Quantities of vitamin D in Japanese meals using gas chromatography-mass spectrometry (GC-MS) and prediction of their sources by multiple logistic regression analysis

Autor: Wang, Sen, Nakamura, Yumi, Shirouchi, Bungo, Hashimoto, Yuri, Tanaka, Yasutake, Nakao, Akiko, Goromaru, Ryoko, Iwamoto, Masako, Sato, Masao
Zdroj: Journal of Food Measurement and Characterization; 20240101, Issue: Preprints p1-14, 14p
Abstrakt: Vitamin D is a hormone-like vitamin that plays a primary physiological role in regulating calcium metabolism. In addition to endogenous synthesis through sunlight exposure, dietary supplementation with vitamin D is indispensable for maintaining optimal vitamin D levels. To ensure precision and efficacy in quantifying daily vitamin D intake among the Japanese people, we established a method to measure vitamin D in food. This method optimizes instrument costs and streamlines cumbersome processes. Initially, thin-layer chromatography (TLC) was used to separate vitamin D from food samples, followed by a quantitative analysis using gas chromatography-mass spectrometry (GC-MS) with deuterated vitamin D as an internal standard. Despite the elevated temperature conditions of GC-MS, which lead to the generation of thermal decomposition compounds, the method remains accurate and effective, with an limit of quantitation (LOQ) as low as 0.8 ng for vitamin D. Subsequently, we applied this method to quantify the vitamin D content in 120 meal samples. The meals, designed by a registered dietitian (RD), were served in the dormitory and cafeteria of a Japanese company. The total vitamin D content {mean ± standard deviation (SD)} was 14.7 ± 13.7 µg/day. The measured total vitamin D content in the meal samples, showing a congruent range of 15.7 ± 12.6 µg/day, closely aligned with the corresponding estimate derived from Excel Eiyou-kun. In summary, this study established a method using GC-MS to simultaneously measure vitamin D2and D3in meals, which may be suitable for analyzing food samples for their vitamin D content in large quantities.
Databáze: Supplemental Index
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