In this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.
A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that geneticalgorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent (R 2 cal ¼ 0.97), (R 2 val ¼ 0.97), low (root mean square error of cross-validation ¼ 0.26), low (root mean square error of Prediction ¼ 0.23), and (ratio of performance to deviation ¼ 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R 2 cal (0.97), R 2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation ¼ 8.27); while for free fatty acids, the 11factor partial least square free fatty acids produced very high R 2 cal (0.97) and R 2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation ¼ 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R 2 cal (0.96) and R 2 val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation ¼ 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.
Aims: The study aims to identifying the microorganisms associated with post-harvest rot of frafra potatoes in Bongo-soe, Upper east region of Ghana.
Place and Duration of Study: Department of Horticulture and the Pathology laboratory of the Faculty of Agriculture, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. The Solenostemon rotundifolius tubers were stored at the Horticulture Department laboratory whiles rot identification was carried out at the Pathology laboratory of the Faculty of Agriculture. The Tubers were stored from 2nd November, 2012 to 22nd March 2013.
Methodology: Four hundred (400) tubers of black cultivar and four hundred (400) tubers of a brown cultivar of Solenostemon rotundifolius tubers showing visible signs of rot during the storage were collected. Pieces of diseased tissues from the margin of the necrotic collected and immersed in 10% commercial bleach solution for sterilisation, for one minute. These were then blotted dry and plated on Potato Dextrose Agar PDA. The plates were sealed with a cellotape until growth occurred.
Results: The microorganisms identified to be responsible for causing rot in Solenostemon rotundifolius tubers were six in number. Colletotrichum gloeosporioides was identified to be responsible for 30.76% of rots observed, followed by Aspergillus niger, 23.07%, Curvularia lunata, 19.23%, Aspergillus flavus, 11.54%, Trichoderma sp and Penicillium sp both recorded 7.70% of rots observed. The percentage incidence of Aspergillus niger (15.38%), Curvularia lunata (11.54%) and Aspergillus flavus (7.69%) was higher in the black cultivar as compared with the brown cultivar which had percentage incidence of 7.69%, 7.69% and 3.85% respectively. Also, the percentage incidence of Colletotrichum gloeosporioides (15.38%) and Penicillium sp (3.85%) was the same in both the black and brown cultivars of Solenostemon rotundifolius tubers used in this study.
Conclusion: The activities of the damaging microorganisms can be reduced by controlling mechanical injury during harvesting, transportation and storage of Solenostemon rotundifolius tubers should be prevented or reduced because they pave the way for tuber infection by the rot causing microorganisms.
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