We investigated a concept for the rapid spoilage detection of minced pork. The volatile compounds of 77 packages of minced pork stored at three temperatures (2 degrees, 10 degrees, 14 degrees C) under modified atmospheric packaging (70 % O2 and 30 % CO2) were determined by headspace GC-IMS. The results of these analyses went through a feature extraction process that utilized the loading values from the principal component analysis, followed by edge detection via Otsu method and obtaining the coordinates of each peak via regionprops from skimage python library. The automatic extraction process resulted in 161 volatile features. The volume of these features was linked to total viable counts that were used in this study as a proxy for spoilage via partial least squares. Iteratively, the volatile features with low importance were eliminated. It resulted in sixteen volatile features, including monomers, dimers, and three overlapping peaks, being selected based on their beta coefficient. Subsequently, the selected features were identified to investigate the feature extraction and selection approach. Twelve out of sixteen features were identified to represent six VOCs commonly found in spoiled meat: acetoin, 1propanol, 2-methylpropanol, 2-butanone, 3-methylbutanal, and acetone. The other four features remained unidentified. Three of the eleven features referring to acetone and its co-elution were highly overlapping. Therefore, they were combined into one feature with a larger area, hence reducing the total feature number from sixteen to fourteen. The regression model trained based on fourteen features resulted in R2 of 0.80/0.77 and RMSE in log CFU/g of 0.73/0.83 for the training and testing datasets, respectively. The results, therefore, underline the potential of GC-IMS-based analyses of changes in the volatilome of food products as a rapid monitoring approach for detecting food spoilage.

In gas measurements, there is inherent competition between sensors and analytical systems, often made difficult by a lack of understanding of approaches and constraints from both sides. This leads to controversies, sometimes simple to avoid or defuse, but sometimes leading to harsh discussions and resentment of "wrong" concepts from the respective other side. Meanwhile, collaboration between disciplines is often a key to scientific and, in the long term, economic and societal success. This is especially true today, when digitization is the pervasive topic in our society and where "sensors act as a bridge between biology and digitization." This article addresses this conflict for the field of high-performance gas measurement systems, where the two worlds of low-cost sensor systems on the one hand and high-performance analytical instruments on the other are still often at odds.

This article investigates methods for sampling volatiles on a compounding extruder to enable the development of a quasi-continuous automated sampling and measurement system for odorous contaminants. A first prototype of a bespoke extraction system was presented in earlier work, comprising four sequential cooling traps and one subsequent sorption trap. This setup allows us to obtain samples from the vacuum degassing port of a Coperion ZSK extruder. Preliminary results indicated that samples contain a plethora of odor-active compounds. This study assesses samples from the processing of post-consumer recycled polypropylene by means of gas chromatography-mass spectrometry/olfactory detection (GC-MS/O), focusing on comparing the composition of condensed and adsorbed samples. The results give an overview of the degassing atmosphere, listing more than 108 volatile compounds including odorants. Qualitative comparison of the sampling techniques indicates significant fractioning between condensates and adsorbates, which is illustrated on an orientation plot for water solubility versus volatility. Based on the results, guidelines for the design of sampling units for automated use in the aspired online monitoring application are proposed to transfer a broad spectrum of relevant contaminants to an attached measurement system.

Quality assurance of polyolefin regrinds requires assessment and control of their odor properties. Aiming to develop efficient tools for this purpose, we applied analytical and sensory methods to investigate odor properties in 51 virgin and recycled plastics, primarily polypropylene. We report on the odor characteristics of polyolefin materials with a differentiation between virgin and post-consumer recycled material, the latter exhibiting higher odor intensities. Gas chromatography combined with software-based odorant detection facilitated a comparison of samples and indicated higher odorant loads in regrinds. Chemometric tools using sensory and analytical datasets classified samples from virgin and PCR material with prediction accuracies of 97.2-100 %. The prediction of overall odor intensity from analytical data using a support vector machine model delivered root mean square errors of 0.46 (training) and 0.22 (test). This represents a crucial step towards instrumental measurement for odor monitoring in plastics, especially in view of the increasing reuse of post-consumer materials.

The measurement of odors offers a high potential for non-destructive, on-line, real-time quality monitoring of many different products, such as food and cosmetics. Although numerous laboratory devices are capable of such odor measurements, required laboratory background and trained workers prevent the widespread use of such devices in the industry and in public. Hence, cheap and commercial instrumental odor monitoring systems (IOMSs) are needed. To ensure a timely and cost-effective experience during the development of such IOMSs, a gas chromatography selective odorant measurement sensor array (GC-SOMSA) combining three detector ports (a mass spectrometer, an odor detection port, and a sensor chamber) is set up as an element of a structured development concept for IOMSs. This device is tested with a commercially available sensor and spiked sunflower oils, which emulate odor-active oxidation of fatty oils that occur during oil aging in food or cosmetics. The sensor was able to detect pentane (4 µL 100 mL−1) and the odor-active oxidation markers hexanal (1.4 µL 100 mL−1) and octanal (8.2 µL 100 mL−1) within a sunflower oil matrix. When applying different sensor temperatures, the sensor was able to detect a more intense signal for hexanal than for pentane at 250 °C. Furthermore, it was found that the (siloxane) protective membrane of the sensor discriminates between different molecules. This has an influence on the synchronicity of the detectors by adding a possible time offset to the sensor signals. This offset could be considered by forming the first derivative of the sensor signal. The odor detection port measurements revealed a weak odor impression at calculated target concentrations. For 10 and 100 times higher concentrations, hexanal (grassy) and octanal (citrus-like) could be detected. All three detectors were in parallel, and odor impressions could be assigned to mass spectrometer and sensor peaks. Thus, the sensor could be characterized sufficiently and is suitable for detecting odor-active compounds in the fat oxidation of a fatty matrix. The GC-SOMSA can be used in the future as an element of a structured IOMS development concept since it can be extended to a wide range of applications for rapid sensor characterization and, due to the flexible design of the sensor chamber, for many different sensors.

The work presented here aims at a process compatible sampling and gas chromatography to enable realtime monitoring of contaminants in plastics recycling. The technique makes use of the fact that the plastic material is molten in a closed compartment which is evacuated continously. Main work items were the identification of the substances in this gas space and the development of a suitable sampling technique that allows the transfer of a representative sample into the gas chromatography part. The fast temperature changes necessary for a real-time monitoring could be realized in a modular demonstrator setup that allows for more than 12 measurements per hour. In contrast to conventional offline laboratory analyses, this enables a targeted identification of contaminated material sections.

This paper presents the development and characterization of a gas sensor characterization platform for gas chromatography (GC) systems. With fluidic modeling we can accurately calculate the flow ratio between two detectors at different outlet pressures - in the presented case a mass spectrometer (MS) at high vacuum and metal oxide semiconductor (MOS) detector at ambient pressure. We further validate the fluidic model through experimental testing with commercially available restrictions and a separation column. A volatile organic calibration mix (EPA 502/524) is used to characterize a commercially available MOS gas sensor as GC detector. The temperature-dependent behavior of MOS sensors is studied, and the overall performance is compared with a laboratory MS. The results show the potential for these sensors in various applications, highlighting the importance of characterizing and verifying system components to ensure reliable results. The study concludes by demonstrating that MOS sensors can be effectively used as GC detectors, with comparable sensitivity and retention times to the total ion current of a traditional MS, paving the way for more accessible and portable GC solutions.

This article presents fundamental work on online assessment of contaminants in recycled plastic processing. Post-consumer recycled plastic can contain various contaminants that prevent closed-loop recycling, which holds especially true for high quality packaging materials like polyolefins. A current German research cluster aims at increasing the recycling quota of these plastic types by gathering comprehensive data and using intelligent methods to optimize the overall recycling process. Due to the heterogenous quality of post-consumer recycling materials, fast online measurement methods are necessary to discriminate smallest possible portions during material processing. This study presents a method to access the recompounding of recycled plastic. A sampling system has been developed and tested to extract samples from the vacuum degassing of the compounding process. Laboratory analyses using GC-MS/O have demonstrated that this system can sample (odorous) volatile compounds from the extrusion process showing potential for the detection of relevant target components. This could give insights into the general composition of the gas phase and therefore enables further work towards an online measurement system and targeted monitoring of contaminants.

Volatile organic compounds (VOCs) are common constituents of many consumer products. Although many VOCs are generally considered harmless at low concentrations, some compound classes represent substances of concern in relation to human (inhalation) exposure and can elicit adverse health effects, especially when concentrations build up, such as in indoor settings. Determining VOC emissions from consumer products, such as toys, utensils or decorative articles, is of utmost importance to enable the assessment of inhalation exposure under real-world scenarios with respect to consumer safety. Due to the diverse sizes and shapes of such products, as well as their differing uses, a one-size-fits-all approach for measuring VOC emissions is not possible, thus, sampling procedures must be chosen carefully to best suit the sample under investigation. This review outlines the different sampling approaches for characterizing VOC emissions from consumer products, including headspace and emission test chamber methods. The advantages and disadvantages of each sampling technique are discussed in relation to their time and cost efficiency, as well as their suitability to realistically assess VOC inhalation exposures.

In this work, we present a concept for a raw-milk monitoring sensor system aiming at demonstrating a generalized approach for low-cost gas sensor system development in future. These systems are expected to be comparatively less expensive than conventional gas chromatography (GC) systems and can therefore likewise be used by farmers to monitor on-site storage as well as by dairy companies for the inspection of incoming milk and can thus play a significant role in counteracting the waste of milk and its products. This generalizable method is based on three steps: identification of potential milk degradation markers, quantification of these markers, and characterization of metal oxide semiconductor (MOS) sensors for these markers. In the first step, gas chromatography-mass spectrometry (GC-MS) and GC-flame ionization detector (GC-FID)/olfactometry (O) were used to tentatively identify 14 volatile substances in the headspace concentrations above the raw milk. From this, 3-methylbutan-1-ol, hexan-1-ol, pentan-1-ol, acetic acid, and additionally ethanol and ethyl acetate were selected by cross-referencing our results with literature data. In addition, hexanal, 2-methyl-1-propanol, limonene, nonanal, 2-ethylhexan-1-ol, butanoic acid, hexanoic acid, octanoic acid, methyl hexadecanoate, and decanoic acid were identified but not selected as potential markers due to their properties being incompatible with gas mixing apparatus (GMA). In the second step, a proton transfer reaction-MS (PTR-MS) analysis was used to determine the concentration in the headspace, which is in the parts per billion (ppb) range. Investigations of good milk samples and bad milk samples from alpine farms showed that ethanol, 3-methylbutan-1-ol, pentan-1-ol, and hexan-1-ol offered an increasing trend from good to bad milk samples. To enable more precise differentiation, further investigations with a higher sample size are necessary to reveal the feasibility of these markers within the complex matrix of raw milk. In the third step, these selected and literature-confirmed markers were presented to a commercially available sensor, run in a temperature-cycled operation and characterized by a self-developed system. When using ethanol, pentan-1-ol, and hexan-1-ol, a regression model with an accuracy of 42.9 ppb using partial least-squares regression (PLSR) analysis could be established, enabling such sensors to be used in raw-milk monitoring systems in the future.

Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for statistical models, but even for trained assessors. Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. Using 16 previously analyzed whisky samples (American or Scotch origin), we apply the linear classifier OWSum to distinguish the samples based on their detected molecules and to gain insights into the key molecular structure characteristics and odor descriptors for sample type. Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). The predictions outperform the inter-panelist agreement and thus demonstrate previously impossible data-driven sensory assessment in mixtures.

The correlation between a gas sensor pattern and its corresponding odor impression on human noses remains a scientific challenge for the development of technical odor detection systems. Small, inexpensive gas sensors, for example, those based on a metal oxide semiconductor (MOS), offer a versatile platform for the development of application-specific sensor systems for odor detection or monitoring. The training of MOS sensors for odor detection remains a challenging task that has been addressed by recent advances. We hereby present a comprehensive method and instrumentation for the characterization and validation of MOS sensors using a gas chromatograph with a mass spectrometer and odor detection port.

Tin oxide nanoparticles are well-established materials with a wide range of applications, including optoelectronic devices and solid-state gas sensors. Conventional synthesis methods of these systems are often based on batch processes. In this study, we compare batch and continuous synthesis methods for tin dioxide nanoparticles using precipitation and sol-gel processes. For the continuous processes we applied the so called microjet reactor method. The nanoparticles were characterized by TEM, elemental analysis and XRD and exhibited particle sizes of 1.7-3.0 nm and crystallite sizes of 1.7-2.3 nm, consisting of tetragonal (P42/mnm) and orthorhombic (Pbcn) phases. We have evaluated different post-synthesis purification methods to remove impurities such as chlorides and carbon-hydrogen species. Each purification method exhibited unique advantages and side effects, providing insight into selecting the most appropriate method for specific applications. We also demonstrated the potential of these SnO2 nanoparticles as ethanol gas sensing materials and compared their performance with a commercial sensor.

A quality monitoring method using gas chromatography - ion mobility spectrometry (GC-IMS) was developed. The results were used to implement a sensor-based quality control system for raw cow milk samples on dairy farms. The system is based on a commercial sensor system (Odor Checker Spot, OCS). GC-IMS was used to establish a profile of characteristic volatile organic compounds (VOCs) found within the headspace of raw milk samples, where the relative heights of 25 peaks corresponding to 13 VOCs were further transformed and reduced to the first principal component, using principal component analysis (PCA). 93% of all milk samples were classified correctly by PCA into either "good / fresh" or "bad / deteriorated" classes. Subsequently, the first principle component was transferred to a quality scoring system, which was used to train the OCS The OCS was able to evaluate raw milk samples and score their quality by grades, based on the GC-IMS reference measurements. Unknown samples could also been evaluated. These scores were in agreement with reference measurements at the GC-IMS.