Thermophilic bacteria play an important role in aroma formation during pile-fermentation process of dark tea. With the aim to reveal the impact of thermophilic bacteria on volatile compounds in dark tea, Bacillus licheniformis (thermophilic bacteria) were inoculated into sun-dried green tea for spontaneous fermentation. In this study, headspace solid phase microextraction combined to gas chromatography mass spectrometry (HS-SPME-GC-MS), odor activity value (OAV), principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) were employed to investigated the characteristics of volatile compounds during thermophilic bacteria pile-fermentation. According to HS-SPME-GC-MS, a total of 64 volatile compounds were identified. PCA revealed that tea samples could be clearly discriminated from each other. Furthermore, sulcatone, hexanoic acid, linalool, 2-methyl-trans-decalin, myrtenal, and α-ionone were found to play an important role in discrimination of tea samples, based on OPLS-DA. In addition, the OAV could effectively characterize the aroma contribution of volatile compounds during thermophilic bacteria pile-fermentation, and (Z)-linalool oxide (furanoid), α-ionone, (E)-2-nonenal, and linalool were the critical volatile compounds of aroma quality in dark tea. This study provides insight into volatile compounds characteristics during thermophilic bacteria pile-fermentation in dark tea.
Tea polyphenols are one of the most important ingredients in Qingzhuan tea. Usually, a chemical method is used to determine tea polyphenols content, but it was time-consuming and laborious. This paper attempted to use near infrared spectroscopy (NIRS) technology combined with three partial least squares methods to predict tea polyphenols content quickly and nondestructively. The partial least squares (PLS), synergy interval PLS (siPLS) and genetic algorithm based PLS (gaPLS) were used to establish prediction models, the performance of the final model was showed by root mean square error of prediction (RMSEP) and determination coefficient (Rp 2 ) in prediction set. The best spectral preprocessing method was multivariate scattering correction (MSC); the RMSEP and R p 2 of PLS model were 0.145% and 0.8974, respectively; the siPLS model was established with four spectral regions (4377.6 cm -1 -4751.7 cm -1 , 4755.6 cm -1 -5129.7 cm -1 , 6262.7 cm -1 -6633.9 cm -1 and 7386 cm -1 -7756.3 cm -1 ), whose RMSEP and R p 2 were 0.0652% and 0.9235, respectively; the gaPLS model was established with 36 spectra dada points and showed the best performance (RMSEP=0.0624%, Rp 2 =0.9769) compared with the PLS and si-PLS models. Therefore, the application of near infrared technology combined with the gaPLS method could predict tea polyphenols content in Qingzhuan tea more accurately and rapidly.
Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.
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