BACKGROUND: Suansun is a traditional salt-free fermented bamboo shoot product that has been widely consumed as a cooking ingredient in south China for centuries. The aim of this study was to evaluate and compare the microbial and metabolic diversity in samples of two kinds of suansun, namely Guangdong suansun (GD) and Yunnan suansun (YN), using high-throughput sequencing (HTS) and headspace solid-phase microextraction-gas chromatograph-mass spectrometry (HS-SPME/GC-MS), respectively, and then to assess the influence of environmental factors on the microbial communities. RESULTS: The results showed that Lactobacillus and Serratia were the most abundant bacterial genera in both the GD and YN groups. For the fungi, Pichia, Candida, and Debaryomyces were the major genera in the GD group, whereas Pichia and Zygosaccharomyces were the dominant genera in the YN group. The canonical correlation analysis (CCA) results demonstrated that three environmental factorstemperature, longitude, and altitudeplay a more important role in affecting the microbial community composition of suansun than physical and chemical factors. The fugal community composition was more influenced by environmental factors than the bacterial community. The volatile profile of the GD group differed from that of the YN group, and the difference was mainly reflected in the relative alcohol, aldehyde, ester, and aromatic compound content. CONCLUSIONS: This study provided insights into the microbial and metabolic profiles of suansun products. The findings might be useful for the improvement and standardization of suansun production.

In view of the difficulty in selecting wavelet base and decomposition level for wavelet-based de-noising method, this paper proposes an adaptive de-noising method based on Ensemble Empirical Mode Decomposition (EEMD). The autocorrelation, cross-correlation method is used to adaptively find the signal-tonoise boundary layer of the EEMD in this method. Then the noise dominant layer is filtered directly and the signal dominant layer is threshold de-noised. Finally, the de-noising signal is reconstructed by each layer component which is de-noised. This method solves the problem of mode mixing in Empirical Mode Decomposition (EMD) by using EEMD and combines the advantage of wavelet threshold. In this paper, we focus on the analysis and verification of the correctness of the adaptive determination of the noise dominant layer. The simulation experiment results prove that this de-noising method is efficient and has good adaptability.

In view of the limitation of many multipitch detection methods in single-channel speech, a robust and accurate multipitch estimation method for multiple voices is processed. In this study, our approach is based on the spectral analysis of the wavelet packet analysis. It utilizes the quasi-periodicity in a short time frame of speech, and gets candidate pitche in every frame by using peak selection and searching algorithm. The multipitch envelope of the mixed signal is obtained by neighborhood analysis and determined from these candidates by virtue of the fact that pitch should change rather smoothly in consecutive frame. Simulation results showed that the proposed method can robustly estimate fundamental frequency for mixed speech from single-channel speech.