| Autor: |
Basri, Katrul Nadia, Khir, Mohd Fared Abdul, Rani, Rozina Abdul, Sharif, Zaiton, Rusop, M., Zoolfakar, Ahmad Sabirin, Mahmood, Mohamad Rusop, Soga, Tetsuo, Nagaoka, Shiro, Mamat, Mohamad Hafiz, Jafar, Salifairus Mohammad |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2018, Vol. 1963 Issue 1, pN.PAG-N.PAG, 4p, 1 Color Photograph, 1 Chart, 2 Graphs |
| Abstrakt: |
This paper demonstrates the application of short near infrared spectroscopy to measure the presence of lard adulteration in soybean oil. Partial least square (PLS) regression was used as supervised learning algorithm to quantify the percentage of adulteration in soybean oil. Spectral data obtained was divided into training and validation dataset using fixed ratio 7:3. R2 for calibration and prediction are 0.9999 while root mean square error of calibration and prediction are 0.3018 and 0.3246. The result obtained showed the robustness of the predictive model. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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