Aggregating-agent-assisted surface-enhanced Raman spectroscopy-based detection of acrylamide in fried foods: A case study with potato chips.

This study developed a simple, rapid, stable, and reliable technique for acrylamide (AAm) detection through surface-enhanced Raman scattering (SERS) on an AgNPs substrate with an aggregating agent. Specifically, the agglomeration effects of five types of salt solutions (NaCl, KCl, MgCl2, Na2SO4, and MgSO4) were investigated at different concentrations and optimized using an orthogonal experiment. The optimal amounts of the aggregating agent, analytes, and AgNPs were 4, 4, and 12 µL, respectively. A linear relationship (peak area I1449 = 7.4197x + 5984.8, R2 = 0.9971) between the characteristic peak area and AAm concentration was established in the range of 10 to 500 µg/L, and the LOD was 2.5 µg/L. The recoveries and relative standard deviations in the analysis of potato chips samples were 94.67 %-117.50 % and 8.43 %-12.29 %, respectively. The results of the proposed method were consistent with those obtained by LC-MS/MS method. This study demonstrated that SERS has excellent potential for application in the qualitative and quantitative analyses of AAm in fried foods.

A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins.

Geographic-label is a remarkable feature for Chinese tea products. In this study, the UHPLC-Q/TOF-MS-based metabolomics approach coupled with chemometrics was used to determine the five narrow-geographic origins of Keemun black tea. Thirty-nine differentiated compounds (VIP > 1) were identified, of which eight were quantified. Chemometric analysis revealed that the linear discriminant analysis (LDA) classification accuracy model is 91.7%, with 84.7% cross-validation accuracy. Three machine learning algorithms, namely feedforward neural network (FNN), random forest (RF) and support vector machine (SVM), were introduced to improve the recognition of narrow-geographic origins, the performances of the model were evaluated by confusion matrix, receiver operating characteristic curve (ROC) and area under the curve (AUC). The recognition of RF, SVM and FNN for Keemun black tea from five narrow-geographic origins were 87.5%, 94.44%, and 100%, respectively. Importantly, FNN exhibited an excellent classification effect with 100% accuracy. The results indicate that metabolomics fingerprints coupled with chemometrics can be used to authenticate the narrow-geographic origins of Keemun black teas.

Efficient isolation and purification of tissue-specific protoplasts from tea plants (Camellia sinensis (L.) O. Kuntze).

BACKGROUND: Plant protoplasts constitute unique single-cell systems that can be subjected to genomic, proteomic, and metabolomic analysis. An effective and sustainable method for preparing protoplasts from tea plants has yet to be established. The protoplasts were osmotically isolated, and the isolation and purification procedures were optimized. Various potential factors affecting protoplast preparation, including enzymatic composition and type, enzymatic hydrolysis duration, mannitol concentration in the enzyme solution, and iodixanol concentration, were evaluated. RESULTS: The optimal conditions were 1.5% (w/v) cellulase and 0.4-0.6% (w/v) macerozyme in a solution containing 0.4 M mannitol, enzymatic hydrolysis over 10 h, and an iodixanol concentration of 65%. The highest protoplast yield was 3.27 × 106 protoplasts g-1 fresh weight. As determined through fluorescein diacetate staining, maximal cell viability was 92.94%. The isolated protoplasts were round and regularly shaped without agglomeration, and they were less than 20 µm in diameter. Differences in preparation, with regard to yield and viability in the tissues (roots, branches, and leaves), cultivars, and cultivation method, were also observed. CONCLUSIONS: In summary, we reported on a simple, efficient method for preparing protoplasts of whole-organ tissue from tea plant. The findings are expected to contribute to the rapid development of tea plant biology.