Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting

Abstract Introduction Ginger ( Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non‐invasive quality and traceability assessment techniques ensure a sustainable value chain. Objective The objective of this study is the development of suitable machine learning models to estimate the concentration of 6‐gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near‐infrared spectrometry. Methods Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6‐gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint‐based traceability identification employed principal component analysis and t ‐distributed stochastic neighbor embedding ( t ‐SNE). Model performance was assessed using RMSE and R 2 values across selective wavelengths and spectral fingerprints. Results The standard normal variate pretreated spectral data over the wavelength region of 1,100–1,250 nm and 1,325–1,550 nm showed the optimal calibration model with root mean square error of calibration and R 2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R 2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t ‐SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. Conclusion The developed NIR spectroscopic model for Indian ginger samples predicts the 6‐gingerol content and provides geographical traceability‐based identification to ensure a sustainable value chain, which can promote efficiency, cost‐effectiveness, consumer confidence, sustainable sourcing, traceability, and data‐driven decision‐making.