Statistical inference on a very small datasets
There are two separate issues: 


Sampling - Picking the optional ingredient level for next experiment to run. Given you have only have 4 explanatory variables, just plot them. Either all pairwise or a couple of 3d charts. With the outcome variable on the y or z axis. You'll then see the trend in the data. You can decide to get more data for interpolation (between the data points you already have) or extrapolation (data outside of the current range). There are frameworks, such as Bayesian Optimization, but that is too much work given the small dimensionality.
Inference - Predicting performance for new data. Given the data you have seen thus far (sample data), estimate parameters. In your example that would the estimating the contribution of each of the 4 ingredients, either individually or interaction. Those parameters could be scalar coefficients or distributions.