AIMC Topic: Cooking

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Machine learning for polycyclic aromatic hydrocarbons analysis in roasted lamb: new insights from spectral and chemical data.

Food chemistry
Polycyclic aromatic hydrocarbons (PAHs) generated during lamb roasting pose health risks but are difficult to predict due to their low concentrations and complex features. Existing models fail to address data scarcity and low-concentration prediction...

Quality enhancement of batter-coated oysters via low temperature vacuum frying technique and machine learning-based prediction models for shelf life and quality dynamics.

Food chemistry
As a widely consumed prepared food, the preservation of nutrient retention and sensory quality in batter fried oysters is a significant concern. Deep frying, air frying, and vacuum frying (VF) were used to prepare battered fried oysters. Guar gum (GG...

Electronic nose, HS-GC-IMS, HS-SPME-GC-MS, and deep learning model were used to analyze and predict the changes and contents of VOCs in in-shell walnut kernels under different roasting conditions.

Food chemistry
Roasting imparts walnuts with an increased amount of hedonic aromas. Consequently, this study comprehensively analyzed aroma characteristics of in-shell walnut kernels during roasting at different conditions using quantitative descriptive analysis, E...

Joint control and machine learning prediction of co-formation and kinetic profiles of typical hazardous Maillard reaction products by catechin treatment in air-fried potato chips.

Food chemistry
The Maillard reaction generates hazardous processing contaminants, including acrylamide (AA) and Nε-(carboxymethyl)lysine (CML), necessitating effective inhibitors. Here we use machine learning approaches to predict how catechin treatment reduces sim...

Enhancing robotic skill acquisition with multimodal sensory data: A novel dataset for kitchen tasks.

Scientific data
The advent of large language models has transformed human-robot interaction by enabling robots to execute tasks via natural language commands. However, these models primarily depend on unimodal data, which limits their ability to integrate diverse an...

Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics.

Food chemistry
This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed und...

Advanced 3D Food Printing with Simultaneous Cooking and Generative AI Design.

Advanced materials (Deerfield Beach, Fla.)
3D food printing is an indispensable technology for emerging food technologies. However, conventional nonconcurrent postprocessing methods limit the final food quality, including the unappealing nature of food ink modification, imperfections in retai...

Towards automated recipe genre classification using semi-supervised learning.

PloS one
Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation. However, categorizing raw recipes found online into appropriate food genres can be challenging due to a lack of adequate labeled data. In...

Cooking loss estimation of semispinalis capitis muscle of pork butt using a deep neural network on hyperspectral data.

Meat science
This study evaluated the performance of a deep-learning-based model that predicted cooking loss in the semispinalis capitis (SC) muscle of pork butts using hyperspectral images captured 24 h postmortem. To overcome low-scale samples, 70 pork butts we...

Robots in the kitchen: The automation of food preparation in restaurants and the compounding effects of perceived love and disgust on consumer evaluations.

Appetite
Restaurants are swiftly embracing automation to prepare food, experimenting with innovations from robotic arms for frying foods to pizza-making robots. While these advances promise to enhance efficiency and productivity, their impact on consumer psyc...