RESUMO
The flow cytometry method (FCM) is a widely renowned practice increasingly used to assess the microbial viability of probiotic products. Additionally, the measurement of water activity (aw) can be used to confirm the presence of viable cells in probiotic products throughout their shelf lives. The aim of this study was to investigate the correlation between changes in aw and variations in active fluorescent units (AFU), a unit commonly used in flow cytometry method, during the aging of probiotic products containing freeze-dried bacteria. We controlled the stability of probiotic products for bacterial counts (using ISO 19344 method) and aw levels in commercially available capsules containing freeze-dried bacteria such as Lactobacillus sp. or combinations of Lactobacillus sp. and Bifidobacterium sp. in standard conditions (25 ± 2 °C and 60% relative humidity) over a period of 24 months. During this time, the bacterial contents decreased by 0.12 Log10 in the single-strain product, by 0.16 Log10 in the two-strain product and by 0.26 Log10 in the multi-strain product. With the increase in aw, the number of bacteria decreased but the aw at the end point of the stability study did not exceed 0.15 in each of the three tested products. FCM combined with aw is a prospective analysis that can be used to assess the stability of probiotic products, both for its ability to detect bacterial viability and for practical (analysis time) and economic reasons.
RESUMO
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
