Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

Identifying the geographical origin and processing technology of Moyao () on the basis of near-infrared spectroscopy combined with chemometrics.

OBJECTIVE: To evaluate the quality of Moyao (Myrrh) in the identification of the geographical origin and processing of the products. METHODS: Raw Moyao (Myrrh) and two kinds of Moyao (Myrrh) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (Myrrh) samples, and their discriminative performance was evaluated. RESULTS: Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively. CONCLUSIONS: NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.

Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

HS-SPME-GC-MS and Chemometrics for the Quality Control and Clustering of Monovarietal Extra Virgin Olive Oil: A 3-Year Study on Terpenes and Pentene Dimers of Italian Cultivars.

Terpenes and pentene dimers are less studied volatile organic compounds (VOCs) but are associated with specific features of extra virgin olive oils (EVOOs). This study aimed to analyze mono- and sesquiterpenes and pentene dimers of Italian monovarietal EVOOs over 3 years (14 cultivars, 225 samples). A head space-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method recently validated was used for terpene and pentene dimer quantitation. The quantitative data collected were used for both the characterization and clustering of the cultivars. Sesquiterpenes were the molecules that most characterized the different cultivars, ranging from 3.908 to 38.215 mg/kg; different groups of cultivars were characterized by different groups of sesquiterpenes. Pentene dimers (1.336 and 3.860 mg/kg) and monoterpenes (0.430 and 1.794 mg/kg) showed much lower contents and variability among cultivars. The application of Kruskal-Wallis test-PCA-LDA-HCA to the experimental data allowed defining 4 clusters of cultivars and building a predictive model to classify the samples (94.3% correct classification). The model was further tested on 33 EVOOs, correctly classifying 91% of them.

Determination of organic acids for predicting sourness intensity of tea beverage by liquid chromatography-tandem mass spectrometry and chemometrics methods.

The contents of organic acids (OAs) in tea beverage and their relationship with taste intensity have not been fully understood. In this work, a rapid (10 min for a single run) and sensitive (limits of quantification: 0.0044-0.4486 µg/mL) method was developed and validated for the simultaneous determination of 17 OAs in four types of tea, based on liquid chromatography-tandem mass spectrometry with multiple reaction monitoring mode. The contents of 17 OAs in 96 tea samples were measured at levels between 0.01 and 11.80 g/kg (dried weight). Quinic acid, citric acid, and malic acid were determined as the major OAs in green, black, and raw pu-erh teas, while oxalic acid and tartaric acid exhibited the highest contents in ripe pu-erh tea. Taking the OAs composition as input features, a partial least squares regression model was proposed to predict the sourness intensity of tea beverages. The model achieved a root-mean-square error of 0.58 and a coefficient of determination of 0.84 for the testing set. The proposed model provides a theoretical way to evaluate the sensory quality of tea infusion based on its chemical composition.

Classification of buckwheat honey produced in Kazakhstan according to their biochemical ingredients and bioactivities by chemometric approach.

Herein, nineteen buckwheat honey samples collected from 19 stations of different ecological zones of Kazakhstan were analysed for their pollen density, physicochemical properties, chemical composition, antioxidant, anticholinesterase, tyrosinase inhibitory, and urease inhibitory activities with chemometric approaches. Twelve phenolic compounds and fumaric acid were identified using HPLC-DAD, and mainly fumaric, p-hydroxybenzoic, p-coumaric, trans-2-hydroxy cinnamic acids, and chrysin were detected in all samples. The honey samples collected from the Northern zone exhibited best antioxidant activity in lipid peroxidation inhibitory (IC50:8.65 ± 0.50 mg/mL), DPPH• (IC50:17.07 ± 1.49 mg/mL), ABTS•+ (IC50:8.90 ± 0.65 mg/mL), CUPRAC (A0.50:7.51 ± 0.30 mg/mL) and metal chelating assay (IC50:10.39 ± 0.71 mg/mL). In contrast, South-eastern zone samples indicated better acetylcholinesterase (55.57 ± 0.83%), butyrylcholinesterase (49.59 ± 1.09%), tyrosinase (44.40 ± 1.21%), and moderate urease (24.57 ± 0.33%) inhibitory activities at 20 mg/mL. The chemometric classification of nineteen buckwheat honey was performed using PCA and HCA techniques. Both were supported by correlation analysis. Thirteen compounds contributed significantly to the clustering of buckwheat honey based on geographical origin.

Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose.

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Differentiation of three commercial tuna species through GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics and chemometrics.

Food fraud is common in the tuna industry because of the economic benefits involved. Ensuring the authenticity of tuna species is crucial for protecting both consumers and tuna stocks. In this study, GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics were used to investigate the metabolite profiles of three commercial tuna species (skipjack tuna, bigeye tuna and yellowfin tuna). A total of 22 and 77 metabolites were identified with high confidence using GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry, respectively. Further screening via chemometrics revealed that 38 metabolites could potentially serve as potential biomarkers. Hierarchical cluster analysis showed that the screened metabolite biomarkers successfully distinguished the three tested tuna species. Furthermore, a total of 27 metabolic pathways were identified through enrichment analysis based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways.

Exploring the bioaccessibility of polyphenols and glucosinolates from Brassicaceae microgreens by combining metabolomics profiling and computational chemometrics.

Microgreens constitute natural-based foods with health-promoting properties mediated by the accumulation of glucosinolates (GLs) and phenolic compounds (PCs), although their bioaccessibility may limit their nutritional potential. This work subjected eight Brassicaceae microgreens to in vitro gastrointestinal digestion and large intestine fermentation before the metabolomics profiling of PCs and GLs. The application of multivariate statistics effectively discriminated among species and their interaction with in vitro digestion phases. The flavonoids associated with arugula and the aliphatic GLs related to red cabbage and cauliflower were identified as discriminant markers among microgreen species. The multi-omics integration along in vitro digestion and fermentation predicted bioaccessible markers, featuring potential candidates that may eventually be responsible for these functional foods' nutritional properties. This combined analytical and computational framework provided a promising platform to predict the nutritional metabolome-wide outcome of functional food consumption, as in the case of microgreens.

[Analysis of chemical compositions in different parts of Isodon japonicus using UPLC-Q-TOF-MS technique combined with chemometrics].

In order to analyze the similarities and differences of chemical compositions between the roots and stems and leaves of Isodon japonicus(IJ), this study utilized UPLC-Q-TOF-MS technology to systematically characterize its chemical compositions, analyzed and identified the structure of its main compounds, and established a method for simultaneous determination of its content by refe-rence substance. A total of 34 major compounds in IJ, including 14 reference compounds, were identified or predicted online. Moreover, an UPLC-UV content determination method was developed for 11 compounds [danshensu, caffeic acid, vicenin-2,(1S,2S)-globoidnan B, rutin,(+)-rabdosiin,(-)-rabdosiin,(1S,2S)-rabdosiin, shimobashiric acid C, rosmarinic acid, and pedalitin]. The method exhibited excellent separation, stability, and repeatability, with a wide linear range(0.10-520.00 µg·mL~(-1)) and high linearity(R~2>0.999). The average recovery rates ranged from 94.72% to 104.2%. The principal component analysis(PCA) demonstrated a clear difference between the roots and stems and leaves of IJ, indicating good separation by cluster. Furthermore, the orthogonal partial least squares discriminant analysis(OPLS-DA) model was employed, and six main differentially identified compounds were identified: rosmarinic acid, shimobashiric acid C, epinodosin, pedalitin, rutin, and(1S,2S)-rabdosiin. In summary, this study established a strategy and method for distinguishing different parts of IJ, providing a valuable tool for quality control of IJ and a basis for the ratio-nal utilization and sustainable development of IJ.

Probing the stability and quality of the cellulose-based Pickering emulsion containing sesamolin-enriched sesame oil by chemometrics-assisted ATR-FTIR spectroscopy.

This study presents the employment of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflection and principal component analysis (PCA) to analyze the stability of a Pickering emulsion stabilized by carboxylated-cellulose nanocrystal (cCNC) comprising sesame oil phases with or without sesamolin. FTIR measurements identified an intermolecular hydrogen bond between the ester group of the triglyceride and the carboxyl group of the cCNC to create the emulsion droplet. The spectral bands from the hydroxyl group vibration (3700-3050 cm-1), carbonyl (1744 cm-1), CO groups of the ester triglyceride and cCNC (1160-998 cm-1) markedly discriminated between stabilized and destabilized emulsions. The PCA of FTIR spectra detected the change of molecular interaction during storage according to creaming, aggregation, and coalescence and changes in physicochemical parameters such as droplet size, refractive index, and zeta potential. Hence, PCA enabled the observation of the destabilization of emulsion in real-time.

A novel headspace solid-phase microextraction arrow method employing comprehensive two-dimensional gas chromatography-mass spectrometry combined with chemometric tools for the investigation of wine aging.

BACKGROUND: Omics is used as an analytical tool to investigate wine authenticity issues. Aging authentication ensures that the wine has undergone the necessary maturation and developed its desired organoleptic characteristics. Considering that aged wines constitute valuable commodities, the development of advanced omics techniques that guarantee aging authenticity and prevent fraud is essential. RESULTS: Α solid phase microextraction Arrow method combined with comprehensive two-dimensional gas chromatography-mass spectrometry was developed to identify volatiles in red wines and investigate how aging affects their volatile fingerprint. The method was optimized by examining the critical parameters that affect the solid phase microextraction Arrow extraction (stirring rate, extraction time) process. Under optimized conditions, extraction took place within 45 min under stirring at 1000 rpm. In all, 24 monovarietal red wine samples belonging to the Xinomavro variety from Naoussa (Imathia regional unit of Macedonia, Greece) produced during four different vintage years (1998, 2005, 2008 and 2015) were analyzed. Overall, 237 volatile compounds were tentatively identified and were treated with chemometric tools. Four major groups, one for each vintage year were revealed using the Hierarchical Clustering Analysis. The first two Principal Components of Principal Component Analysis explained 86.1% of the total variance, showing appropriate grouping of the wine samples produced in the same crop year. A two-way orthogonal partial least square - discriminant analysis model was developed and successfully classified all the samples to the proper class according to the vintage age, establishing 17 volatile markers as the most important features responsible for the classification, with an explained total variance of 88.5%. The developed prediction model was validated and the analyzed samples were classified with 100% accuracy according to the vintage age, based on their volatile fingerprint. SIGNIFICANCE: The developed methodology in combination with chemometric techniques allows to trace back and confirm the vintage year, and is proposed as a novel authenticity tool which opens completely new and hitherto unexplored possibilities for wine authenticity testing and confirmation.

GC-MS and ATR-FTIR Spectroscopy Coupled with Chemometric Analysis for Detection and Quantification of White Turmeric (Curcuma zedoaria) Essential Oils Adulteration.

<b>Background and Objective:</b> White turmeric essential oil (WTEO) is known to have high commercial value since it has been used to improve immunological function, increase blood circulation, ease toxin clearance and stimulate digestion. However, there is no standard to regulate the specific characteristics of white turmeric essential oil. Therefore, the objective of this research was to develop an analytical technique for WTEO authentication from vegetable oils, namely palm oil (PO), coconut oil (VCO) and soybean oil (SO), using FTIR spectroscopy and chemometrics, as well as GC-MS spectroscopy. <b>Materials and Methods:</b> The WTEO was obtained by hydrodistillation method. Pure WTEO and vegetable oils were scanned in the MIR region (4000-650 cm¹) of FTIR spectroscopy and the spectra were further analyzed using chemometrics. <b>Results:</b> The extraction yielded 0.103% v/w WTEO, a dark purple color with a specific pungent odor. Discriminant analysis separated pure WTEO and adulterated WTEO with 100% accuracy at wave numbers 4000-650 cm¹. The best PLS regressions to quantify SO, VCO, PO and concentration in WTEO were at wave numbers 4000-1100, 1400-1050 and 2100-650 cm¹, respectively. <b>Conclusion:</b> The FTIR and chemometrics combination effectively authenticates white turmeric essential oil from any possible adulterants, such as vegetable oil.

Based on metabolomics, chemometrics, and modern separation omics: Identifying key in-pathway and out-pathway points for pesticide residues during solid-state fermentation of baijiu.

By collecting real samples throughout the entire production process and employing chemometrics, metabolomics, and modern separation omic techniques, it unveiled the patterns of pesticide transfer during solid-state fermentation. The results indicated that 12 types of pesticide residues were prevalent during baijiu production, with organochlorine and carbamate pesticides being the most abundant in raw materials. After fermentation, organochlorine pesticides and pyrethroid pesticides exhibited higher content, while carbamate pesticides dominated in the final product. The pathways for pesticide input and elimination were identified, and the intricate mechanisms underlying these changes were further elucidated. Additionally, key control points were defined to facilitate targeted monitoring. The results indicated that pesticide residue primarily originates from raw materials and Daqu, whereas both solid-state fermentation and distillation processes were effective in reducing pesticide residues. The study offers valuable guidance for establishing pesticide residue standards in the context of baijiu production.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach.

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis.

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics.

Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.

Formulation analysis of functional fragrance via polar-gradient extraction method and chemometrics pattern recognition.

In this study, characteristic components of 15 natural flavors was analyzed by the polar-gradient extraction (PGE) technique in combination with GC-MS and chemometrics pattern recognition. The obtained results were utilized for the traceability of 4 functional fragrance formulations. The optimal PGE system consisting of 5 different polar solvents, was developed based on similarity-intermiscibility theory. Four chemometrics pattern recognition models including PCA, HCA, PLS-DA, and OPLS-DA were constructed based on the characteristic component database constituting 15 natural flavors. These models were used to trace 4 functional fragrance formulations. The experimental results obtained were found to be satisfactory and accurate. The combination of PGE technique and chemometric pattern recognition methods provides theoretical guidance for the analysis of characteristic components of natural flavors and the traceability of functional fragrance formulations. This approach can be promoted in various fields such as food, traditional Chinese medicine, and cosmetics.

Chemometric Analysis Evidencing the Variability in the Composition of Essential Oils in 10 Salvia Species from Different Taxonomic Sections or Phylogenetic Clades.

Essential oil (EO) of Salvia spp. has been widely used for culinary purposes and in perfumery and cosmetics, as well as having beneficial effects on human health. The present study aimed to investigate the quantitative and qualitative variations in EOs in wild-growing and cultivated pairs of samples from members in four Salvia sections or three clades, namely S. argentea L. (Sect. Aethiopis; Clade I-C), S. ringens Sm. (Sect. Eusphace; Clade I-D), S. verticillata L. (Sect. Hemisphace; Clade I-B), S. amplexicaulis Lam., and S. pratensis L. (Sect. Plethiosphace; Clade I-C). Furthermore, the natural variability in EO composition due to different genotypes adapted in different geographical and environmental conditions was examined by employing members of three Salvia sections or two phylogenetic clades, namely S. sclarea L. (six samples; Sect. Aethiopis or Clade I-C), S. ringens (three samples; Sect. Eusphace or Clade I-D), and S. amplexicaulis (five samples; Sect. Plethiosphace or Clade I-C). We also investigated the EO composition of four wild-growing species of two Salvia sections, i.e., S. aethiopis L., S. candidissima Vahl, and S. teddii of Sect. Aethiopis, as well as the cultivated material of S. virgata Jacq. (Sect. Plethiosphace), all belonging to Clade I-C. The EO composition of the Greek endemic S. teddii is presented herein only for the first time. Taken together, the findings of previous studies are summarized and critically discussed with the obtained results. Chemometric analysis (PCA, HCA, and clustered heat map) was used to identify the sample relationships based on their chemical classes, resulting in the classification of two distinct groups. These can be further explored in assistance of classical or modern taxonomic Salvia studies.

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.