Gas chromatography/olfactometry (GC/O) has been used in various fields as a valuable method to identify odor-active components from a complex mixture. Since human assessors are employed as detectors to obtain the olfactory perception of separated odorants, the GC/O technique is limited by its subjectivity, variability, and high cost of the trained panelists. Here, we present a proof-of-concept model by which odor information can be obtained by machine-learning-based prediction from molecular parameters (MPs) of odorant molecules. The odor prediction models were established using a database of flavors and fragrances including 1026 odorants and corresponding verbal odor descriptors (ODs). Physicochemical parameters of the odorant molecules were acquired by use of molecular calculation software (DRAGON). Ten representative ODs were selected to build the prediction models based on their high frequency of occurrence in the database. The features of the MPs were extracted via either unsupervised (principal component analysis) or supervised (Boruta, BR) approaches and then used as input to calibrate machine-learning models. Predictions were performed by various machine-learning approaches such as support vector machine (SVM), random forest, and extreme learning machine. All models were optimized via parameter tuning and their prediction accuracies were compared. A SVM model combined with feature extraction by BR-C (confirmed only) was found to afford the best results with an accuracy of 97.08%. Validation of the models was verified by using the GC/O data of an apple sample for comparison between the predicted and measured results. The prediction models can be used as an auxiliary tool in the existing GC/O by suggesting possible OD candidates to the panelists and thus helping to give more objective and correct judgment. In addition, a machine-based GC/O in which the panelist is no longer needed might be expected after further development of the proposed odor prediction technique.
Low temperature curing polyimide (PI) has become a significant target for the advanced electronic package recently. Even though curing accelerators which could significantly reduce the curing temperature have been proved,...
Detection of plant volatile organic compounds (VOCs) enables monitoring of pests and diseases in agriculture. We previously revealed that a localized surface plasmon resonance (LSPR) sensor coated with a molecularly imprinted sol-gel (MISG) can be used for cis-jasmone vapor detection. Although the selectivity of the LSPR sensor was enhanced by the MISG coating, its sensitivity was decreased. Here, gold nanoparticles (AuNPs) were doped in the MISG to enhance the sensitivity of the LSPR sensor through hot spot generation. The size and amount of AuNPs added to the MISG were investigated and optimized. The sensor coated with the MISG containing 20 μL of 30 nm AuNPs exhibited higher sensitivity than that of the sensors coated with other films. Furthermore, an optical multichannel sensor platform containing different channels that were bare and coated with four types of MISGs was developed to detect plant VOCs in single and binary mixtures. Linear discriminant analysis, k-nearest neighbor (KNN), and naïve Bayes classifier approaches were used to establish plant VOC identification models. The results indicated that the KNN model had good potential to identify plant VOCs quickly and efficiently (96.03%). This study demonstrated that an LSPR sensor array coated with a AuNP-embedded MISG combined with a pattern recognition approach can be used for plant VOC detection and identification. This research is expected to provide useful technologies for agricultural applications.
A catalyst-free and oxidant-free C-H arylation of xanthenes and thioxanthenes using electrochemistry has been developed, which affords a number of cross-coupling products in moderate to good yields. This method is...
Heterostructure construction of layered metal chalcogenides can boost their alkali‐metal storage performance, where the charge transfer kinetics can be promoted by the built‐in electric fields. However, these heterostructures usually undergo interface separation due to severe layer expansion, especially for large‐size potassium accommodation, resulting in the deconstruction of heterostructures and battery performance fading. Herein, first a stable interface design strategy where two metal chalcogenides with totally different layer‐morphologies are stacked to form large K+ transport channels, rendering ultralow interlayer expansion, is presented. As a proof of concept, the flat–zigzag MoS2/Bi2S3 heterostructures stacked with zigzag‐morphology Bi2S3 and flat‐morphology MoS2 present an ultralow expansion ratio (1.98%) versus MoS2 (9.66%) and Bi2S3 (9.61%), which deliver an ultrahigh potassium storage capacity of above 600 mAh g−1 and capacity retention of 76% after 500 cycles, together with the built‐in electric field of heterostructures. Once the heterostructures are used as an anode for potassium‐based dual‐ion batteries (K‐DIBs), it achieves a superior full‐cell capacity of ≈166 mAh g−1 with a capacity retention of 71% after 400 cycles, which is an outstanding performance among the reported K‐DIBs. This proposed interface stacking strategy may offer a new way toward stable heterostructure design for metal ions storage and transport applications.
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