Liang Yan, Khanthaphixay Bradley, Reynolds Jocelyn, Leigh Preston J, Lim Melissa L, Yoon Jeong-Yeol
Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, USA.
Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA.
Appl Phys Rev. 2024 Sep;11(3):031412. doi: 10.1063/5.0174176.
The soil microbiome is crucial for nutrient cycling, health, and plant growth. This study presents a smartphone-based approach as a low-cost and portable alternative to traditional methods for classifying bacterial species and characterizing microbial communities in soil samples. By harnessing bacterial autofluorescence detection and machine learning algorithms, the platform achieved an average accuracy of 88% in distinguishing common soil-related bacterial species despite the lack of biomarkers, nucleic acid amplification, or gene sequencing. Furthermore, it successfully identified dominant species within various bacterial mixtures with an accuracy of 76% and three-level soil health identification at an accuracy of 80%-82%, providing insights into microbial community dynamics. The influence of other soil conditions (pH and moisture) was relatively minor, showcasing the platform's robustness. Various field soil samples were also tested with this platform at 80% accuracy compared with the laboratory analyses, demonstrating the practicality and usability of this approach for on-site soil analysis. This study highlights the potential of the smartphone-based system as a valuable tool for soil assessment, microbial monitoring, and environmental management.
土壤微生物群对养分循环、健康和植物生长至关重要。本研究提出了一种基于智能手机的方法，作为传统方法的低成本、便携式替代方案，用于对土壤样本中的细菌物种进行分类和表征微生物群落。通过利用细菌自发荧光检测和机器学习算法，该平台在缺乏生物标志物、核酸扩增或基因测序的情况下，区分常见土壤相关细菌物种的平均准确率达到了88%。此外，它成功地识别了各种细菌混合物中的优势物种，准确率为76%，并以80%-82%的准确率进行了三级土壤健康识别，为微生物群落动态提供了见解。其他土壤条件（pH值和湿度）的影响相对较小，显示了该平台的稳健性。与实验室分析相比，该平台对各种田间土壤样本的测试准确率也达到了80%，证明了这种方法用于现场土壤分析的实用性和可用性。本研究强调了基于智能手机的系统作为土壤评估、微生物监测和环境管理的宝贵工具的潜力。
