Correction: GIS-based multi-criteria analysis for Arabica coffee expansion in Rwanda.
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ABSTRACT:
SUBMITTER: Nzeyimana I
PROVIDER: S-EPMC4746025 | biostudies-other | 2016
REPOSITORIES: biostudies-other
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Project description:Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (-LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%-60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and -LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and -LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.
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Project description:Background: Increasingly, multi-criteria decision analysis has gained importance as a method by which to assess the value of orphan drugs. However, very little attention has been given to the weight (relative preferences) of the individual criteria used in a framework. Aims: This study sought to gain an understanding of the preferential weights that should be allocated in a multi-criteria decision analysis framework for orphan drugs, from a multi-stakeholder perspective. Method: Using key MCDA criteria for orphan drugs reported in the literature, we developed an interactive web-based survey tool to capture preferences for different criteria from a general stakeholder sample who were requested to assign weights from a reimbursement perspective. Each criterion could be assigned a weight on a sliding scale from 0 to 100% as long as the sum of all the criteria was 100%. We subsequently used the interactive tool with an expert focus group, followed up with a group discussion regarding each criterion and their perspectives on the weight that each criterion should be allocated when assessing an orphan drug. The expert focus group participants were then able to adjust their weights, if the group discussion had changed their perspectives. Results: The interactive tool was completed by 120 general stakeholder sample from a wide range of countries and professional backgrounds and an expert focus group of ten members. The results showed the differences in perspectives on the importance of criteria. Both groups considered Treatment efficacy to be the most important criterion. The general stakeholder sample weighted Treatment safety at 12.03% compared to the expert focus group's average of 20%. The results also demonstrated the value of the group discussion, which provided additional insights into the perspectives on the importance of criteria in assessing orphan drugs. Conclusion: This study aimed to contribute to the important aspect of preferences for different criteria in MCDA. This study sheds light on the important aspect of the preferences of the different criteria. All respondents agreed on the relative importance of Treatment efficacy and Treatment safety, criteria that are captured in conventional cost-effectiveness studies, but they also expressed the view that in addition to those, several disease-related and drug-related criteria should be included in MCDA frameworks for assessing orphan drugs.
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