least Squares (PLS) to address spectral interference correction and our efforts are described here in some detail.
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paper_title: Spectral quantitative analysis of complex samples based on the extreme learning machine

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Multivariate calibrations, including linear and non-linear methods, have been widely used in the spectral quantitative analysis of complex samples. Despite their efficiency and few parameters involved, linear methods are inferior for nonlinear problems. Non-linear methods also have disadvantages such as the requirement of many parameters, time-consuming and easily relapses into local optima though the outstanding performance in nonlinearity. Thus, taking the advantages of both linear and non-linear methods, a novel algorithm called the extreme learning machine (ELM) is introduced. The efficiency and stability of this method are investigated first. Then, the optimal activation function and number of hidden layer nodes are determined by a newly defined parameter, which takes into account both the predictive accuracy and stability of the model. The predictive performance of ELM is compared with principal component regression (PCR), partial least squares (PLS), support vector regression (SVR) and back propagation artificial neural network (BP-ANN) by three near-infrared (NIR) spectral datasets of diesel fuel, a ternary mixture and blood. Results show that the efficiency of ELM is mainly affected by the number of nodes for a certain dataset. Despite some instability, ELM becomes stable close to the optimal parameters. Moreover, ELM has a better or comparable performance compared with its competitors in the spectral quantitative analysis of complex samples.
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paper_title: Rapid monitoring of beer-quality attributes based on UV-Vis spectral data

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ABSTRACT This work aimed to determinate eight beer properties using UV-Vis spectra in combination with principal component regression (PCR) or artificial neural network (ANN) models. A statistical experimental design was performed to generate the calibration data. First, principal component analysis (PCA) was applied to the original spectral data, and the scores in significant PCs were utilized to calibrate both models. PCR showed poor correlation for beer parameters (R2 < 0.61). The ANNs showed satisfactory correlations (R2 = 0.74–0.92) and low relative error considering a variable range (< 9%) for most of the beer-quality attributes, but vicinal diketones (R2 = 0.56, = 16.69%). Once implemented, this method would be fast and low cost.
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paper_title: Quantitative analysis of steel samples using laser-induced breakdown spectroscopy with an artificial neural network incorporating a genetic algorithm.

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In this work, a genetic algorithm (GA) was employed to select the intensity ratios of the spectral lines belonging to the target and domain matrix elements, then these selected line-intensity ratios were taken as inputs to construct an analysis model based on an artificial neural network (ANN) to analyze the elements copper (Cu) and vanadium (V) in steel samples. The results revealed that the root mean square errors of prediction (RMSEPs) for the elements Cu and V can reach 0.0040 wt. % and 0.0039 wt. %, respectively. Compared to 0.0190 wt. % and 0.0201 wt. % of the conventional internal calibration approach, the reduction rates of the RMSEP values reached 78.9% and 80.6%, respectively. These results indicate that the GA combining ANN can excellently execute the quantitative analysis in laser-induced breakdown spectroscopy for steel samples and further improve analytical accuracy.
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paper_title: A kinetic spectrofluorometric method, aided by chemometrics, for the analysis of sibutramine, indapamide and hydrochlorothiazide compounds found in weight-reducing tonic samples

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An uncomplicated and sensitive kinetic spectrofluorometric method was developed for the simultaneous analysis of sibutramine, indapamide, and hydrochlorothiazide commonly found in weight-reducing health foods. The mentioned compounds were oxidized by cerium(IV) ammonium sulfate dissolved in an acidic solution. The kinetic-fluorescence spectrum of the produced cerium(III) was recorded between 0 and 600 s reaction time at the excitation wavelength of 250 nm. It was found that the fluorescence intensity was proportional to the concentration of the compounds, and their linear ranges were 0.005–0.12, 0.03–1.44 and 0.01–0.20 μg mL−1; the corresponding detection limits were 1.6, 8.3 and 6.0 ng mL−1 for sibutramine, indapamide and hydrochlorothiazide, respectively. The measured three- and two-way data of the mixtures were processed by chemometrics (included methods: parallel factor analysis (PARAFAC), partial least squares (PLS), principal component regression (PCR), and radial basis function-artificial neural network (RBF-ANN)). The proposed method was applied for the simultaneous determination of the analytes in weight-reducing food samples, and the results were comparable with those from the reference HPLC method.
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paper_title: Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics.

paper_content:

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.
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paper_title: Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

paper_content:

ABSTRACTChinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness...
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paper_title: From chemical markers to quality markers: an integrated approach of UPLC/Q-TOF, NIRS, and chemometrics for the quality assessment of honeysuckle buds

paper_content:

Because of the poor discovery rate of relatively effective components and elusive component-effect correlation, formulating a quality control system for Chinese herbal medicines (CHMs) has been a great challenge for quality management. In this paper, the concept of the quality marker (Q-marker) was used, and a set of integrated strategies to improve the chemical markers of the Q-markers was introduced. Two often confused CHMs, Lonicera japonica flos (LJF) and Lonicera flos (LF), which are of the same genus but different species, are illustrated to quickly evaluate their potency. Ultra-performance liquid chromatography-quadrupole/time-of-flight (UPLC/Q-TOF) with partial least squares-discriminant analysis (PLS-DA) was used to screen the chemical markers for their herbal origin identification; then, a bioactive-guided evaluation method was performed to detect the Q-markers. As a result, four NF-κB inhibitors were proposed to be representative Q-markers for the anti-inflammatories: 3-O-caffeoylquinic acid (CA), 3,5-O-dicaffeoylquinic acid (3,5-diCQA), `iamarin, and vogeloside. After the chemometrics study, near-infrared spectroscopy (NIRS) based on the distinctive wavenumber points from the Q-markers was developed for its distinction and determination capabilities by optimum siPLS-CARS analysis (OPSC). Then, the back propagating-artificial neutral network (BP-ANN) algorithm was used to clarify the non-linear relationship between the Q-markers and their integral anti-inflammation effect. Finally, convenient and reliable fast quantitative analysis and holistic bioactivity assessment patterns were established by NIRS for the quality management of honeysuckle buds. The integrated Q-marker screen and NIRS assessment strategy was suitable for a fast quality evaluation of herbal medicines and was applied to the quality control of botanical functional foods.
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paper_title: UV/Vis spectroscopy combined with chemometrics for monitoring solid‐state fermentation with Rhizopus microsporus var. oligosporus

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BACKGROUND: Difficulties in bioprocess monitoring are a drawback of solid‐state fermentation (SSF). Specifically, monitoring of enzyme activities in SSF is not an easy task. This work aimed to calibrate partial least squares (PLS) and artificial neural network (ANN) models for inferring protease and amylase activities, as well as protein concentration, from UV‐Vis spectra of aqueous extracts of samples removed during SSF using Rhizopus microsporus var. oligosporus. RESULTS: SSFs were performed using single agro‐industrial wastes (wheat bran, type II wheat flour, sugarcane bagasse and soybean meal) and ternary mixtures of them. Enzyme activities and protein concentrations in the aqueous extracts were quantified biochemically. The corresponding UV‐Vis spectra of diluted extracts were also collected. The prediction quality of the ANN was higher than that of the PLS model. The relative errors considering the range for amylolytic and proteolytic enzymes were 4% (3–442 U g⁻¹) and 6% (0–256 U g⁻¹), respectively, for the best ANN architectures (8 and 6 neurons in hidden layer, respectively). CONCLUSION: These results, in combination with correlation coefficients (R > 0.94), suggest that this approach is suitable for developing a chemosensor for monitoring SSFs, reducing the analytical work for quantification of enzyme activities. No satisfactory results were obtained for protein concentration. © 2017 Society of Chemical Industry
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paper_title: Experimental design in chemistry: A tutorial.

paper_content:

In this tutorial the main concepts and applications of experimental design in chemistry will be explained. Unfortunately, nowadays experimental design is not as known and applied as it should be, and many papers can be found in which the “optimization” of a procedure is performed one variable at a time. Goal of this paper is to show the real advantages in terms of reduced experimental effort and of increased quality of information that can be obtained if this approach is followed. To do that, three real examples will be shown. Rather than on the mathematical aspects, this paper will focus on the mental attitude required by experimental design. The readers being interested to deepen their knowledge of the mathematical and algorithmical part can find very good books and tutorials in the references [G.E.P. Box, W.G. Hunter, J.S. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, John Wiley & Sons, New York, 1978; R. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons, New York, 1978; R. Carlson, J.E. Carlson, Design and Optimization in Organic Synthesis: Second Revised and Enlarged Edition, in: Data Handling in Science and Technology, vol. 24, Elsevier, Amsterdam, 2005; J.A. Cornell, Experiments with Mixtures: Designs, Models and the Analysis of Mixture Data, in: Series in Probability and Statistics, John Wiley & Sons, New York, 1991; R.E. Bruns, I.S. Scarminio, B. de Barros Neto, Statistical Design—Chemometrics, in: Data Handling in Science and Technology, vol. 25, Elsevier, Amsterdam, 2006; D.C. Montgomery, Design and Analysis of Experiments, 7th edition, John Wiley & Sons, Inc., 2009; T. Lundstedt, E. Seifert, L. Abramo, B. Thelin, A. Nystrom, J. Pettersen, R. Bergman, Chemolab 42 (1998) 3; Y. Vander Heyden, LC–GC Europe 19 (9) (2006) 469].
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paper_title: Doehlert matrix: a chemometric tool for analytical chemistry-review.

paper_content:

A review of the use of the Doehlert matrix as a chemometric tool for the optimization of methods in analytical chemistry and other sciences is presented. The theoretical principles of Doehlert designs are described, including the coded values for the use of this matrix involving two, three, four and five variables. The advantages of this matrix in comparison with other response surface designs, such as central composite and Box-Behnken, designs are discussed. Finally, 57 references concerning the application of Doehlert matrices in the optimization of procedures involving spectroanalytical, electroanalytical and chromatographic techniques are considered.
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Title: Chemometrics in Analytical Chemistry – An Overview of Applications from 2014 to 2018
Section 1: Introduction
Description 1: This section provides an introductory overview of the technological progress in chemometrics and its applications in analytical chemistry from 2014 to 2018.

Section 2: Medical and Pharmaceutical Applications
Description 2: This section explores the use of chemometrics in medical and pharmaceutical applications, including studies on indole alkaloids, monoclonal antibodies, and pharmaceutical quality control.

Section 3: Food Analysis
Description 3: This section reviews the application of chemometric techniques in food analysis, particularly in honey, oil, milk, and other food products.

Section 4: Fuel
Description 4: This section discusses the utilization of chemometrics in fuel analysis, focusing on techniques such as spectroscopy and multivariate models to determine the properties and quality of fuels.

Section 5: Biological Samples
Description 5: This section covers the application of chemometrics in the analysis of biological samples, including lipidomics, metabolomics, and the identification and quantification of various compounds.

Section 6: Forensic Purposes
Description 6: This section details the use of chemometrics in forensic science, including the authentication and classification of various forensic samples.

Section 7: Artificial Intelligence Methods for Multiple Questions
Description 7: This section highlights the implementation of artificial intelligence methods in chemometrics, such as ANN, SVM, and other advanced algorithms to enhance analytical processes.

Section 8: Design of Experiments (DoE) and Response Surface Methodology (RSM)
Description 8: This section describes the application of DoE and RSM in optimizing analytical methods, focusing on improving analytical signal, signal-to-noise ratio, and other parameters.

Section 9: Conclusions
Description 9: This section summarizes the findings of the review, highlighting the prominence of chemometrics in various fields and its potential for future applications in analytical chemistry.