1. Introduction
Beer is produced worldwide and consumed in over 150 different styles, each one being characterized by a peculiar attribute [
1]. It is the most produced and consumed alcoholic product in the world, with 177.5 million kiloliters being produced every year [
2]. The alcoholics market is continuously growing, and it is expected to globally increase its value by 19% from 2018 to 2024 [
3]. Through alcoholic fermentation, many different volatile compounds are produced, which starts from the interaction between yeast and the matrix; together with the compounds that characterize raw materials, they have a direct impact on final product’s sensory perception. These chemicals belong to different classes, e.g., inorganic compounds, alcohols, organic acids, ester, aldehydes, vicinal diketones including diacetyl, and many oth ers [
4]. The production process and ingredients create significant differences in the flavors, which is an important driver for food acceptability. Molecules such as diacetyl or 2,3-Butanedione have a great impact on the flavor of many foods, from dairy products to fermented matrixes including beer, thanks to its small and highly volatile characteristics. Because of its butter-like aroma, it is commonly used as a flavoring agent, and it is considered as GRAS by the FDA [
5,
6,
7].
Diacetyl is a vicine diketone produced by yeast action on the fermentation process as byproducts of valine and isoleucine amino acid synthesis [
8]. Diacetyl is converted into 2,3-butanediol thanks to various reductase enzymes [
9,
10], and its presence in beer can vary according to the yeast and beer typology [
11]. Like many other molecules, it can be an appreciated characteristic of some food products or become a problem if its presence is detected in others [
6]. This can be noticed in beer samples, where the lager type usually has a high diacetyl content, with a 0.15 ppm threshold [
11,
12]; meanwhile, bock beer should not have it in its formulation because its sensory perception is influenced by different volatile compounds. Pilsner and English-style ale usually have a higher concentration [
13]. Indeed, lighter beers are usually characterized by higher diacetyl perception because in these samples, the hop and malt do not have a preponderant importance on the flavor. When its concentration exceeds the cited thresholds, non-compliance is produced [
4,
8]. This difference in choosing typology characterized by different diacetyl concentrations was considered for sample selection. Lager beer is a low-temperature fermented and matured beer, and nowadays, it is the most consumed beer in the world. It is characterized by a sweet smell and taste with important fresh and malty notes [
14]. Ale is a high fermented beer with fruitier and sweeter notes than lager, and it is referred to a vast amount of sub-classes. The diacetyl flavor threshold on these products are normally 0.10–0.40 ppm [
8]. Indian pale ale (IPA) generally has a 5–8% alcohol volume, and it is characterized by a pronounced hop aroma and high bitterness. In this typology, diacetyl’s presence can easily generate non-compliance, and the thresholds are really low [
4,
15].
Implementing a detection system that is able to recognize diacetyl’s presence is primarily important. Because of its volatility, quantitative analyses are difficult to pursue; nevertheless, it represents one of the most important analyses in the brewing industries. Its evaluation is an important tool for investigating the presence of problems related to pitching and fermentation, for studying the yeast vitality, and as a marker for beer aging [
16,
17]. Chromatographic analyses are the most often used techniques to detect diacetyl’s presence in foods because of its good results; meanwhile, other analytical procedures include colorimetric assays and voltametric detection [
5,
8], which can guarantee excellent results for miniaturizing samples [
18]. Implementing electronic noses for beer evaluation is nowadays a well-known field, as this topic has been investigated in the last 30 years [
19,
20]. Nowadays, many studies are currently undergoing the creation and validation of IoT integrated devices that are able to detect different target molecules in fermented products [
21]; however, only a few have already been implemented in real production processes. This study follows this research field with the aim of scaling up and implementing the preliminary results shown in this paper to a real production process. For this reason, the sample selection was not only based for different levels of diacetyl presence, but also for the production processes where this device will be implemented.
Metal oxide sensors (MOS) have been widely studied for their ability to produce an electrical signal, which starts from chemicals present on sample [
22]. An S3+ (Nano Sensor Systems s.r.l.; Reggio Emilia, Italy;
www.nasys.it (accessed on 15 December 2022 ) device is an innovative tool that has been already used with success in other previous studies concerning quality control and food technology [
23,
24,
25]. Thanks to its electrochemical and nano oxide metal sensors, its ability to interconnect, and possibility to be remotely controlled, this device can be efficiently used for many applications, including quality control in production processes. For this reason, its implementation in diacetyl analysis is based on solid scientific knowledge, starting from its instrumental composition. Before the sensor’s application in inline devices, they must be calibrated to establish the functional relationship between the measured values and specific analytical quantities [
26]. This expensive and time-consuming procedure plays a key role in providing reliable sensor performances [
27].
The S3+ device can contribute important information to different stages of the production process, starting from the arrival of raw materials to process monitoring, as well as the analysis of results. Furthermore, implementing data analysis through the PCA technique makes it possible to have a fast and easy-to-read data visualization. This process control can be a great innovation because it can drastically reduce power consumption and food waste. Indeed, the usually performed traditional analyses are destructive, slow, and expensive. Implementing a continuous way to control a system can recognize undesired chemicals when they are present in traces. In this scenario, corrective actions can be very effective and do not require long and complex treatments. Vice versa, when chemical concentrations are too high, corrective actions cannot be as effective, requiring expensive treatment that have important consequences on the product’s final quality or even the possibility of having to trash the products. Nowadays, the current limitation of electronic noses is the incapability of performing quantitative analyses [
28].
This work aims to analyze the volatilome of different craft beers, specifically investigating how the presence of diacetyl affects final products according to their typology and aromatic profile. Because the beer market is extremely important and it will grow in the future, implementing new and developed technology can bring an important impact on many fields, from reducing resource waste to improving the final quality. An S3+ device equipped with advanced nanosensors was therefore implemented with the aim of looking for new emerging applications fields for this technology, thanks to its capability of ensuring fast, precise, and cost-effective analyses. This study sought to understand sensor response patterns for this spe cific application. Finally, a post-run analysis was conducted using the principal component analysis (PCA). Furthermore, creating a robust database allows for the implementation and design of pattern recognition algorithms in order to provide fast responses and artificial intelligence algorithms that can predict the situation of interest in order to assure a higher level of quality in the standards and safety in the food/feed production chain. These tailormade, noncommercial devices can be implemented for a selected target molecule on a different matrix, which helps to achieve the digitalization and automation of entire production lines.
3. Results and Discussion
Data collected by the chosen six sensing elements were recorded and stored into the web app in order to be easily managed by employers to quickly monitor the production process on any device. An example of the recorded tracks is visible in
Figure 2, where differences among sensing elements are due to the dissimilar interaction with the sample, the non-identical dopant used, and structural characteristics. From the track, it is possible to see how the system is able to make measurements with a good replicability over time. During the withdrawal phase, a drop can be noticed, with a ΔR value always being greater or equal to 75%. Furthermore, the recovery phase takes place correctly, returning the baseline to the starting resistance value.
Figure 2.
Three different recorded tracks made by differently doped sensing elements on the same sample: Sensor S0: SnO
2 + Pd; Sensor S1: SnO
2; Sensor S2: SnO
2 + Au. The
y-axis shows the resistance value (Ω) while the
x axis shows time (s). Once the tracks have been recorded, this project firstly has the aim of correctly selecting the best-performing sensor array for this specific application. As shown in
Figure 3, the selected sensing elements are able to detect and discriminate 5% hydroalcoholic solution samples with different diacetyl concentrations. The selected features were mean last 60, mean, minimum, integral, difference, maximum, and delta-R. All three different concentrations (0.01 mg/L, 0.06 mg/L, and 1 mg/L) are recognized as different among themselves and, for that reason, this specific sensor’s array was implemented for next steps.
Figure 2.
Three different recorded tracks made by differently doped sensing elements on the same sample: Sensor S0: SnO
2 + Pd; Sensor S1: SnO
2; Sensor S2: SnO
2 + Au. The
y-axis shows the resistance value (Ω) while the
x axis shows time (s). Once the tracks have been recorded, this project firstly has the aim of correctly selecting the best-performing sensor array for this specific application. As shown in
Figure 3, the selected sensing elements are able to detect and discriminate 5% hydroalcoholic solution samples with different diacetyl concentrations. The selected features were mean last 60, mean, minimum, integral, difference, maximum, and delta-R. All three different concentrations (0.01 mg/L, 0.06 mg/L, and 1 mg/L) are recognized as different among themselves and, for that reason, this specific sensor’s array was implemented for next steps.
Once the ability to discriminate a simplified solution was demonstrated, the capability to detect a difference among the different beers was tested.
Figure 4 depicts the outcome PCA of untreated ale, IPA, and lager, which produced three different clusters according to the different beers’ aromatic patterns through the difference in the detected resistance by the sensitive elements. The selected features were the integral and delta-R. Through this representation, it is possible to describe all of the collected dataset by both reduced principal components, as the sum of these PCs reached 100% of the variance.
Figure 4.
PCA obtained by the different types of beer. Light blue dots: ale; yellow dots: IPA; brown dots: lager. As described, the second part of the analysis was focused on investigating the S3+ device’s discriminant capacity in the beer samples. We performed 19 different tests and compared the results of different types of beers and different diacetyl concentrations. The results of the analysis show that the S3+ device is able to detect differences in molecule concentrations at levels that are smaller than the human perception threshold. This ability is demonstrated in
Figure 5 with the remarkably high explained variance on the outcome PCA. Indeed, two different clusters are visible in the IPA samples. Nevertheless, two different populations (brown dots and light blue dots) are still visible, showing how the device is able to recognize even very small differences among the samples. The selected features were mean and difference.
Figure 4.
PCA obtained by the different types of beer. Light blue dots: ale; yellow dots: IPA; brown dots: lager. As described, the second part of the analysis was focused on investigating the S3+ device’s discriminant capacity in the beer samples. We performed 19 different tests and compared the results of different types of beers and different diacetyl concentrations. The results of the analysis show that the S3+ device is able to detect differences in molecule concentrations at levels that are smaller than the human perception threshold. This ability is demonstrated in
Figure 5 with the remarkably high explained variance on the outcome PCA. Indeed, two different clusters are visible in the IPA samples. Nevertheless, two different populations (brown dots and light blue dots) are still visible, showing how the device is able to recognize even very small differences among the samples. The selected features were mean and difference.
Figure 5.
Comparison between two different IPA samples. Brown dots: IPA beer in an unaltered state (US); light blue dots: IPA with 0.01 mg/L of diacetyl. All outliers, as shown in
Figure 6, were measured in the first moments of the withdrawals. This can be explained because in the first moments of the analysis, ethanol, CO
2, and other volatile compounds such as diacetyl are accumulated in the headspace. This could mean that the first data analyzed are affected by this quiet moment and, as a consequence, abnormal data are collected that affect the outcome PCA. This specific pattern is recognizable in all of the shown PCA, because no data were considered.
Figure 5.
Comparison between two different IPA samples. Brown dots: IPA beer in an unaltered state (US); light blue dots: IPA with 0.01 mg/L of diacetyl. All outliers, as shown in
Figure 6, were measured in the first moments of the withdrawals. This can be explained because in the first moments of the analysis, ethanol, CO
2, and other volatile compounds such as diacetyl are accumulated in the headspace. This could mean that the first data analyzed are affected by this quiet moment and, as a consequence, abnormal data are collected that affect the outcome PCA. This specific pattern is recognizable in all of the shown PCA, because no data were considered.
Diacetyl’s concentration effectively affects the samples’ volatilome, as shown in
Figure 7. The selected features were the delta-R and integral. Here, the device’s capability to recognize differences between different beer samples and diacetyl concentrations has been tested. The central cluster composed by light blue and purple dots represents both IPA and lager beers with 1 mg/L of diacetyl. The overlap of these two typologies is due to the preponderant importance of diacetyl in the collected data. In fact, with 1 mg/L of the contaminant inserted, the results are extremely affected by these molecules, and other sensorial differences become less important. This has been observed in every sample, and for this reason, beers with important aromatic differences are recognized as a single central cluster.
Diacetyl, as can be observed in
Figure 8, has a direct impact on the sensors’ recorded tracks. Indeed, a small distance between the clusters confirm that all of the used matrix belong to the same typology, and the dissimilarity is attributable to differences in diacetyl increasing the concentration in the lager samples. According to these results, it is possible to implement this device for a real production process to effectively control diacetyl developments. The implemented features were the integral e minimum.
4. Conclusions
Nowadays, there is a limitation mainly with respect to the necessity of using finished products instead of semi-finished products. Because corrective procedures should be implemented on semi-finished products during the production process, a study of how sensors can recognize this non-compliance should be produced in a real matrix. This will be considered during the next step, where the S3+ device will be implemented in a real productive process.
The capability of recognize the difference among different beer types was confirmed by this study. Indeed, all three different beer typologies produced different clusters. This is evidence of the device’s capability to recognize different products that start from its volatilome. Furthermore, this device is able to recognize different diacetyl concentrations, also giving as a result a PCA test with a high value of variance explained. Exceeding a specific concentration determines the formation of non-compliances. This is motivated by the fact that regardless of the selected typology, the presence of a specific concentration turns out to be preponderant and mainly affects the outcome PCA.
Through this device, it is possible to perform and implement a control alongside all of the production process. This can improve not only the final quality, ensuring compliance with the imposed standards, but also reduce food and resource waste. Indeed, by applying a constant and non-destructive control on the process, data can be continuously achieved and used to create an IoT integrated system that is able to manage all of the production process. Finally, an analysis through the S3+ device can be performed on other aspects of beer’s off-flavors such as dimethyl sulfide in order to investigate the MOX sensor’s detection capability for different chemicals. Implementing knowledge on beer’s contaminants can improve the capability of quickly recognizing all of the non-compliances. For this reason, MOX sensors can become an active support and can become a highly functional tool for beer production lines and more.