Comparison of antioxidant capacity and phenolic composition of peel and flesh of some apple varieties

BACKGROUND: Apples contain a large concentration of phenolic compounds, dependent on factors such as cultivar, harvest, storage conditions, and processing. This study aims to identify the essential phenolic compounds present in various apple varieties, to measure their total antioxidant capacity (TAC) with the CUPRAC (cupric ion reducing antioxidant capacity) and ABTS (2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonate)) methods, and to correlate their TAC values with HPLC findings. RESULTS: The order of TAC (mmol Trolox g^?1 fresh weight) of apple peels determined with the CUPRAC method was: Granny Smith > Amasya > Sky Spur > Ervin Spur > King Luscious ≥ Arap Kizi ≥ Lutz Golden. The theoretically calculated TAC values of HPLC‐quantified compounds, with the aid of the combined HPLC‐CUPRAC method, accounted for 18.4–33.5% of the experimentally observed CUPRAC capacity of peel extracts and 19.5–56.3% of flesh extracts, depending on apple variety. CONCLUSION: In synthetic samples of apple antioxidants, the CUPRAC‐TAC values of constituents, identified and analyzed by HPLC, proved to be additive, enabling measurement of the cooperative action of antioxidants using the proposed methodology. Apple peel showed higher contents of phenolics and therefore higher TAC than apple flesh, confirming the health benefit of the consumption of apples together with peel. © 2012 Society of Chemical Industry     

Comparison of total antioxidant capacity and phenolic composition of some apple juices with combined HPLC–CUPRAC assay

It was aimed in this study to identify and quantify various constituents (particularly phenolics) of apple juice and to quantitatively compare the total antioxidant capacities of juices obtained from apple varieties grown in Turkey.     

The effects of processing technology on the patulin content of juice during commercial apple juice concentrate production

 The effects of different treatments on the patulin content of apple juice during the production of industrial apple juice concentrate were investigated. Conventional clarification using a rotary vacuum precoat filter was found to be more effective than using ultrafiltration for the removal of patulin from apple juice. The average losses of patulin were 39% and 25% for conventional clarification and filtration, and ultrafiltration, respectively. Washing and handling appeared to be the most critical steps in removing patulin from apples since up to 54% could be removed using high-pressure water spraying. Received: 22 January 1998 / Revised version: 21 April 1998     

Quantum speed-up for unsupervised learning

We show how the quantum paradigm can be used to speed up unsupervised learning algorithms. More precisely, we explain how it is possible to accelerate learning algorithms by quantizing some of their subroutines. Quantization refers to the process that partially or totally converts a classical algorithm to its quantum counterpart in order to improve performance. In particular, we give quantized versions of clustering via minimum spanning tree, divisive clustering and k-medians that are faster than their classical analogues. We also describe a distributed version of k-medians that allows the participants to save on the global communication cost of the protocol compared to the classical version. Finally, we design quantum algorithms for the construction of a neighbourhood graph, outlier detection as well as smart initialization of the cluster centres.     

Competitiveness analysis of automotive industry in Turkey using Bayesian networks

The purpose of this study is to analyze the relations between the factors that enable national competitive advantage and the establishment of competitive superiority in automotive industry through a comprehensive analytical model. Bayesian networks (BN) are used to investigate the associations of different factors in the automotive industry which lead to competitive advantage. The results of the study focus on building a road map for the automotive sector policy makers in their way to improve the competitiveness through scenario analysis. Using the probabilistic dependency structure of the Bayesian network all of the variables in the model can be estimated. Thus, with the proposed model the automotive industry can be analyzed as a whole system and not only in terms of single variables. Findings of the model indicate that technological developments in automotive industry can alter the nature of competition in this industry.     

Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set. Responsible Editor: Charu Aggarwal.     

Using an optimized queueing network model to support wafer fab design

We develop an Optimized Queueing Network (OQNet) capacity planning tool for supporting the design of new and reconfigured semiconductor fabrication facilities that makes use of queueing network approximations and an optimization routine. The basic problem addressed by this tool is to minimize the facility cost required to meet specified volume and cycle time targets. Features common to semiconductor environments, such as batch processes, re-entrant flows, multiple product classes, and machine setups, are incorporated into the model. Comparisons with simulation show that the queueing and other approximations are reasonably accurate. Tests of the optimization routine demonstrate that it can find good solutions quickly.     

An investigation into the formation of fumaric acid in apple juice concentrates

 In this study, apples inoculated with pure cultures of Rhizopus stolonifer, Penicillium expansum and Lactobacillus plantarum were utilised as the raw material to produce apple juice concentrate. The effects of microorganisms and various processing treatments on the fumaric acid, lactic acid and patulin contents of apple juice were investigated. R. stolonifer was found to be the most responsible causative agent of fumaric acid and lactic acid. L. plantarum also produced certain amounts of lactic acid, but not as much as R. stolonifer. L. plantarum caused a significant decrease in L-malic acid content indicating the occurrence of malolactic fermentation. During this complicated biochemical pathway, a small amount of fumaric acid was also formed by L. plantarum. P. expansum produced only patulin, but did not affect the fumaric acid and lactic acid contents. Fumaric acid contents of the samples decreased throughout the processing steps excepting evaporation. The fumaric acid contents of the samples increased just after evaporation depending on the heat applied. The patulin and lactic acid contents of the samples also showed a decreasing trend throughout processing. However, L. plantarum caused a significant increase in lactic acid content just after depectinization depending on whether the appropriate time-temperature conditions for incubation were available during depectinization. Received: 3 November 1998 / Revised version: 4 January 1999