Use of a handheld near infrared spectrometer and partial least squares regression to quantify metanil yellow adulteration in turmeric powder

Abstract

The efficacy of using a handheld near infrared spectrometer to predict metanil yellow (MY) adulteration levels (0-30% w/w) in dried turmeric powder was tested against a benchtop near infrared spectrometer using partial least squares regression models. The differences between near infrared instruments were resolution (i.e., 1 nm (handheld) vs. 0.5 nm (benchtop)) and sample container during scanning (plastic pouch (handheld) vs. quartz glass cup (benchtop)). Prediction performance of the calibration models developed was evaluated using number of model factors ([Formula: see text]), coefficients of determination of calibration and validation ([Formula: see text] and[Formula: see text], respectively), root-mean-square errors of calibration, cross-validation, and validation (RMSEC, RMSECV, and RMSEP), ratio of prediction error to standard deviation (RPD), and limits of detection (LOD) and quantification (LOQ). The best benchtop calibration models were based on spectral data preprocessed with Savitzky–Golay first derivative algorithm for the benchtop near infrared and standard normal variate for the handheld near infrared, yielding low[Formula: see text], high [Formula: see text] and[Formula: see text], low RMSEC, RMSECV, RMSEP, and high RPD. The LOD and LOQ for both spectrometers were 0.33 and 1.10%, respectively, and no significant difference was found between the predicted [Formula: see text] values by the benchtop and handheld near infrared spectrometers. The models were, in general, not sensitive to sample source and size of the validation set. When spectra from the benchtop near infrared were standardized using a reverse standardization strategy, calibrated against[Formula: see text], and transferred to the handheld near infrared, prediction performance dropped, from [Formula: see text] of 0.99 to 0.98, RMSEP increased from 0.96% to 1.53%, and [Formula: see text] decreased from 10.1 to 6.3. Despite the reduced prediction performance, the handheld near infrared with a transferred calibration model from the benchtop near infrared was still useful for screening, quality control, and process control applications.