Introduction

• Dataset for food products created by everyone for everyone
• Total                  : 119415 products
• Size                    : 267 MB
• Format              : .csv
• Attributes         : 159
• Missing values : Almost 13 Million
• Almost 80% missing values for most attributes

• The Open Food Facts Project was started by Stéphane Gigandet
• Like on Wikipedia, anyone can create an account and add / edit food items

Introduction

Overview

• Introduction

• Problems

• Cleaning & Preprocessing

• Interpretations

• Predictions

• Conclusion

Cleaning

• Missing values
• Stopwords and punctuation
• Translation issues
• Case sensitive ("Lait" and "lAit")
• Inappropriate value in cells

Missing values

• Many attributes have missing values

product_name        : 14787
generic_name        : 60005
quantity            : 21450
packaging           : 39153
packaging_tags      : 39153
brands              : 18298
brands_tags         : 18304
categories          : 35132
categories_tags     : 35153
ingredients_text    : 44506
allergens           : 83246
allergens_en        : 106340
additives_n         : 44542
additives           : 45032
additives_tags      : 73536
additives_en        : 73536
fat_100g            : 53180
saturated.fat_100g  : 57684
X.butyric.acid_100g : 106340
X.sucrose_100g      : 106283

Stopwords and Punctuation

• Library for cleaning:
  • Library (NLP)
  • Library (tm)

Stopwords and Punctuation

• Wordcloud before removing stopwords and punctuantion

Figure 1

Stopwords and Punctuation

• Wordcloud after removing:
  • Stopwords
  • Punctuation
• Also after translating words

Figure 2

Translation issues

• Couldn't find library for translation
• Manually substitute Non-English words to English words

Case - Senstive

• Because anyone can insert data into the OpenFoodFact database, the database has inconsistent input.
• For example: blé and Blé

Inappropriate Value

• Many cells have inappropriate value, for example in countries_en attribute.

Overview

• Introduction

• Problems

• Cleaning & Preprocessing

• Interpretations

• Predictions

• Conclusion

Inspecting Sweets

• Searched database for sweets-related tags
• Found 661 items (in a database of over 100000 foods)
• Obviously there must be missing (untagged) sweets!

Inspecting Sweets

All nutritional values per 100g

Figure 1

Figure 1

Figure 1

Figure 1

Figure 3

Inspecting Sweets

All nutritional values per 100g

Figure 4

Inspect beverage

Searched the dataset for beverage tag

• Found 71187 entries
• Beverage categories based on its tag:
  • Morning drink
    • Milk, Coffee, Chocolate Milk
  • Soda drink
    • Sprite, Coca Cola, Pepsi, Diet Coke
  • Healthy drink
    • Tea, Juice, Smoothie
  • Protein and energy drink
    • Protein shake, Power drink

Inspect beverage

• Formula:
  • Were sent to OpenFoodFact contributor by the team of Prof. Hercberg
  • The formula has been subject of studies and adaptations for the French market
  • There are 2 formulas:
    • For calculating "solid food"
    • For calculation "beverage"
      • Note: based on the website, we need to use formula for calculation solid food to calculate Milk nutrition score

Clustering beverage products

Figure 5

Beverage Nutrition Grade

Threshold                                            

Grade

Beverage Nutrition Grade

Points 	Energy (kJ) 	    Sugars (g) 	            Fruits, vegetables (%)
0 	0 	            0 	                            < = 40
1 	≤30 	            ≤1,5 ​​or sweeteners 	
2 	>30 	            >1.5 	                    >40
3 	>60 	            >3 	
4 	>90 	            >4.5 	                    >60
5 	>120 	            >6 	
6 	>150 	            >7.5 	
7 	>180 	            >9 	
8 	>210 	            >10.5 	
9 	>240 	            >12 	
10 	>270 	            >13.5 	                    >80

Figure 6

Meat product - Types

Figure 7

Meat product - Additives used

Figure 8

Overview

• Introduction

• Problems

• Cleaning & Preprocessing

• Interpretations

• Predictions

• Conclusion

Predicting Chocolate

The first attempt

Predicting Chocolate

Ideas to improve the predictions

Predicting Chocolate

Dark Chocolate

Figure 9

Predicting Chocolate

Dark Chocolate

• Predicting Dark Chocolate only
• Using Proteins and Carbohydrates in addition to the previous attributes

• F1-score: 0.77                                                

Predicting Chocolate

Milk Chocolate

Figure 10

Predicting Chocolate

Milk Chocolate

• Better F1-score when leaving out carbohydrates
• Only chocolate-prediction that had improved results for a higher amount of trees (500 instead of 100)

• Still only F1-score of 0.56                        

Predicting Chocolate

White Chocolate

• Tried different combinations of attributes
• Still only f1-score of 0.23

• why?                                                               

Predicting Chocolate

White Chocolate

• Multiplied every white chocolate in the dataset x20
• Removed half of them before training and added them back in for testing
• The other half was split randomly between training and test set

• Result: F1-score of 0.88                               

• Data consist (after removing empty categories_en cells):
  • 1963 categorized as Juice
  • 69234 categorized as non-Juice
• Split the data into:
  • Train set (60%)
  • Test set (40%)
• Using randomForest:
  • Number of trees : 500
  • F1-score               : 0.695

Predicting Juice in Beverage dataset

• Data consist (after removing non-Beverage items):
  • 1925 categorized as Juice
  • 25852 categorized as non-Juice
• Split the data into:
  • Train set (60%), Test set (20%), Validation set (20%)
• Attributes: fat, sugar, energy, sodium, fruits, fibre, protein
• Use 2 different methods to see which is better:
  • randomForest 
  • naiveBayes

Predicting Juice in Beverage dataset 

Result

• Naive Bayes: 0.22
• Random Forest: 0.73

Predicting missing Plant-based products

• Data :
  • Missing  category entries: 23443
  •  Attributes used: protein, fat, sugar, carbohydrate, energy
• Split the data into:
  • Train set (60%), Test set (20%),Validation set (20%)
• Method : Random Forest Regressor
• Result:
  • Recall: 0.79
  • Precision: 0.85
  • F1-score: 0.82

Predicting missing Plant-based products

• Total missing values : 2315 ( w.r.t to non NAN values for protein, sugar, carbohydrate, fat and energy)

• Labelled : 434
• We manually checked 30 values and 25 out of them were plant based.