Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking

Abstract

Chefs frequently rely on their taste to assess the content and flavor of dishes during cooking. While tasting the food, the mastication process also provides continuous feedback by exposing the taste receptors to food at various stages of chewing. Since different ingredients of the dish undergo specific changes during chewing, the mastication helps to understand the food content. The current methods of electronic tasting, on the contrary, always use a single taste snapshot of a homogenized sample. We propose a robotic setup that uses the mixing to imitate mastication and tastes the dish at two different mastication phases. Each tasting is done using a conductance probe measuring conductance at multiple, spatially distributed points. This data is used to classify 9 varieties of scrambled eggs with tomatoes. We test four different tasting methods and analyze the resulting classification performance, showing a significant improvement over tasting homogenized samples. The experimental results show that tasting at two states of mechanical processing of the food increased classification F1 score to 0.93 in comparison to the traditional tasting of a homogenized sample resulting in F1 score of 0.55. We attribute this performance increase to the fact that different dishes are affected differently by the mixing process, and have different spatial distributions of the salinity. It helps the robot to distinguish between dishes of the same average salinity, but different content of ingredients. This work demonstrates that mastication plays an important role in robotic tasting and implementing it can improve the tasting ability of robotic chefs.

Classification-Based Evaluation of Multi-Ingredient Dish Using Graphene-Modified Interdigital Electrodes

“…PC1 accounted for the most variation in impedance values (76.4%), while PC2 accounted for the next largest variation (23.6%). Other classification algorithm such as K-nearest neighbors, decision trees, and support vector machines were also effective in carrying out such tasks [ 39 ]. The distances of samples in sucrose subsets were closer due to the larger molecular volume, which were difficult to adsorb and desorb under the AC current disturbance of EIS.…”

Towards practical robotic chef: Review of relevant work and future challenges

“…It was also theorized that human taste can be reproduced using a learning robot that produces its energy from organic matter using microbial fuel cells (MFCs) (Sochacki, Abdulali, Cheke, et al, 2023). As the MFC has chemical requirements as comes to its feed, the robot can learn what it likes depending on the current output of the cell.…”

Theoretical Framework for Human-Like Robotic Taste with Reference to Nutritional Needs

“…Robotic taste is a recent development [4], [45], and has so far been used to cook dishes catering to specific human preferences. In this case, we choose a combination of sensors that can sense the substances needed by the fuel cell.…”

“…There was a successful attempt to build a robot that hunted slugs [49]. Moreover, it was shown that mastication can improve taste sensing for robots [45]. Given the scarcity of data, we do not attempt a complicated feeding mechanism for our hypothetical robot but will use liquid feeds only.…”