## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fiber', 'milligrams of vitamin B5', and 'milligrams of vitamin E', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : 'grams of fiber'
- $x_1$ : 'milligrams of vitamin B5'
- $x_2$ : 'milligrams of vitamin E'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $5x_0 + 3x_1 + 8x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $2x_1 + 9x_2 \geq 16$
- $8x_0 + 2x_1 + 9x_2 \geq 16$
- $x_1 + 5x_2 \geq 8$
- $7x_0 + x_1 \geq 14$
- $7x_0 + x_1 + 5x_2 \geq 14$
- $-x_0 + 5x_1 \geq 0$
- $8x_0 + 9x_2 \leq 30$
- $2x_1 + 9x_2 \leq 47$
- $8x_0 + 2x_1 \leq 43$
- $8x_0 + 2x_1 + 9x_2 \leq 29$
- $7x_0 + x_1 \leq 27$

## 5: Define the bounds of the variables
The variables $x_0$, $x_1$, and $x_2$ are allowed to be non-integer, implying $x_0, x_1, x_2 \geq 0$ and no upper bound is specified.

## 6: Write down the problem in a structured format
```json
{
    'sym_variables': [('x0', 'grams of fiber'), ('x1', 'milligrams of vitamin B5'), ('x2', 'milligrams of vitamin E')],
    'objective_function': '5*x0 + 3*x1 + 8*x2',
    'constraints': [
        '2*x1 + 9*x2 >= 16',
        '8*x0 + 2*x1 + 9*x2 >= 16',
        'x1 + 5*x2 >= 8',
        '7*x0 + x1 >= 14',
        '7*x0 + x1 + 5*x2 >= 14',
        '-x0 + 5*x1 >= 0',
        '8*x0 + 9*x2 <= 30',
        '2*x1 + 9*x2 <= 47',
        '8*x0 + 2*x1 <= 43',
        '8*x0 + 2*x1 + 9*x2 <= 29',
        '7*x0 + x1 <= 27'
    ]
}
```

## 7: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # grams of fiber
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B5
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin E

    # Objective function
    model.setObjective(5 * x0 + 3 * x1 + 8 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2 * x1 + 9 * x2 >= 16)
    model.addConstr(8 * x0 + 2 * x1 + 9 * x2 >= 16)
    model.addConstr(x1 + 5 * x2 >= 8)
    model.addConstr(7 * x0 + x1 >= 14)
    model.addConstr(7 * x0 + x1 + 5 * x2 >= 14)
    model.addConstr(-x0 + 5 * x1 >= 0)
    model.addConstr(8 * x0 + 9 * x2 <= 30)
    model.addConstr(2 * x1 + 9 * x2 <= 47)
    model.addConstr(8 * x0 + 2 * x1 <= 43)
    model.addConstr(8 * x0 + 2 * x1 + 9 * x2 <= 29)
    model.addConstr(7 * x0 + x1 <= 27)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Objective: {model.objVal}")
        print(f"Grams of fiber: {x0.varValue}")
        print(f"Milligrams of vitamin B5: {x1.varValue}")
        print(f"Milligrams of vitamin E: {x2.varValue}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```