@article { ISI:000269374500012,
	title = {Changes in volatile composition during fruit development and ripening of {\textquoteleft}Alphonso{\textquoteright} mango},
	journal = {Journal of the Science of Food and Agriculture},
	volume = {89},
	number = {12},
	year = {2009},
	month = {SEP},
	pages = {2071-2081},
	publisher = {JOHN WILEY \& SONS LTD},
	address = {THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND},
	abstract = {<p>BACKGROUND: Volatile blends of five developing and five ripening stages of mango (Mangifera indica L. cv. Alphonso) were investigated along with those of flowers and leaves. Raw and ripe fruits of cv. Sabia were also used for comparison. RESULTS: A total of 55 volatiles belonging to various chemical classes such as aldehydes, alcohols, mono- and sesquiterpene hydrocarbons, lactones and furanones were identified. In all Alphonso tissues monoterpenes quantitatively dominated, with 57-99\% contribution; in particular, (Z)-ocimene was found in the highest amount. Ripeness was characterized by the de novo appearance of lactones and furanones in the blend of monoterpenes. Sabja was distinguished by the abundance of monoterpene hydrocarbons in the raw fruit, and that of sesquiterpene hydrocarbons and their derivatives in the ripe stage. CONCLUSION: Various stages of the Alphonso fruit during transition from flower to ripe fruit are characterized by unique volatile signatures that are distinguished from each other by the qualitative and quantitative appearance of different volatile compounds. Thus volatiles can be highly informative markers while studying the development and ripening of mango. (C) 2009 Society of Chemical Industry</p>},
	keywords = {Deogad, Gas chromatography, Mangifera indica, mango products, ripening indicator, Sabja},
	issn = {0022-5142},
	doi = {10.1002/jsfa.3692},
	author = {Pandit, Sagar S. and Kulkarni, Ram S. and Chidley, Hemangi G. and Giri, Ashok P. and Pujari, Keshav H. and Koellner, Tobias G. and Degenhardt, Joerg and Gershenzon, Jonathan and Gupta, Vidya S.}
}
@article { ISI:000364264800011,
	title = {Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices},
	journal = {Journal of Chromatography A},
	volume = {1420},
	year = {2015},
	month = {NOV},
	pages = {98-109},
	publisher = {ELSEVIER SCIENCE BV},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {<p>The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (\&gt;0.9) values of the coefficient of determination (R-2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. (C) 2015 Elsevier B.V. All rights reserved.</p>},
	keywords = {Artificial intelligence, Gas chromatography, genetic programming, Kovats retention index, Molecular descriptors, Quantitative structure-retention relationships},
	issn = {0021-9673},
	doi = {10.1016/j.chroma.2015.09.086},
	author = {Goel, Purva and Bapat, Sanket and Vyas, Renu and Tambe, Amruta and Tambe, Sanjeev S.}
}
