A robust HS-SPME and GC/MS method was developed for analyzing the composition of volatiles in raw and dry-roasted almonds. Almonds were analyzed directly as ground almonds extracted at room temperature. In total, 58 volatiles were identified in raw and roasted almonds. Straight chain aldehydes and alcohols demonstrated significant but minimal increases, while the levels of branch-chain aldehydes, alcohols, heterocyclic and sulfur containing compounds increased significantly (500-fold) in response to roasting (p<0.05). Benzaldehyde decreased from 2934.6±272.5 ng/g (raw almonds) to 315.8±70.0 ng/g (averaged across the roasting treatments evaluated i.e. 28, 33 and 38 min at 138 °C) after roasting. Pyrazines were detected in only the roasted almonds, with the exception of 2,5-dimethylpyrazine, which was also found in raw almonds. The concentration of most alcohols increased in the roasted samples with the exception of 2-methyl-1-propanol, 3-methyl-1-butanol and 2-phenylethyl alcohol, which decreased 68%, 80%, and 86%, respectively.

In this paper, we investigate software architecture as a set of overlapping design rule spaces, formed by one or more structural or evolutionary relationships and clustered using our design rule hierarchy algorithm. Considering evolutionary coupling as a special type of relationship, we investigated (1) whether design rule spaces can reveal structural relations among error-prone files; (2) whether design rule spaces can reveal structural problems contributing to error-proneness. We studied three large-scale open source projects and found that error-prone files can be captured by just a few design rule sub-spaces. Supported by our tool, Titan, we are able to flexibly visualize design rule spaces formed by different types of relationships, including evolutionary dependencies. This way, we are not only able to visualize which error-prone files belong to which design rule spaces, but also to visualize the structural problems that give insight into why these files are error prone. Design rule spaces provide valuable direction on which parts of the architecture are problematic, and on why, when, and how to refactor.

A sensitive and reliable LC-(ESI)MS/MS method was developed and validated for the simultaneous analysis of five common advanced glycation endproducts (AGEs) after enzymatic digestion in raw and roasted almonds. AGEs included carboxymethyl-lysine (CML), carboxyethyl-lysine (CEL), pyralline (Pyr), argpyrimidine (Arg-p), and pentosidine (Pento-s). This method allows accurate quantitation of free and AGE-protein adducts of target AGEs. Results indicate that CML and CEL are found in both raw and roasted almonds. Pyr was identified for the first time in roasted almonds and accounted for 64.4% of free plus bound measured AGEs. Arg-p and Pento-s were below the limit of detection in all almond samples tested. Free AGEs accounted for 1.3-26.8% of free plus bound measured AGEs, indicating that protein-bound forms predominate. The roasting process significantly increased CML, CEL, and Pyr formation, but no significant correlation was observed between these AGEs and roasting temperature.

In this paper, we propose and empirically validate a suite of hotspot patterns: recurring architecture problems that occur in most complex systems and incur high maintenance costs. In particular, we introduce two novel hotspot patterns, Unstable Interface and Implicit Cross-module Dependency. These patterns are defined based on Baldwin and Clark's design rule theory, and detected by the combination of history and architecture information. Through our tool-supported evaluations, we show that these patterns not only identify the most error-prone and changeprone files, they also pinpoint specific architecture problems that may be the root causes of bugginess and change proneness. Significantly, we show that 1) these structure-history integrated patterns contribute more to error-and change-proneness than other hotspot patterns, and 2) the more hotspot patterns a file is involved in, the more error-and change-prone it is. Finally, we report on an industrial case study to demonstrate the practicality of these hotspot patterns. The architect and developers confirmed that our hotspot detector discovered the majority of the architecture problems causing maintenance pain, and they have started to improve the system's maintainability by refactoring and fixing the identified architecture issues.

Hexanal, peroxide value, and lipid hydroperoxides are common indicators of lipid oxidation in food products. However, these markers are not always reliable as levels are dynamic and often can be detected only after significant oxidation has occurred. Changes in the volatile composition of light- and dark-roast almonds were evaluated during storage over 24 weeks at 25 or 35 °C using headspace solid phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS). Several volatile changes were identified in association with early oxidation events in roasted almonds. Hexenal decreased significantly during the first 6 weeks of storage and did not increase above initial levels until 20-24 weeks of storage depending upon the degree of roast. In contrast, levels of 1-heptanol and 1-octanol increased at 16-20 weeks, depending upon the degree of roast, and no initial losses were observed. Seventeen new compounds, absent in raw and freshly roasted almonds but detectable after 6 weeks of storage, were identified. Of these, 2-octanone, 2-nonanone, 3-octen-2-one, 2-decanone, (E)-2-decenal, 2,4-nonadienal, pentyl oxirane, and especially acetic acid increased significantly (that is, >10 ng/g). The degree of roasting did not correlate with the levels of these compounds. Significant decreases in roasting-related aroma volatiles such as 2-methylbutanal, 3-methylbutanal, furfural, 2-phenylacetaldehyde, 2,3-butanedione, 2-methylpyrazine, and 1-methylthio-2-propanol were observed by 4 weeks of storage independent of the degree of roast or storage conditions.

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Our recent research has shown that, in large-scale software systems, defective files seldom exist alone. They are usually architecturally connected, and their architectural structures exhibit significant design flaws which propagate bugginess among files. We call these flawed structures the architecture roots, a type of technical debt that incurs high maintenance penalties. Removing the architecture roots of bugginess requires refactoring, but the benefits of refactoring have historically been difficult for architects to quantify or justify. In this paper, we present a case study of identifying and quantifying such architecture debts in a large-scale industrial software project. Our approach is to model and analyze software architecture as a set of design rule spaces (DRSpaces). Using data extracted from the project's development artifacts, we were able to identify the files implicated in architecture flaws and suggest refactorings based on removing these flaws. Then we built economic models of the before and (predicted) after states, which gave the organization confidence that doing the refactorings made business sense, in terms of a handsome return on investment.