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.

Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag

“…NIRS has a wide scope of application from product development to food authentication [10]. It has been used to detect yellow metanil in tamarind powder [11], husk in coffee [12], corn flour in paprika powder [13] etc. In the dairy industry, it has been used to detect plant proteins in skimmed milk [14], melamine adulteration in milk [15], predict urea in milk [16], and predict diverse adulterants in cereal products [17].…”

“…Handheld NIRS devices have also been widely applied for quality control purposes in the food industry [11,25,26]. They present advantages of portability and are capable of direct field applications with less complexities but, it is also important to consider some of their major gaps such as, the unavailability of standard cuvettes or sample holders especially when dealing with liquid or powdered samples.…”

Experimentally designed chemometric models for the assay of toxic adulterants in turmeric powder

“…A literature survey revealed several methods to detect and/or quantify the adulteration of turmeric, such as HPLC, 50,51 HPTLC, 52,53 voltammetry, 54 multispectral imaging, 55 spectrophotometry 56 and FTIR. 57–60 However, the reported literature failed to reveal a single analytical method for the simultaneous assay of MY, AO and LCM in turmeric powder.…”

A deep neural network and random forests driven computer vision framework for identification and prediction of metanil yellow adulteration in turmeric powder

“…The amount of MET has been predicted using handheld NIR device and partial least square regression (PLSR) and obtained more than 96% prediction accuracy in terms of R 2 values. 11 In Reference 12, a DNA barcoding-based application of adulteration detection in turmeric has been reported.…”

“…vations under consideration as shown in Equation(11). RMSE is calculated as Equation(12) and it is more appropriate compared to MAE since the squaring of error gives higher weight to large differences.…”