Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

• Published in: Journal of cellular and molecular medicine Vol. 28; no. 8; pp. e18292 - n/a
• Main Authors: Yuan, Quan, Gu, Bin, Liu, Wei, Wen, Xin‐Ru, Wang, Ji‐Liang, Tang, Jia‐Wei, Usman, Muhammad, Liu, Su‐Ling, Tang, Yu‐Rong, Wang, Liang
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.04.2024; John Wiley and Sons Inc
• Subjects: Accuracy; Algorithms; characteristic peaks; Discrimination; Food; Foodborne diseases; Humans; label‐free SERS; Lasers; Learning algorithms; Machine learning; machine learning algorithm; Mass spectrometry; Microorganisms; Neural networks; Original; Pathogens; Public health; Raman spectrometer; Raman spectroscopy; Raman spectrum; Salmonella; Salmonella enterica; Salmonella enterica - isolation & purification; Salmonella serovar; Scientific imaging; Serogroup; Serotyping; Silicon wafers; Silver; Spectrum Analysis, Raman - methods; Support Vector Machine; Testing laboratories; China; Raman spectrometer; label‐free SERS; Salmonella serovar; characteristic peaks; Raman spectrum; machine learning algorithm
• ISSN: 1582-1838; 1582-4934; 1582-4934
• DOI: 10.1111/jcmm.18292

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface‐enhanced Raman spectroscopy (SERS) is a non‐invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost‐effective identification of closely associated S. enterica serovars.