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Three structurally similar catecholamines, adrenaline (AA), dopamine (DA) and noradrenaline (NA), which are neurotransmitters well known for their treatment of neural disorders and many other diseases, were investigated at a glassy carbon electrode (GCE) with the use of differential pulse stripping voltammetry (DPSV). The aim was to develop a simple, rapid and cost effective method of simultaneous analysis of these three substances in typical samples such as human urine. Each analyte showed typical reversible redox behaviour with the use of cyclic voltammetry in aqueous medium in the pH range of 4.0–7.9, and composite voltammograms of the three analytes were found to consist of three significantly overlapping peaks corresponding to the three individual substances. Thus, two- and three-way multi-variate data analysis methods were investigated. These included unfolding methods such as—partial least squares (UPLS), principal component regression (UPCR) and radial basis function-artificial neural networks (URBF-ANN), as well as trilinear models, such as parallel factor analysis (PARAFAC) and N-way PLS (NPLS). The results were compared with those obtained from two-way voltammetric data matrix with the use of conventional models, PLS, PCR and RBF-ANN. It was found that most of the three-way models, such as PARAFAC and UPLS, performed somewhat better than others on the basis of the %RPET (5.6∼5.9) and mean %Recovery (94∼102). The proposed methods were then applied for the determination of human urine samples spiked with the three catecholamines, and the results were satisfactory.

An electrochemical biosensor capable of direct detection of DNA damage induced by clenbuterol (CLB) has been developed. A glassy carbon electrode (GCE) was modified using reduced graphene oxide-Nafion and dsDNA to produce a dsDNA/RGO-Nafion/GCE sensor. The modified electrode was characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV), and the results at each stage of the electrode construction were interpreted. They indicated that the electrochemical oxidation peak currents of guanine and adenine at the electrode (dsDNA/RGO-Nafion/GCE) significantly increased as compared with those at the untreated electrode (dsDNA/GCE). Based on this, the sensor was used to measure the change of the oxidation peak currents for guanine and adenine between the intact and damaged dsDNA in the sensor films, and the DNA damage was successfully detected. Furthermore, the plot of peak current decline for the guanine and adenine versus the concentration of CLB was linear in the range of 5.0 × 10−7 to 4.0 × 10−6 mol L−1, with a limit of detection (LOD) of 3.2 × 10−7 mol L−1, which provided a good method to determine CLB indirectly.

Nanostructured Co3O4 with near-cubic morphology was synthesised using the solvothermal method, and a non-enzymatic electrochemical glucose sensor (Co3O4–CuNPs/Pt) was successfully constructed by dropping and potentiostatic depositing technologies. The obtained Co3O4 and Co3O4–CuNPs were characterised and investigated by X-ray powder diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. Quantitative analysis of glucose was performed using the amperometric (i–t) method, and a plot of current difference versus concentration of glucose was linear in the range 0.5–336 μM, with a linear correlation coefficient (R2) of 0.9989 and limit of detection (LOD) of 0.43 μM. When the linear range was reduced to 0.5–76.5 μM, R2 and LOD were 0.9997 and 0.17 μM, respectively. The sensitivity of the sensor was evaluated as 3.58 × 104 and 4.03 × 104 μA μM−1 cm−2 for the above two linear ranges, respectively. This novel sensor produced satisfactory reproducibility and stability and was applied to monitor trace amounts of glucose in human serum samples.

A novel modified electrode has been successfully constructed with the use of a nano-composite mixture consisting of molybdenum disulfide (MoS2) and gold nanoparticles (AuNPs). Its application as a cholesterol biosensor was investigated. The morphology of the prepared MoS2-AuNPs nanoparticles were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscope (AFM), X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS). The physicochemical properties of the modified electrode at each stage of the construction were characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Quantitative analysis of cholesterol was carried out with the use of the amperometric i–t method, and the calibration plot for cholesterol was linear in the range of 0.5–48 μM with a limit of detection (LOD) of 0.26 ± 0.015 μM. The apparent Michaelis-Menten constant ( K M a p p ) and sensitivity were estimated to be 0.325 mM and 4460 μA mM−1 cm−2, respectively. This novel sensor produced satisfactory reproducibility and stability, and was used successfully for the quantitative analysis of cholesterol in egg yolk and pork liver samples.

A strong red fluorescent nanocomposite, consisting of graphite-like carbon nitride nanosheets (g-C3N4 NSs) and serum albumin-capped Au nanoclusters (AuNCs), was synthesized. Dopamine (DA) can quench the red fluorescence of the nanocomposite, based on the Forster resonance energy transfer (FRET) mechanism. In this quenching process, the energy is transferred from the fluorescent g-C3N4 NSs-AuNCs to the oxidized DA quinine molecules (DA is easily oxidated to form DA quinine in air). The red fluorescence emission at 420 nm decreases dramatically and the quenching ratio (F0 – F)/F0 is linearly related to the concentration of DA in the range of 0.05–8.0 μmol L−1 with a detection limit of 0.018 μmol L−1 (S/N = 3). Additionally, this sensor has a potential of application to assay the DA in the real samples, such as human serum and human urine.

A novel 3D nano-flower-like Cu/multi-layer molybdenum disulfide composite (CuNFs/MoS2) modified glassy carbon electrode (GCE) has been successfully constructed. It was a highly sensitive and selective non-enzymatic hydrogen peroxide (H2O2) and glucose biosensor. The morphology of the obtained CuNFs-MoS2 nano-particles was investigated with the use of a scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS). The physicochemical properties of the modified electrode were characterized at each of the construction stages with the use of an electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) techniques. The new sensor combined the advantages of MoS2 and CuNFs, and exhibited high electro-catalytic activity toward H2O2 and glucose. Quantitative analysis of H2O2 and glucose was carried out with the use of the amperometric i-t method. Linear ranges were obtained between 0.04-1.88 μM and 1.88-35.6 μM for H2O2 and 1-20 μM and 20-70 μM for glucose, and their corresponding limits of detection (LOD) were 0.021 μM and 0.32 μM. This novel sensor was successfully applied for the quantitative analysis of H2O2 in tap water and glucose in human serum samples.

The interactions of the sugars glucose and lactose with the transport protein bovine serum albumin (BSA) were investigated using fluorescence, FT-IR and circular dichroism (CD) techniques. The results indicated that glucose could be bonded and transported by BSA, mainly involving hydrogen bonds and van der Waals interactions (ΔH = -86.13 kJ mol(-1)). The obtained fluorescence data from the binding of sugar and BSA were processed by the multivariate curve resolution-alternating least squares (MCR-ALS) method, and the extracted concentration profiles showed that the equilibrium constant, rglucose:BSA, was about 7. However, the binding of lactose to BSA did not quench the fluorescence significantly, and this indicated that lactose could not be directly transported by BSA. The binding experiments were further performed using the fluorescence titration method in the presence of calcium and BSA. Calcium was added so that the calcium/BSA reactions could be studied in the presence or absence of glucose, lactose or hydrolysis products. The results showed that hydrolyzed lactose seemed to enhance calcium absorption in bovine animals. It would also appear that for children, lactose provides better nutrition; however, glucose is better for adults.

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of spectra of the jujube (Zizyphus jujuba Mill.) fruit samples from four geographical regions. Prediction models were developed for the quantitative prediction of the contents of jujube fruit, i.e., total sugar, total acid, total phenolic content, and total antioxidant activity. Four pattern recognition methods, principal component analysis (PCA), linear discriminant analysis (LDA), least squares-support vector machines (LS-SVM), and back propagation-artificial neural networks (BP-ANN), were used for the geographical origin classification. Furthermore, three multivariate calibration models based on the standard normal variate (SNV) pretreated NIR spectroscopy, partial least squares (PLS), BP-ANN, and LS-SVM were constructed for quantitative analysis of the four analytes described above. PCA provided a useful qualitative plot of the four types of NIR spectra from the fruit. The LS-SVM model produced best quantitative prediction results. Thus, NIR spectroscopy in conjunction with chemometrics, is a very useful and rapid technique for the discrimination of jujube fruit.

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.

Bulk graphitic carbon nitride (g-C3N4) was obtained by pyrolysis of urea at 600 °C. The material was then subjected to protonation and exfoliation to obtain strongly fluorescent g-C3N4 nanosheets. These nanosheets are representing a two-dimensional layered material that emits strong blue fluorescence peaking at 434 nm, and their quantum yield of up to 10.3 % is much higher than that of bulk g-C3N4 (6.0 %). It was found that Cu(II) ions quench the fluorescence of the nanosheets due to photo-induced electron transfer (PET), probably the result of the stronger binding affinity between Cu(II) and the nitrogen and oxygen functional groups of the nanosheets. The probe responds to Cu(II) in the 0.01 to 0.4 nM concentration range, with a 8 pM detection limit. The method was applied to the determination of Cu(II) in spiked water samples.

Tungsten disulfide (WS2) is a two-dimensional transition metal dichalcogenide, which is of particular interest because it has highly anisotropic bonding, which leads to strongly anisotropic electrical and mechanical properties. Thus, in this work, a simple hydrothermal process was developed to produce photoluminescence from WS2 dots. This was achieved in the presence of sodium tungstate and reduced L-glutathione; the emitted fluorescence produced a quantum yield as high as 0.066. The WS2 dots and the associated fluorescence were investigated with the use of transmission electron microscopy, atomic force microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared and UV-vis spectroscopies. The WS2 dots were used as a fluorescent probe to analyze nitrofurazone (NFZ). The associated fluorescence resonance energy transfer (FRET) mechanism was also investigated and the emitted fluorescence was found to be linear in the range of 0.17-166 μmol L(-1) with a detection limit of 0.055 μmol L(-1). The proposed method was successfully applied for analysis of NFZ in nasal drops and water samples.

This work demonstrates how near-infrared reflectance spectroscopy (NIR) in combination with chemometrics can be used to analyse and discriminate powdered, pure and adulterated sweet potato samples. Thus, the principal component analysis (PCA) method, which is able to produce diagrams of objects as a function of PC scores, was able to distinguish and group the NIRS from the many different types of potato sample. Other methods such as the radial basis function-partial least squares (RBF-PLS) as well the successive projection algorithm (SPA) combination, distinguished the purple and white sweet potato varieties from each other as well as, importantly, from the different adulterated purple sweet potato samples. Furthermore, the total anthocyanin (TA) and total antioxidant activity (TAA) parameters were also analysed quantitatively in these samples, and high residual prediction deviation (RPD) values were noted. Also, the two recognition methods, k-nearest neighbours (KNN) and linear discriminant analysis (LDA), were able to discriminate the sweet potato samples. Thus, overall this work demonstrated that NIRS combined with chemometrics methods can be employed for the identification of purple sweet potato, white sweet potato and their adulterated samples; in addition, quantitative analysis for TA and TAA in such samples can be successfully performed.

An electrochemical biosensor capable of indirect detection of DNA damage induced by any one of the three endocrine-disrupting compounds (EDCs) – bisphenol A (BPA), 4-nonylphenol (NP) and 4- t -octylphenol (OP), has been researched and developed. The methylene blue (MB) dye was used as the redox indicator. The glassy carbon electrode (GCE) was modified by the assembled dsDNA/graphene oxide-chitosan/gold nano-particles to produce a dsDNA/GO-CS/AuNPs/GCE sensor. It was characterized with the use of electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and scanning electron microscopy (SEM) techniques. The loading/release of the MB dye by the dsDNA/GO-CS/AuNPs film was investigated, and the results showed that the process was reversible. Based on this, the sensor was used to measure the difference between the loading capabilities of intact and damaged dsDNA in the films. The sensor was then successfully applied to detect DNA damage electrochemically. The differential pulse voltammetry (DPV) peak current ratio for MB, observed before and after DNA damage, increased linearly in the presence the BPA, NP or OP compounds; the treatment range was 10–60 min, and the respective damage rates were 0.0069, 0.0044 and 0.0031 min −1 , respectively. These results were confirmed by the binding constants: 2.09 × 10 6 M −1 (BPA-DNA), 1.28 × 10 6 M −1 (NP-DNA) and 9.33 × 10 5 M −1 (OP-DNA), all of which were obtained with the use of differential pulse stripping voltammetry (DPSV).

In this study, complex substances such as Mint (Mentha haplocalyx Briq.) samples from different growing regions in China were analyzed for phenolic compounds by high-performance liquid chromatography with diode array detection and for the volatile aroma compounds by gas chromatography with mass spectrometry. Chemometrics methods, e.g. principal component analysis, back-propagation artificial neural networks, and partial least squares discriminant analysis, were applied to resolve complex chromatographic profiles of Mint samples. A total of 49 aroma components and 23 phenolic compounds were identified in 79 Mint samples. Principal component analysis score plots from gas chromatography with mass spectrometry and high-performance liquid chromatography with diode array detection data sets showed a clear distinction among Mint from three different regions in China. Classification results showed that satisfactory performance of prediction ability for back-propagation artificial neural networks and partial least squares discriminant analysis. The major compounds that contributed to the discrimination were chlorogenic acid, unknown 3, kaempherol 7-O-rutinoside, salvianolic acid L, hesperidin, diosmetin, unknown 6 and pebrellin in Mint according to regression coefficients of the partial least squares discriminant analysis model. This study indicated that the proposed strategy could provide a simple and rapid technique to distinguish clearly complex profiles from samples such as Mint.

Flos Chrysanthemum tea contains flavonoids, essential oils and caffeoylquinic acids. These substances are pharmacologically active but this activity is cultivar dependent. Seventy-six Flos Chrysanthemum samples collected from four cultivars (Hangju, Taiju, Gongju and Boju) were discriminated with the use of results from high performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). A two-dimensional chromatographic fingerprint dataset of the four kinds of Flos Chrysanthemum cultivar was built from the combined GC/HPLC profiles and thirty variables were selected. Principal component analysis (PCA) and kernel – PCA (KPCA) were used for feature extraction. The score mapping graph indicated that these two PCA methods effectively extracted most information from the samples, and the four Flos Chrysanthemum cultivars were qualitatively differentiated. Furthermore, four supervised pattern recognition techniques, radial basis function-neural network analysis (RBF-NN), least squares support vector machines (LS-SVM), linear discriminant analysis (LDA) and K-nearest neighbors (KNN), successfully predicted the validation of the samples.

Molybdenum disulfide (MoS2) is an emerging material with some unique physical and electronic properties somewhat comparable to those of graphene. It was prepared with the use of a simple hydrothermal process, and has a layered structure similar to that of graphene. Also, it has a peroxidase-like catalytic activity and is able to catalyze the oxidation of the colorless peroxidase substrate, 3,3',5,5'-tetramethylbenzidine (TMB), in the presence of H2O2 to produce a blue product. This reaction used MoS2 as a useful peroxidase, which involved a colorimetric method for trace analysis of H2O2 in water, e.g. lake waters. This method is uncomplicated, inexpensive and highly sensitive for H2O2 in the 0.125-1.75 μM range (LOD: 0.08 μM). It is also quite stable in the presence of many common inorganic and organic potentially interfering compounds, e.g. metal ions as well as amino acids and sugars.

A novel, uncomplicated method for the analysis of lead (Pb2+) and mercury (Hg2+) ions has been developed with the use of gold nano-rods (AuNRs), which were combined with ssDNA. The AuNR–ssDNA assembly was stabilized by electrostatic interactions. The ssDNA has a random coil structure, which was changed to G-quartet and hairpin-like structures after binding with Pb2+ and Hg2+ ions, respectively. The bonding with lead and mercury caused the ssDNA assembled AuNRs (ssDNA–AuNRs) to disperse, and this increased the surface plasmon resonance (SPR) absorption of the AuNRs. Changes in the UV-vis spectral intensities allowed the selective detection of Pb2+ and Hg2+ ions at concentrations as low as 19.8 nM and 3.2 nM, respectively. Linear correlations were observed between the UV-vis spectra and the Pb2+ and Hg2+ analytes in the concentration ranges of 25–250 nM (R2 = 0.9971) for Pb2+ and 4–40 nM (R2 = 0.9956) for Hg2+, respectively. The literature suggests that the novel method is another example in which the ssDNA–AuNR assembly has been used for trace analysis of Pb2+ and Hg2+.

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95-100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

A novel differential pulse voltammetry (DPV) method was developed for the simultaneous analysis of herbicides in water. A mixture of four herbicides, atrazine, simazine, propazine and terbuthylazine was analyzed simultaneously and the complex, overlapping DPV voltammograms were resolved by several chemometrics methods such as partial least squares (PLS), principal component regression (PCR) and principal component-artificial networks (PC-ANN). The complex profiles of the voltammograms collected from a synthetic set of samples were best resolved with the use of the PC-ANN method, and the best predictions of the concentrations of the analytes were obtained with the PC-ANN model (%RPET = 6.1 and average %Recovery = 99.0). The new method was also used for analysis of real samples, and the obtained results were compared well with those from the GC-MS technique. Such conclusions suggest that the novel method is a viable alternative to the other commonly used methods such as GC, HPLC and GC-MS.

Flos Chrysanthemum is a generic name for a particular group of edible plants, which also have medicinal properties. There are, in fact, twenty to thirty different cultivars, which are commonly used in beverages and for medicinal purposes. In this work, four Flos Chrysanthemum cultivars, Hangju, Taiju, Gongju, and Boju, were collected and chromatographic fingerprints were used to distinguish and assess these cultivars for quality control purposes. Chromatography fingerprints contain chemical information but also often have baseline drifts and peak shifts, which complicate data processing, and adaptive iteratively reweighted, penalized least squares, and correlation optimized warping were applied to correct the fingerprint peaks. The adjusted data were submitted to unsupervised and supervised pattern recognition methods. Principal component analysis was used to qualitatively differentiate the Flos Chrysanthemum cultivars. Partial least squares, continuum power regression, and K-nearest neighbors were used t...

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations.

This review is focused on the impact of chemometrics for resolving data sets collected from investigations of the interactions of small molecules with biopolymers. These samples have been analyzed with various instrumental techniques, such as fluorescence, ultraviolet–visible spectroscopy, and voltammetry. The impact of two powerful and demonstrably useful multivariate methods for resolution of complex data—multivariate curve resolution–alternating least squares (MCR–ALS) and parallel factor analysis (PARAFAC)—is highlighted through analysis of applications involving the interactions of small molecules with the biopolymers, serum albumin, and deoxyribonucleic acid. The outcomes illustrated that significant information extracted by the chemometric methods was unattainable by simple, univariate data analysis. In addition, although the techniques used to collect data were confined to ultraviolet–visible spectroscopy, fluorescence spectroscopy, circular dichroism, and voltammetry, data profiles produced by ot...

Single-wavelength high performance liquid chromatography-diode-array-detector (HPLC-DAD) and multi-wavelength combined HPLC methods were researched and developed in order to compare their performance for the classification of complex substances. Thus, the aims of this work were: to compare the performance of the single- and multi-wavelength HPLC-DAD methods for analysis of complex substances – in this context, the Radix Paeoniae herbs, which were classified on the basis of their component species and geographical origin. Three classification methods, Linear discriminant analysis (LDA), Radial basis function artificial neural network (RBF-ANN) and Least squares-support vector machine (LS-SVM), were compared on the basis of their performance in discriminating the Radix Paeoniae samples. The results showed that the multi-wavelength data produced better classification results of the two HPLC methods. This was so, irrespective of the chemometrics method used. However, the LS-SVM models were significantly better in classifying the herb samples. Consequently, the multi-wavelength HPLC-DAD approach is a strong alternative to the more common single-wavelength method, and the LS-SVM was the method of choice for classification of the complex substances such as the Radix Paeoniae herbs.

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

A novel kinetic spectrophotometric method was developed for the simultaneous determination of caffeine, theobromine and theophylline in food samples. This method was based on the different kinetic characteristics between the reactions of analytes with cerium sulphate in sulphuric acid and the associated change in absorbance at 320 nm. Experimental conditions, the effects of sulphuric acid, cerium sulphate and temperature, were optimised. Linear ranges (0.4–8.4 μg mL −1 ) for all three analytes were established, and the limits of detection were: 0.30 μg mL −1 (caffeine), 0.33 μg mL −1 (theobromine) and 0.16 μg mL −1 (theophylline). The recorded data were processed by partial least squares and artificial neural network, and the developed mathematical models were then used for prediction. The proposed, novel method was applied to determine the analytes in commercial food samples, and there were no significant differences between the results from the proposed method and those obtained by high-performance liquid chromatography.

A novel Prussian blue/copper-gold bimetallic nanoparticles hybrid film modified electrode was prepared by electrochemical deposition on a glassy carbon electrode (PB/Cu-AuNPs/GCE). Morphology and electrochemistry of this electrode were studied by UV-vis spectroscopy, scanning electron microscopy, X-ray diffraction, cyclic voltammetry and electrochemical impedance spectroscopy. The sensor showed significantly better electrocatalytic activity for the reduction of hydrogen peroxide in comparison with the single PB/GCE and PB/AuNPs/GCE. This was attributed to the synergistic effect of PB and Cu-Au bimetallic nanoparticles. Also, the sensor demonstrated an overall high level of performance for the analysis of H2O2 in the concentration range from 0.002 to 0.84 mM.

Hydrogen peroxide is an important analyte in biochemical, industrial and environmental systems. Therefore, development of novel rapid and sensitive analytical methods is useful. In this work, a hemin-graphene nano-sheets (H-GNs)/gold nano-particles (AuNPs) electrochemical biosensor for the detection of hydrogen peroxide (H2O2) was researched and developed; it was constructed by consecutive, selective modification of the GCE electrode. Performance of the H-GNs/AuNPs/GCE was investigated by chronoamperometry, and AFM measurements suggested that the graphene flakes thickness was ∼1.3 nm and that of H-GNs was ∼1.8 nm, which ultimately indicated that each hemin layer was ∼0.25 nm. This biosensor exhibited significantly better electrocatalytic activity for the reduction of hydrogen peroxide in comparison with the simpler AuNPs/GCE and H-GNs/GCE; it also displayed a linear response for the reduction of H2O2 in the range of 0.3 μM to 1.8 mM with a detection limit of 0.11 μM (S N−1 = 3), high sensitivity of 2774.8 μA mM−1 cm−2, and a rapid response, which reached 95% of the steady state condition within 5 s. In addition, the biosensor was unaffected by many interfering substances, and was stable over time. Thus, it was demonstrated that this biosensor was potentially suitable for H2O2 analysis in many types of sample.

The interaction of graphene oxide (GO), a medicinal drug (10-hydroxy camptothecin (HCPT)), and bovine serum albumin (BSA) was investigated with the aim of developing a method for the analysis of serum albumin proteins. It was demonstrated that HCPT could be readily loaded onto GO via the π–π stacking interaction, and the delivery of HCPT to BSA was improved in the presence of GO; this, in turn, facilitated the binding interaction of HCPT and BSA. Chemometrics methods, multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC), were applied to resolve spectral data, and this assisted in the elucidation of the above interaction. GO was found to enhance the fluorescence response of HCPT to BSA, and thus, a low cost fluorescence bio-sensing platform was developed for fluorescence-enhanced detection of BSA based on GO. The satisfactory analytical performance of this biosensor for BSA was attributed to the structure and electronic properties of GO.

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

Enantioselective binding interaction of the pesticides, metolachlor (RAC-metolachlor) and its S-enantiomer (S-metolachlor), with bovine serum albumin (BSA) was investigated by fluorescence and UV-vis absorption spectroscopy. Both RAC- and S-metolachlors quenched the intrinsic fluorescence of BSA via a static mechanism, and various binding parameters indicated that electrostatic forces were involved in the binding of both of these compounds. Site marker competitive experiments demonstrated that S-metolachlor bound to site I of BSA, while R-metolachlor bound to site II, indicating the importance of enantiomeric factors for binding site selection. Further experiments showed that S-metolachlor had a higher binding affinity to BSA than R-metolachlor. The obtained spectral data were resolved with use of the multivariate curve resolution-alternating least squares method (MCR-ALS), and the extracted concentration profiles of the reacting species in the interaction were obtained. These profiles indicated that S-metolachlor was the main active constituent of RAC-metolachlor for binding with BSA, and these findings have significant implications in providing an explanation why S-metolachlor is the preferred herbicide in practice than RAC-metolachlor.

A novel method for the discrimination of the three kinds of Indigowoad Root sample, Radix Isatidis (RI), Rhizoma et Radix Baphicacanthis Cusia (RRBC) and simulated adulterated samples (AD) was researched and developed with the use of near infrared spectroscopy (NIR) and chemometrics. Principal component analysis (PCA) was applied to process the NIR data of 75 collected Indigowoad Root samples, and the three kinds of such sample were discriminated along the first principal component (PC1) axis. In addition, the data pretreatment methods - genetic algorithm-partial least squares (GA-PLS), successive projections algorithm (SPA), and wavelet transform (WT), were employed to select the key analytical wavelengths, and then, these were used as input variables for the three kinds of the pattern recognition method, such as K-nearest neighbor (KNN), radial basis function-artificial neural network (RBF-ANN), least squares-support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN). The WT was the method of choice for data pretreatment, and three pretreatment-prediction method combinations performed well (basis: %recognition rate) - WT-KNN (98.2%) and BP-ANN (97.3%) as well as GA-PLS - LS-SVM (97.2). A BP-ANN calibration model was built for the quantitative discrimination of the three types of the complex Indigowoad Root samples, and it was successfully validated.

Chromatographic profiles of Rhizoma et Radix Notoperygii (RRN, “Qianghuo” in Chinese), a complex traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography with diode array detection (HPLC-DAD) at 330 nm. These data profiles were used as fingerprints to investigate quality control classification modeling of the RRN samples. In contrast to the classical methods for discrimination of TCMs, that is, just using common HPLC peaks, all chromatographic profile data were pre-processed by the correlation optimized warping method and polynomial functions; then, these data were submitted as fingerprints (variables) for classification on the basis of sample origin. Chemometrics methods used for calibration modeling and subsequent sample classification-least square support vector machine (LS-SVM), artificial neural network (ANN), and partial least square discriminant analysis (PLS-DA); all produced satisfactory calibrations as well as classification results.

An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λex=215∼395 nm and λem=290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine—a strongly fluorescing substance—as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.

This overview summarizes the application and impact of chemometrics on the extraction and interpretation of analytical data with the use of curve resolution methods from about 2005 onward. The development and usage of well-known and novel chemometric methods have been described and approximately 85 papers have been referenced. Many suggested improvements to some well-known methods, for example, multivariate curve resolution, have been noted as well as the growing software for such methods. Also, these high dimensional resolution methods have found significant application and, arguably, have opened up a new perspective in calibration, that is, extraction of otherwise unobtainable analytical information from strongly overlapping profiles in the presence of interferences. Recent literature suggests that the use of chemometric methods in analytical chemistry for data extraction and interpretation provides indispensable tools for multivariate data processing and extraction of hidden information, which otherwis...

A spectrofluorimetric analytical method was researched and developed for the quantitative, simultaneous analysis of nitrofurazone and nitrofurantoin antibiotics. The method was based on the kinetic analysis of their hydrolysis reaction coupled with multivariate calibration methods. These consisted of several different multiway data models, and their prediction performance was compared on the basis of the above reaction. Nitrofurans do not fluoresce but their alkaline hydrolysis products show strong fluorescence at 245 nm/456 nm (excitation/emission wavelengths) and 240 nm/452 nm. Different multiway data matrices were acquired at various excitation and/or emission wavelengths, and/or reaction times and resolved by multiway calibration methods, i.e. the parallel factor analysis, unfolded principal component analysis–residual bilinearization and unfolded principal component analysis–residual trilinearization methods. All of these procedures facilitate the second-order advantage. The relationship between the concentration of the analytes and the fluorescence output was found to be nonlinear. Hence, the results from the calibration models were submitted to the radial basis function artificial neural networks for prediction. The best performing prediction model, unfolded principal component analysis–residual bilinearization–radial basis function artificial neural networks, was used to analyze the nitrofurans in fish samples and the results compared well with those obtained from the reference HPLC method.

The binding interaction of bovine serum albumin (BSA) with dopamine was studied by different spectroscopic techniques, and a fluorescence quenching mechanism was associated with this process. Estimated thermodynamic parameters indicated the presence of hydrogen bonding and van der Walls forces between dopamine and BSA. The microenvironment of the protein-binding site was studied by the synchronous fluorescence, FT-TR, and three-way fluorescence techniques in the presence of dopamine, and changes in conformation were indicated within the binding cavity. This study provides useful information on the transportation and distribution of dopamine in proteins.

Many complex natural or synthetic products are analysed either by the GC-MS (gas chromatography-mass spectrometry) or HPLC-DAD (high performance liquid chromatography-diode-array detector) technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC-MS and HPLC-DAD analyses of complex, similar substances represented by the three common types of the TCM (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA (Principal component analysis), which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC-MS and HPLC-DAD variables were observed. LDA (linear discriminant analysis), BP-ANN (back propagation-artificial neural networks) and LS-SVM (least squares-support vector machine) chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all training models indicated that several samples would be misclassified; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.

Chromatographic fingerprints of raw, wine-treated and braised rhubarb samples were obtained by liquid chromatography with a diode array detector (LC-DAD). Eight LC peaks were selected for fingerprint analysis, and six compounds were identified as gallic acid, emodin, chrysophanol, palmidin, rhein glucoside and desoxyrhaponticin. A data matrix of the chromatographic fingerprints from the three types of sample was submitted to principal component analysis (PCA) and this indicated that the three types of rhubarb were distinguished in the PC1versusPC2 space. Training, verification and prediction data sets of the three types of HPLC fingerprint were processed successfully by chemometrics data classification methods, K-nearest neighbor (KNN), linear discriminant analysis (LDA) and least squares support vector machines (LS-SVM), and these types of rhubarb sample were classified satisfactorily.

Interaction of isoprenaline (ISO) with calf-thymus DNA was studied by spectroscopic and electrochemical methods. The behavior of ISO was investigated at a glassy carbon electrode (GCE) by cyclic voltammetry (CV) and differential pulse stripping voltammetry (DPSV); ISO was oxidized and an irreversible oxidation peak was observed. The binding constant K and the stoichiometric coefficient m of ISO with DNA were evaluated. Also, with the addition of DNA, hyperchromicity of the UV-vis absorption spectra of ISO was noted, while the fluorescence intensity decreased significantly. Multivariate curve resolution-alternating least squares (MCR-ALS) chemometrics method was applied to resolve the combined spectroscopic data matrix, which was obtained by the UV-vis and fluorescence methods. Pure spectra of ISO, DNA and ISO-DNA complex, and their concentration profiles were then successfully obtained. The results indicated that the ISO molecule intercalated into the base-pairs of DNA, and the complex of ISO-DNA was formed.

High performance liquid chromatography with diode array detector (HPLC-DAD) was employed for the chromatographic fingerprint analysis and characterization of Rhizoma Curcumae, a traditional Chinese medicine (TCM). Genetic algorithm-partial least squares (GA-PLS) were applied for feature variables selection based on the 11 common peaks. Then, two chemometrics pattern recognition methods, principal component analysis (PCA) and K-nearest neighbor (KNN), were applied to investigate the mean chromatograms of Rhizoma Curcumae samples, which were obtained from four different provinces with two different processing methods. It was found that these samples can be successfully compared and distinguished according to their origin and processing methods. The results indicated that the proposed method could be efficiently used to control the quality of Rhizoma Curcumae samples and providing a reference method for chemical pattern recognition for other TCMs.

The interaction of 10-hydroxycamptothecine (HCPT) with DNA under pseudo-physiological conditions (Tris-HCl buffer of pH 7.4), using ethidium bromide (EB) dye as a probe, was investigated with the use of spectrofluorimetry, UV-vis spectrometry and viscosity measurement. The binding constant and binding number for HCPT with DNA were evaluated as (7.1 ± 0.5) × 104 M-1 and 1.1, respectively, by multivariate curve resolution-alternating least squares (MCR-ALS). Moreover, parallel factor analysis (PARAFAC) was applied to resolve the three-way fluorescence data obtained from the interaction system, and the concentration information for the three components of the system at equilibrium was simultaneously obtained. It was found that there was a cooperative interaction between the HCPT-DNA complex and EB, which produced a ternary complex of HCPT-DNA-EB. © 2011 Elsevier B.V.

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D - first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.

An NIR spectroscopic method was researched and developed for the analysis of potato crisps (chips) chosen as an example of a common, cheap but complex product. Four similar types of the ‘original flavour’ potato chips from different manufacturers were analysed by NIR spectroscopy; as well, the quality parameters — fat, moisture, acid and peroxide values of the extracted oil were predicted. Principal component analysis (PCA) of the NIR data displayed the clustering of objects with respect to the type of chips. NIR spectra were rank-ordered with the use of the sparingly applied multiple criteria decision making (MCDM) ranking methods, PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid), and a comprehensive quantitative description of the data was obtained. The four traditional parameters were predicted on the basis of the NIR spectra; the performance of the Partial Least Squares (PLS), and Kernel Partial Least Squares (KPLS) calibrations was compared with those from Least Squares-Support Vector Machines (LS-SVM) method. The LS-SVM calibrations, which model better data linearity and non-linearity, successfully predicted all four parameters. This work has demonstrated that NIR methodology with the use of chemometrics can describe comprehensively qualitative and quantitative properties of complex, processed substances as illustrated by the potato chips example, and indicated that this approach may be applied to other similar complex samples.

The new simple, cost effective and well performing differential pulse stripping voltammetry method for the simultaneous determination of antibiotics – oxytetracycline, tetracycline and chlorotetracycline was researched and developed. It depends on the reduction of these compounds at a hanging mercury drop electrode. The samples were extracted from (i) spiked animal feed, and (ii) fresh fish muscle dosed with the drugs. The voltammograms from the drug mixture produced complex, overlapping profiles, and chemometrics methods were applied for calibration modelling. The analytical linear ranges were within 0.02–0.18 μg mL −1 and the corresponding LODs were 3–5 μg L −1 for the three analytes. These values compare well with those from the HPLC and fluorescence methods in the literature. The % relative prediction errors from the verification trials were between 4% and 9% with % Recoveries being 103–107. Also, the % Recoveries of the antibiotics from animal feeds as measured by the new method were comparable with those from the HPLC analysis (85%), i.e. the method is highly competitive, especially as a screening approach.

Interaction of chlorotetracycline (CTC) with bovine serum albumin (BSA) was investigated under simulated physiological conditions by spectroscopy with the aid of multivariate curve resolution-alternating least squares (MCR-ALS). Eosin Y was selected as an alternative site I marker on the BSA to study the above molecular interaction. The binding of Eosin Y and CTC to BSA showed that CTC was displaced from CTC-BSA complex by Eosin Y, and Eosin Y-BSA complex was formed. However, the recorded fluorescence spectra of Eosin Y and Eosin Y-BSA overlapped and MCR-ALS was applied to resolve the two-way fluorescence spectra. From the resolved equilibrium concentration profiles, it was observed that Eosin Y competed with CTC in the binding process with BSA; it was also shown that the binding site of CTC on BSA was site I, and this was further confirmed by the fluorescence polarization method. Compared with some common site I markers for BSA, the fluorescence and UV-vis spectral shapes of the Eosin Y-BSA complex were quite different from that of Eosin Y, and this feature facilitated the investigation of the small molecule-BSA interaction.

Interaction of a steroid drug, Ractopamine (RAC), and DNA was investigated by electroanalysis-differential pulse and cyclic voltammetry (DPV and CV), and UV-vis and fluorescence spectroscopy. DPV showed that RAC intercalated with DNA, and CV indicated that the reaction mechanism of RAC and dsDNA involved irreversible oxidation with the loss of two H+ and a transfer of two electrons. Reaction binding parameters were obtained. Pure spectra of RAC, DNA and the RAC-DNA complex, and their concentrations were extracted by multivariate curve resolution-alternating least squares method (MCR-ALS). Concentration profiles indicated quantitatively the course of the reaction.

To investigate the interaction between Ractopamine (RAC), an animal growth promoter, and bovine serum albumin (BSA), three spectroscopic approaches (fluorescence, UV–vis and FT-IR) and three different experiments (two mole-ratio and a Job's methods) were used to monitor the biological kinetic interaction procedure. The Stern–Volmer quenching constants K SV , the binding constants K a , and the number of binding sites n at 298, 301 and 304 K were evaluated by molecular spectroscopic approaches. The values of enthalpy (−13.47 kJ mol −1 ) and entropy (78.39 J mol −1 K −1 ) in the reaction indicated that RAC bound to BSA mainly by electrostatic and hydrophobic interaction. The site markers competitive experiments indicated that the binding of RAC to BSA primarily took place in site I. The spectra data matrix was further investigated with multivariate curve resolution-alternating least squares (MCR-ALS), and the concentration profiles and the pure spectra for three species (BSA, RAC and RAC-BSA) existed in the kinetic interaction procedure, as well as the apparent equilibrium constants, were obtained.

Raw and roasted Semen Cassiae seeds, a complex traditional Chinese medicine (TCM), are used as examples to research and develop a method of classification analysis based on measurements of Fourier transform infrared (FT-IR) spectral fingerprints. Eighty samples of the TCM were measured in the mid-infrared range, 400–2000 cm−1(KBr pellets), and the complex overlapping spectra were submitted for interpretation to a principal component analysis least squares support vector machine (PC-LS-SVM), kernel principal component analysis least squares support vector machine (KPC-LS-SVM), and radial basis function artificial neural networks (RBF-ANN). The LS-SVM models were developed with an RBF kernel function and a grid search technique. Training models were constructed with the use of raw and first-derivative spectra and these were then verified by another data set containing both raw and roasted spectral objects. It was demonstrated that the first-derivative data set produced the best separation of the spectral objects. In general, satisfactory analytical performance was obtained with the PC-LS-SVM, KPC-LS-SVM, and RBF-ANN training models and with the classification of the verification spectral objects. With regard to chemometrics modeling, the performance of KPC-LS-SVM was somewhat more economical than that of the PC-LS-SVM model. It would appear that the latter relatively simple model would be sufficient for application to most small to medium sized FT-IR fingerprint data sets, but with larger matrices the more complex models, such as the RBF-ANN and KPC-LS-SVM, may be more advantageous on a computational basis.

This overview focuses on the application of chemometrics techniques to the investigation of soils contaminated by polycyclic aromatic hydrocarbons (PAHs) and metals, because these two important and very diverse groups of pollutants are ubiquitous in soils. We highlight the salient features of studies carried out in the microenvironments and recreational environments of humans in the context of the multivariate statistical techniques available across the boundaries of disciplines used in soil studies. We pay particular attention to techniques employed in the geosciences that may be effectively utilized for environmental soil studies. We also discuss classical multivariate approaches that may be used in isolation or as complementary methods to these. Chemometrics techniques widely applied in atmospheric studies for identifying sources of pollutants or for determining the importance of contributions of contaminant sources to a particular site have seen little use in soil studies, but they may be employed effectively in such investigations. Suitable programs are also available for suggesting measures of mitigation in cases of soil contamination, and we also consider these. Specific techniques reviewed include: • pattern-recognition techniques [e.g., Principal Components Analysis (PCA), Fuzzy Clustering (FC) and Cluster Analysis (CA); • geostatistical tools [e.g., variograms, Geographical Information Systems (GISs), contour mapping and kriging]; and, • source-identification and contribution-estimation methods [e.g., Positive Matrix Factorization (PMF), and PCA on Absolute Principal Component Scores (PCA/APCS)]. Mitigating measures to limit or to eliminate pollutant sources may be suggested through the use of ranking analysis and multi-criteria decision-making methods (MCDMs), mainly represented in this review by studies employing the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) and its associated graphic output, Geometrical Analysis for Interactive Aid (GAIA).