jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Evaluating Coronavirus Stability: Insights from Raman Spectroscopy and Multivariate Analysis

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Evaluating Coronavirus Stability: Insights from Raman Spectroscopy and Multivariate Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 November 2025 V1 Latest version Share on jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Evaluating Coronavirus Stability: Insights from Raman Spectroscopy and Multivariate Analysis Authors : Ali Haneen Issmer 0000-0002-7459-3203 [email protected] , Carolina Guerrero-Amelin 0000-0003-1498-8518 , Pedro Antonio Mateos-Gomez , Gemma Montalvo , Fernando E. Ortega-Ojeda , Pablo Navarro Caceres , and Carmen García Ruiz Authors Info & Affiliations https://doi.org/10.22541/au.176358713.31161656/v1 201 views 118 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The global COVID-19 pandemic, caused by SARS-CoV-2, has increased scientific interest in coronaviruses. Controlling environmental conditions is crucial to prevent viral transmission via surfaces or air. This study aimed to evaluate the effect of storage time and heat on the stability of human coronavirus-229E (HCoV-229E) using Raman spectroscopy. Wild-type and GFP-tagged HCoV-229E were stored at −20 °C for 24, 168, and 504 h, and exposed to 37 °C for 120 min, and 95 °C for 10 min. Raman spectra were collected at 532 nm excitation (3 s exposure, 7 mW). Multivariate analyses (PCA, OPLS-DA) distinguished virus types, concentrations, and environmental effects, identifying structural changes linked to stress conditions. Continuous OPLS-DA models explained 97% of variance (R 2 Y) and predicted 95% (Q 2 ), demonstrating Raman spectroscopy’s potential for detecting molecular changes in coronaviruses under varying conditions. Evaluating Coronavirus Stability: Insights from Raman Spectroscopy and Multivariate Analysis Ali Haneen Issmer a,b⃰ | Carolina Guerrero-Amelin c | Pedro Antonio Mateos-Gomez c | Gemma Montalvo a,d | Fernando E. Ortega-Ojeda a,d,e | Pablo Navarro Caceres f,g | Carmen García-Ruiz a,d a Universidad de Alcalá, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km 33.6, 28871, Alcalá de Henares, Madrid, Spain | b University of Thi-Qar, College of Science, Department of Chemistry, Thi-Qar, Iraq | c Biochemistry and Molecular Biology Unit, Department of Systems Biology School of Medicine and Health Sciences, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain | d Universidad de Alcalá, Instituto Universitario de Investigación en Ciencias Policiales (IUICP), Calle Libreros 27, 28801 Alcalá de Henares, Madrid, Spain | e Universidad de Alcalá, Departamento de Ciencias de la Computación, Ctra. Madrid-Barcelona km 33.6, 28871 Alcalá de Henares, Madrid, Spain | f Research Center for Dental Sciences (CICO), Dental School, Universidad de La Frontera, Temuco 4811230, Chile | g Facultad de Ciencias de la Salud, Universidad Autonoma de Chile, Temuco 4780000, Chile –⃰ Corresponding author : Ali Haneen Issmer Universidad de Alcalá, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km 33.6, 28871, Alcalá de Henares, Madrid, Spain | University of Thi-Qar, College of Science, Department of Chemistry, Thi-Qar, Iraq Phone: +964 7827982101 Email: [email protected] | [email protected] Abstract The global COVID-19 pandemic, caused by SARS-CoV-2, has increased scientific interest in coronaviruses. Controlling environmental conditions is crucial to prevent viral transmission via surfaces or air. This study aimed to evaluate the effect of storage time and heat on the stability of human coronavirus-229E (HCoV-229E) using Raman spectroscopy. Wild-type and GFP-tagged HCoV-229E were stored at −20 °C for 24, 168, and 504 h, and exposed to 37 °C for 120 min, and 95 °C for 10 min. Raman spectra were collected at 532 nm excitation (3 s exposure, 7 mW). Multivariate analyses (PCA, OPLS-DA) distinguished virus types, concentrations, and environmental effects, identifying structural changes linked to stress conditions. Continuous OPLS-DA models explained 97% of variance (R²Y) and predicted 95% (Q²), demonstrating Raman spectroscopy’s potential for detecting molecular changes in coronaviruses under varying conditions. Keywords : coronavirus | Huh7(RRID:CVCL_B7TI) | HCoV-229E | multivariate analyses | Raman spectroscopy | SIMCA (RRID:SCR_014688) | Introduction The heightened academic interest in coronaviruses has been driven by the global COVID-19 pandemic, which caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronaviruses are characterized as enveloped viruses with a spherical or pleomorphic morphology, encompassing more than 30 strains primarily associated with respiratory tract infections. Preceding the onset of the COVID-19 pandemic, six distinct strains of coronaviruses had been identified, among them HCoV-229E. According to the universal system of viral taxonomy, HCoV-229E is classified within the realm Riborivia, order Nidovirales, suborder Cornidovirineae, family Coronaviridae, subfamily Orthocoronavirinae, and genus Alphacoronavirus. Belonging to the species Alphacoronavirus, HCoV-229E represents a prototypical alphacoronavirus typically responsible for causing mild upper respiratory tract illnesses, although it may occasionally precipitate severe infections in vulnerable demographics such as infants, young children, and the elderly [1],[2]. In contrast to the pronounced severity and impact observed in the lower respiratory tract associated with SARS-CoV-2 infection, HCoV-229E typically causes a mild to moderate upper respiratory tract infection. Upon attachment to its cellular receptor, aminopeptidase N (APN), HCoV-229E initiates infection of the host cells. As the other coronaviruses, the virion of HCoV-229E comprises four primary structural proteins: the nucleocapsid (N) protein, the transmembrane (M) protein, the envelope (E) protein, and the spike (S) protein [3]. The viral detection assays predominantly utilize antibody-based techniques, such as enzyme-linked immunosorbent assays (ELISA), fluorescent antibody assays, or serologic testing. However, these methods often exhibit limitations in sensitivity and specificity, particularly for detecting viruses at low level [4]. Polymerase Chain Reaction (PCR) techniques are widely regarded as indispensable tools for the detection and assessment of microorganisms, including viruses. These techniques serve as valuable modalities in disease diagnosis and in monitoring treatment or recovery progress [5]. Strict control of environmental conditions (context) is crucial in preventing the transmission of viruses between individuals through materials or ambient air. Investigations into these environmental factors, particularly emphasized during high-burden situations like the COVID-19 pandemic, have been conducted using PCR techniques. These works have uncovered that storage conditions may introduce pre-analytical errors, compromising virus detection and diminishing the reliability of diagnostic methodologies. Furthermore, the regulation of environmental variables influencing viral activity is crucial for comprehending pathogen transmission dynamics across various substrates and ambient air methodologies [6] [7]. Both the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) recommend refrigerated storage of samples at 2–8 °C and transportation in viral transport medium (VTM) for up to 5 days to ensure accurate molecular testing results. These measures are implemented to safeguard the reliability and precision of diagnostic testing procedures [5]. Thermal effects can result in the inactivation or destruction of virus. These effects induced by elevated temperature or other sources like radiation, can break nucleic acid chain or separate the base pairs, consequently inhibiting virus replication or damaging the virus envelope [8]. In this context, Markt et al. observed significant fluctuations in virus detection via RT-qPCR after freeze-thaw cycles. This phenomenon is ascribed to cellular disruption within specimens, leading to an elevated concentration of RNA in frozen samples. The release of cellular contents, including proteases and RNases, has the potential to compromise the subsequent detection of SARS-CoV-2 in specimens. Consequently, they advocate for the storage of wastewater samples intended for SARS-CoV-2 analysis at 4 °C during analysis, as opposed to -20 °C [9]. Gulec et al. have highlighted the significance of the storage temperature regulation considering that the cycle threshold (CT) is a parameter used in quantitative PCR. The CT value is commonly used to quantify the initial amount of target nucleic acid in a sample and is crucial for comparing the levels of gene expression or viral load between different samples. According to their findings, specimens exhibiting high viral loads with CT values below 25 can be stored at both room temperature and 4 °C for up to 12 days without compromising their positivity. However, specimens with lower viral loads, characterized by CT values above 25, are not suitable for extended storage periods. In cases where the specimens have CT values exceeding 32, the authors recommended retesting the samples to mitigate the risk of misinterpretation [10]. Anagoni et al. found that the storage temperature does not significantly affect the results of nasopharyngeal swabs sample in the positive SARS COV-2 specific genes test when stored at 4 °C and at room temperature up to 5 days after their collection [5]. PCR, while widely used, does have limitations such as the need for advanced equipment, susceptibility to contamination, and relatively long processing times. This has led researchers to explore alternative techniques that offer improved sensitivity, cost-effectiveness, and time efficiency. Among these alternatives, spectroscopic methodologies have garnered significant attention, with Raman spectroscopy standing out as a promising candidate. Raman spectroscopy is based on the inelastic scattering of light from molecules caused by vibrational changes of the electrons in the molecular bond. This phenomenon is rather specific and useful because it can help in the molecular structure identification. This technique has advantages in a wide range of sample phases like solid, liquid, gas, gel, etc. and has a great potential in Biomedical and Forensic Sciences due to its non-destructive nature [11]. Raman spectroscopy offers several advantages over PCR, including rapid analysis, minimal sample preparation requirements, and the ability to analyze samples size of about 1 × 1 × 5 μm. By utilizing the unique spectral fingerprints of molecules, Raman spectroscopy enables the identification and differentiation of viruses based on their structural and chemical properties. Moreover, recent advancements in the data analysis techniques have further enhanced the capabilities of Raman spectroscopy, making it increasingly attractive for use in biomedical and clinical settings. Based on our search in scientific databases, there is no data utilizing Raman spectroscopy to describe HCoV-229E stability when exposed to varying storage times and heating conditions (context). Therefore, this study aimed to evaluate the effect of storage conditions (time & heat) on the stability of the HCoV-229E using Raman spectroscopy assisted by multivariate analysis. | Materials and Methods 2.1 | Materials Agarose (Nzytech, ref: MB052), crystal violet (Merck, ref: C3886), Dulbecco’s modified Eagle’s medium (Merck, ref: D6429), Dulbecco’s modified Eagle’s medium 2X (Merck, ref: SLM-202-B), foetal bovine serum (Merck, ref: F7524), formaldehyde (Alfa Aesar, ref: 33314), methanol (Panreac, ref: 8.111.952). All eluents, stocks, standards and samples solutions were prepared using ultrapure water (18.2 MΩ cm) delivered from a Millipore Synergy UV ultra-purification system (Millipore, Bedford, MA, USA). 2.2 | Samples Preparation The HCoV-229E and HCoV-229E-GFP [12] samples were kindly provided by the CoV lab at the National Centre of Biotechnology (CNB-CSIC, Madrid, Spain) under the permission of Prof. Dr. Volker Thiel (Institute of Virology and Immunology, University of Berlin). The virus was amplified using Huh7 human cells (RRID:CVCL_B7TI) grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10 % foetal bovine serum (FBS), 5 % CO2, and at 33 ºC. Confluent 10 cm plates were infected with a multiplicity of infection (MOI) of 0.1. After 1 h of infection in 4 mL of 2 % FBS in DMEM medium and frequent rocking, the cells were supplemented with 2 % FBS in DMEM to a final volume of 10 mL. Then, 48 h later, half of the viral soup was collected in plates were supplemented with 5 mL of 2 % FBS in DMEM. After 24 h, all the viral soup was collected. The collected media were stored at -80 °C. For the virus titration , the Huh7 cells were plated on 12 well plates. Once they were fully confluent, 10-fold dilution series of the virus in DMEM with 2 % FBS were used to infect the cells as described above. After 2 h, the medium was removed from the wells and 1.5 mL of overlay medium was added. Then, 96 h later, the cells were fixed with a 10 % formaldehyde solution in phosphate-buffered saline (PBS) for 20 min. The agarose plugs were removed, and the cells were stained with a crystal violet staining solution (0.1 % in methanol-water, 20 and 80 % respectively). Once dried, the viral plaques were counted, and the plaque forming units (PFU) was calculated for every mL unit. PFU presents a number of viral particles capable of forming visible plaques (localized areas of cell death) on a cell monolayer in a culture dish. Each plaque typically originates from a single infectious viral particle. Hence, counting the number of plaques allows for the estimation of viral titer or concentration in a sample The viral titers of HCoV-229E (No GFP) and HCoV-229E-GFP (GFP) were determined to be 8 000 000 and 4 000 000 PFU/ mL, respectively. For the subsequent measurements, four serial dilutions were prepared from each stock solution. | Raman Spectra Measurements Raman spectroscopy was performed using a confocal microscope Raman Spectrometer ThermoFisher Scientific, (Waltham, MA, USA) using excitation laser sources at 532 nm of 7 mW power. The single spectra were obtained using a 10 x MPlan achromat confocal objective lens (Olympus, Hamburg, Germany) confocal a slit aperture of 25 µm, and a grating of 900 lines/mm that resulted to a Raman spectral resolution of around 2 cm -1 , an exposure time of 3 s, and 45 scans. The Raman signal detector was the Syncerity CCD Deep Cooled Camera by Horiba (Piscataway, NJ, USA), operating at -50 °C. The experiment design is illustrated in (Figure1). For every measurement, a capillary tube was immersed in 10 µL of the sample, and then placed on the aluminium foil-coated microscope glass slide. The slide was then placed under the laser light for the spectral acquisition. About 5-10 replicates from 5 different spots were taken per sample. Raman spectra were acquired for both types of viruses in addition to DME media. Four serial dilutions were achieved by serial diluting the stock solutions of both viruses (No GFP = 8 000 000 and GFP = 4 000 000 PFU/ mL) in DMEM containing 2% FBS. In order to monitor the virus stability at different storage times, the Raman spectra were obtained from four concentrations of GFP stored for 24, 168, and 504 h at -20 °C. After that, the Raman spectra were obtained from two samples of No GFP treated thermally at 37 °C, and 95 °C, for 120 and 10 min, respectively. [Insert FIGURE 1] FIGURE1 | Scheme of the experiment design (Own design). 2.4 | Data Analysis All spectra were acquired using the Thermo Scientific Omnic for dispersive Raman 8 software (Waltham, MA, USA), then organized using MS Excel (Microsoft, USA) and The Unscrambler (AspenTech, Bedford, MA, USA). Multivariate analysis, using principal components analysis (PCA) and orthogonal projection to latent structures and discriminant analysis (OPLS-DA), was used to interpret to what extent the possible groups can be distinguished whilst identifying the critical variables that allow such differentiation. The different variables used in this study were: type of virus, its concentration, storage time, and heating. The multivariate analysis was performed with SIMCA 18 (Sartorius Stedim Biotech, France) (RRID:SCR_014688). The following data preprocessing steps were applied on the entire dataset: (1) standard normal variate (SNV) data normalization; (2) Baseline correction using the Asymmetric Least Squares (ALS) method; and (3) smoothing using a Savitzky–Golay filter with a 3 rd order polynomial and a window size of 11. The preprocessing with SNV is essential to reduce the differences in the global signal intensities and remove multiplicative interferences without altering the spectrum shape, while the ALS method enhances the peak observation by correcting the baseline fluctuations [13]. Subsequently, the average spectral curves were plotted using Origin 2022 (OriginLab, Massachusetts, USA) the average from the replicate measurements taken at a given spatial point. The R language (Vienna, Austria) was used for conducting several multivariate analyses and for presenting the results using more visual graphics [14]. | Results 3.1 | Discrimination Based on Type and Concentration of Viruses Raman spectra were obtained from No GFP and GFP viruses, in addition to from medium as a control. Figure 2A shows the Raman spectra obtained from the considered experimental samples: DMEM (Med), No GFP (80 000 PFU/ mL) and GFP (40 000 PFU/ mL). Raman spectra were acquired by taking 10 µL of each sample by the capillary tube, and then measured by the Raman spectroscopy technique. Raman microspectrometer is a spectrometer coupled to a microscope to yield Raman spectra with a spatial resolution that can be modulated by the use of objectives with different magnifications. Raman spectrum can be obtained from a discrete point within a sample using a 10× objective, where the focusing was done in the same level used to focus on the cell. A data was compiled by analyzing Raman signals and identifying bands, derived from literature. The assignations of the major different bands are present in Table S 1. The different bands in spectra of GFP virus in comparison with spectra of No GFP virus are present at 734 cm -1 (adenine), 1019 cm -1 (glucose, phenylalanine), 1193 cm -1 (aromatic C-H bending), 1210 cm -1 (phenylalanine, tryptophan), 1370 cm -1 (tryptophan), 1382 cm -1 (C-H rocking lipid), 1394 cm -1 , 1416 (adenine), 1452 cm -1 (CH 2 deformation), and 1518 cm -1 (COO - Tryptophan). To discriminate between the three experimental samples by their type, we explored the chemometric modeling technique known as OPLS-DA. OPLS-DA is a supervised regression method that enables the differentiation among groups by combining orthogonal signal correction (OSC) and partial least squares with discriminant analysis (PLS-DA). An OPLS-DA model was made for the whole training data set, with different types of samples (Med, No GFP and GFP). Figure 2B represents the 3D scores plot for OPLS-DA model that discriminate the three samples clearly according to the type of sample. The model explained 95 % of the variance in Y (R2Y), with a prediction capacity of 71 % (Q2). [Insert FIGURE 2] FIGURE 2A | Raman spectra acquired for three experimental samples: Media (red), No GFP (green) and GFP (blue). B . 3D scores plot of OPLS-DA model for the three samples discriminated by their type: Media, GFP virus, and No GFP virus. Virus concentrations of GFP and No GFP viruses were 40 000 and 80 000 PFU/ mL. The model explained 95 % of the variance in Y (R2Y), with a prediction capacity of 71 % (Q2). The test dataset was also employed for making predictions, specifically to assess the ability of the trained OPLS-DA models to accurately predict the type of sample. The findings of these predictions were represented in misclassification tables. Concerning the OPLS-DA model, the misclassification table demonstrated 100 % accuracy in classifying samples correctly (Table 1). [Insert TABLE 1] TABLE I | Misclassification table for the OPLS-DA model that discriminates the three samples (Media, No GFP, and GFP virus) by their type. For both types of viruses, four serial dilutions were achieved by diluting the virus stock solutions into DMEM. Figure 3 represents the developed OPLS-DA model using 5-10 replicates of Raman spectra acquired from the No GFP virus to discriminate four concentrations; 0.08, 800, 80 000, 800 000 PFU/ mL, as well as the media (virus concentration = 0). Visual differences among the different concentrations were observed by analyzing their Raman spectra by OPLS-DA. [Insert FIGURE 3] FIGURE 3 | 3-D scores plot of OPLS-DA model shows a discrimination of No GFP virus samples by their concentration at 0.08, 800, 80 000, 800 000 PFU/ mL, as well as the media (virus concentration = 0). The model showed 92 % of the variance in Y (R2Y) and a prediction capacity of 43 % (Q2). This indicated that the prediction of the test data set was perfect, with 100 % of the samples correctly classified as shown in Table 2. [Insert TABLE 2] TABLE 2 | Misclassification table for the OPLS-DA model that discriminates No GFP virus samples by their concentration, as well as the media (virus concentration = 0). 3.2 | Discrimination Based on Storage Time Raman spectra were recorded and analyzed for various concentrations of GFP virus stored for three different storage times at -20 °C, in comparison with the non-stored sample. Figure 4A shows the Raman spectra of GFP virus with 400 000 PFU/ mL acquired after 24, 168, and 504 h at -20 °C, as well as the non-stored GFP virus. The bands in the yellow area showed the greatest increase in intensity in the GFP virus stored for 24 h. [Insert FIGURE 4] FIGURE 4A | Raman spectra acquired for 400 000 PFU/ mL of GFP virus at different storage times; non-stored GFP virus, GFP virus stored for 24, 168, and 504 h at -20 °C. The main bands that increased are indicated in the gray area. B . 3-D score plote of the OPLS-DA model for Raman spectra shows the discrimination of 400 000 PFU/ mL of GFP virus according to the storage time. A new OPLS-DA model was made for non-stored GFP virus (0 h), GFP virus stored for 24 h, 168 h and 504 h at -20 °C. Figure 4B represents 3-D score plot for Raman spectra of non-stored GFP virus (0 h), GFP virus stored for 24 h, 168 h and 504 h at -20 °C. The model explained 97 % of the variance in Y (R2Y), with a prediction capacity of 96 % (Q2). For this OPLS-DA model, the classification of GFP virus samples based on the storage time achieved 100 % of correct classification of components studied (Table 3). [Insert TABLE 3] TABLE 3| Misclassification table for the OPLS-DA model that discriminates 800 000 PFU/ mL of GFP virus samples by the storage time, non-stored GFP virus, GFP virus stored for 24, 168, and 504 h at -20 °C. Figure S 1 was constructed based on data analysis with R software, it represents the behavior of the GFP Virus at 400 000 PFU/ mL in different storage times (0, 24, 168 and 504 h). Figure S2 represents behavior of the different concentrations of GFP virus; A: 0.04. B: 400. C: 40 000 PFU/ mL according to the storage times. ‎ 3.3 | Discrimination Based on the Virus Heating To examine the effect of heat on virus stability, we used the highest concentration of No GFP virus. Two samples were exposed to heat at 37 °C, and 95 °C, for 10 and 120 min, respectively. Subsequently, Raman spectra measurements were acquired and compared with the unprocessed sample to detect molecular changes resulting from the heat effect. Raman spectra of these three samples are showed in Figure 5A. [Insert FIGURE 5] FIGURE 5A | Raman spectra acquired for three No GFP virus samples; unprocessed (green), warmed at 37 °C for 120 min (blue), and at 95 °C for 10 min (light purple). Virus concentration was 800 000 PFU/ mL for each sample. B . 3-D scores plot of the OPLS-DA model for of Raman spectra obtained from the three No GFP samples; unprocessed (green), warmed at 37 °C for 120 min (purple), and at 95 °C for 10 min (orange). Figure 5B represents 3-D scores plot derived from OPLS-DA model of Raman spectra acquired for unprocessed No GFP virus, No GFP virus warmed at 37 °C for 120 min, and No GFP virus deactivated at 95 °C for 10 min. Notably, the OPLS-DA model achieved 95 % classification accuracy for GFP virus samples based on their thermal stability (Table 4). [Insert TABLE 4] TABLE 4 | Misclassification table for the OPLS-DA model that discriminates of 800 000 PFU/ mL of No GFP virus samples by the heating temperature. The samples were unprocessed No GFP virus, warmed at 37 °C for 120 min, and at 95 °C for 10 min. According to the data analysis with R software, Figure S 3 shows the evolution of unprocessed No GFP, No GFP warmed at 37 °C for 120 min and at 95 °C for 10 min, under a storage time of 0h. The virus concentration was 800 000 PFU/ml. To enhance clarity, we standardized the concentrations using exponential notation for both viruses; for instance, 0.08 and 0.04 are represented as 1e-8. Figure 6 presents 3-D score plot of the OPLS-DA model for Raman spectra showing the discrimination of all experimental samples by the concentration of virus, regardless the type of virus and the conditions (storage time and temperature). [Insert FIGURE 6] FIGURE 6 | 3-D score plote of the OPLS-DA model for Raman spectra shows the discrimination of all experimental samples by concentration of virus, regardless the type of virus and the conditions. The model had 95 % of the variance in Y (R2Y) and demonstrating a predictive capacity of 66 % (Q2). Remarkably, Table 5 showed that the OPLS-DA model achieved a perfect 100 % classification accuracy for the experimental samples according to concentration of virus. [Insert TABLE 5] TABLE 5 | Misclassification table for the OPLS-DA model that discriminates all experimental samples by virus concentration, regardless the virus type and the conditions. jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf 4 | Discussion 4.1 | Discrimination Based on Type and Concentration of Viruses Dulbecco’s Modified Eagle Medium (DMEM) is a heterogeneous mixture comprising amino acids, vitamins, inorganic salts, carbohydrates, and supplementary materials such as fetal bovine serum (FBS) and 5% CO 2 , which are crucial for cell viability. Based on the results shown in 3.1, DMEM spectra exhibit numerous bands due to the abundance of its components [15]. The most bands were shared among the three types of samples, since the two types of viruses were collected and stored in DMEM. Furthermore, the additional gene encoding GFP made the virus genome more complex and producing a variation in the Raman signals obtained for identical virus types [16]. In the acquired spectra, prominent bands relating to phenylalanine and tryptophan were observed, and their presence is in line with their roles in viral infection and replication [13]. The discrimination between three experimental samples by their type achieved 95 % of the variance in Y (R2Y), with a prediction capacity of 71 % (Q2), demonstrating model capability of distinguishing between the virus with and without GFP extra gene. This is an interesting result since small modifications in a same virus type can be differentiated by analyzing their Raman spectra by OPLS-DA. This result could be encouraging for the use of Raman spectroscopy in detecting variants of the coronavirus. Hence, OPLS-DA proves useful for discrimination purposes as well [17]. 4.2 | Discrimination Based on Storage Time According to the results shown in 3.2, it is noteworthy that discernible spectral alterations indicative of changes in molecular composition within the cell are evident only 24 h post-infection. This implies that viral infection prompts the synthesis of specific proteins within infected cells, a process that can be rapidly activated within a 24 h timeframe following viral infiltration. Such findings underscore the intricate and evolving interplay between the virus and its host cell, shedding light on the expanding roles of host lipids in the viral life cycle [18]. Clear discrimination was observed among the four data groups where the model explained 97 % of the variance in Y (R2Y), with a prediction capacity of 96 % (Q2). These observations demonstrate the capability of Raman spectroscopy enhanced by multivariate analysis to detect the change of GFP virus stability at different storage times due to alterations in its chemical composition. For this OPLS-DA model, the classification of GFP virus samples based on the storage time achieved 100 % of correct classification of components studied (Table 3). There was changing in virus stability depending on the storage time. The largest bands and greatest variance are observed for storage times of 24 and 168 h, respectively. ‎ The rest of GFP virus concentrations recorded showed the same results, where the samples stored for 24 h at -20 °C had highest intensities in comparison with other samples stored for 0, 168, and 504 h. It is well known that -80 °C is considered the temperature to prevent the metabolic activity of virus. Thus, storage for 24 h at -20 °C may not damage virus enough to decrease its capacity for infection. Consequently, this sample records more intensity compared to other samples stored at different storage times. To the best for our knowledge, no studies have reported on determining the stability of the coronavirus using Raman spectroscopy. Hence, the comparison is conducted with the findings obtained from studies that utilized PCR. The literature reported that storage conditions significantly impact the accuracy and sensitivity of detecting or analyzing the virus sample by PCR. When virus is stored fresh as opposed to frozen, accuracy remains high at 93%, while freezing the samples leads to a substantial decrease in accuracy to 64 %. Similarly, sensitivity is maximally retained at 100 % under fresh storage conditions but drastically drops to 18 % when samples are frozen. Therefore, the conclusion is that the conditions of virus storage play a critical role in the reliability of subsequent analyses and maintain the integrity and detectability of the virus [13]. In contrast, Markt et al. showed that storing wastewater at 4 °C for up to 9 days did not significantly impact the detection of SARS-CoV-2 [9]. 4.3 | Discrimination Based on the Virus Heating According to the results shown in 3.3, the high intensities in the spectra were observed for samples in the absence of heat, while disappearance of bands and significant decreases of intensities were observed for the sample heated at 95°C. Subsequent to the application of thermal processing, it is possible that the virus was inactivated under the tested conditions, whereas the unprocessed sample maintained viral infectivity. This observation suggests that exposure to heat reduced the viability of the virus [19]. Evident discrimination was observed among the three samples, with the model explaining 90% of the variance in Y (R2Y) and demonstrating a predictive capacity of 77% (Q2). These findings underscore the efficacy of Raman spectroscopy, supported by multivariate analysis, in detecting changes in GFP virus stability over various storage times due to alterations in its chemical composition. It shows the impact of varying the temperature (37 °C and 95 °C) on the aforementioned sequences. There is impact on the seasonality - prior to the blow-up (peaks of greater magnitude) that are produced by No. 2000 - of the sequences, at 37 °C it has an amplifying effect while at 95 °C its impact is to reduce its fluctuations. In total, 177 spectra were recorded for various samples in our experiment, which included three types (media, No GFP and GFP viruses), four concentrations for each virus type, thermally processed, and stored for different storage time. The spectral dataset was subjected to an OPLS-DA model, allowing discrimination based on concentration. To enhance clarity, we standardized the concentrations using exponential notation for both viruses; for instance, 0.08 and 0.04 are represented as 1e-8. The model successfully discriminated the spectra of highest concentration samples from the rest of spectra, although with less power in discriminating the other concentrations. The model had 95 % of the variance in Y (R2Y) and demonstrating a predictive capacity of 66 % (Q2). It is important to note that the literature does not include studies on determining the stability of the coronavirus using Raman spectroscopy. Therefore, comparison is made with the results of studies that utilized PCR. The propagation of virus typically occurs at a temperature of 37 °C, which provides optimal conditions for their growth and replication. Following production, viruses are commonly stored by freezing them at a temperature of -80 °C to maintain their viability and prevent degradation. Biryukov et al . reported, at 24 °C, that the virus half-life ranged from 6.3 to 18.6 h depending on relative humidity. However, this half-life was reduced to 1.0 to 8.9 h when the temperature was raised to 35 °C [20]. Future studies should therefore aim to incorporate a more precise method such as Surface-Enhanced Raman spectrophotometry (SERS), which offers superior molecular sensitivity and the ability to detect trace-level viral components through signal enhancement at the nanostructured surface. This would enable a meaningful comparison between the two methods. In addition, more advance data analysis software could be employed, with the potential to include a boarder range of coronavirus variants, considering the rapid rate of mutations and genetic variations observed in these viruses recently. 5 | Conclusions The coronavirus has a complex structure that is sensitive to environmental conditions such as temperature, \RL humidity, and light. Therefore, treating the virus using a light-based analytical method like Raman spectroscopy poses a significant challenge. Environmental changes affect the molecular structure of the virus, with proteins in the virus envelope constituting the majority of the structure. These proteins are primarily responsible for the virus’s interaction with laser light. This article has proved the capability of Raman spectroscopy to study those variables for the HCoV-229E coronavirus utilizing multivariate analysis. Main achievements in this work are the possibility of detecting changes that occur in the virus structure due to environmental changes, as well as its ability to identify the virus type and distinguish between different concentrations of the same virus. Multivariate analysis using the continuous OPLS-DA models was successful to discriminate the studied groups, where the models were able to explain 97 % of the variance in Y (R2Y) and demonstrate a predictive capacity of 95 % (Q2). This could be very useful in virus research, allowing fast analysis of virus degradation and viability after storage or exposure to different conditions, a crucial fact for reproducibility and success of the experiments. Additional developments may lead to a method capable of detecting molecular changes in the coronavirus with lower virus concentrations and a wide range of environmental conditions. Author Contributions Ali Haneen Issmer : methodology, measurement, writing – original draft preparation, visualization, data curation \RL and analysis, formal analysis, analysis of results. Carolina Guerrero-Amelin : virus samples preparation, and writing – original draft preparation. Pedro Antonio Mateos-Gomez : methodology, virus samples preparation, investigation, reviewing and editing. Gemma Montalvo : project administration, validation and writing – reviewing and editing. Fernando E. Ortega-Ojeda: methodology, validation, data analysis, writing – reviewing and editing, and supervision. Pablo Navarro Caceres : data analysis. Carmen García-Ruiz : resources, project administration, conceptualization, methodology, data curation, validation, reviewing and editing and supervision. jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Acknowledgments Ali H. I. expresses his appreciation to University of Thi-Qar and Ministry of Higher Education & Scientific Research in IRAQ for his financial support. 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