Abstract
This study evaluates the feasibility of visible and near-infrared spectroscopy (Vis-NIRS), an eco-friendly and efficient analytical technique, for the simultaneous quantification of five key bioactive compounds (bisacurone, ar-turmerone, bisdemethoxycurcumin, demethoxycurcumin, and curcumin) in turmeric. A total of 82 turmeric samples were analyzed by high-performance liquid chromatography (HPLC), with 62 used for Vis-NIRS calibration and 20 for external validation. Various combinations of spectral preprocessing techniques, such as smoothing and derivatives, were applied to improve spectral quality. Multivariate statistical analysis, such as principal component regression (PCR) and partial least squares (PLS) regression, was used to predict target compound concentrations. Optimal models were selected based on the coefficient of determination (R2), standard error of calibration (SEC), standard error of cross-validation (SECV), standard error of prediction(SEP), and ratio of prediction to deviation (RPD). In this study, PLS model exhibited strong predictive performance for most compounds except for bisacurone, with calibration results showing R2 ≥ 0.914 (SEC ≤ 0.11 g/100 g), cross-validation R2 ≥ 0.807 (SECV ≤ 0.22 g/100 g), and external validation R2 ≥ 0.888 (SEP ≤ 0.20 g/100 g). These findings demonstrate that Vis-NIRS, when combined with appropriate spectral preprocessing and chemometric modeling, can serve as a reliable, non-destructive, and sustainable alternative to HPLC for the simultaneous quantification of major turmeric bioactive compounds. Practical Applications: This study suggests that Vis-NIRS can serve as a rapid and non-destructive method for measuring key bioactive compounds in turmeric. This technique could provide a cost-effective and sustainable alternative for routine quality control of turmeric products.
| Original language | English |
|---|---|
| Article number | e70763 |
| Journal | Journal of Food Science |
| Volume | 90 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2025 |
Keywords
- bioactive compounds
- multivariate statistical analysis
- non-destructive
- PLS
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