The biogeography of soil microbiome potential growth rates

Safety evaluation of Weissella confusa SY628 and the effect of its fermentation on the taste and quality of soy yogurt

In the global context, the demand for sustainable protein sources is rising, spotlighting plant-based foods, especially legume products. Fermentation is crucial in developing these foods, as it reduces anti-nutritional factors and improves flavor. But the available fermentation strains for plant-based foods are limited. This study aims to address this knowledge gap by evaluating the safety of Weissella confusa SY628 as a fermentation strain and its impact on the quality characteristics of soy yogurt. The safety evaluation of W. confusa SY628 demonstrated that it possessed no hemolytic activity and was sensitive to a variety of antibiotics, no biogenic amines were produced, suggesting an extremely low pathogenic risk. Furthermore, W. confusa SY628 demonstrated enhanced acid and bile tolerance, characteristics that are indicative of its probiotic properties. The fermentation of soy yogurt was conducted using three distinct organisms: W. confusa SY628, commercial bacterial powder CHS, and a combined starter SYCHS composed of the two aforementioned organisms. The physical, and chemical properties and taste quality of the samples were measured. The results demonstrated that in the SYCHS group, after a 21-day storage period, the pH level was 4.49, the total acidity reached 76.77 °T, and the viable count was 5.81 × 107 CFU/mL, indicating good storage stability. The cohesiveness, viscosity, elasticity, and storage modulus of the SYCHS group were found to be significantly higher than those of the other groups, and the internal network structure was found to be stable. In the SYCHS group, the total amino acid content was determined to be 308.57 μg/g, with umami-tasting amino acids accounting for 22.95%. The total fatty acid content was found to be 1818.95 μg/g, with a notably high polyunsaturated fatty acid content, indicating significant nutritional value. The SYCHS group exhibited the highest number of key flavor components. Substances such as 2,3-butanedione exhibited high ROAV values, contributing to a rich flavor profile. In conclusion, the co-fermentation of W. confusa and commercial bacteria significantly improved the overall quality of soy yogurt, providing a theoretical and practical basis for the innovative development of plant-based foods.

The Vineyard Microbiome: How Climate and the Main Edaphic Factors Shape Microbial Communities

The soil microbiome is a complex system that encompasses millions of microbes including archaea, bacteria, fungi, protozoa and viruses. The role of abiotic factors is crucial in shaping the distribution patterns of microorganisms, its abundance and also the interactions between species, from local to the global level. In the particular case of the vineyard, the microbial communities have a potential impact in both the grapevine development and health and, later on, in the grape production and quality. The present review focuses on how the composition of soil microbial communities is influenced by climate and several edaphic factors, such as soil moisture, soil nutrients and soil pH. It also discusses the role of microorganisms and their metabolic activity on the fermentation process, influencing the sensorial characterisation of the wine and suggesting the definition of a microbial terroir.

Pair-Soil-Spectra: An Approach for NIRS-Based Soil Total Nitrogen Content Detection with Feature Metrics in Cases of Small Sample Sizes

Soil total nitrogen (STN) plays an important role in plant growth, and rapid and nondestructive detection of STN content is essential for agricultural production. Near-infrared spectroscopy (NIRS) takes advantage of the fast detection speed, low cost, and nondestructiveness, and it can be used for STN content detection. Typically, NIRS-based approaches require a large number of samples for detection model training. However, it is difficult to collect sufficient samples due to various causes (e.g., time-varying state, high assay costs, etc.) in practical application. To tackle this problem, a feature metric approach is introduced to detect the STN content based on NIRS in this work, and a new approach (named Pair-Soil-Spectra) is proposed to mine fine-grained features by contrasting different soil sample pairs, which takes full advantage of soil particle heterogeneity and NIRS penetration. For the validation of this study, three different soil datasets with various collection sources are selected as research subjects, and the performance of Pair-Soil-Spectra is analyzed from different perspectives. According to the results, Pair-Soil-Spectra has significantly improved the performance of STN content detection models (e.g., partial least-squares (PLS), Cubist, extreme learning machine (ELM), and random forest (RF)) in small sample cases. Of these, the coefficient of determination of RF has improved by 0.13, 0.42, and 0.10, and the root-mean-square of prediction has decreased by 0.15, 0.52, and 0.01 g/kg with different datasets, which has gained the greatest improvement. Meanwhile, this approach can be easily expanded to cover other domains.