Hairpin DNA (hpDNA) loops were used for the first time as molecular binding elements in gas analysis. The hpDNA loops sequences of unpaired bases were studied in-silico to evaluate the binding versus four chemical classes (alcohols, aldehydes, esters and ketones) of volatile organic compounds (VOCs). The virtual binding score trend was correlated to the oligonucleotide size and increased of about 25% from tetramer to hexamer. Two tetramer and pentamer and three hexamer loops were selected to test the recognition ability of the DNA motif. The selection was carried out trying to maximize differences among chemical classes in order to evaluate the ability of the sensors to work as an array. All oligonucleotides showed similar trends with best binding scores for alcohols followed by esters, aldehydes and ketones. The seven ssDNA loops (CCAG, TTCT, CCCGA, TAAGT, ATAATC, CATGTC and CTGCAA) were then extended with the same double helix stem of four base pair DNA (GAAG to 5' end and CTTC to 3' end) and covalently bound to gold nanoparticles (AuNPs) using a thiol spacer attached to 5' end of the hpDNA. HpDNA-AuNPs were deposited onto 20 MHz quartz crystal microbalances (QCMs) to form the gas piezoelectric sensors. An estimation of relative binding affinities was obtained using different amounts of eight VOCs (ethanol, 3-methylbutan-1-ol, 1-pentanol, octanal, nonanal, ethyl acetate, ethyl octanoate, and butane-2,3-dione) representative of the four chemical classes. In agreement with the predicted simulation, hexamer DNA loops improved by two orders of magnitude the binding affinity highlighting the key role of the hpDNA loop size. Using the sensors as an array a clear discrimination of VOCs on the basis of molecular weight and functional groups was achieved, analyzing the experimental with principal components analysis (PCA) demonstrating that HpDNA is a promising molecular binding element for analysis of VOCs.

In this work a peptide based gas sensor array based of ZnO nanoparticles (ZnONPs) has been realized. Four different pentapeptides molecularly modeled for alcohols and esters having cysteine as a common spacer have been immobilized onto ZnONPs. ZnONPs have been morphologically and spectroscopically characterized. Modified nanoparticles have been then deposited onto quartz crystal microbalances (QCMs) and used as gas sensors with nitrogen as carrier gas. Analysis of the pure compounds modeled demonstrated a nice fitting of modeling with real data. The peptide based ZnONPs had very low sensitivity to water, compared to previously studied AuNPs peptide based gas sensors allowing the use of the array on samples with high water content. Real samples of fruit juices have been assayed; stability of the signal, good repeatability, and discrimination ability of the array was achieved.

Nowadays, the analysis of volatile organic compounds (VOCs) is very important in various domains. For this, in the last decades, electronic noses have emerged as promising alternatives to traditional analytical methods. Nevertheless, their wide use is still limited by their performances such as low selectivity. Herein, we developed an optoelectronic nose using virtually screened peptides and hairpin DNA (hpDNA) with improved selectivity as sensing materials and surface plasmon resonance imaging (SPRi) as the detection system. Thanks to the complementarity of their binding properties towards target VOCs, the obtained optoelectronic nose has very good selectivity, being able to discriminate not only between VOCs of different chemical families, but also VOCs of the same family with only 1-carbon difference. We thus confirmed that computational virtual screening, which allows 'in silico' testing of VOC-peptide binding in a fast and low-cost way, is very promising for the selection of sensing elements with higher sensitivity and selectivity as well as great diversity. The combination of these sensing materials with SPRi is relevant for the development of optoelectronic nose with large sensor arrays and improved performances.

The performances of a quartz crystal microbalances (QCMs) based on an electronic nose (E-nose), modified with hairpin-DNA (hpDNA) for carrot aroma profiling has been evaluated. Solid phase micro-extraction (SPME) headspace sampling, combined with gas chromatography (GC), was used as a reference method. The changes in carrot aroma profiles stored at different temperatures (−18 °C, 4 °C, 25 °C, and 40 °C) were monitored during time up to 26 days. The principal component analysis of the data evidenced the different aroma patterns related to the presence of different key compounds. The output data achieved with the hpDNA-based E-nose were able to detect aroma patterns similar to gas chromatography with mass spectrometry (GC-MS). This work demonstrates that hpDNA has different sizes of loops that can be used for the development of sensor arrays able to detect aroma patterns in food and their changes with advantages in terms of easiness of usage, rapidity, and cost of analysis versus classical methods.

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in ‘point of care’ diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases.