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Enhancing of the rapid evaluation of sugarcane energy content for energy cane varieties selection purposes in breeding program using Near-Infrared spectroscopy (NIRS) |
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| รหัสดีโอไอ | |
| Creator | 1. Kantisa Phoomwarin 2. Jetsada Posom 3. Khwantri Saengprachatanarug 4. Arthit Phuphaphud |
| Title | Enhancing of the rapid evaluation of sugarcane energy content for energy cane varieties selection purposes in breeding program using Near-Infrared spectroscopy (NIRS) |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2567 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 51 |
| Journal No. | 1 |
| Page no. | 58-65 |
| Keyword | Sugar mill, Heating values, Fourier transform near infrared spectroscopy, Energy characteristics, Rapid measurement |
| URL Website | https://ph01.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | The integration of Near-Infrared spectroscopy (NIRs) as a predictive tool for rapidly assessing the gross calorific value (GCV) of dried shredded sugarcane addresses a significant bottleneck in sugarcane breeding programs. A total of 110 samples were collected, and a lab-type Fourier Transform near infrared (FT-NIR) spectrometer operating across a wavenumber range of 12,800 to 3,600 cm-1 was utilized for spectral acquisition. This study explored various variable selection algorithms with partial least squares (PLS) regression for model development. Variable selection methods included variable importance in projection (VIP), successive projections algorithm (SPA), genetic method (GA), and correlation (r). The r-PLS algorithm along with standard normal variate and first derivative (SNV + D1) pre-treatment yielded a highly effective model exhibiting a coefficient of determination in prediction (R2p) of 0.88 and the root mean square error of prediction (RMSEP) of 278.0 J/g. This study showed that the NIRs could provide a reliable method for rapid, fairly accurate, and precise GCV estimation. However, future research should investigate the use of additional modeling algorithms, such as non-linear regression, to improve model accuracy and utilize sample data with an evenly spread range of GCV values to validate the model performance with greater confidence. |
