Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 10⁴ CFU/mL commercially available lateral-flow assays.

沙门氏菌属 是一种食源性病原体，常见于生肉、蛋制品和牛奶中。沙门氏菌导致了多次爆发，成为常见的主要公共卫生问题。许多研究最近报道了用于微生物检测的手持式快速设备。本研究探索了一种基于智能手机的侧流分析仪，该分析仪采用机器学习算法来检测各种浓度的沙门氏菌属 从测试线图像。当细胞数量较少时，难以检测到微弱的测试线，从而导致误导性结果。因此，本研究侧重于开发基于智能手机的侧向流动测定 (SLFA)，以更高的置信度区分模糊浓度的测试线。智能手机支架设计有斜槽以最大化强度，并找到最佳入射光的最佳方向。此外，颜色空间和机器学习算法的组合被应用于 SLFA 进行分类。发现L*a*b和RGB色彩空间与SVM的组合KNN 分类器达到了高准确率（95.56%）。进行盲测以评估设备的性能；机器学习技术的结果报告的错误比目视检查少。基于智能手机的横向流动测定提供了准确的解释，检测限为 5 × 10 ⁴ CFU/mL 市售横向流动测定。