Nondestructive detection of lead chrome green in tea by Raman spectroscopy

Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review

This review covers recent developments in the field of non-invasive techniques for the quality assessment of processed horticultural products over the past decade. The concept of quality and various quality characteristics related to evaluating processed horticultural products are detailed. A brief overview of non-invasive methods, including spectroscopic techniques, nuclear magnetic resonance, and hyperspectral imaging techniques, is presented. This review highlights their application to predict quality attributes of different processed horticultural products (e.g., powders, juices, and oils). A concise summary of their potential commercial application for quality assessment, control, and monitoring of processed agricultural products is provided. Finally, we discuss their limitations and highlight other emerging non-invasive techniques applicable for monitoring and evaluating the quality attributes of processed horticultural products. Our findings suggest that infrared spectroscopy (both near and mid) has been the preferred choice for the non-invasive assessment of processed horticultural products, such as juices, oils, and powders, and can be adapted for on-line quality control. Raman spectroscopy has shown potential in the analysis of powdered products. However, imaging techniques, such as hyperspectral imaging and X-ray computed tomography, require improvement on data acquisition, processing times, and reduction in the cost and size of the devices so that they can be adopted for on-line measurements at processing facilities. Overall, this review suggests that non-invasive techniques have the potential for industrial application and can be used for quality assessment.

Comparative analysis of linear and nonlinear equilibrium models for the removal of metronidazole by tea waste activated carbon

Tea waste was carbonized at 400 °C for 45 min and modified with potassium hydroxide (KOH), to enhance the active sites for the adsorption of antibiotics. The developed tea waste activated carbon (TWAC) was used as a novel eco-friendly and cost-effective adsorbent for metronidazole (MZN) removal from aqueous solution. The textural and surface properties of the adsorbent were determined using Brunauer-Emmett-Teller (BET) and FT-Raman analysis. The BET surface was found to have increased from 24.670 to 349.585 after carbonization and KOH modification. The batch experimental parameters were optimized and equilibrium time was found to be 75 min. Linear and non-linear models were carried out on the adsorption isotherm and kinetics to determine the best fit for the adsorption data. The adsorption equilibrium data were well fitted by the Freundlich isotherm and pseudo-second order models, with higher regression correlation (R²) and smaller chi-square (χ²), as predicted by the non-linear model. The thermodynamic results revealed the adsorption of MZN as spontaneous, physical, and consistently exothermic in character. The activation energy value of 7.610 kJ/mol further revealed that the adsorption process is dominated majorly by physical adsorption. The removal of MZN onto TWAC was best described by the non-linear adsorption isotherm and kinetics model.

Potential of vibrational spectroscopy for rapid and accurate determination of the hydrogen peroxide treatment of plant leaves

Detection and characterization of interactions between crop plants and hydrogen peroxide (H₂O₂) is significant for the exploration of the mechanisms in plant pathology. The objective of this research is to estimate spectral characteristics of rapeseed leaves (Brassica napus L.) during treatment with different H₂O₂ concentrations (0, 0.5, 1.0, and 3.0 mmol/L) by using Raman spectroscopy (RS) (800-1800 cm^-1) and hyperspectral imaging (HSI) (400-1000 nm). Cluster analysis of RS and HSI data between the control and treated samples was conducted using kernel principal component analysis (KPCA) and principal component analysis (PCA), respectively. Characteristic Raman shifts at 1012, 1163, and 1530 cm^-1 and hyperspectral featured wavelengths at 452, 558, 655, and 703 nm were selected for discriminating control and treated samples. The one-way analysis of variance (ANOVA) was applied to demonstrate the significant difference in spectral signatures of samples, and results showed that 452 nm is promising to assess the control and treated samples at the p < 0.05 level. The featured Raman shifts and hyperspectral wavelengths were employed to establish least squares-support vector machine (LS-SVM) discriminative models. The approach of multiple-level data fusion of 1163 cm^-1 combined with 452 nm produced the best recognize rate (RR) of 81.7% to detect the control and treated leaves than other models. Therefore, the results encouraged multiple sensor fusion to improve models for better model performance and to detect plant treatment situations with H₂O₂ solutions.

"Turn-off" fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics

Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.

Optical Determination of Lead Chrome Green in Green Tea by Fourier Transform Infrared (FT-IR) Transmission Spectroscopy

The potential of Fourier transform infrared (FT-IR) transmission spectroscopy for determination of lead chrome green in green tea was investigated based on chemometric methods. Firstly, the qualitative analysis of lead chrome green in tea was performed based on partial least squares discriminant analysis (PLS-DA), and the correct rate of classification was 100%. And then, a hybrid method of interval partial least squares (iPLS) regression and successive projections algorithm (SPA) was proposed to select characteristic wavenumbers for the quantitative analysis of lead chrome green in green tea, and 19 wavenumbers were obtained finally. Among these wavenumbers, 1384 (C = C), 1456, 1438, 1419(C = N), and 1506 (CNH) cm-1 were the characteristic wavenumbers of lead chrome green. Then, these 19 wavenumbers were used to build determination models. The best model was achieved by least squares support vector machine (LS-SVM)algorithm with high coefficient of determination and low root-mean square error of prediction set (R2p = 0.864 and RMSEP = 0.291). All these results indicated the feasibility of IR spectra for detecting lead chrome green in green tea.