Simple and sensitive determination of Cr (III), Cu (II) and Pb (II) in tea infusions using AgNPs-modified resin combined with laser-induced breakdown spectroscopy.

The detection of heavy metals in tea infusions is important because of the potential health risks associated with their consumption. Existing highly sensitive detection methods pose challenges because they are complicated and time-consuming. In this study, we developed an innovative and simple method using Ag nanoparticles-modified resin (AgNPs-MR) for pre-enrichment prior to laser-induced breakdown spectroscopy for the simultaneous analysis of Cr (III), Cu (II), and Pb (II) in tea infusions. Signal enhancement using AgNPs-MR resulted in amplification with limits of detection of 0.22 µg L-1 for Cr (III), 0.33 µg L-1 for Cu (II), and 1.25 µg L-1 for Pb (II). Quantitative analyses of these ions in infusions of black tea from various brands yielded recoveries ranging from 83.3% to 114.5%. This method is effective as a direct and highly sensitive technique for precisely quantifying trace concentrations of heavy metals in tea infusions.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation.

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Detection of heavy metal ions using laser-induced breakdown spectroscopy combined with filter paper modified with PtAg bimetallic nanoparticles.

The rapid and sensitive detection of heavy metal ions is important for environment and human health. Hence, the rapid and sensitive detection of multiple heavy metals simultaneously has become a critical issue. Here, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with PtAg bimetallic nanoparticles (BNPs) (LIBS-FP-PtAgBNPs) for the ultrasensitive detection of Hg2+, Cr3+, and Pb2+. The PtAgBNPs-modified filter paper was used to efficiently and specifically adsorb Hg, Cr, and Pb, and LIBS was used to detect the Hg, Cr, and Pb simultaneously. The limits of detection for Hg, Cr, and Pb were 0.5 µg/L (2.5 nM), 8 µg/L (0.15 µM), and 2 µg/L (9 nM), respectively. Furthermore, this method was successfully applied to determine the concentrations of Hg, Cr, and Pb in real spiked water samples. Compared with other methods based on nanoparticle sensing, LIBS-FP-PtAgBNPs is simpler to use and can achieve highly efficient enrichment, rapid separation, and sensitive detection of heavy metal ions. The optimal detections of Hg, Cr, and Pb were achieved in the pH range of 1-6. The developed method provides a new avenue to realize the rapid and sensitive detection of trace heavy metals in the environment.

Time-resolved spectral-image laser-induced breakdown spectroscopy for precise qualitative and quantitative analysis of milk powder quality by fully excavating the matrix information.

A novel and effective method named time-resolved spectral-image laser-induced breakdown spectroscopy (TRSI-LIBS) was proposed to achieve precise qualitative and quantitative analysis of milk powder quality. To verify the feasibility of TRSI-LIBS, qualitative and quantitative analysis of milk powder quality was carried out. For qualitative analysis of foreign protein adulteration, the accuracy of models based on TRSI-LIBS was higher than those based on LIBS, with an accuracy improvement of about 5% to 10%. For the quantitative analysis of foreign protein adulteration and element content, the quantitative analysis models based on TSRI-LIBS also had better effect. For instance, limit of detection (LOD),determination coefficient of prediction (R2p), root-mean-square error of prediction (RMSEP) and average relative error of prediction (AREP) of quantitative model of calcium (Ca) content based on TRSI-LIBS improved from 1.47 mg/g, 0.95, 0.35 mg/g and 23.29% to 0.81 mg/g, 0.98, 0.20 mg/g and 12.60%.

Visualizing changes of metabolites during iron deficiency chlorosis in field-grown pear leaves using micro-Raman spectral imaging.

Owing to iron chlorosis, pear trees are some of the most severely impacted by iron deficiency, and they suffer significant losses every year. While it is possible to determine the iron content of leaves using laboratory-standard analytical techniques, the sampling and analysis process is time-consuming and labor-intensive, and it does not quickly and accurately identify the physiological state of iron-deficient leaves. Therefore, it is crucial to find a precise and quick visualization approach for metabolites linked to leaf iron to comprehend the mechanism of iron deficiency and create management strategies for pear-tree planting. In this paper, we propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves. According to our findings, iron deficiency significantly decreased the Raman peak intensities of chlorophylls and lipids in leaves. The spatial distributions of chlorophylls and lipids in the leaves changed significantly as the symptoms of iron insufficiency worsened. The technique offers a new, prospective tool for rapid recognition of iron deficiency in pear trees because it is capable of visual detection of plant physiological metabolites induced by iron deficiency.