Oct 10, 2019 21:42
4 yrs ago
Spanish term
análisis del espectro infrarrojo por componentes principales
Spanish to English
Science
Chemistry; Chem Sci/Eng
El análisis del espectro infrarrojo por componentes principales mostró que al realizar la normalización por área de la información se obtuvo la discriminación de las muestras con una explicación del 84% de la varianza; los defectos de café con las muestras de café comercial molido se separaron del café de alta calidad y el café instantáneo. Mediante los descriptores químicos obtenidos del espectro infrarrojo se logró diferenciar las muestras de café de alta calidad, comercial e instantáneo
Proposed translations
(English)
4 +3 | Principal component analysis of infrared spectra | John Druce |
3 -1 | main components of infrared spectral analysis | Juan Arturo Blackmore Zerón |
Proposed translations
+3
1 hr
Selected
Principal component analysis of infrared spectra
As an abstract, functional description; Principal component analysis (PCA) is a statistical technique which aims to minimise the number of variables to describe the data set by taking advantage of correlations in the data. Eg if you have intensities for A, B and C, but A+B are correlated, you will get (A+B) as one of your principal components (PCs). This means you can now describe the data with two variables (PCs); (A+B) and C.
The idea is then to use these to separate different classes of sample (e.g. the different types of coffee). Say, group 1 is high in (A+B) and low in C, group 2 has high (A+B) and high C, and group 3 has low (A+B) and high C
By finding a minimum number of variables to describe as much of the data as possible, it becomes easier to spot the trends.
I’m not sure how easily you will understand the Wikipedia page, it is a little heavy on the maths, but I’m not convinced it (or I) explain the conceptul background well enough.
https://en.m.wikipedia.org/wiki/Principal_component_analysis
The idea is then to use these to separate different classes of sample (e.g. the different types of coffee). Say, group 1 is high in (A+B) and low in C, group 2 has high (A+B) and high C, and group 3 has low (A+B) and high C
By finding a minimum number of variables to describe as much of the data as possible, it becomes easier to spot the trends.
I’m not sure how easily you will understand the Wikipedia page, it is a little heavy on the maths, but I’m not convinced it (or I) explain the conceptul background well enough.
https://en.m.wikipedia.org/wiki/Principal_component_analysis
4 KudoZ points awarded for this answer.
Comment: "thank you!"
-1
24 mins
main components of infrared spectral analysis
Peer comment(s):
disagree |
Neil Ashby
: It's "infrared spectroscopy", not "infrared spectral analysis". Secondly, the word order is wrong, giving the wrong meaning.
11 hrs
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