Discrimination of Free-Range and Caged Eggs by Chemometrics Analysis of the Elemental Profiles of Eggshell.

As one of the foods commonly eaten all over the world, eggs have attracted more and more attention for their quality and price. A method based on elemental profiles and chemometrics to discriminate between free-range and caged eggs was established. Free-range (n1 = 127) and caged (n2 = 122) eggs were collected from different producing areas in China. The content of 16 elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in the eggshell was determined using a inductively coupled plasma atomic emission spectrometer (ICP-AES). Outlier diagnosis is performed by robust Stahel-Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classification of the two types of eggs. As a result, Cd, Mn, Mg, Se, and K make an important contribution to the classification of free-range and caged eggs. By combining column-wise and row-wise rescaling of the elemental data, the sensitivity, specificity, and accuracy were 91.9%, 91.1%, and 92.7% for PLS-DA, while the results of LS-SVM were 95.3%, 95.6%, and 95.1%, respectively. The result indicates that chemometrics analysis of the elemental profiles of eggshells could provide a useful and effective method to discriminate between free-range and caged eggs.

A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine.

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0-3 kilometers, 7.52-88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0-0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins.

The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, Gastrodia elata BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.

ZnCdSe-CdTe quantum dots: A "turn-off" fluorescent probe for the detection of multiple adulterants in an herbal honey.

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds.

Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics.

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.

High-performance liquid chromatographic analysis of as-synthesised N,N'-dimethylformamide-stabilised gold nanoclusters product.

Reverse-phase high-performance liquid chromatographic (RP-HPLC) separation and analysis of polydisperse water-soluble gold nanoclusters (AuNCs) stabilised with N,N'-dimethylformamide (DMF) were investigated. Under optimal elution gradient conditions, the separation of DMF-AuNCs was monitored by absorption and fluorescence spectroscopy. The UV-vis spectral characteristics of the separated DMF-AuNCs have been captured and they do not possess distinct surface plasmon resonance bands, indicating that all DMF-AuNCs are small AuNCs. The photoluminescence emission spectra of the separated DMF-AuNCs are in the blue-light region. Moreover, cationic DMF-AuNCs are for the first time identified by ion chromatography. Our proposed RP-HPLC methodology has been successfully applied to separate AuNCs of various Au atoms as well as DMF-stabilised ligands. Finally, the composition of the separated DMF-AuNCs was confirmed by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and electrospray ionisation mass spectrometry, proving that the as-synthesised DMF-AuNCs product consists of Au10⁺, Au10, Au11, Au12, Au13, and Au14 NCs stabilised with various numbers of DMF ligands.

Ultrahigh performance liquid chromatographic analysis and magnetic preconcentration of polycyclic aromatic hydrocarbons by Fe3O4-doped polymeric nanoparticles.

This paper reports the synthesis of hydrophilic-hydrophobic magnetic Fe3O4-doped polymeric nanoparticles (MPNP) and its application for preconcentration of polycyclic aromatic hydrocarbons (PAHs) in environmental water samples for ultrahigh performance liquid chromatographic (UHPLC) analysis. The MPNP were prepared from highly charged poly(styrene-divinylbenzene-co-4-vinylbenzenesulfonic acid sodium salt) nanoparticles impregnated with Fe²âº via the electrostatic attraction and by microwave heating. The MPNP are relatively uniform in size with an average diameter of 50 nm and have a magnetic saturation value of 24.5 emu/g. The hydrophilic-hydrophobic MPNP could easily disperse in water. The phenyl moieties of MPNP assist the adsorption of PAHs via both hydrophobic and π-π interactions. The separation of the PAHs-adsorbed MPNP from water could be easily achieved by a permanent magnet and the adsorbed PAHs were back extracted into acetonitrile for UHPLC analysis. The UHPLC separation of PAHs is very quick and could be achieved within 1.6 min. Factors affecting the extraction and desorption were investigated in detail. Under the optimum experimental conditions, the recoveries of various PAHs including acenaphthylene, anthracene, fluoranthene, fluorene, phenanthrene, and pyrene in water samples at three different concentrations are 75.7-102.9, 77.8-101.2, 86.3-100.7, 88.5-99.7, 92.0-106.4, and 81.6-98.5%, respectively. The recovery SDs are 0.30-8.20% and the instrumental limits of detection are 10.83-18.53 nM. The proposed technique combining hydrophobic extraction and magnetic separation coupled with UHPLC could provide a fast, convenient and sensitive method for the determination of PAHs in water samples.

Electrogenerated chemiluminescence behavior of graphite-like carbon nitride and its application in selective sensing Cu2+.

This paper reports for the first time the electrogenerated chemiluminescence (ECL) behavior of graphite-like carbon nitride (g-C(3)N(4)) with K(2)S(2)O(8) as the coreactant. The possible ECL reaction mechanisms are proposed. The spectral features of the ECL emission and photoluminescence (PL) of g-C(3)N(4) are compared, and their resemblance demonstrates that the excited states of g-C(3)N(4) from both ECL and photoexcitation are the same. The effects of K(2)S(2)O(8) concentration, pH, g-C(3)N(4)/carbon powder ratio, and scan rate on the ECL intensity have been studied in detail. Furthermore, it is observed that the ECL intensity is efficiently quenched by trace amounts of Cu(2+). g-C(3)N(4) is thus employed to fabricate an ECL sensor which shows high selectivity to Cu(2+) determination. The limit of detection is determined as 0.9 nM. It is anticipated that g-C(3)N(4) could be a new class of promising material for fabricating ECL sensors.

Simultaneous determination of L-ascorbic acid, dopamine and uric acid with gold nanoparticles-ß-cyclodextrin-graphene-modified electrode by square wave voltammetry.

Graphene decorated with gold nanoparticles (AuNPs-ß-CD-Gra) has been synthesized by in situ thermal reduction of graphene oxide and HAuCl(4) with ß-cyclodextrin (ß-CD) under alkaline condition. The AuNPs-ß-CD-Gra product was well characterized by infrared spectroscopy, X-ray powder diffraction, scanning electron microscopy, high-resolution transmission electron microscopy, and selected area electron diffraction. This material was used to fabricate an AuNPs-ß-CD-Gra-modified glassy carbon electrode (GCE) which showed excellent electro-oxidation of l-ascorbic acid (AA), dopamine (DA) and uric acid (UA) in 0.10 M NaH(2)PO(4)-HCl buffer solution (pH 2.0) by square wave voltammetry (SWV). Three well-resolved oxidation peaks of AA and DA and UA were obtained. The AuNPs-ß-CD-Gra/GCE exhibits linear responses to AA, DA and UA in the ranges 30-2000, 0.5-150 and 0.5-60 µM, respectively. The detection limits (based on S/N=3 and preconcentration time=3.0 min) for AA, DA and UA are 10, 0.15 and 0.21 µM, respectively. The AuNPs-ß-CD-Gra/GCE has been successfully applied to determine UA in human urine with satisfactory results. Our work provides a simple, convenient and green route to synthesize AuNPs on Gra which is potentially useful in electroanalysis.

Electrogenerated chemiluminescence of anatase TiO2 nanotubes film.

Highly ordered titanium dioxide (TiO(2)) nanotubes film was successfully synthesized via anodic oxidation of a Ti foil in an ammonium fluoride-based ethylene glycol solution. The electrogenerated chemiluminescence (ECL) behavior of the resulting TiO(2) nanotubes film was subsequently studied. Strong ECL emission was observed at -1.40V (vs. Ag/AgCl) and the ECL spectrum displayed three emission peaks which were bathochromatically shifted by ca. 140nm as compared to its corresponding photoluminescence (PL) emission peaks, indicating that the surface state plays an important role in the emission process. The ECL emission can also occur in a deareated solution attributing to the surface adsorbed O(2) molecules. The ECL emission intensity was quenched by dopamine and greatly enhanced in the presence of dissolved O(2) and H(2)O(2), making it possible to detect these analytes. The TiO(2) nanotubes film has been successfully applied to determine the dissolved O(2) content in river and pond water samples, the H(2)O(2) concentration in commercial disinfectant samples and the dopamine concentration in commercial dopamine injections with satisfactory results. The plausible ECL mechanisms of TiO(2) nanotubes film in aqueous solution are discussed.

Separation and preconcentration of persistent organic pollutants by cloud point extraction.

Persistent organic pollutants (POPs) are recognized as a class of poisonous compounds which pose risks of causing adverse effects to human health and the environment. Thus, it is very important to detect POPs in environmental and biological samples. The identification and determination of very low levels of POPs in complex matrices is extremely difficult. Recently a promising environmentally benign extraction and preconcentration methodology based on cloud point extraction (CPE) has emerged as an efficient sample pretreatment technique for the determination of trace/ultra-trace POPs in complex matrices. The purpose of this paper is to review the past and latest use of CPE for preconcentrating POPs and its coupling to different contemporary instrumental methods of analysis. First, the comparison of various extraction techniques for POPs is described. Next, the general concept, influence factors and other methods associated with CPE technique are outlined and described. Last, the hyphenations of CPE to various instrumental methods for their determination are summarized and discussed.