如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？

wzm — Fri, 25 Oct 2024 04:27:32 GMT

如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？例如：氨基酸作为一个自变量，其中有20种不同的氨基酸，维生素作为一个自变量，其中有8种，筛选氨基酸和维生素的最佳组合，可否使用正交设计，谢谢！

Re: 如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？

Victor_G — Sat, 26 Oct 2024 13:49:06 GMT

Just to be sure to have understood your objective : you want to select one amino-acid and one vitamin among a set of these raw materials to have the best combination between the two and obtain the best results ?

If yes, as you seem to mention "screening" in your question and post (or at least the automatic translation tool has translated your original question as : "If the experimental independent variable is categorical and the response is continuous, can an orthogonal design be used for screening?") and considering the relatively large amount of different raw materials you want to study, I would not try to find the best combination between amino acids and vitamins in a first design (or only in one design), as it can represent a large number of experiments to do to test all combinations (160 unique combinations).

One option could be to subset a number of different amino-acids and vitamins in order to study the variation of your continuous response with limited number of levels for each factors. Instead of testing all 20 different amino-acids and all 8 vitamins, you could select 5 amino-acids and 4 vitamins (for example), so that these molecules express the maximum variability in terms of molecular properties/descriptors. There are multiple ways to select the raw materials, but using the chemical structures, molecular/chemical properties, molecular descriptors values can help in comparing the raw materials and select only those with the highest chemical variability.

I described this option in a previous discussion and this method is also explained in a presentation :

You can then analyze the results of the continuous response thanks to the continuous molecular descriptors/values that you can use as covariates. Using continuous factors instead of categorical factors represents a great gain of information :

https://community.jmp.com/t5/Abstracts/Coding-with-Continuous-and-Mixture-Variables-to-Explore-More-of/ev-p/756156/redirect_from_archived_page/true

The big advantage of this approach is that you avoid a single DoE "brute-force" approach involving a lot of experiments, and you can build your knowledge and start optimising by iteration : once you understand which properties of your different molecules have an impact on your response, you can then select other raw materials (amino-acids & vitamins) and refine your model to have more in-depth understanding about interactions and specific order effects, and optimize the response.

Hope this discussion starter and perspective may help you,

Re: 如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？

wzm — Wed, 30 Oct 2024 02:35:11 GMT

谢谢，感谢您的耐心解答，我需要筛选氨基酸的组合，筛选维生素的组合，氨基酸和维生素的组合，最终的目的是得到一个配比，这个配比中有对标签影响最大几个氨基酸和维生素的配伍，如果设计实验，还要考虑氨基酸和维生素之间有无交互，需要考虑氨基酸之间有无交互作用，谢谢！

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如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？

Re: 如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？

Re: 如果试验自变量为分类变量，响应为连续变量，是否可以使用正交设计进行筛选？