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

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

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

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

An efficient method for the analysis of hydroquinone at trace levels in water samples has been developed in the form of a fluorescent probe based on graphene quantum dots (GQDs). The analytical variable, fluorescence quenching, was generated from the formation of benzoquinone intermediates, which formed during the catalytic oxidation of hydroquinone by horseradish peroxidase (HRP). In general, the reaction mechanism involved hydroquinone, as an electron acceptor, which affected the surface state of GQDs via an electron transfer effect. The water-soluble GQDs were directly prepared by the pyrolysis of citric acid and with the use of the mentioned hybrid enzyme system, the detection limit for hydroquinone was as low as 8.4 × 10−8 M. Furthermore, this analysis was almost unaffected by other phenol and quinine compounds, such as phenol, resorcinol and other quinines, and therefore, the developed GQD method produced satisfactory results for the analysis of hydroquinone in several different lake water samples.

Standard chemical methods of analysis for marker alkaloids and flavonoids such as rutin, apigenin, quercetin, kaempferol, and leonurine were insufficient to discriminate the geographical origins of the complex Herba leonuri (HL) samples. This led to the research and development of a novel, quantitative HPLC fingerprint method for the simultaneous analysis of the content of important markers present in the HL samples. Seven HPLC peaks were selected and a structure was assigned to five. Fifty-six HL samples collected from three provinces, Anhui, Hubei, and Jiangxi, were analyzed by the above HPLC method and the resulting 56 × 7 matrix of the HPLC fingerprints and the seven marker compounds was submitted to principal component analysis (PCA) with consequent discrimination of the HL samples according to their geographical origins; interestingly, it was found that the highly correlated L6 (kaempferol) and L7 (apigenin) loadings were independent of the other five marker compound loadings (L1–L5). In addition, the L6 and L7 variables were related only to the Jiangxi group, while the L1–L5 loadings were associated with Jiangxi and Anhui; the Hubei cluster was not directly related to any loading. For quantitative analysis, the least squares-support vector machine (LS-SVM) calibration model was able to predict 94.4% of the samples correctly.

Performance of a novel analytical method for complex samples, based on combined or fused high-performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FT-IR) data, was compared with that based on measurements from either of the techniques. The analytes were different varieties of Radix Paeoniae (Shaoyao), traditional Chinese medicines (TCM), from various regions. When the HPLC and FT-IR datasets (L and R) were separately submitted to either PCA or several supervised classification and prediction methods, the recognition rates were unsatisfactory. Thus, the potential of combined data of these two techniques was investigated, and two statistical methods for reducing the dimensions of the fused data were developed based on: (i) four PCs from the L and R matrices, and (ii) selection of optimum variables by the genetic algorithm-partial least squares (GA-PLS). PCA of these two fused sets indicated improved discrimination of the different sample clusters. Subsequent application of the supervised classification and prediction methods—linear discriminant analysis (LDA), least squares-support vector machine (LS-SVM), and radial basis function neural network (RBF-ANN)—indicated significantly higher recognition rates. Thus, the best discrimination of the complex Shaoyao TCM samples on the basis of their variety and geographical origin, was obtained with the use of non-linear RBF-ANN and LS-SVP models.

A new kinetic spectrophotometric method for simultaneous determination of chlortetracycline, tetracycline and oxytetracycline in real samples has been researched and developed. The method was based on the kinetic reaction involving the oxidation of the drugs by alkaline potassium permanganate at 40 °C. After the addition of these drugs, an increasing absorbance at 526 nm and a decreasing absorbance at 608 nm caused by manganate and permanganate, respectively, were observed. The three-way kinetic-spectral data in the 325–725 nm range from 0 to 360 s were collected and applied to build chemometrics models. The plot of absorbance versus concentration was rectilinear over the 0.05–0.75 µg mL−1 range for chlortetracycline and tetracycline, and 0.02–0.34 µg mL−1 for oxytetracycline, with the limit of detection of 0.013, 0.015 and 0.011 µg mL−1, respectively. The experimental conditions were carefully investigated to optimize the development and stability of the color products (potassium manganate). The proposed method was successfully applied to the simultaneous determination of tetracyclines (TCs) in feed and muscle samples, and the results showed satisfactory agreement with those from of the HPLC method.