Colorimetric Sensing of Volatile Organic Compounds Produced from Heated Cooking Oils

Fingerprinting the emissions of volatile organic compounds emitted from the cooking of oils, herbs, and spices

Emission rates for volatile organic compounds (VOCs) have been quantified from frying, spice and herb cooking, and cooking a chicken curry, using real-time selected-ion flow-tube mass spectrometry (SIFT-MS) for controlled, laboratory-based experiments in a semi-realistic kitchen. Emissions from 7 different cooking oils were investigated during the frying of wheat flatbread (puri). These emissions were dominated by ethanol, octane, nonane and a variety of aldehydes, including acetaldehyde, heptenal and hexanal, and the average concentration of acetaldehyde (0.059-0.296 mg m-3) and hexanal (0.059-0.307 mg m-3) measured during the frying was 2-10 times higher than the recommended limits for indoor environments. Total VOC emission rates were greatest for ghee (14 mg min-1), and lowest for groundnut oil (8 mg min-1). In a second series of experiments, 16 herbs and spices were individually shallow-fried in rapeseed oil. Over 100 VOCs were identified by offline gas chromatography-mass spectrometry (GC-MS), and absolute emission rates as well as oxidant reactivity for a subset of four spices were determined. These experiments allowed distinct indoor air quality profiles to be calculated for individual oils, herbs and spices, which were used to inform and interpret more realistic cooking experiments where a full recipe of chicken curry was prepared. Total-mass VOC emissions from chicken curry were dominated by methanol (62%), monoterpenes (13%) and ethanol (10%). Additionally, a clear relationship between the cooking events and the chemical classes of VOC was observed, e.g. heating the oil (aldehydes), frying spices (monoterpenes) and adding vegetables (alcohols).

Metal-Organic Frameworks (MOFs)-Based Piezoelectric-Colorimetric Hybrid Sensor for Monitoring Green Leaf Volatiles

Green leaf volatiles (GLVs) are organic compounds emitted by plants in response to insect attacks, offering early detection potential. Current GLV detection methods like gas chromatography-mass spectroscopy (GC-MS) are costly and complex and lack real-time monitoring capability. There is an unmet need for affordable and portable sensors with high sensitivity to monitor GLVs in real time. In this study, we developed a novel sensor capable of capturing piezoelectric and colorimetric signals for the sensitive and selective detection of 1-hexanol, a well-known green leaf volatile. We used a piezoelectric micro quartz tuning fork (MQTF) as the multifunctional transducer. The MQTF's two prongs were coated with a metal-organic framework (MOF)-thymol blue hybrid sensing material, enabling detection through both color change and resonating frequency shift upon 1-hexanol binding. MOFs offer a high surface area and tunable pore size, which enhance sensor sensitivity and selectivity. The sensor's frequency shift indicates mass change due to 1-hexanol binding to MOFs, while the colorimetric sensing signal relies on thymol blue's reaction with 1-hexanol. Our test results demonstrate the sensor's ability to detect 1-hexanol from 62.5 ppb to 250 ppm with high sensitivity and selectivity when the colorimetric and piezoelectric sensing signals are integrated. Due to its compact size, affordability, easy fabrication, wide detection range, and high sensitivity and selectivity, this colorimetric-piezoelectric sensor could serve as an effective tool for early detection of insect herbivore attacks and timely crop protection strategies.

Classification of Sesame Oil Based on Processing-Originated Differences in the Volatile Organic Compound Profile by a Colorimetric Sensor

Fragrant edible sesame oil is popular for its unique aroma. The aroma of sesame oil is determined by its volatile organic compound (VOC) profile. Sesame oils produced by different techniques could have different VOC profiles. In addition, blending fragrant sesame oil with refined oil could also alter the VOC profile of the final product. Current practices in aroma analysis, such as sensory evaluation and gas chromatography (GC), still face many restraints. Hence, there is a need for alternatives. We present a novel 14-unit multiplexed paper-based colorimetric sensor for fragrant sesame oil VOC analysis. The sensor was designed to visualize the VOC profile as a color "fingerprint". The sensor was validated with 55 branded sesame oil samples produced by two different techniques, i.e., hot pressing and small milling; the experimental results suggested a processing dependency in color VOC fingerprints. The sensor also demonstrated the potential to detect the change in sesame oil VOC profile due to blending with refined oil, with an estimated limit of detection down to 20% v/v of the refined oil. The colorimetric sensor might be used as a simple, rapid, and cost-effective analytical tool in the production and quality control of fragrant sesame oil.

ZIF-8-Based Surface Plasmon Resonance and Fabry-Pérot Sensors for Volatile Organic Compounds

This work explores the use of ZIF-8, a metal-organic framework (MOF) material, for its use in the optical detection of volatile organic compounds (VOCs) in Fabry-Pérot and surface plasmon resonance (SPR)-based sensors. The experiments have been carried out with ethanol (EtOH) and show response times as low as 30 s under VOC-saturated atmospheres, and the estimated limit of detection is below 4000 ppm for both sensor types. The selectivity towards other VOCs is relatively poor, although the dynamics of adsorption/desorption differ for each VOC and could be used for selectivity purposes. Furthermore, the hydrophobicity of ZIF-8 has been confirmed and the fabricated sensors are insensitive to this compound, which is a very attractive result for its practical use in gas sensing devices.

Highly Sensitive Sub-ppm CH3COOH Detection by Improved Assembly of Sn3O4-RGO Nanocomposite

Detection of sub-ppm acetic acid (CH₃COOH) is in demand for environmental gas monitoring. In this article, we propose a CH₃COOH gas sensor based on Sn₃O₄ and reduced graphene oxide (RGO), where the assembly of Sn₃O₄-RGO nanocomposites is dependent on the synthesis method. Three nanocomposites prepared by three different synthesis methods are investigated. The optimum assembly is by hydrothermal reactions of Sn⁴⁺ salts and pre-reduced RGO (designated as RS nanocomposite). Raman spectra verified the fingerprint of RGO in the synthesized RS nanocomposite. The Sn₃O₄ planes of (111), (210), (130), (13¯2) are observed from the X-ray diffractogram, and its average crystallite size is 3.94 nm. X-ray photoelectron spectroscopy on Sn3d and O1s spectra confirm the stoichiometry of Sn₃O₄ with Sn:O ratio = 0.76. Sn₃O₄-RGO-RS exhibits the highest response of 74% and 4% at 2 and 0.3 ppm, respectively. The sensitivity within sub-ppm CH₃COOH is 64%/ppm. Its superior sensing performance is owing to the embedded and uniformly wrapped Sn₃O₄ nanoparticles on RGO sheets. This allows a massive relative change in electron concentration at the Sn₃O₄-RGO heterojunction during the on/off exposure of CH₃COOH. Additionally, the operation is performed at room temperature, possesses good repeatability, and consumes only ~4 µW, and is a step closer to the development of a commercial CH₃COOH sensor.

Visualising household air pollution: Colorimetric sensor arrays for monitoring volatile organic compounds indoors

Indoor air quality monitoring as it relates to the domestic setting is an integral part of human exposure monitoring and health risk assessment. Hence there is a great need for easy to use, fast and economical indoor air quality sensors to monitor the volatile organic compound composition of the air which is known to be significantly perturbed by the various source emissions from activities in the home. To meet this need, paper-based colorimetric sensor arrays were deployed as volatile organic compound detectors in a field study aiming to understand which activities elicit responses from these sensor arrays in household settings. The sensor array itself is composed of pH indicators and aniline dyes that enable molecular recognition of carboxylic acids, amines and carbonyl-containing compounds. The sensor arrays were initially deployed in different rooms in a single household having different occupant activity types and levels. Sensor responses were shown to differ for different room settings on the basis of occupancy levels and the nature of the room emission sources. Sensor responses relating to specific activities such as cooking, cleaning, office work, etc were noted in the temporal response. Subsequently, the colorimetric sensor arrays were deployed in a broader study across 9 different households and, using multivariate analysis, the sensor responses were shown to correlate strongly with household occupant activity and year of house build. Overall, this study demonstrates the significant potential for this type of simple approach to indoor air pollution monitoring in residential environments.