Near-Infrared Spectroscopy with Machine Learning for Classifying and Quantifying Nutmeg Adulteration
| dc.contributor.author | Sitorus, Agustami | |
| dc.contributor.author | Pambudi, Suluh | |
| dc.contributor.author | Boodnon, Wutthiphong | |
| dc.contributor.author | Lapcharoensuk, Ravipat | |
| dc.date.accessioned | 2026-08-06T10:45:15Z | |
| dc.date.available | 2026-08-06T10:45:15Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | Near-infrared spectroscopy (NIRS) provides broadbands, overtones, and combinations of organic-bond vibrations and has been used to characterize agricultural and food products. The adulteration of grated nutmeg with cinnamon is extremely profitable and difficult to detect; to prevent retail fraud, it is vital to differentiate between these materials. This study proposes a model for classifying the adulteration of nutmeg with cinnamon and predicting the level of adulteration. NIR spectra were characterized with six machine learning (ML) algorithms, namely, the principal component-multilayer perceptron (PC-MLP), principal component-linear discriminant analysis (PC-LDA), partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and decision tree (DT) methods. PC-MLP provided 100% accuracy in calibration and prediction in distinguishing nutmeg from cinnamon. In addition, this approach showed excellent performance in predicting the adulteration ratio of nutmeg and cinnamon with a high coefficient of determination of prediction (R <sup>2</sup><inf>pred</inf>) value of 0.9969, low root mean square error of prediction (RMSEP) value of 0.5728%, and high ratio of prediction to deviation (RPD) value of 17.9605. Therefore, this study indicates the potential of integrating NIR spectroscopy with PC-MLP to classify and quantify the adulteration of nutmeg. | |
| dc.identifier.citation | Analytical Letters, 57(2), 285-306, 2024 | |
| dc.identifier.doi | 10.1080/00032719.2023.2206665 | |
| dc.identifier.issn | 00032719 | |
| dc.identifier.other | 2-s2.0-85154611695 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15415 | |
| dc.source | Analytical Letters | |
| dc.subject | Adulteration detection | |
| dc.subject | chemometrics | |
| dc.subject | herbs | |
| dc.subject | near-infrared spectroscopy (NIRS) | |
| dc.subject | nutmeg | |
| dc.subject | principal component–multilayer perceptron (PC-MLP) | |
| dc.subject | spices | |
| dc.title | Near-Infrared Spectroscopy with Machine Learning for Classifying and Quantifying Nutmeg Adulteration | |
| dc.type | Article |