Solo mia

Manufactured by Eigenvector Research

Sourced in United States

The SOLO + MIA is a combined instrument that provides data acquisition and analysis capabilities. It is designed to capture and process data from various analytical techniques. The core function of the SOLO + MIA is to facilitate data collection and interpretation, but a detailed description of its intended use is not available.

Automatically generated - may contain errors

Lab products found in correlation

Spelling variants of this product

Solo+MIA software (4 citations)

5 protocols using solo mia

PCA Analysis of XRF Data

Check if the same lab product or an alternative is used in the 5 most similar protocols

The software SOLO + MIA (release 9.0) from Eigenvector Research Inc. (Manson, WA, USA) was used to perform Principal Component Analysis. This exploratory tool was used to extract information from the elemental semi-quantitative data provided by the in-built factory calibrations of the XRF instrument. Concentration data were preprocessed by autoscaling, that is, mean-centered and divided by standard deviation.

Arjonilla P., Domínguez-Vidal A., Rubio Domene R., Correa Gómez E., de la Torre-López M.J, & Ayora-Cañada M.J. (2022). Characterization of Wall Paintings of the Harem Court in the Alhambra Monumental Ensemble: Advantages and Limitations of In Situ Analysis. Molecules, 27(5), 1490.

+ Open protocol

+ Expand

Spectral Data Processing and Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

SOLO + MIA (version 9.2.1.0, Eigenvector Research, Inc., Manson, WA, USA) was used for data processing and analysis.

Wang H, & Jeffery D.W. (2024). Machine Learning Model Stability for Sub-Regional Classification of Barossa Valley Shiraz Wine Using A-TEEM Spectroscopy. Foods, 13(9), 1376.

+ Open protocol

+ Expand

Multivariate Analysis of Cannabinoid Profiles

Check if the same lab product or an alternative is used in the 5 most similar protocols

For multivariate analysis, missing values were replaced with the method detection limit (MDL) divided by 2 for each assigned cannabinoid. In the cannabinoid profiles, where the MDL has not been determined for unassigned peaks, the missing data was replaced with half of the MDL of THC. Pearson correlation coefficients to determine relationships between metabolites were calculated using the cor script in R. As the concentration of a given metabolite does not necessarily correlate with pharmacological activity, the data were autoscaled by mean centering and scaling to unit variance in order to give each metabolite equal weight prior to multivariate analyses. Principal component analysis (PCA) and multiple linear regression (MLR) analysis were subsequently modeled using Solo + MIA (Eigenvector Research).

Mudge E.M., Murch S.J, & Brown P.N. (2018). Chemometric Analysis of Cannabinoids: Chemotaxonomy and Domestication Syndrome. Scientific Reports, 8, 13090.

+ Open protocol

+ Expand

Raman Spectral PCA Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

PCA was performed with a commercial multivariate analysis package (Solo+MIA, Eigenvector Research, Inc). The intensity-scaled Raman spectra were mean-centered before performing PCA.

Jin Y., Kotula A.P., Hight Walker A.R., Migler K.B, & Lee Y.J. (2016). Phase-Specific Raman Analysis of n-Alkane Melting by Moving-Window Two-Dimensional Correlation Spectroscopy. Journal of Raman spectroscopy : JRS, 47(11), 1375-1384.

+ Open protocol

+ Expand

Multivariate Analysis of Phytochemicals

Check if the same lab product or an alternative is used in the 5 most similar protocols

The flavonol and phenolic acid contents were determined with external calibration using Microsoft Excel (Microsoft, Redmond WA, USA). The mean of each sample was converted to wet weight from the loss on drying data from the freeze drier. Data was imported into Solo+MIA (Eigenvector Research Inc., Manson, WA, USA). The data were preprocessed using autoscaling prior to multivariate statistical analysis. Principal component analysis (PCA) was applied to the datasets and score plots were generated for the entire data set to visualize proximity in all cases. Analysis of variance-PCA (ANOVA-PCA) was utilized to determine whether any of the factors identified had an impact on the dataset (Harrington et al., 2005 ). The variations in the contents between different locations were evaluated using Kruskal-Wallis one-way analysis of variance and Tukey-Kramer method for unequal sample sizes. These statistical tests were performed using Microsoft Excel 2011.

Mudge E., Applequist W.L., Finley J., Lister P., Townesmith A.K., Walker K.M, & Brown P.N. (2016). Variation of Select Flavonols and Chlorogenic Acid Content of Elderberry Collected Throughout the Eastern United States. Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems, 47, 52-59.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!