PCA-SCG-ANN for Detection of Non-structural Protein 1 from SERS Salivary Spectra

Methods in Raman spectroscopy for saliva studies – a review

“…Radzol et al [131] ask volunteers to perform 1 min of gargling before unstimulated collection. Hern andez-Arteaga et al [200] implemented "vigorous teeth brushing," an approach which could introduce unwanted blood residues into the saliva, which is in the exclusion criteria of Othman et al [238] Salivary pH upon collection, in general, is not considered, barring ref., [50] the spectral impact of which has been discussed in Buchan. [157] Establishing a universal "standard collection time" will be helpful in the consistency and comparison of the many studies exploiting saliva, as has been noted in the context of CSF and serum acquisition.…”

“…Sample numbers Raman-saliva studies in the literature involve both small sample numbers (n < 20) [60,96,118,153,154,224,241] as well as larger (n > 150) cohorts (Figure 3B). [200][201][202]222,238] Regarding small sample sizes of saliva, Kah et al, [60] who study five patients with oral cancer and five healthy subjects, indicate that a larger cohort is needed for statistically significant cancer staging. On the other hand, Maitra et al [83] collected nearly 500 samples for a biofluid cross-comparison study, including 114 saliva samples, further divided into 35 healthy and 79 diseased cohorts, spread across different stages of esophageal cancer.…”

“…[89,246,359,363] Notably, deep learning includes artificial neural networks (ANNs), which mimic the neuronal networks in the brain. [103,238,239] Here, layers of interconnected nodes are activated to various degrees contingent on the data supplied. Inter-nodal connections, "synapses," are assigned weights, which may be modified via a backpropagation algorithm designed to minimize error, a loss function, between predicted outputs and desired outputs.…”

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

“…Since the spectra have high dimensionality, dimension reduction is a frequent preprocessing step ( Figure 12 ). PCA is again popularly used as a dimension reduction or feature extraction step [ 60 , 61 , 64 , 65 , 68 , 70 , 71 , 73 ], or for exploratory analysis [ 62 , 72 , 157 ]. Once the spectra are remapped using PCA, a classifier or regression model is employed such as an extreme learning machine (ELM) [ 71 ], LDA [ 68 ], SVM [ 60 , 64 , 73 ], PLSR [ 65 ], or ANN [ 70 ].…”

“…PCA is again popularly used as a dimension reduction or feature extraction step [ 60 , 61 , 64 , 65 , 68 , 70 , 71 , 73 ], or for exploratory analysis [ 62 , 72 , 157 ]. Once the spectra are remapped using PCA, a classifier or regression model is employed such as an extreme learning machine (ELM) [ 71 ], LDA [ 68 ], SVM [ 60 , 64 , 73 ], PLSR [ 65 ], or ANN [ 70 ]. An alternative to dimension reduction is utilizing the high dimensionality spectral data directly with a node-based algorithm such as ANN [ 72 , 158 , 159 ] and CNN [ 160 , 161 ].…”