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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 report on a simple approach of time-delayed reservoir computing (RC) based on a two-element phased laser array for image identification. Here the phased laser array with optical feedback and injection is trained according to the representative characteristics extracted through histograms of oriented gradients. These characteristic vectors are multiplied by a random mask signal to form input data, which are subsequently trained in the reservoir. By optimizing the parameters of the RC, we achieve an identification accuracy of 97.44% on the MNIST dataset and 85.46% on the Fashion-MNIST dataset. These results indicate that our proposed RC indeed allows accurate classification of handwritten digit and fashion production. Moreover, we further forecast an RC scheme based on a larger-scale phased laser array, which is expected to tackle more complex tasks at a high speed. Our work offers a possibility for advanced image processing using highly integrated neuromorphic photonic systems. [ABSTRACT FROM AUTHOR]