Kar Saumita, Tudu Bipan, Bandyopadhyay Rajib
Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Block LB, Sector III, Plot 8, Salt Lake, Kolkata, 700 098 India.
J Food Sci Technol. 2024 Oct;61(10):1955-1964. doi: 10.1007/s13197-024-05971-9. Epub 2024 Mar 19.
Machine learning techniques were applied systematically to the spectral data of near-infrared (NIR) spectroscopy to find out the sudan dye I adulterants in turmeric powders. Turmeric powder is one of the most commonly used spice and a simple target for adulteration. Pure turmeric powder was prepared at the laboratory and spiked with sudan dye I adulterants. The spectral data of these adulterated mixtures were obtained by NIR spectrometer and investigated accordingly. The concentrations of the adulterants were 1%, 5%, 10%, 15%, 20%, 25%, 30% (w/w) respectively. Exploratory data analysis was done for the visualization of the adulterant classes by principal component analysis (PCA). Optimization of the pre-processing and wavelength selection was done by cross-validation techniques using a partial least squares regression (PLSR) model. For quantitative analysis four different regression techniques were applied namely ensemble tree regression (ENTR), support vector regression (SVR), principal component regression (PCR), and PLSR, and a comparative analysis was done. The best method was found to be PLSR. The accuracy of the PLSR analysis was determined with the coefficients of determination (R) of greater than 0.97 and with root mean square error (RMSE) of less than 0.93 respectively. For the verification of the robustness of the model, the (FOM) of the model was derived with the help of the Net analyte signal (NAS) theory. The current study established that the NIR spectroscopy can be applied to detect and quantify the amount of sudan dye I adulterants added to the turmeric powders with satisfactory accuracy.
The online version contains supplementary material available at 10.1007/s13197-024-05971-9.
将机器学习技术系统地应用于近红外（NIR）光谱的光谱数据，以找出姜黄粉中的苏丹红I掺假物。姜黄粉是最常用的香料之一，也是掺假的一个简单目标。在实验室制备了纯姜黄粉，并掺入苏丹红I掺假物。通过近红外光谱仪获得这些掺假混合物的光谱数据并进行相应研究。掺假物的浓度分别为1%、5%、10%、15%、20%、25%、30%（w/w）。通过主成分分析（PCA）进行探索性数据分析，以可视化掺假物类别。使用偏最小二乘回归（PLSR）模型，通过交叉验证技术对预处理和波长选择进行优化。为进行定量分析，应用了四种不同的回归技术，即集成树回归（ENTR）、支持向量回归（SVR）、主成分回归（PCR）和PLSR，并进行了对比分析。发现最佳方法是PLSR。PLSR分析的准确性分别由大于0.97的决定系数（R）和小于0.93的均方根误差（RMSE）确定。为验证模型的稳健性，借助净分析物信号（NAS）理论得出模型的品质因数（FOM）。当前研究表明，近红外光谱可用于检测和定量姜黄粉中添加的苏丹红I掺假物的量，准确性令人满意。
在线版本包含可在10.1007/s13197-024-05971-9获取的补充材料。
