Detection of volatile organic compounds in adulterated tea using Fourier transform infrared spectroscopy and Proton-transfer-reaction mass spectrometry.

Aroma is a key factor used to evaluate tea quality. Illegal traders usually add essence to expired or substandard tea to improve its aroma so as to gain more profit. Traditional physical and chemical testing methods are time-consuming and costly. Furthermore, rapid detection techniques, such as near-infrared spectroscopy and machine vision, can only be used to detect adulterated powdered solid essences in tea. In this study, proton-transfer reaction mass spectrometry (PTR-MS) and Fourier-transform infrared spectroscopy (FTIR) were employed to detect volatile organic compounds (VOCs) in samples, and rapid detection of different tea adulterated liquid essence was achieved. The prediction accuracies of PTR-MS and FTIR reached over 0.941 and 0.957, respectively, and the minimum detection limits were lower than the actual used values in both. In this study, the different application scenarios of the two technologies are discussed based on their performance characteristics.

Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging.

The rapid and non-destructive detection of moisture in withering leaves is an unsolved problem because the leaves are stacked together and have random orientation. To address this issue, this study aimed to establish more robust and accurate models. The performance of front side, back side and multi-region models were compared, and the front side model showed the worst transferability. Therefore, five effective wavelength (EW) selection algorithms were combined with a successive projection algorithm (SPA) to select EWs. It was found that the shuffled frog leaping algorithm (SFLA) combined with SPA was the best method for the front side model for moisture analyses. Based on the selected EWs, the extreme learning machine (ELM) became the model with the best self-verification result. Subsequently, moisture distribution maps of withering leaves were successfully generated. Considering the processing demand of withering leaves, local region models developed based on partial least squares and the SFLA-SPA method were applied to predict the moisture of withering leaves in the local and stacked region. The results showed that the RPD, Rcv and Rp values were above 1.6, 0.870 and 0.897, respectively. These results provide a useful reference for the non-destructive detection of moisture in withering leaves.

Optimization of informative spectral variables for the quantification of EGCG in green tea using Fourier transform near-infrared (FT-NIR) spectroscopy and multivariate calibration.

Epigallocatechin-3-gallate (EGCG) is credited with the majority of the health benefits associated with green tea consumption. It has a high economic and medicinal value. The feasibility of using different variable selection approaches in Fourier transform near-infrared (FT-NIR) spectroscopy for a rapid and conclusive quantitative determination of EGCG in green tea was investigated. Graphically oriented multivariate calibration modeling procedures such as interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and genetic algorithm optimization combined with siPLS (siPLS-GA) were applied to select the most efficient spectral variables that provided the lowest prediction error. The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and coefficient of determination (R(2)) for the prediction set. Experimental results showed that the siPLS-GA model obtained the best results in comparison to other models. The optimal models were achieved with R(2)(p) = 0.97 and RMSEP = 0.32. The model can be obtained with only 36 variables retained and it provides a robust model with good estimation accuracy. This demonstrates the potential of NIR spectroscopy with multivariate calibration methods to quickly detect the bioactive component in green tea.

[Real-time analysis of soil N and P with near infrared diffuse reflectance spectroscopy].

Improving the efficiency of fertilization is an effective method of enhancing income for farmers, but it depends on measuring the soil nutrients accurately and rapidly. Near infrared reflectance spectroscopy (NIRS) is a fast method to detect the soil nutrients. In order to evaluate the feasibility of using NIRS to determine the soil N and P contents, the soil samples were collected from different LULC (land use and land cover) types in Daxing district, Beijing, and their biochemical parameters were determined by traditional chemical method. Then, the near infrared reflectance spectra of the samples were acquired, and NIRS models were built using partial least square regression (PLS) and Fourier transform technology for the total N and total P. The determination coefficients of cross validation for the total N and total P were 0.862 6 and 0.668 5 respectively. Ten samples were used to test the performance of the models. The coefficients of correlation between the chemically determined value and the NIRS predicted one were 0.969 8(N) and 0.830 7(P) respectively. The root mean standard error of prediction for N and P were 0.009 5(%) and 0.008 6(%), respectively. The ratios of RMSEP to SD(RPD) were 3.78(N) and 1.69(P). The results showed that the NIRS could be used to evaluate the soil N accurately and the soil P roughly.