Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Abstract

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

“…All six models based on NIR and e-nose resulted in high accuracies (>95% for NIR and e-nose, Models 1 and 2; >98% for NIR Models 3 and 4, and >96% for e-nose, Models 5 and 6). These accuracies and performances are consistent with those presented in previous studies using NIR and e-nose for beer to assess aroma compounds and for the classification of commercial beers [22,29].…”

“…Regarding sensory analysis, it requires a trained panel, which can also be cost-prohibitive and can assess only a few samples at any time to avoid increasing bias due to fatigue. The implementation of new and emerging technologies for beer analysis [14], such as artificial intelligence (AI) [15][16][17] using robotics [18], near-infrared spectroscopy (NIR) [19][20][21], integrated gas sensors or low-cost electronic noses (e-noses) [22][23][24], and machine learning [25] is gaining traction recently for research and practical application purposes. One of those applications is the early detection of beer faults using e-noses [26,27] or beer classification [28].…”

Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications

“…The data are stored on a local computer/online platform for further analysis. Due to the multivariate data obtained from the gas sensor array of the E-nose system, data analysis is usually performed via supervised/unsupervised machine learning algorithms with statistical methods such as principal component analysis (PCA) [49][50][51], hierarchical cluster analysis (CA) [52,53], analysis of variance (ANOVA) [54], linear discriminant analysis (LDA) [55], partial least squares discriminant analysis (PLS-DA) [56], simple visualization techniques [57], multivariate data analysis [58], artificial neural networks (ANNs) [59][60][61], artificial intelligence (AI) [62] and F-test [63]. A photograph and schematic diagram of a prototype portable E-nose system are displayed in Figure 3.…”

“…analysis (PCA) [49][50][51], hierarchical cluster analysis (CA) [52,53], analysis of variance (ANOVA) [54], linear discriminant analysis (LDA) [55], partial least squares discriminant analysis (PLS-DA) [56], simple visualization techniques [57], multivariate data analysis [58], artificial neural networks (ANNs) [59][60][61], artificial intelligence (AI) [62] and F-test [63]. A photograph and schematic diagram of a prototype portable E-nose system are displayed in Figure 3.…”

Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method

“…Previous research developed articial intelligence models based on aroma proles, chemometrics, and chemical ngerprinting to assess beers. 46 At the same time, researchers continue to improve the methods of separating and identifying beer avor substances. However, these methods either detect several avor substances, or detect some types of avor substances.…”