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How beverage industry is affected by machine learning & AI
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Wednesday, 28 April, 2021, 12 : 00 PM [IST]
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Sanjay Indani &Arya Indani
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What is artificial intelligence?
Artificial intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Simply put, AI’s goal is to make computers/computer programs smart enough to imitate the human mind behaviour.Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.
What is machine learning?
Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction.
ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analysing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.
Safety & quality:
Artificial intelligence frameworks deliver more secure, more precise production lines results with more prominent speed and more consistency than human workers. What’s more, on the processing plant floor, AI-based detection can be utilised to keep employees and equipment safer, recognising likely dangers, for example, a worker who has forgotten to wear the appropriate safety gear.
Waste reduction and transparency:
Obviously, the beverage industry’s waste angle is a profoundly discussed and scrutinised part of the business. All through the world’s supply chains, AI is being utilised to follow each and every stage of the manufacturing and supply chain process, for example, tracking costs, overseeing stock levels, and even nations of origin.
Production optimisation:
Application of artificial intelligence and machine learning could incorporate faster production changeovers – decreasing the amount of time expected to change starting with one product then onto the next – and identifying production bottlenecks before they become an issue. Today, an operator is as yet needed to ‘tune’ the recipe or process yet in the future, models will be prepared to calibrate production automatically, enhancing output quality and speed.
Improving food safety standards:
Regardless of where you go on the planet, food safety standards are consistently imperative to follow, and guidelines appear to be turning out to be stricter constantly. Particularly with Covid-19, and nations become more mindful of how contaminated food can be.
Luckily, robots that utilise AI and machine learning can handle and process food, fundamentally disposing of the odds that contamination can happen through touch. Robots and machinery can’t communicate infections and such that people can, accordingly limiting the risk of it turning into an issue.
Packaging
Artificial intelligence-driven robotics are proving key to meeting the pressing and picking demands quickened by customers’ expanding utilisation of e-commerce. The complex and work escalated nature of the process offers remarkable potential for intelligent automation.
Supply chain management
AI is essentially programming computers so they can receive data, evaluate it, make a decision based on the evaluation, and then perform a given task based on the decision. This emerging technology helps the beverage industry with Supply Chain Management through logistics, predictive analytics, and transparency. The dairy industry is using AI to improve quality assurance, provide better forecasting models, and keep up with consumer trends.
Rising technologies based on artificial intelligence & machine learning in beverage industry
Robotics
Robots refer to machines that can perform tasks or operations by themselves after being programmed using a computer. Those tasks may be either simple and repetitive, or adaptive and more complex, in which the latter requires the integration of other AI methods, such as CV and ML, to continually retrain and learn to carry out more advanced operations.
In tea and coffee, robots have been developed for brewing and dispensing purposes. a tea brewing machine is able to make cups of tea in specific times with adequate water temperature and record the consumption patterns. Another recent development was a robot named Teforia; it consists of an in-home tea maker in which the user is able to add any combination of tea leaves and water, then the robot is controlled using a smartphone application to start the process; it claims to be able to brew the beverage to achieve the optimal flavour profile for each consumer. In the case of coffee, a robotic coffee maker, Mugsy, was developed using Raspberry Pi and it is possible to integrate it with different applications such as text messages, Twitter or an Alexa device, which are used to indicate to the robot to start brewing the coffee.
A commercial robotic pourer has been developed, which consists of two arms and a screen; the authors integrated it with a camera, which is able to detect the level of the liquid to predict when to stop serving. A robotic arm was designed to pick a cup and serve the beverage ordered (orange juice, apple juice or iced tea), a second robot was programmed to pick the bottled soft drinks and take them to the clients’ table, while a third machine was intended to have a conversation with clients while waiting for the order.
Computer vision techniques
The CV technique refers to a subdivision of AI, which consists of automatic information extraction from either images or videos by imitating the human eye functions. It can be coupled with robotics, specific equations or algorithms, basic statistics, and ML algorithms, to fully automate the technique as an AI system; this may allow the procedure to be stand-alone and to classify or predict the quality parameters of the product. Some advantages include that it is non-destructive, non-contact, may be replicated, is automatic, and therefore, considered as a rapid method, which is more accurate and reliable than some traditional procedures such as visual inspection and sensory analysis, which include human error as a possible drawback.
Machine learning
Machine learning (ML) is a branch of artificial intelligence, which refers to a computer-based system that may be trained to find patterns among a dataset to classify or predict specific parameters, and it is able to improve its performance by feeding new data. Machine learning may be divided into supervised and unsupervised algorithms, which, at the same time, may be classified into different subtypes.The use of ML has been increasing in recent years in the food and beverage industry due to its ability to improve production and assess the quality in a faster, more accurate, objective, and cost-effective way.
1. Machine learning in hot beverages
Compared to other types of beverages, the application of ML has been less explored in hot drinks. By using machine learning, the quality of green or black tea can be predicted with the help of different inputs. Green teas can be classified according to the quality grade using both back propagation neural networks and probabilistic neural networks with the outputs of an e-nose as inputs of the models.
A model has been developed using the flavonoids, catechins, and total methyl-xanthines content to predict the antioxidant activity. CIELab colour parameters of coffee beans are used to classify them according to their colour through ANN with a 100% accuracy; however, that perfect classification is due to the direct relationship of the categories and the inputs, which makes the model senseless and useless. A model is developed to predict the roasting degree of coffee using results from hyperspectral images (874–1734 nm) through support vector machine with a 90% accuracy.
2. Machine learning in non-alcoholic beverages
There are several studies using ML in non-alcoholic beverages; however, it has not been applied extensively for water quality assessment, and nothing has been done in bottled water. An ANN model is used to assess the quality of drinking water when entering the distribution system, using microbiological and chemical data as inputs with 100% accuracy. No recent studies have been published regarding the application of ML in soft drinks; however, there are some papers in fruit juices. The use of e-nose outputs as inputs to model fruit juices’ quality has been popular. ML has been used to classify strawberry juice samples according to the processing treatment with 100% accuracy.
Conclusion:
Despite the increasing trend in the applications of emerging technologies, which involve the use of AL, robotics, ML and CV in the beverage industry, there are still several gaps to be covered. Robotics science needs to be more explored in beverages as a tool to aid other AI components, which would maximise the use of some emerging methods. Regarding CV, most approaches developed mainly for the assessment of hot drinks and non-alcoholic beverages are based on the analysis of colour; however, more research needs to be conducted to apply this technology to measure other parameters related to the quality traits specific to each product.
The main issue with ML is that there is still a lack of knowledge among researchers concerning the proper development techniques, usage, and interpretation of the algorithms and modelling, as well as the way to select the best models to avoid over- or under-fitting, which are common problems within the existing publications. Furthermore, the combination of two or more of the aforementioned methods should be considered to be implemented as an approach to AI in the different beverage categories, especially for hot and other non-alcoholic drinks in which these technologies have not been very popular among the companies.
(Sanjay is head, SafeFoodz Solutions, a food safety-regulatory advisor-trainer. The article is co-authored by Arya and supported by Saisha Raut, They can be reached at safefoodz@gmail.com)
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