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1¡¢Determination of A in B samples by using C method with uniform design combining with single factor test to optimize analysis conditions¡££¨¾ä×ÓÒâ˼£º²ÉÓþùÔÈÉè¼ÆÓëµ¥Òò×ÓÊÔÑé½áºÏÓÅ»¯ÊÔÑéÌõ¼þºó£¬ÓÃC·½·¨²â¶¨BÑùÆ·ÖÐA£©

2¡¢Mathematical model was established and quadratic polynomial stepwise regression analysis by making use of the D software was employed to obtain each factor¡¯s impact on  the signal of A, then analyzed in combination with the result conducted by single factor test. £¨¾ä×ÓÒâ˼£º½¨Á¢ÊýѧģÐͺóÓÃDÈí¼þ½øÐжþ´ÎÏîÖ𲽻عé·ÖÎö£¬µÃµ½¸÷¸öÒòËØÓëAµÄÐźÅÖµÖ®¼äµÄ¹ØÏµ£¬È»ºóÓëµ¥ÒòËØÊÔÑé½áºÏÆðÀ´½øÐзÖÎö¡££©

3¡¢Only signal factor tests were used to obtain the optimized experimental conditions in most papers. However, there is particular interaction among the various factors. Therefore, the experimental factors should be fully considered to get high signals and accurate results. Orthogonal design and uniform design are widely employed as multi-factor experimental design methods. Orthogonal design has the advantages of easy use and quick results when it is used to fewer levels of experiment. £¨¾ä×ÓÒâ˼£º´ó¶àÊýÎÄÏ×Ö»²ÉÓõ¥ÒòËØÊÔÑéÀ´µÃµ½ÓÅ»¯µÄÊÔÑéÌõ¼þ¡£È»¶ø£¬¸÷¸öÒòËØÖ®¼ä´æÔÚÏ໥ӰÏì¡£Òò´Ë£¬¸÷¸öÊÔÑéÒòËØÖ®¼äµÄÓ°ÏìÐèÒªÈ«Ãæ¿¼ÂDzÅÄÜ»ñµÃ½Ï¸ßµÄÐźÅÖµºÍ׼ȷµÄ½á¹û¡£Õý½»ÊÔÑéºÍ¾ùÔÈÉè¼ÆÊÔÑéÊÇĿǰӦÓù㷺µÄ¶àÒòËØÊÔÑéÉè¼Æ·½·¨¡£Õý½»ÊÔÑéÔÚ²ÉÓýÏÉÙˮƽʱ¾ßÓмÆËã¼òµ¥ºÍ¿ÉÒÔ¿ìËÙÈ¡µÃ½á¹ûµÄÌØÐÔ¡££©

4¡¢In this work, these 5 factors were optimized by uniform experimental design, with uniform design tabulation of  F and corresponding use tabulation. According to the test schedule, the above-mentioned factors were selected . At the same time, 8 levels were chosen from each factor.£¨¾ä×ÓÒâ˼£º±¾ÎĶÔÕâ5¸öÒòËØÓþùÔÈÉè¼ÆÊÔÑéÖеľùÔÈÉè¼ÆÊÔÑéF¼°ÆäʹÓÃ±í½øÐÐÓÅ»¯¡£°´ÕÕÊÔÑé·½°¸£¬²ÉÓÃÁËÉÏÊöÒòËØ£¬Ã¿¸öÒòËØÑ¡Ôñ8¸öˮƽ¡££©

5¡¢Fig. 3 showed E concentration¡¯s impact on the signal. From the given curve made by D software, E concentration and the signal grew linearly. On the other hand, the results of single factor test showed that the signal increased with the increasing concentrations of E up to 5.0 % (m/v). A further increase of the E concentration did not change the signal obviously.£¨¾ä×ÓÒâ˼£ºEµÄŨ¶ÈÓëÐźÅÖµÖ®¼äÓ°Ïì¼ûͼ3¡£ÓÉDÈí¼þ¸ø³öµÄÇúÏß¿ÉÒÔ¿´³ö£¬EµÄŨ¶ÈÓëÐźÅÖµ³ÉÏßÐÔÉÏÉýÇ÷ÊÆ¡£¶øµ¥ÒòËØÊÔÑé±íÃ÷ÔÚEµÄŨ¶È²»³¬¹ý5.0%ʱ£¬Ëæ×ÅEµÄŨ¶ÈµÄÔö¼Ó£¬ÐźÅÖµÖð½¥Ôö¼Ó¡£EµÄŨ¶È³¬¹ý5.0%ʱ£¬ÐźÅÖµ±ä»¯²»Ã÷ÏÔ¡££©

[ Last edited by qhd511 on 2011-10-17 at 12:40 ]

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qhd511(½ð±Ò+25, ·­ÒëEPI+1): µÚÒ»¾äÊÇÎÄÕ±êÌ⣬Õâ¸ö¿ÉÄܲ»ÊǺܺÏÊÊ¡£ÆäËû¶¼²»´í 2011-10-18 15:39:06
1. After adopting uniform design combining with single factor test to optimize experimental conditions, use method C to determine A in sample B.
2. Via using software D to process quadratic polynomial stepwise regression analysis after establishing mathematical model, the relationships between each factor and signal A are known and could be analyzed together with single factor test.
3. Most documents only adopted single factor test to obtain optimized test condition. However, each factor has interaction with others. Therefore, it needs comprehensive consideration of influences among each test factor to obtain higher signals and accurate results. Nowadays, the orthogonal design and uniform design are popular multi-factor experimental design methods. And orthogonal design can have simple calculation and quick results when used to fewer levels of experiments.
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qhd511(½ð±Ò+25): ¸Ðл°ïÖú£¡ 2011-10-18 15:39:33
4. This paper optimizes theses 5 factors by using uniform tabulation of F and its use tabulation in uniform design. According to the test plan, the factors mentioned above are adopted and each factor use 8 levels.
5. Figure 3 showed the influences between concentration of E and signal. According to curve drawn by software D, the concentration of E has linear increasing relationship with signal. On the other hand, single factor test showed that signal increases with concentration of E increasing, when the concentration of E is at most 5.0%. The further increase in concentration of E has no obvious change in signal.

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