Evaluation of Chemical, Biochemical and Microbiological Quality in Tomato Using Multivariate Analysis

Abstract

Tomato quality is determined by metabolite content which is governed by post-harvest physiological changes. A 30-day full factorial experiment investigated pre-packaging, disinfection and storage temperatures on 18 different biochemical, microbiological, enzymatic and subjective quality attributes of tomato quality. Principal component analysis revealed associations among the variables such as PC1 (28.85%): coliform/enzymatic softening; PC2 (21.52%): free sugars/sweetness; and PC3 (18.20%): sucrose hydrolysis/microbial spoilage/defense metabolites. Discriminant analysis showed that some specific parameters were highly significant (P < 0.001) in determining quality changes in relation to the washing procedures and storage temperature. The prominence of ascorbic acid was observed in the equations which discriminate mostly on the basis of microbial deterioration. Further works in this respect entail fine-tuning through model verification of the equations. Multivariate analysis techniques are, therefore, recommended in studies whereby understanding of the phenomenon driving the post-harvest system's dynamics has to be understood through diverse interrelated metabolic parameters.

Modelling effects of pre-storage treatments, maturity stage, low-cost storage technology environment and storage period on the quality of tomato fruit

“…Alimi, Shittu and Sanni (2014) reported that the final quality is dependent on the interaction effects of the different treatments. Building multiple factors, statistical analysis to study the effects of continuous and categorical variables and covariates is vital for the determination of tomato quality (Binder, Sauerbrei, & Royston, 2013;Sobratee & Workneh, 2015). A multivariable regression model development is dependent on the selection of covariates that related to the response of interests and searching for an appropriate efficient model for continuous covariates (Binder et al, 2013).…”

“…This approach can be used to reduce the number of continuous variables to put in the representative group by identifying patterns in a data set and help to eliminate the repetition in a univariate analysis (Iezzoni & Pritts, 1991;Martens & Martens, 2001). The PC is the linear combination of variables that has maximum variance subject to normalization constraints on the coefficients (eigenvectors) (Harrell, 2015) and the total variance created by each PC evaluated as eigenvalue (Iezzoni & Pritts, 1991;Martens & Martens, 2001;Sobratee & Workneh, 2015).…”