• Food Science and Preservation
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• v.23 no.1
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• pp.1-6
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• 2016

The physiological activities of 70% ethanol extracts of grape seed (GS) prepared by freeze-drying (GSFD), infrared drying (GSIR), hot-air drying (GSHD), or sun-drying (GSSD) were investigated. The moisture contents of GSFD, GSIR, GSHD and GSSD powders were 4.53, 6.71, 6.91 and 5.55% respectively. Hunter's color value analysis revealed that the $L^*$ value of GSIR was lower, and the $a^*$ and $b^*$ values of GSIR were higher, than those of GSFD, GSHD, and GSSD. The total polyphenol and proanthocyanidin contents of GSFD were significantly higher than those of the other extracts. The flavonoid related substance contents were in the order of GSFD (7.68 g/100g) = GSSD (7.59 g/100g) = GSHD (7.33 g/100g) > GSIR (6.45 g/100g). The electron donating abilities of $500{\mu}g/mL$ solutions of GSFD, GSIR, GSHD and GSSD were 88.71, 52.62, 65.20, and 65.22%, respectively, while their reducing powers ($OD_{700}$) were 1.633, 1.097, 1.217 and 1.054 absorbance units, respectively. Additionally, the same trend was observed for the ABTS radical-scavenging abilities of the extracts as that observed for their electron-donating abilities and reducing powers. These results suggest that GSFD is the best method for preparing GS extracts with enhanced antioxidant activities, and that GS extracts may be used as a natural antioxidant material for use in health foods.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.247-262
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• 2024

In this paper, we explore nonlinear sufficient dimension reduction (SDR) methods, with a primary focus on establishing a foundational framework that integrates various nonlinear SDR methods. We illustrate the generalized sliced inverse regression (GSIR) and the generalized sliced average variance estimation (GSAVE) which are fitted by the framework. Further, we delve into nonlinear extensions of inverse moments through the kernel trick, specifically examining the kernel sliced inverse regression (KSIR) and kernel canonical correlation analysis (KCCA), and explore their relationships within the established framework. We also briefly explain the nonlinear SDR for functional data. In addition, we present practical aspects such as algorithmic implementations. This paper concludes with remarks on the dimensionality problem of the target function class.