Portable Near Infrared Spectroscopy Study for Turmeric Adulteration Detection

BACKGROUND: Turmeric powder is commonly adulterated with low-cost plant-based materials for economic gain; however, rapid on-site detection methods remain limited. OBJECTIVE: This study is based on the development of a rapid, non-destructive method that uses portable near infrared spectroscopy, chemometrics, and deep learning, enabling the detection of corn flour adulteration in turmeric powder. METHODS: A total of 330 adulterated samples (0-50% corn flour) were prepared, and corresponding NIR spectra were collected across the range of 900-1700 nm. A systematic comparison was carried out using eight pre-processing methods, three feature selection strategies (PCA, CARS, UVE), and multiple modeling algorithms. A dual-attention CNN-LSTM multi-task network was developed to simultaneously perform adulteration classification and content regression. RESULTS: The optimization of the PLS model was performed through second derivative (D2) pre-processing and UVE feature selection (78.5% compression). The CNN-LSTM dual-attention model achieved a classification accuracy of 98.9% and a remarkable regression performance (R2 = 0.9927, RMSE = 1.3825, and RPD = 11.64), significantly outperforming traditional PLS models. Weight visualization revealed that the learned attention maps corresponded closely to the characteristic spectral features associated with the curcumin attenuation and starch enhancement. CONCLUSIONS: The integration of portable NIR spectroscopy with a multi-task deep learning model offers a robust, efficient, and accurate strategy for on-site rapid screening of turmeric adulteration under controlled experimental conditions.