Non-targeted volatile profiles for the classification of the botanical origin of Chinese honey by solid-phase microextraction and gas chromatography-mass spectrometry combined with chemometrics.

A potential method for the discrimination and prediction of honey samples of various botanical origins was developed based on the non-targeted volatile profiles obtained by solid-phase microextraction with gas chromatography and mass spectrometry combined with chemometrics. The blind analysis of non-targeted volatile profiles was carried out using solid-phase microextraction with gas chromatography and mass spectrometry for 87 authentic honey samples from four botanical origins (acacia, linden, vitex, and rape). The number of variables was reduced from 2734 to 70 by using a series of filters. Based on the optimized 70 variables, 79.12% of the variance was explained by the first four principal components. Partial least squares discriminant analysis, naïve Bayes analysis, and back-propagation artificial neural network were used to develop the classification and prediction models. The 100% accuracy revealed a perfect classification of the botanical origins. In addition, the reliability and practicability of the models were validated by an independent set of additional 20 authentic honey samples. All 20 samples were accurately classified. The confidence measures indicated that the performance of the naïve Bayes model was better than the other two models. Finally, the characteristic volatile compounds of linden honey were tentatively identified. The proposed method is reliable and accurate for the classification of honey of various botanical origins.

Discrimination of botanical origins for Chinese honey according to free amino acids content by high-performance liquid chromatography with fluorescence detection with chemometric approaches.

BACKGROUND: The contents of 18 free amino acids in 87 Chinese honey samples from four botanical origins (linden, acacia, vitex and rape) were determined by developing a high-performance liquid chromatography with fluorescence detector (HPLC-FLD) method with an in-loop automated pre-column derivatization. The free amino acid profiles of these samples were used to construct a statistical model to distinguish honeys from various floral origins. RESULTS: The average contents of all free amino acids in linden honey were lower than in the other three types of honey. Phenylalanine was particularly useful in the present study because its average content in vitex honey was far higher than in any other honey samples. There is no doubt that both phenylalanine and tyrosine can be considered as the marker free amino acid in Chinese vitex honey. Principal component analysis (PCA) was conducted based on 15 free amino acids and showed significant differences among the honey samples. The cumulative variance for the first two components was 80.62%, and the four principal components can explain 94.18% of the total variance. In the two first component scores, the honey samples can be separated according to their botanical origins. Cluster analysis of amino acid data also revealed that the botanical origins of honey samples correlated with their amino acid content. Back-propagation artificial neural network (BP-ANN) and naïve Bayes methods were employed to construct the classification models. The results revealed an excellent separation among honey samples according to their botanical origin with 100% accuracy in model training for both BP-ANN and naïve Bayes. CONCLUSION: It indicated that the free amino acid profile determined by HPLC-FLD can provide sufficient information to discriminate honey samples according to their botanical origins. © 2016 Society of Chemical Industry.

[Application of ICP-MS method in the determination of mineral elements in vitex honey for the classification of their geographical origins with chemometric approach].

In the present work, the contents of 38 elements of 65 vitex (Vitex negundo var. heterophylla Rehd. ) honey samples from Shunyi of Beijing, Fuping and Pingshan of Hebei province were determined by inductively coupled plasma mass spectrometry (ICP-MS). Among them, B, Na, Mg, P, K, Ca, Fe and Zn were the most abundant elements with mean contents more than 1 mg kg-1. It can be found that there were relationships between the contents of elements and the geographical origin of vitex honey samples. Taking the contents of 29 out of 38 mineral elements (Na, Mg, Al, K, Ti, V, Mn, Fe, Ni, Cu, Zn, Ga, As, Sr, Y, Mo, Cd, Ba, La, Ce, Pr, Nd, Sm, Gd, Dy, Ho, T1, Pb and U) as variables, the chemometric methods, such as principal component analysis (PCA) and back-propagation artificial neural network (BP-ANN), were applied to classify vitex honey samples according to their geographical origins. PCA reduced all of the variables to four principal components and could explain 81. 6% of the total variances. The results indicated that PCA could mainly classify the vitex honey samples into three groups. BP-ANN was explored to construct classification model of vitex honeys according to their geographical origin. For the whole data set, the overall correct classification rate and cross-validation (leave one out method) rate of proposed BP-ANN model was 100% and 95. 4%, respectively. To further test the stability of the model developed for prediction, 75% of honey samples of each geographical origin were randomly selected for the model training set, and the remaining samples were classified with the use of the constructed model. Both the overall correct classification rate and prediction rate of proposed BP-ANN model were 100%. It is concluded that the profiles of multi-element by ICP-MS with chemometric methods could be a potential and powerful tool for the classification of vitex honey samples from different geographical origins.

Screening of 439 Pesticide Residues in Fruits and Vegetables by Gas Chromatography-Quadrupole-Time-of-Flight Mass Spectrometry Based on TOF Accurate Mass Database and Q-TOF Spectrum Library.

Because of its unique characteristics of accurate mass full-spectrum acquisition, high resolution, and fast acquisition rates, GC-quadrupole-time-of-flight MS (GC-Q-TOF/MS) has become a powerful tool for pesticide residue analysis. In this study, a TOF accurate mass database and Q-TOF spectrum library of 439 pesticides were established, and the parameters of the TOF database were optimized. Through solid-phase extraction (SPE), whereby pesticides are extracted from fruit and vegetable substrates by using 40 mL 1% acetic acid in acetonitrile (v/v), purified by the Carbon/NH2 SPE cartridge, and finally detected by GC-Q-TOF/MS, the rapid analysis of 439 pesticides in fruits and vegetables can be achieved. The methodology verification results show that more than 70 and 91% of pesticides, spiked in fruits and vegetables with concentrations of 10 and 100 µg/kg, respectively, saw recoveries that conform to the European Commission's criterion of between 70 and 120% with RSD ≤20%. Eighty-one percent of pesticides have screening detection limits lower than 10 µg/kg, which makes this a reliable analysis technology for the monitoring of pesticide residues in fruits and vegetables. This technology was further validated for its characteristics of high precision, high speed, and high throughput through successful detection of 9817 samples during 2013-2015.

[Comparison of the performances of gas chromatography-quadrupole time of flight mass spectrometry and gas chromatography-tandem mass spectrometry in rapid screening and confirmation of 208 pesticide residues in fruits and vegetables].

The performances of gas chromatography-tandem mass spectrometry (GC-MS/MS) and gas chromatography quadrupole time of flight mass spectrometry (GC-QTOF/MS) for the determination of 208 pesticide residues in fruit and vegetable samples, including apple, orange, tomato and cucumber, were compared comprehensively. Based on the differences of the two instruments, their respective characteristics and scopes of application in the detection of the pesticide residues were presented, which provided the reference for the analysis of pesticide residues. The performance parameters of the two instruments, such as overall recoveries, precisions, limits of detection, linear ranges, identification points and matrix effects, were evaluated according to a designed experiment. At three spiked levels (5.0, 10.0 and 20.0 µg/kg), the average recoveries for the majority of pesticides (93.0%) ranged from 70% to 120% in the four matrices with relative standard deviations below 20%. The limits of detection for most of the pesticides by GC-MS/MS and GC-Q-TOF/MS were less than 5.0 µg/kg. Compared with GC-QTOF/MS, GC-MS/MS showed relatively lower limits of detection and wider linear ranges, and its performance was more satisfactory in accurate quantitative analysis due to its superior sensitivity. On the other hand, GC-QTOF/MS provided accurate mass measurement, which was proved to be an efficient analytical tool on the rapid screening and confirmation of a large number of pesticides and non-target compounds.