Development of a Non-Destructive Optical Measurement Technique for Identifying Talcum and Their Coating Types in Baby Powder and Edible Starch
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Abstract
This article proposes a method for detecting talcum and classifying their coating types, where talcum is a primary component used in industrial and personal care products, using non-destructive optical spectroscopy techniques in the mid-infrared (Mid-IR) and far-infrared (Far-IR) ranges. In this study, a Fourier Transform Infrared Spectroscopy (FT-IR) device was used to collect the complete spectral data, which was then analyzed using machine learning technique. Experimental results show that, by using Gaussian Naive Bayes (Gaussian NB) as a trained machine learning mode, the proposed technique can classify the coating types used in pure talcum substances with 98.18% and 95.27% accuracy in mid-infrared and far-infrared ranges, respectively. Consequently, the Gaussian NB model was utilized along with spectra collected from various commercial powder products in order to identify the existence of talcum. Results indicate that, in the mid-infrared range, the model achieves a classification accuracy of 95.56% for baby powder products and 91.73% for consumable powder products. In the far-infrared range, the model achieves a better accuracy of 97.78% for baby powder products and 100% for consumable powder products. In conclusion, the proposed technique demonstrates its potential in detecting talcum and coating types in powder products, which can help reduce reliance on experts, lower costs, and increase confidence in the quality and safety of powder products.
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