Rapid, Sensitive, Specific, and Visual Detection of Salmonella in Retail Meat with Loop-Mediated Isothermal Amplification, Targeting the invA Gene

Development of a closed-tube, calcein-based loop-mediated isothermal amplification assay to detect Salmonella spp. in raw meat samples

Foodborne pathogens compromise food safety and public health, and Salmonella spp. are among the major pathogenic bacteria that cause outbreaks worldwide. Proper surveillance through timely and cost-effective detection methods across the food animal production chain is crucial to prevent Salmonella outbreaks and agricultural losses. Traditional culture methods are labor- and resource-intensive, with lengthy turnaround times. Meanwhile, conventional molecular tools, such as PCR and qPCR, are expensive and require technical skills and equipment. Loop-mediated isothermal amplification (LAMP) is a simple, rapid, inexpensive, highly sensitive, and specific molecular assay that does not require expensive equipment. Hence, this study developed and optimized a closed-tube, calcein-based LAMP assay to detect Salmonella using the invA gene and performed evaluation and validation against conventional PCR. The LAMP assay showed high specificity and sensitivity. It showed 10-fold higher sensitivity than conventional PCR, at <1 ng/μL DNA concentrations. Meanwhile, for CFU/mL, LAMP assay showed 1000-fold higher sensitivity than conventional PCR at 4.8 × 103 cells/mL than 4.8 × 107 cells/mL, respectively. For parallel testing of 341 raw meat samples, after conventional culture enrichment (until Rappaport-Vassiliadis broth), the optimized LAMP assay showed 100% detection on all samples while conventional PCR showed 100%, 99.04%, and 96.64% for raw chicken, beef, and pork samples, respectively. Meanwhile, a shortened enrichment protocol involving 3-h incubation in buffered peptone water only, showed lower accuracy in tandem with the optimized LAMP assay ranging from 55 to 75% positivity rates among samples. These suggest that the optimized LAMP assay possesses higher sensitivity over conventional PCR for invA gene detection when coupled with conventional enrichment culture methods. Hence, this assay has potential as a powerful complementary or alternative Salmonella detection method to increase surveillance capacity and protect consumer food safety and public health worldwide.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.

A loop-mediated isothermal amplification-enabled analytical assay for the detection of SARS-CoV-2: A review

The number of words: 4645, the number of figures: 4, the number of tables: 1The outbreak of COVID-19 in December 2019 caused a global pandemic of acute respiratory disease, and with the increasing virulence of mutant strains and the number of confirmed cases, this has resulted in a tremendous threat to global public health. Therefore, an accurate diagnosis of COVID-19 is urgently needed for rapid control of SARS-CoV-2 transmission. As a new molecular biology technology, loop-mediated isothermal amplification (LAMP) has the advantages of convenient operation, speed, low cost and high sensitivity and specificity. In the past two years, rampant COVID-19 and the continuous variation in the virus strains have demanded higher requirements for the rapid detection of pathogens. Compared with conventional RT-PCR and real-time RT-PCR methods, genotyping RT-LAMP method and LAMP plus peptide nucleic acid (PNA) probe detection methods have been developed to correctly identified SARS-CoV-2 variants, which is also why LAMP technology has attracted much attention. LAMP detection technology combined with lateral flow assay, microfluidic technology and other sensing technologies can effectively enhance signals by nucleic acid amplification and help to give the resulting output in a faster, more convenient and user-friendly way. At present, LAMP plays an important role in the detection of SARS-CoV-2.