Establishment of Indirect Competitive Enzyme-linked Immunosorbent Assay (ic-ELISA) for Copper ion (Cu2+) in Raw Meat Products.

Adding an appropriate amount of copper to feed can promote the growth and development of livestock; however, a large amount of heavy metal copper can accumulate in livestock through the enrichment effect, which poses a serious threat to human health. Traditional Cu2+ detection relies heavily on complex and expensive instruments, such as inductively coupled plasma-optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS); thus, convenient and simple rapid detection technologies are urgently needed. In this paper, synthesized copper antigens were used to immunize mice and highly specific anticopper monoclonal antibodies were obtained, which were verified to exhibit high affinity and specificity. Based on the above antibodies, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was established for the rapid detection of copper content in pork. The standard inhibition curve of the method was obtained by antigen-antibody working concentration screening, in which the half inhibitory concentration (IC50) was 11.888 ng/mL, the limit of detection (LOD) was 0.841 ng/mL and the correlation coefficient R2 of the curve was 0.998. In the additive recovery experiment, the recovery rate ranged from 90% to 110%, and the coefficient of variation (CV) was less than 10%, indicating that the method achieved high accuracy and precision. Finally, the results of ic-ELISA combined with Bland-Altman analysis showed a high correlation with ICP-MS, and the correlation coefficient (R2) reached 0.990 when the copper concentration was less than 200 ng/mL. Thus, the ic-ELISA method exhibits high reliability.

Application of an improved hollow fiber liquid phase microextraction technique coupled to LC-MS/MS to studying migration of fluorescent whitening agents from plastic food contact materials.

In this paper, a new hollow fiber liquid-phase microextraction method was developed to improve the extraction of five fluorescent whitening agents that migrated from plastics food contact materials. Influencing factors, such as the types of membrane, the extraction solvent, the stirring speed, the addition of salt ion, and extraction time, were investigated in detail. Under the optimal conditions, high enrichment factors (71-205) can be obtained with 15 µL extraction solvent. The new method is advantageous; the polypropylene hollow fiber membrane modified by sepiolite nanoparticles had excellent solvent binding force and mass transfer effect compared with the conventional extraction technique. The extracts were analyzed by high performance liquid chromatography-tandem mass spectrometry, the limits of detection were 0.3 or 0.9 ng kg-1 with good correlation coefficients (r2 ≥ 0.9940) for the five fluorescent whitening agents. The intra-day and inter-day recoveries ranged between 82.6% and 112%, with a relative standard deviation of less than 12%. The established method was successfully applied to the analysis of fluorescent whitening agent migration from four types of plastic food contact materials immersed in three food simulants.

Tracing Geographical Origins of Teas Based on FT-NIR Spectroscopy: Introduction of Model Updating and Imbalanced Data Handling Approaches.

This work presents a reliable approach to trace teas' geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches-one-class SVM (OC-SVM), isolation forest and elliptic envelope-were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate.

Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea).

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n = 99) collected from nine producing areas and non-Wuyi-Rock tea (n = 33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.

Rapid discrimination of the geographical origins of an oolong tea (anxi-tieguanyin) by near-infrared spectroscopy and partial least squares discriminant analysis.

This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

Combining electronic tongue array and chemometrics for discriminating the specific geographical origins of green tea.

The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing.

Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration.

Class modeling techniques are required to tackle various one-class problems. Because the training of class models is based on the target class and the origins of future test objects usually cannot be exactly predefined, the criteria for feature selection of class models are not very straightforward. Although feature reduction can be expected to improve class models performance, more features retained can provide a sufficient description of the sought-for class. This paper suggests a strategy to balance class description and model specificity by ensemble learning of sub-models based on separate local wavelength intervals. The acceptance or rejection of a future object can be explicitly determined by examining its acceptance frequency by sub-models. Considering the lack of information about sub-model independence, we propose to use a data-driven method to control the sensitivity of the ensemble model by cross validation. In this way, all the wavelength intervals are used for class description and the local wavelength intervals are highlighted to enhance the ability to detect out-of-class objects. The proposed strategy was performed on one-class partial least squares (OCPLS) and soft independent modeling of class analogy (SIMCA). By analysis of two infrared spectral data sets, one for geographical origin identification of white tea and the other for discrimination of adulterations in pure sesame oil, the proposed ensemble class modeling method was demonstrated to have similar sensitivity and better specificity compared with total-spectrum SIMCA and OCPLS models. The results indicate local spectral information can be extracted to enhance class model specificity.

Automatic and rapid discrimination of cotton genotypes by near infrared spectroscopy and chemometrics.

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n = 120) and leaves (n = 123) were measured in the range of 4000-12000 cm(-1). A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

4,5,6,7-Tetra-chloro-2-(2,2,2-trifluoro-eth-yl)isoindoline-1,3-dione.

In the title compound, C(10)H(2)Cl(4)F(3)NO(2), the isoindoline ring system is almostplanar, the maximum atomic deviation being 0.064 (2) Å. The C-C bond of the ethyl-ene group is twisted with respect to the isoindoline plane by a dihedral angle of 59.58 (12)°. In the crystal, weak inter-molecular C-H⋯F hydrogen bonding links the mol-ecules into supra-molecular chains running along the a axis. A short inter-molecular Cl⋯O contact of 2.950 (3) Šis also observed.

4,5,6,7-Tetra-chloro-2-(4-fluoro-phen-yl)isoindoline-1,3-dione.

The title compound, C(14)H(4)Cl(4)FNO(2), has crystallographic twofold symmetry with the N and F atoms and two C atoms of the benzene ring located on a twofold rotation axis. The isoindole-dione ring system is almost planar [maximum atomic deviation = 0.036 (3) Å], and is twisted with respect to the florobenzene ring, making a dihedral angle of 58.56 (16)°. Weak inter-molecular C-H⋯Cl hydrogen bonding is present in the crystal structure.

2-(2,2,2-Trifluoro-eth-yl)isoindoline-1,3-dione.

In the title compound, C(10)H(6)F(3)NO(2), the isoindole ring system is planar, the maximum atomic deviation being 0.012 (2) Å. The C-C bond of the trifluoro-ethyl group is twisted with respect to the isoindole ring by a dihedral angle of 62.58 (17)°. Weak inter-molecular C-H⋯O and C-H⋯F hydrogen bonding is present in the crystal structure.

N-(3,4-Difluoro-phen-yl)phthalimide.

In the title compound, C(14)H(7)F(2)NO(2), the phthalimide ring system is nearly planar [maximum atomic deviation = 0.028 (1) Å] and it is twisted with respect to the attached benzene ring, making a dihedral angle of 55.70 (6)°. Weak inter-molecular C-H⋯F hydrogen bonds are present in the crystal structure.

N-(2,3,4-Trifluoro-phen-yl)phthalimide.

In the title compound, C(14)H(6)F(3)NO(2), the benzene ring and the phthalimide ring system make a dihedral angle of 60.12 (7)°. Weak inter-molecular C-H⋯O and C-H⋯F hydrogen bonds are present in the crystal structure.

4,5,6,7-Tetra-chloro-N-(2-fluoro-phen-yl)phthalimide.

In the title compound, C(14)H(4)Cl(4)FNO(2), the benzene ring and the phthalimide plane are nearly planar, the maximum deviations being 0.005 (2) and 0.010 (2) Å, respectively, but the mol-ecule as a whole is not planar: the dihedral angle between the two planar ring systems is 68.06 (10)°. A short Cl⋯O contact of 2.914 (2) Šexists in the crystal structure.