A untargeted metabolomics approach was proposed in this study based on ultra-high performance liquid chromatography quadrupole time-of-flight (UHPLC-QTOF) and rapid evaporative ionization mass spectrometry (REIMS) to discriminate lamb and mutton meat and investigate their subtle metabolic differences, considering the higher popularity of lamb meat than mutton in the market. Multivariate statistical analysis was performed for data processing in order to distinguish between the two sample types. A total of 42 potential metabolites (20 in positive and 22 in negative ion mode) were defined for UHPLC-QTOF analysis, which provided references for discriminating the two kinds of meat. Furthermore, three potential markers were tentatively identified using LC/MS data against chemical databases. In addition, 14 potential metabolites were putatively identified in negative ion mode using the LipidMaps database. Meanwhile, the data-driven soft independent modeling of class analogy (DD-SIMCA) model was established, which could rapidly differentiate non-pretreated lamb meat and mutton with 92% specificity, rendering REIMS a promising technique for meat identification.

Milk adulteration is a common phenomenon in many countries, which draws extensive attention from humans due to health hazards that might result in some fatal diseases. In this study, a portable near-infrared (NIR) spectrometer combined with multivariate analysis was used to detect and quantify milk adulteration. Fresh cow milk samples were collected from eight dairy farms in Beijing and Hebei province of China. Water, urea, starch and goat milk were used to adulterate milk at 11 different concentrations. The data driven soft independent modeling of class analogy (DD-SIMCA) method was employed for qualitative analysis. Partial least squares regression (PLSR) was applied for statistical analysis of the obtained NIR spectral data. The results showed that the DD-SIMCA approach achieved satisfactory classification. By the PLSR model, standard error of prediction (SEP) values of 4.35, 0.34, 4.74 and 5.56 g/L were obtained for water, urea, starch and goat milk, respectively. These results demonstrated the feasibility and reliability of NIR spectroscopy combined with multivariate analysis in the prediction of the total contents of the investigated adulterants in cow milk. Key words: Portable near-infrared (NIR)-spectroscopy, milk adulteration, DD-SIMCA, Partial least squares (PLS) regression. Milk adulteration is a common phenomenon in many countries, which draws extensive attention from humans due to health hazards that might result in some fatal diseases. In this study, a portable near-infrared (NIR) spectrometer combined with multivariate analysis was used to detect and quantify milk adulteration. Fresh cow milk samples were collected from eight dairy farms in Beijing and Hebei province of China. Water, urea, starch and goat milk were used to adulterate milk at 11 different concentrations. The data driven soft independent modeling of class analogy (DD-SIMCA) method was employed for qualitative analysis. Partial least squares regression (PLSR) was applied for statistical analysis of the obtained NIR spectral data. The results showed that the DD-SIMCA approach achieved satisfactory classification. By the PLSR model, standard error of prediction (SEP) values of 4.35, 0.34, 4.74 and 5.56 g/L were obtained for water, urea, starch and goat milk, respectively. These results demonstrated the feasibility and reliability of NIR spectroscopy combined with multivariate analysis in the prediction of the total contents of the investigated adulterants in cow milk. Key words: Portable near-infrared (NIR)-spectroscopy, milk adulteration, DD-SIMCA, Partial least squares (PLS) regression.

Natural antimicrobials (NAM) are promising candidates for the successful control of poultry-borne bacteria, carrying potent antimicrobial activity (AMA) against a wide range of multidrug-resistant pathogens. Individual activities of carvacrol, eugenol, trans-cinnamaldehyde, oregano, and thymol, along with the combined activity of paired compounds, were examined using broth microdilution and checkerboard techniques. The characteristic interactions between the compounds were calculated using an improved method, based on combination index (CI) values. The bacteria examined herein were selected due to their known genetic resistance to at least one antibiotic. Our results indicated that thymol was most effective, exhibiting the lowest minimum inhibitory concentration (MIC) value against Salmonella pullorum, Escherichia coli, and Klebsiella pneumoniae, establishing the order of antimicrobial efficacy as: thymol > oregano > carvacrol > trans-cinnamaldehyde > eugenol. In the interaction study, the paired combination of carvacrol and thymol showed synergistic effects and was highly effective in reducing the antibiotic resistance of all the evaluated pathogens. Notably, all CI values were <1.0 in evaluations of S. pullorum, indicating the absence of antagonism between eugenol and thymol (or oregano). In K. pneumoniae, majority of CI values, which had a few concentration points, were smaller than 1.0, indicating a synergistic effect between eugenol and carvacrol (oregano or thymol), and trans-cinnamaldehyde and carvacrol. In E. coli, apart from some concentration points, some CI values were smaller than 1.0, demonstrating a synergistic effect between eugenol and carvacrol, and thymol and carvacrol (eugenol or oregano). It is therefore of great significance to investigate and illuminate the minimal effect concentration of these five components when they are used in combination as feed additives. Moreover, the improved evaluation method of this study provides a precise and extensive means to assess the synergistic effects of NAM.