Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

Formaldehyde gas sensors based on a quartz crystal microbalance modified with aniline-doped polyvinyl acetate nanofibers

Real-time detection of formaldehyde in the atmosphere remains challenging. The available gaseous formaldehyde sensing methods offer limited sensitivity, selectivity, and robustness. We modified a quartz crystal microbalance (QCM) system for selective detection of formaldehyde in air. The QCM surface was functionalized with polyvinyl acetate (PVAc) nanofibers and doped with 2, 4, and 6 wt% aniline to improve the selectivity and sensitivity of the sensor. The chemical content and morphological structure of PVAc nanofibers doped with aniline were confirmed by Fourier-transform infrared (FTIR) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and scanning electron microscopy (SEM). The results showed that the modified QCM sensor had a sensitivity of 0.056 Hz ppm-1 with a response and recovery times of 200 s and 90 s, respectively. It gave limits of detection (LOD) and limit of quantification (LOQ) of 28 ppm and 96 ppm, respectively. Moreover, the modified QCM was selective towards formaldehyde compared to the other gases. The current workplace exposure limit (WEL) for formaldehyde is 2 ppm, with a time-weighted average over eight hours. Future work will focus on improving the reported QCM sensor to meet the required LOD for formaldehyde detection in the environment and industrial sites.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.