A review on fermented vegetables: Microbial community and potential upgrading strategy via inoculated fermentation.

Fermentation is a traditional method utilized for vegetable preservation, with microorganisms playing a crucial role in the process. Nowadays, traditional spontaneous fermentation methods are widely employed, which excessively depend on the microorganisms attached to the surface of raw materials, resulting in great difficulties in ideal control over the fermentation process. To achieve standardized production and improve product quality, it is essential to promote inoculated fermentation. In this way, starter cultures can dominate the fermentation processes successfully. Unfortunately, inoculated fermentation has not been thoroughly studied and applied. Therefore, this paper provides a systematic review of the potential upgrading strategy of vegetable fermentation technology. First, we disclose the microbial community structures and succession rules in some typical spontaneously fermented vegetables to comprehend the microbial fermentation processes well. Then, internal and external factors affecting microorganisms are explored to provide references for the selection of fermented materials and conditions. Besides, we widely summarize the potential starter candidates with various characteristics isolated from spontaneously fermented products. Subsequently, we exhibited the inoculated fermentation strategies with those isolations. To optimize the product quality, not only lactic acid bacteria that lead the fermentation, but also yeasts that contribute to aroma formation should be combined for inoculation. The inoculation order of the starter cultures also affects the microbial fermentation. It is equally important to choose a proper processing method to guarantee the activity and convenience of starter cultures. Only in this way can we achieve the transition from traditional spontaneous fermentation to modern inoculated fermentation.

Remote sensing estimates of global sea surface nitrate: Methodology and validation.

Information about sea surface nitrate (SSN) concentrations is crucial for estimating oceanic new productivity and for carbon cycle studies. Due to the absence of optical properties in SSN and the intricate relationships with environmental factors affecting spatiotemporal dynamics, developing a more representative and widely applicable remote sensing inversion algorithm for SSN is challenging. Most methods for the remote estimation of SSN are based on data-driven neural networks or deep learning and lack mechanistic descriptions. Since fitting functions between the SSN and sea surface temperature (SST), mixed layer depth (MLD), and chlorophyll (Chl) content have been established for the open ocean, it is important to include the remote sensing indicator photosynthetically active radiation (PAR), which is critical in nitrate biogeochemical processes. In this study, we employed an algorithm for estimating the monthly average SSN on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1° × 1° grid and the estimated monthly SST and PAR datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and MLD from the Hybrid Coordinate Ocean Model (HYCOM). These results indicated that PAR potentially affects SSN. Furthermore, validation of the SSN model with measured nitrate data from different months and locations for the years 2018-2023 yielded a high prediction accuracy (N = 12,846, R2 = 0.93, root mean square difference (RMSE) = 3.12 µmol/L, and mean absolute error (MAE) = 2.22 µmol/L). Further independent validation and sensitivity tests demonstrated the validity of the algorithm for retrieving SSN.