Researchers from Penn State University and NASA Goddard Space Flight Center recently developed an 'electronic tongue' based on a graphene-based ion-sensitive field-effect transistor, capable of identifying differences in similar liquids, such as milk with varying water content; diverse products, including soda types and coffee blends; signs of spoilage in fruit juices; and instances of food safety concerns. The team also found that results were even more accurate when artificial intelligence (AI) used its own assessment parameters to interpret the data generated by the electronic tongue.
Graphene ISFET chip mounted on a printed circuit board (PCB). Image from: Nature
The sensor and AI can broadly detect and classify various substances while collectively assessing their respective quality, authenticity and freshness. This assessment has also provided the researchers with a view into how AI makes decisions, which could lead to better AI development and applications, they said.
The gustatory cortex is the region of the brain that perceives and interprets various tastes beyond what can be sensed by taste receptors, which primarily categorize foods via the five broad categories of sweet, sour, bitter, salty and savory. As the brain learns the nuances of the tastes, it can better differentiate the subtlety of flavors. To artificially imitate the gustatory cortex, the researchers developed a neural network, which is a machine learning algorithm that mimics the human brain in assessing and understanding data.
The tongue comprises a graphene-based ion-sensitive field-effect transistor, or a conductive device that can detect chemical ions, linked to an artificial neural network, trained on various datasets. Critically, the sensors are non-functionalized, meaning that one sensor can detect different types of chemicals, rather than having a specific sensor dedicated to each potential chemical.
The researchers provided the neural network with 20 specific parameters to assess, all of which are related to how a sample liquid interacts with the sensor’s electrical properties. Based on these researcher-specified parameters, the AI could accurately detect samples — including watered-down milks, different types of sodas, blends of coffee and multiple fruit juices at several levels of freshness — and report on their content with greater than 80% accuracy in about a minute.
“After achieving a reasonable accuracy with human-selected parameters, we decided to let the neural network define its own figures of merit by providing it with the raw sensor data. We found that the neural network reached a near ideal inference accuracy of more than 95% when utilizing the machine-derived figures of merit rather than the ones provided by humans,” said co-author Andrew Pannone, a doctoral student in engineering science and mechanics. “So, we used a method called Shapley additive explanations, which allows us to ask the neural network what it was thinking after it makes a decision.”
This approach uses game theory, a decision-making process that considers the choices of others to predict the outcome of a single participant, to assign values to the data under consideration. With these explanations, the researchers could reverse engineer an understanding of how the neural network weighed various components of the sample to make a final determination — giving the team a glimpse into the neural network’s decision-making process, which has remained largely opaque in the field of AI, according to the researchers. They found that, instead of simply assessing individual human-assigned parameters, the neural network considered the data it determined were most important together, with the Shapley additive explanations revealing how important the neural network considered each input data.
The researchers explained that this assessment could be compared to two people drinking milk. They can both identify that it is milk, but one person may think it is skim that has gone off while the other thinks it is 2% that is still fresh. The nuances of why are not easily explained even by the individual making the assessment.
According to the team, the tongue’s capabilities are limited only by the data on which it is trained, meaning that while the focus of this study was on food assessment, it could be applied to medical diagnostics, too. And while sensitivity is important no matter where the sensor is applied, their sensors’ robustness provides a path forward for broad deployment in different industries, the researchers said. They explained that the sensors don’t need to be precisely identical because machine learning algorithms can look at all information together and still produce the right answer. This makes for a more practical — and less expensive — manufacturing process.
The scientists anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.
