Severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has
caused the worst global health crisis in living memory. The reverse
transcription polymerase chain reaction (RT-qPCR) is considered the
gold standard diagnostic method, but it exhibits limitations in the
face of enormous demands. We evaluated a mid-infrared (MIR) data set
of 237 saliva samples obtained from symptomatic patients (138 COVID-19
infections diagnosed via RT-qPCR). MIR spectra were evaluated via
unsupervised random forest (URF) and classification models. Linear
discriminant analysis (LDA) was applied following the genetic algorithm
(GA-LDA), successive projection algorithm (SPA-LDA), partial least
squares (PLS-DA), and a combination of dimension reduction and variable
selection methods by particle swarm optimization (PSO-PLS-DA). Additionally,
a consensus class was used. URF models can identify structures even
in highly complex data. Individual models performed well, but the
consensus class improved the validation performance to 85% accuracy,
93% sensitivity, 83% specificity, and a Matthew’s correlation
coefficient value of 0.69, with information at different spectral
regions. Therefore, through this unsupervised and supervised framework
methodology, it is possible to better highlight the spectral regions
associated with positive samples, including lipid (∼1700 cm
–1
), protein (∼1400 cm
–1
),
and nucleic acid (∼1200–950 cm
–1
)
regions. This methodology presents an important tool for a fast, noninvasive
diagnostic technique, reducing costs and allowing for risk reduction
strategies.
Here, we combine angular search algorithm and variance inflation factor (ASA-VIF) with support vector regression (SVR) (ASA-VIF-SVR) to estimate total acid number (TAN), basic nitrogen content (BNC), and sulfur content (SC) in Brazilian crude oils. To prevent the interference of outliers, we further developed a strategy for outlier identification and applied it to nonlinear models based on RMSE (root mean square error). ASA-VIF-SVR was applied to near-and mid-infrared spectroscopy (NIR and MIR) and hydrogen nuclear magnetic resonance (1 H NMR) spectroscopy data available in a range of 93-194 samples. The models were evaluated for accuracy (root mean square error of calibration [RMSEC] and root mean square error of prediction [RMSEP]) and linearity (coefficient of determination, R 2). The removal of outliers increased accuracy and linearity of our models. The ASA-VIF model for TAN, BNC, and SC selected 0.37%, 0.93%, and 0.30% of variables from full NIR spectra; 0.21%, 0.27%, and 0.21% from full MIR; and 0.20%, 0.42%, and 0.15% from full 1 H NMR. In most cases, the best results were obtained with variable selection compared with the full dataset. Also, 1 H NMR generated more accurate and linear models with RMSEP and R 2 p of 0.0071 wt% and 0.86 for BNC and 0.0623 wt% and 0.79 for SC. TAN showed a better MIR result with RMSEP of 0.1426 mg KOH g-1 and R 2 p of 0.47. The most important region for 1 H NMR and MIR was the one with the largest quantity of unpaired electrons (aromatic region).
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