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.

Artificial neural networks (ANN) have become popular for optimization and prediction of parameters in foods, beverages, agriculture and medicine. For brewing, they have been explored to develop rapid methods to assess product quality and acceptability. Different beers (N = 17) were analyzed in triplicates using a robotic pourer, RoboBEER (University of Melbourne, Melbourne, Australia), to assess 15 color and foam-related parameters using computer-vision. Those samples were tested using sensory analysis for acceptability of carbonation mouthfeel, bitterness, flavor and overall liking with 30 consumers using a 9-point hedonic scale. ANN models were developed using 17 different training algorithms with 15 color and foam-related parameters as inputs and liking of four descriptors obtained from consumers as targets. Each algorithm was tested using five, seven and ten neurons and compared to select the best model based on correlation coefficients, slope and performance (mean squared error (MSE). Bayesian Regularization algorithm with seven neurons presented the best correlation (R = 0.98) and highest performance (MSE = 0.03) with no overfitting. These models may be used as a cost-effective method for fast-screening of beers during processing to assess acceptability more efficiently. The use of RoboBEER, computer-vision algorithms and ANN will allow the implementation of an artificial intelligence system for the brewing industry to assess its effectiveness.

Yogurt, readily available in plant and dairy-based formulations, is widely consumed and linked with health benefits. This research is aimed to understand the sensory and textural spectrum of commercially available dairy and plant-based yogurts. In a preliminary study, qualitative focus group discussions (4 groups; n = 32) were used to determine perceptions of 28 dairy and plant-based yogurts, identifying positive consumer perceptions of plant-based yogurts. A smaller subset of five spoonable and one drinkable yogurts—(Reference, Soy, Coconut, Cookies, Berry, and Drinkable) was subsequently selected for rheological and structural measurements, showing wide variations in the microstructure and rheology of selected yogurt samples. A quantitative blind sensory tasting (n = 117) showed varying yogurt acceptability, with Berry being the least-liked and Cookies being the most-liked yogurt, in terms of overall liking. The multi-factor analysis confirmed that compositional and textural elements, including protein content, gel firmness, and consistency coefficient, displayed a positive relationship with overall liking. In contrast, fat, sugar, and calories were negatively correlated to the overall liking. This research showed that texture and other compositional factors are significant determinants of the consumer acceptability of yogurt products and are essential properties to consider in product development.