As AI continues to make its way into all sectors of the world, the food industry is no exception. AI has the potential to revolutionize the complex science of food labelling.
Artificial Intelligence (AI) is rapidly changing the face of the food industry in multiple ways through its ability to automate tasks and increase efficiency. AI is impacting the food manufacturing industry with robotics in food manufacturing, smart sensors for production failures, automated packaging, tracking the food supply chain, segregation of food on select metrics, quality inspection and more. With Chat GPT and GPT4 as the latest sensation, AI is expected to continue transforming significant portions of the food industry.
Here are some more specific ways in which AI and Chat GPT can impact the food labelling industry using Natural Language Processing (NLP) and Machine Learning (ML) techniques –
- Consumer Sentiment & Feedback Analysis on Food Labels
Sentiment analysis is a powerful application of AI that can be used to analyse consumer reviews and feedback related to food labelling. By analysing large volumes of consumer reviews, feedback and grievances, companies can gain insights into the common complaints or issues that consumers face with food labelling.
E.g., Let us review a particular brand of breakfast cereal – sentiment analysis could be used to analyse reviews to identify common complaints related to the product’s nutritional claims. By analysing the language used in consumer reviews, sentiment analysis could identify complaints related to false or misleading nutrition claims, which could prompt the manufacturer to improve its labelling practices to better reflect the product’s nutritional content.
Another example of the use of sentiment analysis in the food labelling industry is the analysis of consumer reviews related to allergen labelling. By analysing consumer feedback related to food allergies, sentiment analysis could identify common complaints related to allergen labelling practices, such as unclear or misleading allergen labels. This could prompt food manufacturers to improve their allergen labelling practices to better meet the needs of consumers with food allergies.
- Information Recognition & Compliance of Food Regulations
Named Entity Recognition (NER) is a subfield of natural language processing (NLP) that involves identifying and extracting specific entities from text. In the context of food labelling, NER can be used to identify specific entities mentioned on food labels, such as ingredients or allergens, which can help ensure that labels are accurate and compliant with regulations.
By using NER to identify specific entities on food labels, food and beverage companies and regulators can ensure that labels are accurate and compliant with regulations. This can help strengthen food regulatory compliance, and enhance food safety by preventing allergic reactions and ensures that consumers have access to clear and accurate information about the ingredients and nutritional content of the food products they consume.
- Translation of Information on Food Labels
With increasing impetus on food exports and imports, machine translation will be immensely useful to translate food labels into different languages, which can help manufacturers expand their markets and reach a wider audience. Machine translation is a technology that uses artificial intelligence (AI) to automatically translate text from one language to another.
E.g., Suppose a food manufacturer in India wants to export its products to a country where the primary language is Spanish. By using machine translation, the manufacturer can translate its food labels into Spanish, making it easier for Spanish-speaking consumers to understand the ingredients and nutritional information in the products. This can help to increase the manufacturer’s sales, expand its market reach, and make their products more accessible to consumers around the world.
However, it is important to note that machine translation is not always 100% accurate, and there may be errors or inaccuracies in the translation. Therefore, it is important for manufacturers to have their translated labels reviewed by a professional translator or language expert to ensure that the translation is accurate and compliant with local regulations.
- Extracting Information from Food Labels to Make Healthier Food Choices
In the context of food labelling, information extraction can be used to extract nutritional content or manufacturing processes from food labels, which can help consumers make more informed decisions about the products they purchase.
For example, suppose a consumer wants to purchase a snack bar that is high in protein and low in sugar. By using information extraction techniques, the consumer can quickly and easily identify snack bars that meet their nutritional preferences by scanning the labels for relevant information. This can help the consumer make a more informed decision about which products to purchase.
Such systems can also power various front-of-pack labelling models across the world and make it far easier for consumers to comprehend product constitution, nutrition value and make a healthy choice.
- Use of Image Recognition on Counterfeit Food Labels
Image recognition can be used to automatically identify a brand’s logo on a food label. By analysing images of product packaging or labels, machine learning algorithms can detect and identify any discrepancies or irregularities that may indicate counterfeit products. For example, if a counterfeit product uses a similar logo or design to a legitimate product, image recognition can identify the differences in colour, font, or placement of elements that may indicate the product is not authentic. This can help manufacturers and regulatory authorities to identify and remove counterfeit products from the market, thereby protecting consumer safety and preserving brand reputation.
As we have explored the use of AI and its components such as natural language processing, machine learning, and image recognition technologies, we have seen how AI can greatly impact the food labelling industry. By leveraging these tools, manufacturers can ensure that their products are accurately and compliantly labelled, while consumers can make more informed decisions about the products they purchase.
From sentiment analysis to information extraction, these technologies provide a range of benefits for both manufacturers and consumers. Machine translation services can also help manufacturers expand their reach and enter new markets, while image recognition can help ensure that authentic products are hitting the markets.
The future of the food labelling is undoubtedly tied to the integration of advanced technologies based on artificial intelligence. As AI continues to advance, we can expect to see even more sophisticated applications of these tools in the food labelling industry. By embracing these technologies, manufacturers and regulatory authorities can create safer, more transparent, and more consumer-friendly food labelling practices. Ultimately, this will not only benefit manufacturers and consumers, but also help to build a stronger, more sustainable food industry for everyone.
(This article is written by Rashida Vapiwala, Founder – FoLSol by LabelBlind, and the views expressed in this article are her own)
