2023
DOI: 10.1016/j.tifs.2023.07.012
|View full text |Cite
|
Sign up to set email alerts
|

Recent advances and application of machine learning in food flavor prediction and regulation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
4
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 43 publications
(4 citation statements)
references
References 91 publications
0
4
0
Order By: Relevance
“…There are considerable applications of NMR for ingredient organoleptic quality such as flavor profiles related to metabolite profiles. This has been demonstrated and is now utilized in the industry for many commercial foods and spices [ 102 , 103 , 104 ]. Changes in cinnamon NMR spectra may be used to predict quality and flavor profiles through time for high-quality brands that seek to provide consumers with the best culinary experience.…”
Section: Discussionmentioning
confidence: 99%
“…There are considerable applications of NMR for ingredient organoleptic quality such as flavor profiles related to metabolite profiles. This has been demonstrated and is now utilized in the industry for many commercial foods and spices [ 102 , 103 , 104 ]. Changes in cinnamon NMR spectra may be used to predict quality and flavor profiles through time for high-quality brands that seek to provide consumers with the best culinary experience.…”
Section: Discussionmentioning
confidence: 99%
“…To some extent, it can reflect the degree of food preference, which can help research food recipes that are more popular with consumers (this benefit was consistent with the evaluation method for its saltiness intensity) [ 38 , 39 ]. Machine learning can amalgamate data from electronic tongues and other technologies to predict the salty taste intensity of food products and consumer acceptance [ 40 ].…”
Section: Salty Taste Perception Mechanism and Sodium Salt Reduction S...mentioning
confidence: 99%
“…Machine learning has succeeded in various aroma-related parameters, including precise kind, intensity, and profile predictions. Among them, it is possible to assess and extract food’s gustatory and olfactory properties using general electronic tongue and electronic nose techniques as inputs to machine learning models . Furthermore, recent advancements in gas chromatography ion mobility spectrometry (GC-IMS) technology allow for exact qualitative and quantitative characterization of volatile flavor compounds in meat products, laying the groundwork for predictive models. , It will be less likely for fresh and frozen/thawed meat products to be adulterated if flavor-predictive models are linked to the freshness of muscle-based foods, which bodes well for the future of the meat products industry.…”
Section: Impact Of Ice On the Quality Of Muscle Foodmentioning
confidence: 99%