Browsing by Author "Myburgh, Lindie"
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- ItemPrediction of post-storage quality in canning apricots and peaches using near infrared spectroscopy (NIRS) and chemometrics(Stellenbosch : Stellenbosch University, 2003-12) Myburgh, Lindie; Manley, Marena; Joubert, Elizabeth; Stellenbosch University. Faculty of AgriScience. Dept. of Food Science.ENGLISH ABSTRACT: Post-storage quality of the stone fruit, apricots and peaches, is the major factor determining their suitability for canning after cold storage in South Africa. Short harvesting periods and the limited capacity of the factory to process the large quantities of fruit within two days after delivery, necessitates cold storage until canning. Apricots develop internal breakdown, whereas peaches develop internal breakdown accompanied by loosening of the skin and adhesion of the flesh to the stone. The deterioration takes place within the fruit during a cold storage period of one to two weeks. The tendency of the fruit to develop internal defects can, to date, not be identified prior to storage and are only discovered after destoning during canning. Near infrared spectroscopy (NIRS) combined with chemometrics were investigated as a non-destructive method to predict post-storage quality in Bulida apricots and clingstone peach cultivars. Near infrared (NIR) spectra (645-1201 nm), measured on the intact fruit just after harvesting, were correlated with subjective quality evaluations performed on the cut and destoned fruit after cold storage. The cold storage periods for apricots were four weeks (2002 season) and three and two weeks for peach cultivars for the 2002 and 2003 seasons, respectively. Soft independent modelling by class analogy (SIMCA) and multivariate adaptive regression splines (MARS) were applied to the spectral and reference data to develop models for good and poor post-storage quality. The ability of these models to predict post-storage quality was evaluated in terms of recognition (sensitivity) and rejection (specificity) of the samples in independent validation sets. Total correct classification rates of 50.00% and 69.00% were obtained with Bulida apricots, using SIMCA and MARS, respectively. Classification results with apricots showed that MARS performed better than SIMCA and is thus recommended for this application. Total correct classification rates of 53.00% to 60.00% (SIMCA) and 57.65% to 65.12% (MARS) were obtained for data sets of combined peach cultivars within seasons and over both seasons. Additional aspects of fruit quality were investigated to identify possible indices of post-storage quality. Classification trees were used to find correlations between the post-storage quality and the fruit mass, diameter, firmness and soluble solids content (SSC). Among these, fruit diameter and firmness were the major indices of post-storage quality. Accurate predictions of firmness could not be achieved by near infrared spectroscopy (NlRS), making the combination of NIRS and classification trees not yet suitable for predicting post-storage quality. NIRS was further used to predict poststorage SSC within seasons in Bulida apricots and intact peach cultivars. This confirmed sufficient NIR light penetration into the intact fruit and also provided a further application of NIRS for ripeness evaluation in the canning industry. Validations on peach samples obtained correlation coefficients (r) of 0.77-0.85 and SEP-values of 1.35-1.60 °Brix using partial least squares (PLS) regression. MARS obtained r = 0.77-0.82 and SEP = 1.42-1.55 °Brix. Predictions of sse in apricots were less accurate, with r = 0.39-0.88, SEP = 1.24-2.21 °Brix (PLS) and r = 0.51-0.82, SEP = 1.54-2.19 °Brix (MARS). It is suggested that the accuracy of sse measurements, and the subsequent predictions, were affected by the cold storage periods as well as internal variation within the fruit. This study showed that a combination of NIRS and chemometrics can be used to predict post-storage quality in intact peaches and apricots. A small scale feasibility study showed that 4% (R117 720) (apricot industry) and 3% (R610 740) (peach industry) of production losses can be saved if this method is implemented in the South African canning industry. Although it was difficult to assign specific chemical components or quality attributes to the formulation of the storage potential models, important hidden information in the spectra could be revealed by chemometric classification methods. NIRS promises to be a useful and unique quality evaluation tool for the South African fruit canning industry. Several recommendations are made for the canning practices to reduce losses and for future research to improve the current prediction models.