Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy
| dc.contributor.author | Sitorus, Agustami | |
| dc.contributor.author | Lapcharoensuk, Ravipat | |
| dc.date.accessioned | 2026-08-06T10:45:50Z | |
| dc.date.available | 2026-08-06T10:45:50Z | |
| dc.date.issued | 2024-04-01 | |
| dc.description.abstract | Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R<sup>2</sup> of 0.886–0.999, RMSE of 0.370–6.108%, and Bias of −0.176–1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future. | |
| dc.identifier.citation | Sensors, 24(7), 2024 | |
| dc.identifier.doi | 10.3390/s24072362 | |
| dc.identifier.issn | 14248220 | |
| dc.identifier.other | 2-s2.0-85190253048 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15565 | |
| dc.source | Sensors | |
| dc.subject | adulteration | |
| dc.subject | chemometric | |
| dc.subject | coconut milk | |
| dc.subject | deep learning | |
| dc.subject | food | |
| dc.subject | non-destructive | |
| dc.title | Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy | |
| dc.type | Article |