RESUMEN
The objective of this study was to quantify the chemical content of multiple products using one single calibration model. This study involved seven tuber and root powders from arrowroot, Canna edulis, cassava, taro, as well as purple, yellow, and white sweet potato, for partial least square (PLS) regression to predict polysaccharide contents (i.e., amylose, starch, and cellulose). The developed PLS models showed acceptable results, with Rc 2 of 0.9, 0.95, and 0.85 and SEC of 2.7%, 3.33%, and 3.22%, for amylose, starch, and cellulose, respectively. The models also successfully predicted polysaccharide contents with Rp 2 of 0.89, 0.95, and 0.79; SEP of 2.83%, 3.33%, and 3.55%; and RPD of 3.02, 4.47, and 2.18 for amylose, starch, and cellulose, respectively. These results showed the potential of Fourier transform near-infrared spectroscopy to quantify the chemical composition of multiple products instead of using one individual model.
RESUMEN
This study evaluated the mechanical accuracy of an in-house-developed couch-based tracking system (CBTS) according to respiration data. The overall delay time of the CBTS was calculated, and the accuracy, reproducibility, and loading effect of the CBTS were evaluated according to the sinusoidal waveform and various respiratory motion data of real patients with and without a volunteer weighing 75kg. The root mean square (rms) error of the accuracy, the reproducibility, and the sagging measurements were calculated for the three axes (X, Y, and Z directions) of the CBTS. The overall delay time of the CBTS was 0.251 sec. The accuracy and reproducibility in the Z direction in real patient data were poor, as indicated by high rms errors. The results of the loading effect were within 1.0 mm in all directions. This novel CBTS has the potential for clinical application for tumor tracking in radiation therapy.
