ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin

Salivary Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy Combined with Chemometric Analysis: A Potential Point-of-Care Approach for Chronic Kidney Disease Screening

BACKGROUND

The increasing prevalence of chronic kidney disease (CKD) and its terminal stage, end-stage renal disease (ESRD), raises the importance of an accurate, early, and point-of-care method to diagnose and monitor patients. Saliva is a potential point-of-care diagnostic biofluid for its simple collection and ability to reflect systemic health status. This study investigated salivary spectral signatures in ESRD patients and their diagnostic potential compared to healthy controls.

METHODS

Saliva samples were collected from 24 ESRD patients undergoing hemodialysis and 24 age/sex-matched healthy controls. The dried saliva samples were analyzed using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy in the 4000-400 cm⁻¹ range. Chemometric analyses, including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), were applied to preprocessed spectra to identify discriminatory spectral features and establish classification models.

RESULTS

Second derivative spectroscopic analysis of ATR-FTIR spectra revealed distinctive spectral patterns in dried ESRD saliva samples, including characteristic peak shifts observed in both the amide I secondary structures (from 1636 cm-1 in controls to 1629 cm-1 in ESRD) and carbohydrate (from 1037 cm-1 in controls to 1042 cm-1 in ESRD) regions. PCA demonstrated clear clustering patterns across key biological spectral regions, including the lipid CH stretching region (3000-2800 cm-1), the fingerprint region (1800-900 cm-1), and their combination (3000-2800 cm-1 + 1800-900 cm-1). PLS models based on the fingerprint region achieved optimal diagnostic performance (87.5-100% accuracy, 75-100% sensitivity, and 100% specificity). Biochemical markers associated with ESRD revealed variations in lipids, protein, sugar moieties, carbohydrates, and nucleic acids, reflecting the underlying pathological changes in CKD, with the most prominent band at ∼1405 cm-1.

CONCLUSION

ATR-FTIR analysis of dried saliva demonstrated potential as a non-invasive diagnostic tool for ESRD. This approach could complement existing diagnostic methods, particularly in resource-limited settings or for frequent monitoring requirements.

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm-1, 1013 cm-1, 1002 cm-1, 1004 cm-1, and 1011 cm-1 in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool.

Infrared spectroscopy combined with chemometrics in transflectance mode: An alternative approach in the photodiagnosis of COVID-19 using saliva

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

Molecular Interactions Leading to Advancements in the Techniques for COVID-19 Detection: A Review

Since 2019 the world has been in a combat with the highly contagious disease COVID-19 which is caused by the rapid transmission of the SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2). Detection of this disease in an early stage helps to control its spread and management. To combat this epidemic with one-time effective medication, improved quick analytical procedures must be developed and validated. The requirement for accurate and precise analytical methods for the diagnosis of the virus and antibodies in infected patients has been a matter of concern. The global impact of this virus has motivated scientists and researchers to investigate and develop various analytical diagnostic techniques. This review includes the study of standard methods which are reliable and accredited for the analytical recognition of the said virus. For early detection of SARS-CoV-2 RNA, RT-PCR (Real-time reverse transcriptase-polymerase chain reaction) is an accurate method among other methods and, thus, considered as the "gold standard" technique. Here, we outline the most extensively used analytical methods for diagnosing COVID-19, along with a brief description of each technique and its analytical aspects/perspective.

Rapid discrimination of Lentilactobacillus parabuchneri biofilms via in situ infrared spectroscopy

Microbial contamination in food industry is a source of foodborne illnesses and biofilm-related diseases. In particular, biogenic amines (BAs) accumulated in fermented foods via lactic acid bacterial activity exert toxic effects on human health. Among these, biofilms of histamine-producer Lentilactobacillus parabuchneri strains adherent at food processing equipment surfaces can cause food spoilage and poisoning. Understanding the chain of contamination is closely related to elucidating molecular mechanisms of biofilm formation. In the present study, an innovative approach using integrated chemical sensing technologies is demonstrated to fundamentally understand the temporal behavior of biofilms at the molecular level by combining mid-infrared (MIR) spectroscopy and fluorescence sensing strategies. Using these concepts, the biofilm forming capacity of six cheese-isolated L. parabuchneri strains (IPLA 11151, 11150, 11129, 11125, 11122 and 11117) was examined. The cut-off values for the biofilm production ability of each strain were quantified using crystal violet (CV) assays. Real-time infrared attenuated total reflection spectroscopy (IR-ATR) combined with fluorescence quenching oxygen sensing provides insight into distinct molecular mechanisms for each strain. IR spectra showed significant changes in characteristic bands of amides, lactate, nucleic acids, and extracellular polymeric substances (i.e., lipopolysaccharides, phospholipids, phosphodiester, peptidoglycan, etc.), which are major contributors to biofilm maturation involved in the initial adhesion processes. Chemometric methods including principal component analysis and partial least square-discriminant analysis facilitated the rapid determination and classification of cheese isolated L. parabuchneri strains unambiguously differentiating the IR signatures based on their ability to produce biofilm. All biofilms were morphologically characterized by confocal laser scanning microscopy on relevant industrial equipment surfaces. In summary, this innovative approach combining MIR spectroscopy with luminescence sensing enables real-time insight into the molecular composition and formation of L. parabuchneri biofilms.

Applications of Fourier Transform-Infrared spectroscopy in microbial cell biology and environmental microbiology: advances, challenges, and future perspectives

Microorganisms play pivotal roles in shaping ecosystems and biogeochemical cycles. Their intricate interactions involve complex biochemical processes. Fourier Transform-Infrared (FT-IR) spectroscopy is a powerful tool for monitoring these interactions, revealing microorganism composition and responses to the environment. This review explores the diversity of applications of FT-IR spectroscopy within the field of microbiology, highlighting its specific utility in microbial cell biology and environmental microbiology. It emphasizes key applications such as microbial identification, process monitoring, cell wall analysis, biofilm examination, stress response assessment, and environmental interaction investigation, showcasing the crucial role of FT-IR in advancing our understanding of microbial systems. Furthermore, we address challenges including sample complexity, data interpretation nuances, and the need for integration with complementary techniques. Future prospects for FT-IR in environmental microbiology include a wide range of transformative applications and advancements. These include the development of comprehensive and standardized FT-IR libraries for precise microbial identification, the integration of advanced analytical techniques, the adoption of high-throughput and single-cell analysis, real-time environmental monitoring using portable FT-IR systems and the incorporation of FT-IR data into ecological modeling for predictive insights into microbial responses to environmental changes. These innovative avenues promise to significantly advance our understanding of microorganisms and their complex interactions within various ecosystems.