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A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits
Research article (Sensors and Actuators B Chemical, 2019) · cited 20× · AI/ML
A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits
Summary
A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits is a scholarly article[1].
Key Facts
A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-drift-correction-method-of-e-nose-data-based-on-wavelet-packet-decomposition-and-no-load-data-case-study-on-the-robust
MLA“A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-drift-correction-method-of-e-nose-data-based-on-wavelet-packet-decomposition-and-no-load-data-case-study-on-the-robust.
BibTeX@misc{4ortxyz_a-drift-correction-method-of-e-nose-data-based-on-wavelet-packet-decomposition-and-no-load-data-case-study-on-the-robust_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits}}, year = {2026}, url = {https://4ort.xyz/entity/a-drift-correction-method-of-e-nose-data-based-on-wavelet-packet-decomposition-and-no-load-data-case-study-on-the-robust}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A drift correction method of E-nose data based on wavelet packet decomposition and no-load data: Case study on the robust identification of Chinese spirits — https://4ort.xyz/entity/a-drift-correction-method-of-e-nose-data-based-on-wavelet-packet-decomposition-and-no-load-data-case-study-on-the-robust (retrieved 2026-05-24)
