2020
DOI: 10.1039/d0ra06318e
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The statistical fusion identification of dairy products based on extracted Raman spectroscopy

Abstract: At present, practical and rapid identification techniques for dairy products are still scarce.

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Cited by 12 publications
(5 citation statements)
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“…When it comes to the application of AI and ML potential, data fusion has reported significant improvement not only in the dairy industry [40], [41], [42] but also in several industrial sectors (e.g., automotive [43], construction [44], and food industry [45]) in which data comes from many different sources and instruments that when combined can provide compositional valuable insights [46]. However, incorporated noise in the data must be monitored so as not to affect subsequent analysis.…”
Section: Discussion Of Recent ML Solutions In the Dairy Industrymentioning
confidence: 99%
“…When it comes to the application of AI and ML potential, data fusion has reported significant improvement not only in the dairy industry [40], [41], [42] but also in several industrial sectors (e.g., automotive [43], construction [44], and food industry [45]) in which data comes from many different sources and instruments that when combined can provide compositional valuable insights [46]. However, incorporated noise in the data must be monitored so as not to affect subsequent analysis.…”
Section: Discussion Of Recent ML Solutions In the Dairy Industrymentioning
confidence: 99%
“…Choose a positive component of ( ) and calculate the threshold (3). Construct a decision function to calculate the brand discrimination results of experimental samples (4) ( Xiaofeng et al, 2023 ; Zheng-Yong, 2020 ).…”
Section: Methodsmentioning
confidence: 99%
“…High level data fusion often outperforms mid and low level data fusion as it removes unwanted data while including all relevant data. Mid level and high level data fusion gave better classification performance than those produced on individual datasets ( 13 ).…”
Section: Data Fusionmentioning
confidence: 99%