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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.

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.

Near-infrared (NIR) spectroscopy is a well-known, rapid and non-destructive technique suitable for analyses of many different food products. In this work, it was used to develop a novel method for the analysis of dehydrated tomato samples, collected from four different producers. The NIR spectra from such samples were discriminated according to the four production sources, with the use of principal component analysis (PCA). Also, hierarchical cluster analysis (HCA), linear discriminant analysis (LDA) and K-nearest neighbors (KNN) were successfully used for pattern recognition. The prediction rates were 100% for assigning producers to samples. Two multivariate calibration models—partial least squares regression (PLSR) and radial basis function neural networks (RBF-NN), were applied for quantitative analysis. NIRS calibration models were established for the determination of lycopene and total acid, sugar, phenols and antioxidant activity in dehydrated tomatoes. These calibrations were then used for prediction of unknown, dehydrated tomato samples with satisfactory results. The RBF-NN results were better than those obtained from the PLSR models, and the better predictions suggested that the novel NIR spectroscopic method supported by chemometrics, is suitable for the discrimination and prediction of the five quality parameters in the dehydrated tomato products.

A simple, environmentally friendly hydrothermal method was used to prepare strongly luminescent, nitrogen-doped carbon dots (NCDs) with the use of Chinese yams as a source of carbon and nitrogen. Such NCDs have an average size of 2.7±1.4 nm; they emit blue light at 420 nm and have a quantum yield of up to 9.3%. Thus, carboxyfluorescein (FAM)-DNA macro-molecules were assembled on the surfaces of the NCDs, and stabilised by strong π-π stacking; the so formed hybrid nano-sensors were found to have an ultra-sensitive response to 6-mercaptopurine (6-MP). A strong emission and enhancement of yellow radiation was observed from FAM. Furthermore, due to the specific interactions between DNA and Hg(2+), which resulted in the formation of the T-Hg(2+)-T (T: thymine base) complex - a large, conjugated system, which formed between NCDs, DNA and 6-MP, was broken up. Thus, the fluorescence from FAM was quenched. The detection limits for 6-MP and Hg(2+) were 0.67 and 1.26 nM, respectively. The proposed method was applied for the determination of 6-MP in human serum and Hg(2+) in water samples with satisfactory results.

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).

A simple and rapid method of analysis for mercury ions (Hg 2+ ) and cysteine (Cys) was developed with the use of graphene quantum dots (GQDs) as a fluorescent probe. In the presence of GQDs, Hg 2+ cations are absorbed on their negatively charged surface by means of electrostatic interactions. Thus, the fluorescence (FL) of the GQDs would be significantly quenched as a result of the FL charge transfer, e.g. 92% quenching at 450 nm occurs for a 5 μmol L −1 Hg 2+ solution. However, when Cys was added, a significant FL enhancement was observed (510% at 450 nm for a 8.0 μmol L −1 Cys solution), and Hg 2+ combined with Cys rather than with the GQDs in an aqueous solution. This occurred because a strong metal thiol bond formed, displacing the weak electrostatic interactions, and this resulted in an FL enhancement of the GQDs. The limits of detection (LOD) for Hg 2+ and Cys were 0.439 nmol L −1 and 4.5 nmol L −1 , respectively. Also, this method was used successfully to analyze Hg 2+ and Cys in spiked water samples.

A novel electrochemical sensor was constructed by depositing copper/copper oxide nano-particles on a glassy carbon electrode (GCE) to produce a Cu@CuO/GCE. The morphology and composition of this modified electrode were characterized with the use of scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) techniques. The electrochemical properties of the novel modified GCE electrode were studied using various electrochemical techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The catalytic performance of the sensor was studied with the use of differential pulse voltammetry (DPV) under optimized conditions. The constructed Cu@CuO/GCE sensor showed significantly better electrocatalytic activity for the analysis of the H 2 O 2 in comparison to the Cu/GCE or GCE. The novel sensor produced a linear response for H 2 O 2 analysis in the range of 0.005–8 mM and the detection limit was 0.23 μM (S/N = 3). Satisfactory results were obtained for H 2 O 2 analyses of tap and lake waters. This performance compares very favorably with the results from several other commonly used techniques for analysis of H 2 O 2 .

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 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.

A simple one-step electrodeposition method was used to construct a glassy carbon electrode (GCE), which has been modified with Cu doped gold nanoparticles (GNPs), i.e. a Cu@AuNPs/GCE. This electrode was characterized with the use of scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. The eugenol was electrocatalytically oxidized at the Cu@AuNPs/GCE. At this electrode, in comparison with the behavior at the GCE alone, the corresponding oxidation peak current was enhanced and the shift of the oxidation potentials to lower values was observed. Electrochemical behavior of eugenol at the Cu@AuNPs/GCE was investigated with the use of the cyclic voltammetry (CV) technique, and additionally, in order to confirm the electrochemical reaction mechanism for o-methoxy phenols, CVs for catechol, guaiacol and vanillin were investigated consecutively. Based on this work, an electrochemical reaction mechanism for o-methoxy phenols was suggested, and in addition, the above Cu@AuNPs/GCE was successfully employed for the analysis of eugenol in food samples.

A novel, uncomplicated and rapid method of analysis for organophosphorus (OP) pesticides was researched and developed using the important, common OP, dipterex, as a typical example. The basis of the method involved the citrate-capped silver nanoparticles (citrate-capped AgNPs) and Acetylthiocholine (ATCh). The latter compound can be catalyzed by Acetylcholinesterase (AChE) to form thiocholine (TCh), which induces the aggregation of AgNPs. Correspondingly, the color of AgNPs in solution changes from bright yellow to pink, and the UV–vis characteristic absorption peak of AgNPs at about 400 nm decreases; simultaneously, a new absorption band appears at about 520 nm. Irreversible inhibition of AChE activity caused by dipterex, prevents the aggregation of AgNPs. Thus, a UV–vis spectrophotometric method was developed for the analysis of dipterex. The absorbance ratio A396 nm/A520 nm was found to be linearly related to the concentration of dipterex in the range of 0.25–37.5 ng mL−1 with a detection limit of 0.18 ng mL−1. This method was used successfully to analyse dipterex in spiked, different water samples.

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.

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.

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.

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.

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.

A reliable method for simultaneous determination of three antibiotic drugs (levofloxacin, gatifloxacin and lomefloxacin) by differential pulse stripping voltammetry (DPSV) in Britton–Robinson buffer (pH 7.96) was presented. The method is based on adsorptive accumulation of the antibacterial drugs on a hanging mercury dropping electrode (HMDE), followed by the reduction of the adsorptive species by the technique of DPSV. Optimal conditions, the deposition time of 80 s, the deposition potential of −1250 mV, and the scan rate of 25 mV/s, were obtained. The linear concentration ranges of 0.010–0.080 μg/mL were obtained for all these three antibiotic drugs, while the detection limits were 2.38, 3.20 and 1.60 ng/mL for levofloxacin, gatifloxacin and lomefloxacin, respectively. In this work, chemometrics methods, such as classical least squares (CLS), partial least squares (PLS), principle component regression (PCR) and radial basis function-artificial neural networks (RBF-ANN), were used to quantitatively resolve the overlapping signals. It was found that PCR gave the best results with total relative prediction error (RPE T ) of 7.71%. The proposed method was applied to determine these three drugs in several commercial food samples with spiked method and yielded satisfactory recoveries.

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.

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.

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 kinetic spectrophotometric method was developed for determination of pyrocatechol, resorcin, hydroquinone and phenol based on their inhibitory effect on the oxidation of Rhodamine B (RhB) in acid medium at pH=3.0. A linear relationship was observed between the inhibitory effect and the concentrations of the compounds. The absorbance associated with the kinetic reactions was monitored at the maximum wavelength of 557nm. The effects of different parameters such as pH, concentration of RhB and KBrO(3), and temperature of the reaction were investigated and optimum conditions were established. The linear ranges were 0.22-3.30, 0.108-0.828, 0.36-3.96 and 1.52-19.76μg mL(-1) for pyrocatechol, resorcin, hydroquinone and phenol, respectively, and their corresponding detection limits were 0.15, 0.044, 0.16 and 0.60μg mL(-1). The measured data were processed by several chemometrics methods, such as principal component regression (PCR), partial least squares (PLS) and artificial neural network (ANN), and a set of synthetic mixtures of these compounds was used to verify the established models. It was found that the prediction ability of PLS, PCR and RBF-ANN was similar, however, the RBF-ANN model did perform somewhat better than the other methods. The proposed method was also applied satisfactorily for the simultaneous determination of pyrocatechol, resorcin, hydroquinone and phenol in real 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.

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.

This article discusses the outcomes of a research project on nutrients build-up on urban road surfaces. Nutrient build-up was investigated on road sites belonging to residential, industrial and commercial land use. Collected build-up samples were separated into five particle size ranges and were tested for total nitrogen (TN), total phosphorus (TP) and sub species of nutrients, namely, NO2-, NO3-, TKN and PO4(3-). Multivariate analytical techniques were used to analyse the data and to develop detailed understanding on buildup. Data analysis revealed that the solids loads on urban road surfaces are highly influenced by factors such as land use, antecedent dry period and traffic volume. However, the nutrient build-up process was found to be independent of the type of land use. It was solely dependent on the particle size of solids build-up. Most of the nutrients were associated with the particle size range150 microm. Therefore, the removal of particles below 150 microm from road surfaces is of importance for the removal of nitrogen and phosphorus from road surface solids build-up. It is also important to consider the difference in the composition of nitrogen and phosphorus build-up in the context of designing effective stormwater quality mitigation strategies.

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).

High performance liquid chromatographic (HPLC) fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were developed by means of the chromatograms at two wavelengths of 238 and 282 nm. Then, the two data sets were combined into one matrix. The application of principal component analysis (PCA) for this data matrix showed that the samples were clustered into four groups in accordance with the plant sources and preparation procedures. Furthermore, partial least squares (PLS), back propagation artificial neural network (BP-ANN), and radial basis function artificial neural network (RBF-ANN) were effectively applied to predict the category of the four different samples in the test set.

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics. To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics’ interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided.

Films found on the windows of residential buildings have been studied. The main aim of the paper was to assess the roles of the films in the accumulation of potentially toxic chemicals in residential buildings. Thus the elemental and polycyclic aromatic hydrocarbon compositions of the surface films from the glass windows of eighteen residential buildings were examined. The presence of sample amounts of inorganic elements (4.0–1.2 × 106 μg m−2) and polycyclic aromatic hydrocarbons in the films (BDL - 620.1 ng m−2) has implications for human exposure and the fate of pollutants in the urban environment. To facilitate the interpretation of the results, data matrices consisting of the chemical composition of the films and the building characteristics were subjected to multivariate data analysis methods, and these revealed that the accumulation of the chemicals was strongly dependent on building characteristics such as the type of glass used for the window, the distance from a major road, age of the building, distance from an industrial activity, number of smokers in the building and frequency of cooking in the buildings. Thus, building characteristics which minimize the accumulation of pollutants on the surface films need to be encouraged.

A procedure for the simultaneous kinetic spectrophotometric determination of aminocarb and carbaryl in vegetable and water samples was described. The method was based on the differential oxidation rate of aminocarb and carbaryl when they were reacted with the oxidant, potassium ferricyanide (K 3 Fe(CN) 6 ), in an appropriate alkaline medium. Both species were instantly oxidized, and resulted in a decrease of ferricyanide concentration. This anion has a maximum spectral absorbance at about 420 nm. Under the optimum experimental conditions, the linear ranges were 0.05–0.6 mg L −1 and 0.1–1.2 mg L −1 for aminocarb and carbaryl, respectively. The kinetic data collected were processed by chemometrics methods, such as classical least squares (CLS), partial least squares (PLS), principal components regression (PCR), back propagation-artificial neural network (BP-ANN), radial basis function-artificial neural network (RBF-ANN), and principal component-radial basis function-artificial neural network (PC-RBF-ANN). These methods were applied for the prediction of the two carbamate pesticides. The results showed that the PLS and PC-RBF-ANN calibration models gave the lowest prediction errors. The proposed method was successfully applied to the simultaneous determination of aminocarb and carbaryl in vegetable and water samples, and satisfactory results were obtained.

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.

This paper describes a simple and sensitive kinetic spectrophotometric method for the simultaneous determination of Amaranth, Ponceau 4R, Sunset Yellow, Tartrazine and Brilliant Blue in mixtures with the aid of chemometrics. The method involved two coupled reactions, viz. the reduction of iron(III) by the analytes to iron(II) in sodium acetate/hydrochloric acid solution (pH 1.71) and the chromogenic reaction between iron(II) and hexacyanoferrate(III) ions to yield a Prussian blue peak at 760 nm. The spectral data were recorded over the 500-1000 nm wavelength range every 2s for 600 s. The kinetic data were collected at 760 nm and 600 s, and linear calibration models were satisfactorily constructed for each of the dyes with detection limits in the range of 0.04-0.50 mg L(-1). Multivariate calibration models for kinetic data were established and verified for methods such as the Iterative target transform factor analysis (ITTFA), principal component regression (PCR), partial least squares (PLS), and principal component-radial basis function-artificial neural network (PC-RBF-ANN) with and without wavelet packet transform (WPT) pre-treatment. The PC-RBF-ANN with WPT calibration performed somewhat better than others on the basis of the %RPE(T) (approximately 9) and %Recovery parameters (approximately 108), although the effect of the WPT pre-treatment was marginal (approximately 0.5% RPE(T)). The proposed method was applied for the simultaneous determination of the five colorants in foodstuff samples, and the results were comparable with those from a reference HPLC method.

A simple and sensitive spectrophotometric method for the simultaneous determination of acesulfame-K, sodium cyclamate and saccharin sodium sweeteners in foodstuff samples has been researched and developed. This analytical method relies on the different kinetic rates of the analytes in their oxidative reaction with KMnO4 to produce the green manganate product in an alkaline solution. As the kinetic rates of acesulfame-K, sodium cyclamate and saccharin sodium were similar and their kinetic data seriously overlapped, chemometrics methods, such as partial least squares (PLS), principal component regression (PCR) and classical least squares (CLS), were applied to resolve the kinetic data. The results showed that the PLS prediction model performed somewhat better. The proposed method was then applied for the determination of the three sweeteners in foodstuff samples, and the results compared well with those obtained by the reference HPLC method.

The interaction of quercetin, which is a bioflavonoid, with bovine serum albumin (BSA) was investigated under pseudo-physiological conditions by the application of UV-vis spectrometry, spectrofluorimetry and cyclic voltammetry (CV). These studies indicated a cooperative interaction between the quercetin-BSA complex and warfarin, which produced a ternary complex, quercetin-BSA-warfarin. It was found that both quercetin and warfarin were located in site I. However, the spectra of these three components overlapped and the chemometrics method - multivariate curve resolution-alternating least squares (MCR-ALS) was applied to resolve the spectra. The resolved spectra of quercetin-BSA and warfarin agreed well with their measured spectra, and importantly, the spectrum of the quercetin-BSA-warfarin complex was extracted. These results allowed the rationalization of the behaviour of the overlapping spectra. At lower concentrations ([warfarin]

A method for determination of lactose in food samples by Osteryoung square wave voltammetry (OSWV) was developed. It was based on the nucleophilic addition reaction between lactose and aqua ammonia. The carbonyl group of lactose can be changed into imido group, and this increases the electrochemical activity in reduction and the sensitivity. The optimal condition for the nucleophilic addition reaction was investigated and it was found that in NH4Cl–NH3 buffer of pH 10.1, the linear range between the peak current and the concentration of lactose was 0.6–8.4 mg L−1, and the detection limits was 0.44 mg L−1. The proposed method was applied to the determination of lactose in food samples and satisfactory results were obtained.

A fast and accurate procedure has been researched and developed for the simultaneous determination of maltol and ethyl maltol, based on their reaction with iron(III) in the presence of o-phenanthroline in sulfuric acid medium. This reaction was the basis for an indirect kinetic spectrophotometric method, which followed the development of the pink ferroin product (λmax = 524 nm). The kinetic data were collected in the 370–900 nm range over 0–30 s. The optimized method indicates that individual analytes followed Beer’s law in the concentration range of 4.0–76.0 mg L−1 for both maltol and ethyl maltol. The LOD values of 1.6 mg L−1 for maltol and 1.4 mg L−1 for ethyl maltol agree well with those obtained by the alternative high performance liquid chromatography with ultraviolet detection (HPLC-UV). Three chemometrics methods, principal component regression (PCR), partial least squares (PLS) and principal component analysis–radial basis function–artificial neural networks (PC–RBF–ANN), were used to resolve the measured data with small kinetic differences between the two analytes as reflected by the development of the pink ferroin product. All three performed satisfactorily in the case of the synthetic verification samples, and in their application for the prediction of the analytes in several food products. The figures of merit for the analytes based on the multivariate models agreed well with those from the alternative HPLC-UV method involving the same samples.

Synchronous fluorescence spectroscopy (SFS) was applied for the investigation of interactions of the antibiotic, tetracycline (TC), with DNA in the presence of aluminium ions Al3+. The study was facilitated by the use of the Methylene Blue (MB) dye probe, and the interpretation of the spectral data with the aid of the chemometrics method, parallel factor analysis (PARAFAC). Three-way synchronous fluorescence analysis extracted the important optimum constant wavelength differences, deltalambda, and showed that for the TC-Al3+-DNA, TC-Al3+ and MB dye systems, the associated deltalambda values were different (deltalambda=80, 75 and 30 nm, respectively). Subsequent PARAFAC analysis demonstrated the extraction of the equilibrium concentration profiles for the TC-Al3+, TC-Al3+-DNA and MB probe systems. This information is unobtainable by conventional means of data interpretation. The results indicated that the MB dye interacted with the TC-Al3+-DNA surface complex, presumably via a reaction intermediate, TC-Al3+-DNA-MB, leading to the displacement of the TC-Al3+ by the incoming MB dye probe.

In this paper, chemometrics methods were applied to resolve the high performance liquid chromatography (HPLC) fingerprints of complex, many-component substances to compare samples from a batch from a given manufacturer, or from those of different producers. As an example of such complex substances, we used a common Chinese traditional medicine, Huoxiang Zhengqi Tincture (HZT) for this research. Twenty-one samples, each representing a separate HZT production batch from one of three manufacturers were analyzed by HPLC with the aid of a diode array detector (DAD). An Agilent Zorbax Eclipse XDB-C18 column with an Agilent Zorbax high pressure reliance cartridge guard-column were used. The mobile phase consisted of water (A) and methanol (B) with a gradient program of 25-65% (v/v, B) during 0-30min, 65-55% (v/v, B) during 30-35min and 55-100% (v/v, B) during 35-60min (flow rate, 1.0mlmin(-1); injection volume, 20mul; and column temperature-ambient). The detection wavelength was adjusted for maximum sensitivity at different time periods. A peak area matrix with 21objectsx14HPLC variables was obtained by sampling each chromatogram at 14 common retention times. Similarities were then calculated to discriminate the batch-to-batch samples and also, a more informative multi-criteria decision making methodology (MCDM), PROMETHEE and GAIA, was applied to obtain more information from the chromatograms in order to rank and compare the complex HZT profiles. The results showed that with the MCDM analysis, it was possible to match and discriminate correctly the batch samples from the three different manufacturers. Fourier transform infrared (FT-IR) spectra taken from samples from several batches were compared by the common similarity method with the HPLC results. It was found that the FT-IR spectra did not discriminate the samples from the different batches.

The need to find the most efficient material for the clean-up of oil/fuel spills both at sea and on land is of extreme importance. Generally, this requires material selection based upon the adsorption properties of selected sorbent materials such as sand, swelling clays, organo-clays and cotton fibres. These adsorption properties are a function of the surface characterisation where hydrophobic and oleophilic properties are essential. From BET analysis, the adsorption isotherm of the selected materials was Types II and IV in the IUPAC classification scheme. The main adsorption mechanism for these sorbents occurred on the external surface of the material in the pores or capillaries. ESEM studies indicate that cotton capillaries contribute significantly to the adsorption process of oil. In addition, the presence of surface wax on cotton–cellulose fibre facilitated the uptake by: (a) providing a relatively hydrophobic surface for sorption of organics; and (b) providing a low surface energy environment for the capillaries to aid in oil transport. Cotton fibre was observed to have several key properties such as hydrophobicity, good affinity for hydrocarbons, rapid adsorption on contact, and high adsorption and retention through interfibre capillaries. This research provides the basis for selection of cotton–cellulose fibres compared to common and other novel alternatives such as sand and organo-clays, respectively.

This paper demonstrates the crucial role of chemometrics in facilitating the knowledge transfer of a laboratory derived rating for sugarcane clonal performance (based on GC measurements) towards a rapid near infrared (NIR) spectroscopic methodology for at-field screening applications. NIR sugarcane stalk spectra were collected from 65 sugarcane clones, which were obtained from two widely different geographical regions. An exploratory principal component analysis (PCA) of an NIR spectral data matrix of the sugarcane clones showed a clear trend along PCI similar to that found from the GC measurements, with the resistant clone (Q90) being well discriminated from the susceptible one (NCo310). Biplots related the NIR spectral frequencies with the spectral objects. Cellulose and lignin bands (4248 and 4406 cm(-1) respectively) were associated with the well performing clones and epicuticular wax bands (4346 and 5781 cm(-1)) with the poorly performing ones. Several partial least squares (PLS) regression models were constructed from the spectral objects, and satisfactorily predicted the traditional sugarcane performance ratings (SEP range +/-0.7-0.9 traditional units for the verification data, and the Measured versus Predicted R-2 range 0.880-0.910). In addition, the stalk spectra were investigated with the use of multicriteria decision-making methods, PROMETHEE and GAIA. This demonstrated that the spectral objects could be ranked on the basis of the NIR information alone, and that this rank order was in agreement with traditionally determined ratings. Thus, the performance of sugarcane clones could be assessed from molecular information as from the earlier laboratory GC measurements, and therefore, can be obtained completely independently of any conventional nominal ratings scales. (C) 2006 Elsevier B.V. All rights reserved.

A sensitive and selective enzymatic kinetic method for the simultaneous determination of mixtures of carbaryl and phoxim pesticides was researched and developed. It was based on the inhibitory effect of the pesticides on acetylcholinesterase (AChE), and the use of 5,5′-dithiobis(2-nitrobenzoic) acid (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The DTNB-thiocholine reaction was investigated by a spectrophotometric-kinetic approach. The complex rate equation for the formation of the chromogenic product, P, was solved under certain experimental conditions, which enabled the absorbance ( A P , at λ max = 412 nm) from the mixtures of the two pesticide inhibitors to be directly related to their concentrations provided the absorbance additivity was followed. The spectra were measured for mixtures of carbaryl and phoxim at different concentrations, and at t = 904 s, T = 35 °C, pH = 7.5, c ATChI = 0.14, and c AChE = 0.10 mg mL −1 . The detection limits of the enzymatic kinetic spectrophotometric procedures for the determination of the carbaryl and phoxim were 4.7 and 0.59 μg L −1 , respectively. Calibration models for chemometrics methods, such as principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural network (RBF-ANN) were constructed and verified with synthetic samples of the mixtures of the two pesticides. The best performing model was based on the RBF-ANN method yielding at approximately 10 ppb analyte concentrations, %RPE T (carbaryl = 5.2; phoxim = 6.5), %Recovery (approx.105%) and %RPE T (6.5). Various spiked town-water samples produced recoveries in the range of 98.8–103% for each pesticide.

Summary Physico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia–nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.

The presence of the pesticide ethion was determined using an electroanalytical method, i.e. , linear sweep stripping voltammetry at a hanging mercury drop electrode in the Britton-Robbinson buffer with pH = 8.36, based on its reduction peak observed at - 690 mV following its alkaline hydrolysis. The experimental parameters, namely, the preconcentration potential, the preconcentration time, and the scan rate were investigated and - 300 mV ( vs. Ag/AgCl), 270 s, and 100 mV/s were selected as the optimal values, respectively. The linear relationship between the peak current and the concentration was found to be in the range of 0.02-0.16 mg/L, with a detection limit of 0.0087 mg/L. The proposed method was applied to the determination of ethion in spiked vegetable and fruit samples via a preextraction with anhydrous ethanol.