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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.

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.

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 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, 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.

A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint ( Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods—K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

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.

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.

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.

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.

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 paper demonstrates how inferential measurements or indirect methods using near-infrared (NIR) methodology and chemometrics can be used to predict sugarcane clonal performance. Fiji leaf gall resistance is used in this study as an example. Fiji leaf gall is one of Australia's most serious sugarcane diseases, representing a significant problem in almost half of the total area under production. Traditional rating of sugarcane clones for resistance/susceptibility is difficult and expensive because of the nature of field-based methods and variable infection levels of the trials. Thus, the aim of this work was to investigate the potential of NIR spectroscopy as an alternative means to rate clones from direct measurement of sugarcane leaf spectra and to examine its ability to successfully predict traditional resistance ratings using a calibration model based on a chemometrics method such as partial least squares (PLS). A scanning electron microscopy (SEM) study of the leaf substrate was undertaken to elucidate the nature of the NIR sample site. In addition, an NIR study of freeze-dried sugarcane leaf samples resolved the heavily overlapping O–H bands present in the NIR spectrum due to water/cellulose interaction. A significant decrease in the spectral intensity between 5205 and 5393 cm−1 was observed and a similar decrease was noted in the OH stretching overtone (7114 cm−1) with an accompanying shift to lower wavenumbers. PLS modeling based on traditional ratings as the dependent variable and the corresponding NIR spectra showed satisfactory results with standard error of validation (SEV) and standard error of prediction (SEP) values being 0.98 ( R2 = 0.97) and 1.20 ( R2 = 0.88), respectively. This methodology has now been recommended for more extensive field trials.

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]

The Chinese yam ( Dioscorea opposita) is a basic food in Asia and especially China. Consequently, an uncomplicated, reliable method should be available for the analysis of the quality and origin of the yams. Thus, near-infrared (NIR) and mid-infrared (mid-IR) spectroscopic methods were developed to discriminate among Chinese yam samples collected from four geographical regions. The yam samples were analyzed also for total sugar, polysaccharides, and flavonoids. These three analytes were used to compare the performance of the analytical methods. Overlapping spectra were resolved using chemometrics methods. Such spectra were compared qualitatively using principal component analysis (PCA) and quantitatively using partial least squares (PLS) and least squares-support vector machine (LS-SVM) models. We discriminated among the four sets of yam data using PCA, and the NIR data performed somewhat better than the mid-IR data. We constructed the PLS and LS-SVM calibration models for the prediction of the three key variables, and the LS-SVM model produced better results. Also, the NIR prediction model produced better outcomes than the mid-IR prediction model. Thus, both infrared (IR) techniques performed well for the analysis of the three key analytes, and the samples were qualitatively discriminated according to their provinces of origin. Both techniques may be recommended for the analysis of Chinese yams, although the NIR technique would be preferred.

Studies of kaolinite surfaces are of industrial importance. One useful method for studying the changes in kaolinite surface properties is to apply chemometric analyses to the kaolinite surface infrared spectra. A comparison is made between the mechanochemical activation of Kiralyhegy kaolinites with significant amounts of natural quartz and the mechanochemical activation of Zettlitz kaolinite with added quartz. Diffuse reflectance infrared Fourier transform (DRIFT) spectra were analyzed using principal component analysis (PCA) and multi-criteria decision making (MCDM) methods, the preference ranking organization method for enrichment evaluations (PROMETHEE) and geometrical analysis for interactive assistance (GAIA). The clear discrimination of the Kiralyhegy spectral objects on the two PC scores plots (400–800 and 800–2030 cm−1) indicated the dominance of quartz. Importantly, no ordering of any spectral objects appeared to be related to grinding time in the PC plots of these spectral regions. Thus, neither the kaolinite nor the quartz are systematically responsive to grinding time according to the spectral criteria investigated. The third spectral region (2600–3800 cm−1, OH vibrations), showed apparent systematic ordering of the Kiralyhegy and, to a lesser extent, Zettlitz spectral objects with grinding time. This was attributed to the effect of the natural quartz on the delamination of kaolinite and the accompanying phenomena (i.e., formation of kaolinite spheres and water). The mechanochemical activation of kaolinite and quartz, through dry grinding, results in changes to the surface structure. Different grinding times were adopted to study the rate of destruction of the kaolinite and quartz structures. This relationship (i.e., grinding time) was classified using PROMETHEE and GAIA methodology.

Raman spectroscopy has been used to investigate raw cotton acetylation using acetic anhydride/4-dimethylaminopyridine (DMAP) catalyst blend without solvent. The Raman data further confirm successful acetylation as shown by FTIR that was demonstrated previously to be highly sensitive for determining the level of acetylation. However, the Raman peaks are much weaker than the FTIR bands. Nevertheless, the variations of the extent of acetylation estimated from both Raman and FTIR spectra with weight percent gain due to acetylation (WPG) were observed to follow the same pattern. The degrees of acetylation calculated from Raman data were also found to increase linearly with that calculated from the more sensitive FTIR technique. Raman technique is thus suitable for further development as an analytical tool for determining the acetylation level of natural cellulose fibres. Raman data have also shown that the acetylation reaction reduces the crystallinity of cotton.

A rapid and effective sensor for the analysis of nitrophenol-based explosive substances, represented by trinitrophenol (TNP), has been developed with the use of the blue luminescent graphene quantum dots (GQDs); these GQDs are derived from citric acid by a pyrolysis procedure. They emit strong blue fluorescence at 450 nm after excitation at 365 nm, and TNP can quench this fluorescence because a fluorescence resonance energy transfer occurs. The quenching ratio ( F 0 − F )/ F 0 was related linearly to the concentration of TNP in the range of 0.1–15 μmol L −1 with a detection limit of 0.091 μmol L −1 ( S / N = 3). The developed method exhibits high sensitivity, good linearity and reliable reproducibility for the quantitative analysis of TNP in water samples. The GQDs were used directly without any further treatment or complicated modification.

A simple, inexpensive and sensitive kinetic spectrophotometric method was developed for the simultaneous determination of three anti-carcinogenic flavonoids: catechin, quercetin and naringenin, in fruit samples. A yellow chelate product was produced in the presence neocuproine and Cu(I) - a reduction product of the reaction between the flavonoids with Cu(II), and this enabled the quantitative measurements with UV-vis spectrophotometry. The overlapping spectra obtained, were resolved with chemometrics calibration models, and the best performing method was the fast independent component analysis (fast-ICA/PCR (Principal component regression)); the limits of detection were 0.075, 0.057 and 0.063 mg L(-1) for catechin, quercetin and naringenin, respectively. The novel method was found to outperform significantly the common HPLC procedure.

Interactions between the anti-carcinogens, bendamustine (BDM) and dexamethasone (DXM), with bovine serum albumin (BSA) were investigated with the use of fluorescence and UV–vis spectroscopies under pseudo-physiological conditions (Tris–HCl buffer, pH 7.4). The static mechanism was responsible for the fluorescence quenching during the interactions; the binding formation constant of the BSA–BDM complex and the binding number were 5.14 × 105 L mol−1 and 1.0, respectively. Spectroscopic studies for the formation of BDM–BSA complex were interpreted with the use of multivariate curve resolution – alternating least squares (MCR–ALS), which supported the complex formation. The BSA samples treated with site markers (warfarin – site I and ibuprofen – site II) were reacted separately with BDM and DXM; while both anti-carcinogens bound to site I, the binding constants suggested that DXM formed a more stable complex. Relative concentration profiles and the fluorescence spectra associated with BDM, DXM and BSA, were recovered simultaneously from the full fluorescence excitation–emission data with the use of the parallel factor analysis (PARAFAC) method. The results confirmed that on addition of DXM to the BDM–BSA complex, the BDM was replaced and the DXM–BSA complex formed; free BDM was released. This finding may have consequences for the transport of these drugs during any anti-cancer treatment.

Two sets of cotton fabrics—one consisting of different types of fabric and the other of a fabric sampled sequentially during its normal processing stages—were investigated by DRIFT spectroscopy. The band assignments were consistent with previous work but, in general, the spectra could not be differentiated on visual examination. However, they were successfully qualitatively and quantitatively distinguished with the use of chemometrics; a chemical basis for fabric clustering was also indicated. This approach is a significant improvement on previously reported work with KBr disk IR sampling.

DRIFT spectra were collected from dye mixtures extracted from small yarn samples taken from several areas of a worn test garment. Raw and pretreated data matrices were interpreted with the use of chemometrics (PCA, SIMCA, fuzzy clustering). This approach showed that spectra cluster and match according to their sampling area on the test textile. The DRIFT results provide detailed information that is not available from the application of the usual techniques (e.g., TLC) with such samples and therefore should be of use in forensics or similar work where comparisons of unknown, small dye mixtures are required.

A rapid NIR method for the determination of octane number of reformate samples of Australian refined motor gasoline is described. Results of predictions using calibration sets based on ASTM full blends and actual reformate samples are presented. Interpretation of spectral data using principal component analysis facilitates the selection of a suitable analytical spectral range and demonstrates that the reformate samples may be distinguished on the basis of the geographical origins of the parent crude oils. The described method forms a basis for practical application and is a possible alternative to the standard CFR engine test method.

The interaction between fluoroquinolones (FQs), ofloxacin and enrofloxacin, and bovine serum albumin (BSA) was investigated by fluorescence and UV–vis spectroscopy. It was demonstrated that the fluorescence quenching of BSA by FQ is a result of the formation of the FQ–BSA complex stabilized, in the main, by hydrogen bonds and van der Waals forces. The Stern–Volmer quenching constant, K SV , and the corresponding thermodynamic parameters, Δ H , Δ S and Δ G , were estimated. The distance, r , between the donor, BSA, and the acceptor, FQ, was estimated from fluorescence resonance energy transfer (FRET). The effect of FQ on the conformation of BSA was analyzed with the aid of UV–vis absorbance spectra and synchronous fluorescence spectroscopy. Spectral analysis showed that the two FQs affected the conformation of the BSA but in a different manner. Thus, with ofloxacin, the polarity around the tryptophan residues decreased and the hydrophobicity increased, while for enrofloxacin, the opposite effect was observed.

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526 nm, and the accompanying increase of the product, potassium manganate, at 608 nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0-11.5 mg L(-1) at 526 and 608 nm for pefloxacin, and 0.15-1.8 mg L(-1) at 526 and 608 nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526 nm, were the preferred methods--%RPE(T) approximately 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06 mg L(-1), respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.