2021
DOI: 10.1007/s12247-020-09504-7
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Starch Products as Candidate Excipients in a Continuous Direct Compression Line

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“…In a bi-rotated space orthogonal to PC1 and PC2, over 70% of the dataset's variability is explained, with PC1 accounting for 43.2% and PC2 for 27.6% of the variability. Principal component analysis, or PCA, has been found to be useful for understanding the flow properties of various food powders, including starches, flour, fruit powder, and coffee powder [35][36][37][38][39][40]. In this study as well, PCA was able to describe the differences in the granular flow properties of corn meal, wheat farina, and granulated sugar under different moisture regimens, and pointed to a higher sensitivity of wheat farina to moisture as compared to corn meal.…”
Section: Principal Component Analysismentioning
confidence: 99%
“…In a bi-rotated space orthogonal to PC1 and PC2, over 70% of the dataset's variability is explained, with PC1 accounting for 43.2% and PC2 for 27.6% of the variability. Principal component analysis, or PCA, has been found to be useful for understanding the flow properties of various food powders, including starches, flour, fruit powder, and coffee powder [35][36][37][38][39][40]. In this study as well, PCA was able to describe the differences in the granular flow properties of corn meal, wheat farina, and granulated sugar under different moisture regimens, and pointed to a higher sensitivity of wheat farina to moisture as compared to corn meal.…”
Section: Principal Component Analysismentioning
confidence: 99%