2021
DOI: 10.1590/fst.35319
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Physico-chemical and Nutraceutical Characterization of Selected Indigenous Guava (Psidium guajava L.) Cultivars

Abstract: In order to ascertain physicochemical and nutraceutical attributes, indigenous guava (Psidium guajava L.) cultivars were comprehensively characterized. Eight cultivars namely Gola, Chota Gola, Surahi, Choti Surahi, Sufaida, Sdabahar, Lal Badshah and Karela were selected due to their climatic adaptability and commercial suitability. All the cultivars showed significant variations in terms of their studied quality attributes. Amongst physical characteristics, Gola exhibited highest (79.9 mm 3) GMD with lowest (5… Show more

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Cited by 18 publications
(14 citation statements)
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“…The four factors explained 83.73% of the variation in the total SBs quality. The results of the current investigation were in line with the finding that investigated the principal component physicochemical and nutraceutical in guava products [42]. Nutraceutical pigmentation (the correlation of color changes and biological activity) highly contributed to the quality of the snack bars.…”
Section: Principal Component Analysis (Pca)supporting
confidence: 87%
“…The four factors explained 83.73% of the variation in the total SBs quality. The results of the current investigation were in line with the finding that investigated the principal component physicochemical and nutraceutical in guava products [42]. Nutraceutical pigmentation (the correlation of color changes and biological activity) highly contributed to the quality of the snack bars.…”
Section: Principal Component Analysis (Pca)supporting
confidence: 87%
“…Color is the most important sensory attribute perceived by the consumer and grower (Yousaf et al, 2021). In general, Fresh qamgur is white with globular roots, qamgur powders is yellowish-brown powders after low temperature drying, and FD qamgur slices appear in white flakes via ultra-low temperature and vacuum environment, as is shown in Figure 1.…”
Section: Resultsmentioning
confidence: 99%
“…If the degree of correlation between some indices and all other indices is low, it indicates that they provide a weak amount of information for the overall evaluation of quality, and when there are more factors in the evaluation index set, it will reduce the calculation efficiency of the model and increase the difficulty of the problem analysis, so it is necessary to analyze the relationship between the indices, so as to select the factors prominent in the expression of wheat quality. Related studies show that PCA has strong advantages in multivariate correlation analysis and validity verification, and without the need for a priori knowledge (Yousaf et al, 2021;Sun et al, 2020). Therefore, this paper uses PCA to analyze and select wheat physical and chemical indices of wheat, as shown in the following steps.…”
Section: Establishment Of a Pca-based Wheat Quality Evaluation Index Setmentioning
confidence: 99%