AChemS: the beginning. Association for Chemoreception Sciences

This paper unfolds the events, the people and the times that led up to the founding of AChemS and fashioned its character during its early formative years. It describes the path over which AChemS came, going from the original assertions and denials for the need of such an organization to its later inception and nascent development. This narration highlights such topics as the debate over the need for AChemS, the role of National Science Foundation in the founding of AChemS, the derivation of the Association's name, the choice of Sarasota and the Hyatt House as the meeting site, the generation of the programs for the early annual meetings, the adoption of the bylaws, the process of incorporation and tax deferment, and the birth of the Givaudan Lectureship. Most emphatically highlighted, however, is the enthusiasm, commitment and hard work that the members of the chemosensory research community displayed in bringing AChemS to fruition.      

Modeling changes in biomass composition during microwave‐based alkali pretreatment of switchgrass

This study used two different approaches to model changes in biomass composition during microwave‐based pretreatment of switchgrass: kinetic modeling using a time‐dependent rate coefficient, and a Mamdani‐type fuzzy inference system. In both modeling approaches, the dielectric loss tangent of the alkali reagent and pretreatment time were used as predictors for changes in amounts of lignin, cellulose, and xylan during the pretreatment. Training and testing data sets for development and validation of the models were obtained from pretreatment experiments conducted using 1–3% w/v NaOH (sodium hydroxide) and pretreatment times ranging from 5 to 20 min. The kinetic modeling approach for lignin and xylan gave comparable results for training and testing data sets, and the differences between the predictions and experimental values were within 2%. The kinetic modeling approach for cellulose was not as effective, and the differences were within 5–7%. The time‐dependent rate coefficients of the kinetic models estimated from experimental data were consistent with the heterogeneity of individual biomass components. The Mamdani‐type fuzzy inference was shown to be an effective approach to model the pretreatment process and yielded predictions with less than 2% deviation from the experimental values for lignin and with less than 3% deviation from the experimental values for cellulose and xylan. The entropies of the fuzzy outputs from the Mamdani‐type fuzzy inference system were calculated to quantify the uncertainty associated with the predictions. Results indicate that there is no significant difference between the entropies associated with the predictions for lignin, cellulose, and xylan. It is anticipated that these models could be used in process simulations of bioethanol production from lignocellulosic materials. Biotechnol. Bioeng. 2010;105: 88–97. © 2009 Wiley Periodicals, Inc.