Loading...
Quantitative analysis based on image processing combined with machine learning and deep learning to determine the adulteration in nutmeg powder
Author(s)
Date Issued
October 1, 2025
Type
Article
Abstract
The global demand for nutmeg powder has increased, raising the risk of adulteration and necessitating an efficient and cost-effective screening method. The objective of this study is to develop a calibration model to predict the adulteration of nutmeg powder by cinnamon powder using a novel approach by integrating image processing with machine learning (ML) and deep learning (DL). Eight regressors, including four ML regressors (multiple linear ridge regression–MLRR, partial least squares regression–PLSR, multi-layer perceptron–MLP, and adaptive boosting–ABS) and four DL regressors (convolutional neural networks–CNN, AlexNET, residual networks–ResNET, and GoogleNET), were employed to analyze 1800 images of adulteration samples ranging from 0 % to 35 % (w/w). Among ML models, MLP achieved the highest accuracy in prediction (Rp²=0.922, RMSEP=2.804 %, RPD=3.59), while PLSR (Rp²=0.876, RMSEP=3.538 %, RPD=2.84), MLRR (Rp²=0.872, RMSEP=3.596 %, RPD=2.80), and ABS (Rp²=0.849, RMSEP=3.904 %, RPD=2.58) underperformed. For DL, ResNET (Rp²=0.882, RMSEP=3.416 %, RPD=2.91) surpassed CNN (Rp²=0.876, RMSEP=3.505 %, RPD=2.84), AlexNET (Rp²=0.801, RMSEP=4.429 %, RPD=2.24), and GoogleNET (Rp²=0.751, RMSEP=4.963 %, RPD=2.00). The MLP's superiority highlights its compatibility with ORB-based feature extraction for nonlinear adulteration patterns, outperforming complex DL architectures. This research highlights the potential of image processing supported by ML and DL as a rapid and low-cost tool for future nutmeg powder adulteration screening.
Citation
Journal of Food Composition and Analysis, 146, 2025