Q2 Agricultural and Biological Sciences
{"title":"From Data to Finished Product","authors":"","doi":"10.1002/fsat.3804_5.x","DOIUrl":null,"url":null,"abstract":"<p><b><i>Richard Marshall gives an overview on AI and how it is transforming food product development in the UK, enhancing everything from recipe optimisation data-driven innovation. Advanced techniques such as inverse design allow food scientists to create new products by working backwards from desired characteristics, accelerating development and boosting success rates</i></b>.</p><p>Artificial Intelligence (AI) is playing an increasingly significant role in both personal lives and in all areas of industry. Many people will have already used it perhaps unknowingly, for example when using an internet search engine or using automotive parking assistance. AI includes a number of different levels of data manipulation. It forms the basis of expert systems that analyse complex data to produce results. At the next level, machine learning (ML) algorithms learn from data to make predictions or decisions. A more advanced version of machine learning has artificial neural networks (ANN) which mimic human brain function, taking data in, analysing it in some form of ‘black box’ and then presenting the results. More recently, we have seen the development of large language models (LLM) that use natural language inputs and outputs – they ‘understand’ discursive questions. Within such systems, data is often analysed using fuzzy logic when it has varying levels of ‘truth’. AI is very suitable for use in product development particularly using inverse design, that is starting from knowing about products, recipes and ingredients.</p><p>Artificial intelligence (AI) is the simulation of human intelligence in machines. It involves the development of methods and software that enable computers to perceive their environment and react to it. AI aims to mimic human thinking and problem-solving abilities<sup>(</sup><span><sup>1</sup></span><sup>)</sup>.</p><p>AI is becoming more and more prominent in our lives. It has the potential to give significant benefits to the way we live, to make industry more competitive and more efficient in ways that we never imagined a few years ago. As with any emerging technology, there are risks and fears as well as positive opportunities. The UK Government has recognised this and hosted an international conference on AI in 2023<sup>(</sup><span><sup>2</sup></span><sup>)</sup>. We are generally unaware that AI is already playing a major role in many activities. Internet search engines, such as Google, use it to present results to users, often in a way that biases towards favouring advertisers, promoting certain views or suppressing certain sites<sup>(</sup><span><sup>3</sup></span><sup>)</sup>. The Internet of Things (IoT) enables smart devices, such as domestic fridges, smart watches and autonomous vehicles to communicate with the world, sharing data, providing assistance and information. The food industry is no exception in this regard. Robotics has been used for some considerable time to move product around factories, control process operations through automation and optimising supply chain management<sup>(</sup><span><sup>4</sup></span><sup>)</sup>.</p><p>At a basic level, expert systems use a form of AI in which users feed information into a human-machine interface, i.e. a computer keyboard, touch screen etc<sup>(</sup><span><sup>4</sup></span><sup>)</sup>. The initial knowledge for expert systems is acquired from a human expert. Within the system, an inference engine compares the input from the interface with rules in the knowledge base. The knowledge is in the form IF (condition)….THEN (conclusion) followed by logic operations AND, OR, NOT. Inside the system, data is held during the operations until a result has been generated which is then presented to the operator via an interpreter module, which may display on a screen, give a signal or print out. Expert systems are already widely used across the food industry (Table 1).</p><p>ML and ANNs can use ‘fuzzy logic’ where the ‘truth’ value of variables can range from 0 (not true) to 1 (completely true). The ‘truth’ can be thought of as a weighting that describes how true a value is. In standard Boolean logic, variables are either true or false, 0 or 1. Fuzzy logic allows for uncertainty in variable values, enabling the processing of imprecise data.</p><p>In a fuzzy logic system, sharp data (i.e. data that has real numbers) is transformed by a ‘fuzzifier’ into fuzzy data that indicates how reliable the values are. The fuzzy data input set is used to infer the meaning of the data based on a set of rules. Once this has been generated, the fuzzy output set is passed through a ‘de-fuzzifier’ that generates sharp or crisp output that can be used by the operator.</p><p>Examples of the use of fuzzy logic systems include modelling food control, to classify products and in handling fresh produce. The advantage of fuzzy systems is that they can use natural language for processing and they are good at managing multivariables and non-linear situations. For example, such a system can use sensory terms such as ‘not satisfactory’, ‘fair’, ‘medium’, ‘good’, and ‘excellent’ and extract useful information<sup>(</sup><span><sup>6</sup></span><sup>)</sup>.</p><p>Managing food safety is complex: it needs extensive knowledge and experience. Food safety management systems cover processing, monitoring, testing, training and maintenance. Safety relies on HACCP to evaluate hazards and the risk of occurrence. Safety is only improved after hazardous events through the evaluation of ‘lagging indicators’. The use of AI enables a more proactive approach using ‘leading indicators’. Such a system is not stand-alone but must work in conjunction with food safety experts. Examples of the challenge from microbiology facing the use of AI can be illustrated by considering that <i>Salmonella</i> has over 2500 serovars and <i>Listeria monocytogenes</i> has as many strain-dependent virulence factors<sup>(</sup><span><sup>7</sup></span><sup>)</sup>. According to these authors, AI is being used for rapid monitoring of microbial contamination in chicken liver meat, rapid verification of fresh produce wash water sanitation, predicting foodborne disease outbreaks, and predicting food safety compliance in food outlets.</p><p>As shown by Flynn (2023)<sup>(</sup><span><sup>8</sup></span><sup>)</sup> in this journal, there are a number of applications for preventing food recalls or managing food safety systems or identifying fish species. On the farm, an AI-based system called Chirrup (chirrup.ai) can be used to identify bird species and so indicate the level of biodiversity.</p><p>LLMs are based on ANNs. They are able to communicate with users via natural languages and work by learning statistical relationships between words in text. They make use of fuzzy logic to generate output. Development started from about 2017 with Google being one of the first developers. They introduced Generative Pre-trained Transformer 1 (GPT-1) in 2018 followed by GPT-2 (2019), GPT-3 (2020) and GPT-4 (2023). Access is limited to GPT because of fears of abuse but ChatGPT is the free, publicly-available version, however this uses data from GPT-3.5, which only goes up to 2022. A pay-to-use version, ChatGPT Plus, is up-to-date.</p><p>As Flynn (2023)<sup>(</sup><span><sup>8</sup></span><sup>)</sup> indicated, ChatGPT and similar LLMs can be interrogated to find information on food topics. This is equally true for food product development (FPD). However, the free-to-use ChatGPT cannot give up-to-date information on, say, market trends in FPD as its database is about two years old. On the other hand, asking ‘How can I use ChatGPT for food product development’ gives a help list that can act as a starting point (Table 4). This would be particularly useful for food science and technology students planning to develop a product as part of their coursework.</p><p>If a paid version of LLM is used, e.g. ChatGPT Plus, more current information can be obtained. It can provide trend spotting, discover new ingredients and technologies, emerging flavours, help choosing the best/right ingredients, choosing the process conditions, e.g. cooking times and temperatures, improving sustainability and carbon footprint and overall market trends and consumer preferences<sup>(</sup><span><sup>9</sup></span><sup>)</sup>.</p><p>AI can be very effective in supporting product innovation from idea generation, through building the business case, to the design, engineering, development and testing of the new product<sup>(</sup><span><sup>9</sup></span><sup>)</sup>. In the initial stages of new product development (NPD), LLMs can be used to generate novel ideas by scanning the internet, finding market opportunities from sources such as blogs, forums, reports, complaint lines and comments from product users. One area that LLMs excel in analysing is discursive data from surveys. They can generate the first draft of a customer interview guide. With information from these areas, LLMs can set out and evaluate concepts and link these to R &amp; D data held by a company, exploiting intellectual property (IP).</p><p>With these first steps completed, LLMs can then be used to build the business case for the development of new product. This includes evaluating market data, predicting potential sales, learning about competitors’ activities and projecting income and profit. The AI can even prepare the business case for the NPD to be presented to company management.</p><p>After the go-ahead decision, AI has a further role in creating virtual prototypes, setting design parameters, defining development iterations and optimising the process. Initial product evaluation by test panels of consumers can be fed back into the design process with AI indicating further improvements.</p><p>Examples of recent applications of AI in food design include using ML models to predict sensory scores of chocolate cookie recipes<sup>(</sup><span><sup>10</sup></span><sup>)</sup> and to identify optimal conditions for the ideal yoghurt sauce considering 36 combinations and 22 sensory attributes. ML models have also been used to predict co-occurrences of ingredients in recipes to discover novel food pairings. It has been noted on various occasions that new food products fail in the market place up to 75% of the time<sup>(</sup><span><sup>11</sup></span><sup>)</sup>. Al-Sarayeh et al. (2023)<sup>(</sup><span><sup>10</sup></span><sup>)</sup> argue that understanding consumer demands and using these to design a food product with appropriate chemical and physical characteristics is challenging for the food industry. 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引用次数: 0

摘要

食品安全管理系统包括加工、监控、检测、培训和维护。安全依靠 HACCP 来评估危害和发生风险。只有在危险事件发生后,才能通过评估 "滞后指标 "来提高安全性。人工智能的使用可以利用 "先行指标 "采取更加积极主动的方法。这种系统不是独立的,必须与食品安全专家合作。沙门氏菌有超过 2500 个血清型,单核细胞增生李斯特菌也有同样多的依赖于菌株的毒力因子(7)。根据这些作者的说法,人工智能正被用于快速监测鸡肝肉中的微生物污染、快速验证新鲜农产品清洗水的卫生状况、预测食源性疾病的爆发以及预测食品店的食品安全合规性。正如 Flynn(2023 年)(8) 在本期刊上指出的那样,人工智能在防止食品召回、管理食品安全系统或识别鱼类物种方面有许多应用。在农场,一个名为 Chirrup(chirrup.ai)的人工智能系统可用于识别鸟类物种,从而显示生物多样性水平。它们能够通过自然语言与用户交流,并通过学习文本中单词之间的统计关系来工作。它们利用模糊逻辑生成输出。开发工作大约从 2017 年开始,谷歌是最早的开发者之一。他们在 2018 年推出了生成式预训练转换器 1(GPT-1),随后又推出了 GPT-2(2019 年)、GPT-3(2020 年)和 GPT-4(2023 年)。由于担心被滥用,GPT 的访问受到了限制,但 ChatGPT 是免费的公开版本,不过它使用的是 GPT-3.5 的数据,而 GPT-3.5 只到 2022 年。正如 Flynn(2023)(8) 所指出的,ChatGPT 和类似的 LLM 可用于查询食品主题信息。这对食品产品开发 (FPD) 同样适用。不过,免费使用的 ChatGPT 无法提供最新信息,例如,FPD 的市场趋势,因为其数据库已有两年左右的历史。另一方面,询问 "如何使用 ChatGPT 进行食品产品开发 "可以得到一个帮助列表,作为一个起点(表 4)。如果使用 LLM 的付费版本,如 ChatGPT Plus,可以获得更多最新信息。如果使用付费版本的 LLM,如 ChatGPT Plus,可以获得更多最新信息。它可以提供趋势预测、发现新配料和技术、新口味、帮助选择最佳/正确配料、选择加工条件(如烹饪时间和温度)、改善可持续性和碳足迹以及整体市场趋势和消费者偏好(9)。在新产品开发(NPD)的初始阶段,LLMs 可以通过扫描互联网,从博客、论坛、报告、投诉电话和产品用户评论等来源中寻找市场机会,从而产生新颖的想法。法律硕士最擅长分析的一个领域是来自调查的话语数据。他们可以编写客户访谈指南的初稿。有了这些方面的信息,法律硕士就可以提出和评估概念,并将这些概念与公司拥有的研发数据联系起来,从而利用知识产权(IP)。这包括评估市场数据、预测潜在销售额、了解竞争对手的活动以及预测收入和利润。在做出批准决定后,人工智能还可以在创建虚拟原型、设定设计参数、定义开发迭代和优化流程方面发挥进一步的作用。人工智能在食品设计中的最新应用实例包括使用 ML 模型预测巧克力饼干配方的感官评分(10),以及考虑 36 种组合和 22 种感官属性确定理想酸奶酱的最佳条件。ML 模型还被用于预测食谱中配料的共同出现,以发现新的食物搭配。人们在不同场合注意到,新食品在市场上的失败率高达 75%(11)。Al-Sarayeh 等人(2023 年)(10) 认为,了解消费者的需求,并利用这些需求设计出具有适当化学和物理特性的食品,对食品行业来说具有挑战性。他们提出了一种不同的开发新产品的方法,称为 "逆向设计"。 在传统的 NPD 中,感官、营养、健康、便利、心理和社会信息被用来推动新概念的创造。在一个需要多学科知识的迭代过程中,可能会产生一个或多个原型。经过数次开发和加工试验后,产品就生产出来了。反向设计 "从确定产品的预期功能开始,致力于优化设计。为了有效地做到这一点,必须对输入和输出之间的关系进行建模,而这对于人类来说过于复杂,无法手动建模。应用人工智能可以更高效、更有效地开发新产品。来自公共配方数据库、食品成分、结构和分子特性的信息可以通过编码器输入虚拟设计空间。在这个空间中,人工智能可以找到具有目标属性的新产品。结果通过解码器输出,并以新产品配方的形式呈现,其中包括所需加工、感官特性、营养和健康方面的详细信息。然后可以对这些属性进行测量,以确定产品是否符合规范。消费者的反应也可用于评估设计。Al-Sarayeh 等人(2023)(10) 列举了 Marin 等人(2019)(12) 的一个例子,其中食谱的成分、加工方向和视觉特性都是由人工智能深度学习模型编码的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

From Data to Finished Product

From Data to Finished Product

Richard Marshall gives an overview on AI and how it is transforming food product development in the UK, enhancing everything from recipe optimisation data-driven innovation. Advanced techniques such as inverse design allow food scientists to create new products by working backwards from desired characteristics, accelerating development and boosting success rates.

Artificial Intelligence (AI) is playing an increasingly significant role in both personal lives and in all areas of industry. Many people will have already used it perhaps unknowingly, for example when using an internet search engine or using automotive parking assistance. AI includes a number of different levels of data manipulation. It forms the basis of expert systems that analyse complex data to produce results. At the next level, machine learning (ML) algorithms learn from data to make predictions or decisions. A more advanced version of machine learning has artificial neural networks (ANN) which mimic human brain function, taking data in, analysing it in some form of ‘black box’ and then presenting the results. More recently, we have seen the development of large language models (LLM) that use natural language inputs and outputs – they ‘understand’ discursive questions. Within such systems, data is often analysed using fuzzy logic when it has varying levels of ‘truth’. AI is very suitable for use in product development particularly using inverse design, that is starting from knowing about products, recipes and ingredients.

Artificial intelligence (AI) is the simulation of human intelligence in machines. It involves the development of methods and software that enable computers to perceive their environment and react to it. AI aims to mimic human thinking and problem-solving abilities(1).

AI is becoming more and more prominent in our lives. It has the potential to give significant benefits to the way we live, to make industry more competitive and more efficient in ways that we never imagined a few years ago. As with any emerging technology, there are risks and fears as well as positive opportunities. The UK Government has recognised this and hosted an international conference on AI in 2023(2). We are generally unaware that AI is already playing a major role in many activities. Internet search engines, such as Google, use it to present results to users, often in a way that biases towards favouring advertisers, promoting certain views or suppressing certain sites(3). The Internet of Things (IoT) enables smart devices, such as domestic fridges, smart watches and autonomous vehicles to communicate with the world, sharing data, providing assistance and information. The food industry is no exception in this regard. Robotics has been used for some considerable time to move product around factories, control process operations through automation and optimising supply chain management(4).

At a basic level, expert systems use a form of AI in which users feed information into a human-machine interface, i.e. a computer keyboard, touch screen etc(4). The initial knowledge for expert systems is acquired from a human expert. Within the system, an inference engine compares the input from the interface with rules in the knowledge base. The knowledge is in the form IF (condition)….THEN (conclusion) followed by logic operations AND, OR, NOT. Inside the system, data is held during the operations until a result has been generated which is then presented to the operator via an interpreter module, which may display on a screen, give a signal or print out. Expert systems are already widely used across the food industry (Table 1).

ML and ANNs can use ‘fuzzy logic’ where the ‘truth’ value of variables can range from 0 (not true) to 1 (completely true). The ‘truth’ can be thought of as a weighting that describes how true a value is. In standard Boolean logic, variables are either true or false, 0 or 1. Fuzzy logic allows for uncertainty in variable values, enabling the processing of imprecise data.

In a fuzzy logic system, sharp data (i.e. data that has real numbers) is transformed by a ‘fuzzifier’ into fuzzy data that indicates how reliable the values are. The fuzzy data input set is used to infer the meaning of the data based on a set of rules. Once this has been generated, the fuzzy output set is passed through a ‘de-fuzzifier’ that generates sharp or crisp output that can be used by the operator.

Examples of the use of fuzzy logic systems include modelling food control, to classify products and in handling fresh produce. The advantage of fuzzy systems is that they can use natural language for processing and they are good at managing multivariables and non-linear situations. For example, such a system can use sensory terms such as ‘not satisfactory’, ‘fair’, ‘medium’, ‘good’, and ‘excellent’ and extract useful information(6).

Managing food safety is complex: it needs extensive knowledge and experience. Food safety management systems cover processing, monitoring, testing, training and maintenance. Safety relies on HACCP to evaluate hazards and the risk of occurrence. Safety is only improved after hazardous events through the evaluation of ‘lagging indicators’. The use of AI enables a more proactive approach using ‘leading indicators’. Such a system is not stand-alone but must work in conjunction with food safety experts. Examples of the challenge from microbiology facing the use of AI can be illustrated by considering that Salmonella has over 2500 serovars and Listeria monocytogenes has as many strain-dependent virulence factors(7). According to these authors, AI is being used for rapid monitoring of microbial contamination in chicken liver meat, rapid verification of fresh produce wash water sanitation, predicting foodborne disease outbreaks, and predicting food safety compliance in food outlets.

As shown by Flynn (2023)(8) in this journal, there are a number of applications for preventing food recalls or managing food safety systems or identifying fish species. On the farm, an AI-based system called Chirrup (chirrup.ai) can be used to identify bird species and so indicate the level of biodiversity.

LLMs are based on ANNs. They are able to communicate with users via natural languages and work by learning statistical relationships between words in text. They make use of fuzzy logic to generate output. Development started from about 2017 with Google being one of the first developers. They introduced Generative Pre-trained Transformer 1 (GPT-1) in 2018 followed by GPT-2 (2019), GPT-3 (2020) and GPT-4 (2023). Access is limited to GPT because of fears of abuse but ChatGPT is the free, publicly-available version, however this uses data from GPT-3.5, which only goes up to 2022. A pay-to-use version, ChatGPT Plus, is up-to-date.

As Flynn (2023)(8) indicated, ChatGPT and similar LLMs can be interrogated to find information on food topics. This is equally true for food product development (FPD). However, the free-to-use ChatGPT cannot give up-to-date information on, say, market trends in FPD as its database is about two years old. On the other hand, asking ‘How can I use ChatGPT for food product development’ gives a help list that can act as a starting point (Table 4). This would be particularly useful for food science and technology students planning to develop a product as part of their coursework.

If a paid version of LLM is used, e.g. ChatGPT Plus, more current information can be obtained. It can provide trend spotting, discover new ingredients and technologies, emerging flavours, help choosing the best/right ingredients, choosing the process conditions, e.g. cooking times and temperatures, improving sustainability and carbon footprint and overall market trends and consumer preferences(9).

AI can be very effective in supporting product innovation from idea generation, through building the business case, to the design, engineering, development and testing of the new product(9). In the initial stages of new product development (NPD), LLMs can be used to generate novel ideas by scanning the internet, finding market opportunities from sources such as blogs, forums, reports, complaint lines and comments from product users. One area that LLMs excel in analysing is discursive data from surveys. They can generate the first draft of a customer interview guide. With information from these areas, LLMs can set out and evaluate concepts and link these to R & D data held by a company, exploiting intellectual property (IP).

With these first steps completed, LLMs can then be used to build the business case for the development of new product. This includes evaluating market data, predicting potential sales, learning about competitors’ activities and projecting income and profit. The AI can even prepare the business case for the NPD to be presented to company management.

After the go-ahead decision, AI has a further role in creating virtual prototypes, setting design parameters, defining development iterations and optimising the process. Initial product evaluation by test panels of consumers can be fed back into the design process with AI indicating further improvements.

Examples of recent applications of AI in food design include using ML models to predict sensory scores of chocolate cookie recipes(10) and to identify optimal conditions for the ideal yoghurt sauce considering 36 combinations and 22 sensory attributes. ML models have also been used to predict co-occurrences of ingredients in recipes to discover novel food pairings. It has been noted on various occasions that new food products fail in the market place up to 75% of the time(11). Al-Sarayeh et al. (2023)(10) argue that understanding consumer demands and using these to design a food product with appropriate chemical and physical characteristics is challenging for the food industry. They propose a different way of developing new products called ‘inverse design’. In conventional NPD, sensory, nutrition, health, convenience, psychology and social information are used to drive the creation of a new concept. One or more prototypes may be produced in an iterative process requiring multidisciplinary knowledge. After several development and processing trials, a product is produced. This complex process, involving many factors, is often costly in terms of both time and money, with potential for failure at any stage.

‘Inverse design’ begins by identifying the desired functionalities of a product and works towards optimising the design. In order to do this effectively, the relationships between inputs and outputs have to be modelled, which is too complex for humans to model manually. The application of AI enables more efficient and more effective development of the new product. Information from public recipe databases, food composition, structural and molecular properties can be fed via an encoder to the virtual design space. In that space, the AI can find novel products with targeted attributes. The results pass out through a decoder and are presented as a recipe for the new product with details of processing required, the sensory properties, nutrition and health aspects. These properties can then be measured to see if the product meets the specification. Consumer responses can also be used to assess the design. Al-Sarayeh et al. (2023)(10) give an example from Marin et al. (2019)(12) in which the ingredients of a recipe, the processing directions and visual properties are encoded by an AI deep learning model.

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来源期刊
Food Science and Technology
Food Science and Technology 农林科学-食品科技
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