Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra.

The problem of excessive lead content in tea has become more and more serious with the development of society and industry. This paper investigated the ability of visible and near-infrared (Vis-NIR) spectroscopy to evaluate foliar lead uptake by tea plants through simulating real air pollution. Lead content of tea leaves in different treatment groups during stress time was measured by inductively coupled plasma mass spectrometry (ICP-MS). It was determined that stomata can be a channel for lead particles in the air and most of the lead entering through the stomata accumulates in the leaves. The spectral variation of treated samples was measured, and it was found that a combination of partial least squares-discriminant analysis (PLS-DA) and spectral responses can perfectly classify the tea samples under different lead concentrations stress with an overall accuracy of 0.979. Then the Vis-NIR spectra were used for fast monitoring physiological and biochemical indicators in tea leaves under atmospheric deposition. Relevant spectra pretreatment methods and characteristic wavelength selection approaches were evaluated for quantitative analysis and then optimal prediction models to instantly detect quality indicators in tea samples were built. Among predictive models, PLS had the best results (RMSE = 0.139 mg/g, 0.663 mmol/g, and 1.494 µmol/g) for the prediction of chlorophyll a (Chl-a), ascorbic acid (ASA), and glutathione (GSH), respectively. Also, principal component regression (PCR) gave the best results (RMSE = 0.053 mg/g, 0.024 mg/g, and 0.011%) for prediction of chlorophyll b (Chl-b), carotenoid (Car) and moisture content (MC), respectively. Results of this study can be applied for developing an effective and reliable approach for monitoring atmospheric deposition in plants.

Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer.

BACKGROUND: Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress. RESULTS: In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents. CONCLUSIONS: It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.

Quantitative visualization of subcellular lignocellulose revealing the mechanism of alkali pretreatment to promote methane production of rice straw.

BACKGROUND: As a renewable carbon source, biomass energy not only helps in resolving the management problems of lignocellulosic wastes, but also helps to alleviate the global climate change by controlling environmental pollution raised by their generation on a large scale. However, the bottleneck problem of extensive production of biofuels lies in the filamentous crystal structure of cellulose and the embedded connection with lignin in biomass that leads to poor accessibility, weak degradation and digestion by microorganisms. Some pretreatment methods have shown significant improvement of methane yield and production rate, but the promotion mechanism has not been thoroughly studied. Revealing the temporal and spatial effects of pretreatment on lignocellulose will greatly help deepen our understanding of the optimization mechanism of pretreatment, and promote efficient utilization of lignocellulosic biomass. Here, we propose an approach for qualitative, quantitative, and location analysis of subcellular lignocellulosic changes induced by alkali treatment based on label-free Raman microspectroscopy combined with chemometrics. RESULTS: Firstly, the variations of rice straw induced by alkali treatment were characterized by the Raman spectra, and the Raman fingerprint characteristics for classification of rice straw were captured. Then, a label-free Raman chemical imaging strategy was executed to obtain subcellular distribution of the lignocellulose, in the strategy a serious interference of plant tissues' fluorescence background was effectively removed. Finally, the effects of alkali pretreatment on the subcellular spatial distribution of lignocellulose in different types of cells were discovered. CONCLUSIONS: The results demonstrated the mechanism of alkali treatment that promotes methane production in rice straw through anaerobic digestion by means of a systemic study of the evidence from the macroscopic measurement and Raman microscopic quantitative and localization two-angle views. Raman chemical imaging combined with chemometrics could nondestructively realize qualitative, quantitative, and location analysis of the lignocellulose of rice straw at a subcellular level in a label-free way, which was beneficial to optimize pretreatment for the improvement of biomass conversion efficiency and promote extensive utilization of biofuel.