How to handle nutrition data

[ajama01]

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Hello everyone, I'm currently working on a data from an FFQ (Food Frequency Questionnaire). I've tried to perform PCA on this data, the results of the PCA are not really interpretable (overlapping of variables and individuals) the problem is that some columns corresponding to foods are mostly 0, I don't know how I can handle this kind of data. What do you suggest?

[MostlyKIGuess]

• Log Transformation: ( log(x+1) to handle zeros) can sometimes stabilize the variance and make the data more standerd.
• Normalization/Standardization: PCA is sensitive to the scale of the data, so it might be beneficial to standardize your data (e.g., using z-scores).

import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('ffq_data.csv')

# Log transformation
data_log_transformed = np.log1p(data)

# Standardize
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data_log_transformed)

# PCA time
pca = PCA(n_components=2)
principal_components = pca.fit_transform(data_scaled)

# Convert to DataFrame
pca_df = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])

# Plot it
plt.scatter(pca_df['PC1'], pca_df['PC2'])
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.title('PCA of FFQ Data')
plt.show()

[ajama01]

Thank u !

[MostlyKIGuess]

Hope that helps! :3

[LiteBrite82]

Thanks for posting in the GitHub Community, @ajama01 !

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[innoutmenu9]

When working with FFQ (Food Frequency Questionnaire) data where many food-related variables are mostly zeros (sparse data), standard PCA can struggle because it assumes continuous, normally distributed data and can be heavily influenced by such sparsity. Here are a few suggestions to make your analysis more meaningful:

Remove Rarely Consumed Foods: Filter out food items (columns) that are consumed by very few individuals. Set a threshold (e.g., remove columns where over 90% of values are zero).

Group Similar Foods: Instead of using individual food items, group them into broader food categories (e.g., “leafy greens,” “processed meats”) to reduce sparsity and noise.

Consider Nonlinear Methods: Use dimensionality reduction methods better suited for sparse or binary data such as Multiple Correspondence Analysis (MCA), Non-negative Matrix Factorization (NMF), or t-SNE/UMAP for visual exploration.

Use Binary/Ordinal PCA: If your data is binary or ordinal, consider PCA variants that work for such scales, like logistic PCA or ordinal factor analysis.

Transformation Before PCA: Log-transform or center-scale the data if it's appropriate. Sometimes even simple normalization (e.g., z-scores) can help structure emerge more clearly.

[alaxendermatthew]

Maybe try clustering or zero-inflated models to handle the sparse data,like segmenting by frequent items, similar to a max meny approach in menus.

[Harrysharmax]

Do not use PCA on raw FFQ data.

Transform your data first. The Centred Log-Ratio (CLR) transformation is the recommended approach.

Perform PCA on the CLR-transformed data. This will reveal interpretable dietary patterns.

Consider using Factor Analysis as it is conceptually better suited for identifying underlying dietary patterns from FFQ data.

[proseo2050]

ChatGPT said:

For FFQ data, sparse matrices with many zeros often reduce PCA interpretability. A common approach is applying transformations (e.g., log, square-root) or using methods like Multiple Correspondence Analysis (MCA) and Non-negative Matrix Factorization (NMF), which are designed for categorical or sparse nutritional data (Wikipedia – Principal component analysis
). In practice, exploring alternative dimensionality reduction tailored to dietary data can improve clarity—similar to how menu datasets, like panda express-menuprices, structure items for easier comparison