Artificial neural network and convolutional neural network for prediction of dental caries.

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries.

Multivariate versus univariate spectrum analysis of dentine sialophosphoprotein (DSPP) for root resorption prediction: a clinical trial.

Background: A force applied during orthodontic treatment induces inflammation to root area and lead to root resorption known as orthodontically induced inflammatory root resorption (OIIRR). Dentine sialophosphoprotein (DSPP) is one of the most abundant non-collagenous proteins in dentine that was released into gingival crevicular fluid (GCF) during OIIRR. The aim of this research is to compare DSPP detection using the univariate and multivariate analysis in predicting classification level of root resorption.

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

Caries is one of the non-communicable diseases that has a high prevalence trend. The current methods used to detect caries require sophisticated laboratory equipment, professional inspection, and expensive equipment such as X-ray imaging device. A non-invasive and economical method is required to substitute the conventional methods for the detection of caries.

Classification and quantification of palm oil adulteration via portable NIR spectroscopy.

Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.