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In order to explore the effect of different distillation methods on the key aroma compounds of whisky, the aroma composition of base whisky produced by two different distillation methods: double distillation in pot stills and continuous distillation in tower stills, and whisky aged with oak chips was detected by headspace-solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The characteristic aroma compounds were determined by odor activity values (OAVs), and the effect of distillation methods on the characteristic aroma of whisky was investigated by sensory evaluation. The results demonstrated that the distillation methods led to significant differences in the raw material-like, fermented and distilled aromas of base whisky. Pot-distilled whisky retained more aroma characteristics of the raw material. The difference in fermented aroma was mainly reflected by the ethyl ester content. More medium-chain and long-chain fatty acid ethyl esters were retained during pot distillation, while more short-chain fatty acid ethyl esters contributing to the fermented aroma were retained during tower distillation. The aroma differences brought about by the distillation methods affected the change of aroma compounds during subsequent aging. After aging, more fermented aroma compounds in pot-distilled whisky were lost, but the distilled aroma and the aged aroma from oak chips were more prominent. Although the aged aroma could attenuate the aroma difference between base whisky produced by different distillation methods, the effect of the distillation methods on the aroma was still evident. The choice of the appropriate distillation process is essential for the production of whisky.

We propose and numerically demonstrate a photonic time-delay reservoir computing (TDRC) system exhibiting enhanced parallel task processing performance, where an optically injected vertical-cavity surface-emitting laser (VCSEL) under random distributed optical feedback acts as the reservoir computer. To assess its effectiveness, we perform two benchmark tasks including chaotic time-series prediction and waveform recognition task, where the TDRC is associated with two different random feedback structures, i.e., orthogonally polarized optical feedback (OPOF) and parallelly polarized optical feedback (PPOF). Benefiting from the enhanced nonlinearity offered by the random distributed optical feedback, the proposed TDRC excels at parallel task processing with the PPOF structure, whereas the performance of the OPOF structure may be deteriorated. Additionally, we reveal the effect of the injection strength, feedback strength, pump current, and number of virtual nodes on the proposed TDRC. Our work paves the way for the performance enhancement of parallel task processing based on polarization multiplexing in a VCSEL-based TDRC.