TY - JOUR
T1 - Emerging technologies based on artificial intelligence to assess the quality and consumer preference of beverages
AU - Viejo, Claudia Gonzalez
AU - Torrico, Damir D.
AU - Dunshea, Frank R.
AU - Fuentes, Sigfredo
N1 - Acknowledgments:review. C.G.V. was supported by the Melbourne Research Scholarship from the University of Melbourne.
PY - 2019/12
Y1 - 2019/12
N2 - Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over-or under-fitted models.
AB - Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over-or under-fitted models.
KW - Artificial intelligence
KW - Biometrics
KW - Computer vision
KW - Machine learning
KW - Robotics
UR - https://www.scopus.com/pages/publications/85087562003
UR - https://www.scopus.com/pages/publications/85087562003#tab=citedBy
U2 - 10.3390/beverages5040062
DO - 10.3390/beverages5040062
M3 - Review article
AN - SCOPUS:85087562003
SN - 2306-5710
VL - 5
JO - Beverages
JF - Beverages
IS - 4
M1 - 62
ER -