Abstract:
The application of multivariate Chemometric techniques on spectroscopic data obtained from the analysis of food, plant and fuel samples enhanced the visibility of hidden information in the data sets. Principal Component Analysis (PCA) is a standard multivariate data analysis exploration tool was used to reduce the dimensionality of spectroscopic data to visualize the pattern of grouping based on similarities and dis-similarities in the data set. The procedure of PCA is based on converting a set of correlated variables into a new set of uncorrelated variables called principal components. PCA redistributes the total variance of the data set in such a way that the first principal component has maximum variance, followed by second component and so on. PLS-DA is another technique was used to optimize separation between different groups of samples, which is accomplished by linking two data matrices X (i.e., raw data) and Y (i.e., groups, class membership etc.). The method in fact is extension of PLS. This approach aims to maximize the covariance between the independent variables X (sample readings; that is spectra) and the corresponding dependent variable Y (classes, groups; that is to say the targets that one wants to predict) of highly multidimensional data by finding a linear subspace of the explanatory variables. This new subspace permits the prediction of the Y variable based on a reduced number of factors (PLS components, or what are also known as latent variables). These factors describe the behaviors of dependent variables Y and they span the subspace onto which the independent variables X are projected. Cluster analysis is an unsupervised exploratory data analysis tool and was used to classify the samples into groups based on their similarities of specified characteristics (variables). It grew out work by biologists working on numerical taxonomy, and is a valuable visualization tool in data mining. Partial least squares (PLS) regression is a technique that reduces the predictors to a smaller set of uncorrelated components or factors and performs least squares regression on these components, instead of on the original data. PLS regression is especially useful when your predictors are highly collinear, or when you have more predictors than observations and ordinary least-squares regression either produces coefficients with high standard errors or fails completely. PLS does not assume that the predictors are fixed, unlike multiple regression. This means that the predictors can be measured with error, making PLS more robust to measurement uncertainty. PLS regression was primarily used to model the relationship between spectral measurements (NIR, IR, UV), which include many variables that are often correlated with each other, and chemical composition or other physio-chemical properties. In PLS regression, the emphasis was on developing predictive models. Therefore, it is not usually used to screen out variables that
are not useful in explaining the response.
Page(s):
11-12
DOI:
DOI not available
Published:
Journal: 4th International Conference of Sciences “Revamped Scientific Outlook of 21st Century, 2025” , November 12,2025, Volume: 1, Issue: 1, Year: 2025
Keywords:
Physicochemical properties
,
predictive models
,
Spectroscopic Analysis
,
Chemometric Technique