Chemometrics analysis for the detection of dental caries via UV absorption spectroscopy

Basri, Katrul Nadia; Yazid, Farinawati; Wahab, Rohaya Megat Abdul; Zain, Mohd Norzaliman Mohd; Yusof, Zalhan Md; Zoolfakar, Ahmad Sabirin

doi:10.1016/j.saa.2021.120464

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…Different methods of preprocessing were applied to the UV spectra to determine the optimized prediction model. The performance measure of the prediction model was accuracy, precision, sensitivity and the specificity based on the calculation used by the previous work [22]. The calibration data performance has shown the optimized performance for the accuracy, precision, sensitivity and specificity for each type of preprocessing method.…”

Section: Fig 3 -The Decision Tree For the Classification Of Uv Spectr...mentioning

confidence: 99%

Decision Tree Algorithm for the Classification of Dental Caries Severity via Saliva

Basri

Zain

Yusof

et al. 2022

IJIE

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Dental caries is one of the most prevalent chronic diseases. Early detection is prominent to avoid the tooth weakening or worst the tooth loss. UV absorption spectroscopy is a non-invasive techniqueused for the detection of salivary alpha-amylase which are increasing in the presence of caries. Spectrum acquired from patient at Faculty of dentistry, UKM showed significant peak around 260-300nm which are correspond to the absorption of amino acid found in salivary alpha-amylase.The spectra arepreprocesses using autoscale and multiplicative scatter correction (MSC) to optimize the signal. Decision tree algorithm was implemented on the UV absorption spectra. The best model of decision tree obtained when using autoscale preprocessing method. The accuracy, precision, sensitivity and specificity for the validation data obtained were 0.70, 1.00, 0.14and 1.00 respectively. The decision tree requiresmore tuning for the robustness for future application.

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Section: Fig 3 -The Decision Tree For the Classification Of Uv Spectr...mentioning

confidence: 99%

Decision Tree Algorithm for the Classification of Dental Caries Severity via Saliva

Basri

Zain

Yusof

et al. 2022

IJIE

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“…These methods are extremely subjective, expensive, and require trained personnel. 5 The rapid advancement in the methods and development of novel materials have revolutionized the course of history for the clinical and diagnostic applications. 6−12 Lately, scientists are studying the potential of saliva as an alternative, noninvasive biofluid for the diagnosis of several pathological conditions by the evaluation of biomarkers and monitoring the therapeutic outcomes.…”

Section: Introductionmentioning

confidence: 99%

Host–Guest Mechanism via Induced Fit Fullerene Complexation in Porphin Receptor to Probe Salivary Alpha-Amylase in Dental Caries for Clinical Applications

Asghar,

Bilal,

Nawaz

et al. 2024

ACS Appl. Bio Mater.

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Salivary α-amylase is the most abundant protein of human saliva that potentially binds to streptococcus and other bacteria via specific surface-exposed α-amylase-binding proteins and plays a significant role in caries development. The detection of α-amylase in saliva can be used as a bioindicator of caries development. Herein, a facile strategy has been applied, tailoring the photochemical properties of 5,10,15,20-tetrakis(4-hydroxyphenyl)-21H,23H-porphine (TPPOH) and the fullerene C60 complex. The fluorescence emission of TPPOH is quenched by starch-coated fullerene C60 via charge-transfer effects, as determined by UV absorption and fluorescence spectroscopic studies. The starch-coated C60 has been thoroughly characterized via Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), optical microscopy, thermal gravimetric analysis (TGA), static water contact angle measurements, and zeta potential measurements. The analytical response of the assay showed a linear fluorescent response in α-amylase concentrations ranging from 0.001–0.1 Units/mL, with an LOD of 0.001 Units/mL. The applicability of the method was tested using artificial saliva with quantitative recoveries in the range 95–100%. The practicability of the procedure was verified by inspecting saliva samples of real clinical samples covering all age groups. We believe that the proposed method can serve as an alternative analytical method for caries detection and risk assessment that would also minimize the cost of professional preventive measures and treatments.

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“…To explore which of the commonly used machine learning algorithms is more effective for blood classification of singlecell Raman spectrum, these algorithms are deeply analyzed and compared in this paper. Seven commonly used machine learning algorithms and a deep learning algorithms were studied, including random forest (RF) [15] , support vector machine (SVM) [16] , linear discriminant analysis (LDA) [17] , quadratic discriminant analysis (QDA) [18] , naive Bayes (NB) [19] , artificial neural network (ANN) [20] , multinomial logistic regression (MLR) [21] , and convolutional neural network (CNN) [22] . Laser tweezers Raman spectroscopy (LTRS) combines Optical Tweezers and Raman spectroscopy, which can collect Raman spectra of single cells in liquid phase environment.…”

Section: Introductionmentioning

confidence: 99%

Identification of individual red blood cells by single-cell Raman spectroscopy combined with machine learning

Liu

Zhou²

2023

AOPC 2022: Biomedical Optics

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Raman spectroscopy, a "fingerprint" spectrum of substances, can be used to characterize various biological and chemical samples. To allow for blood classification using single-cell Raman spectroscopy, several machine learning algorithms were implemented and compared. A single-cell laser optical tweezer Raman spectroscopy system was established to obtain the Raman spectra of red blood cells. The Boruta algorithm extracted the spectral feature frequency shift, reduced the spectral dimension, and determined the essential features that affect classification. Next, seven machine learning classification models and deep learning model without dimensionality reduction are analyzed and compared based on the classification accuracy, precision, and recall indicators. The results show that support vector machines and convolutional neural network are the two most appropriate machine learning algorithms for single-cell Raman spectrum blood classification, and the findings provide essential guidance for future research studies.

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Chemometrics analysis for the detection of dental caries via UV absorption spectroscopy

Cited by 12 publications

References 24 publications

Decision Tree Algorithm for the Classification of Dental Caries Severity via Saliva

Decision Tree Algorithm for the Classification of Dental Caries Severity via Saliva

Host–Guest Mechanism via Induced Fit Fullerene Complexation in Porphin Receptor to Probe Salivary Alpha-Amylase in Dental Caries for Clinical Applications

Identification of individual red blood cells by single-cell Raman spectroscopy combined with machine learning

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