ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models.

Attenuated Total Reflectance-Fourier transform infrared spectroscopy coupled with chemometrics for the rapid detection of coconut water adulteration.

Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR), in combination with chemometrics, was explored as a rapid method of detecting sugar adulteration in coconut water. In a simulated experiment, coconut water was substituted with binary sugars, mixed sugars, and high fructose corn syrup and discriminated using the fingerprint infrared band region between 1200 and 950 cm. Principal component analysis (PCA) performed on data pre-processed by the Savitzky-Golay smoothing and gap-segment derivative, revealed data clusters discernible by the type and level of substituted sugars, enabling visual diagnosis of the similarity and anomalous features in the dataset.