Bibliographic citations
Pereda, J., (2016). Calibración para determinar composición proximal de la quinua (Chenopodium quinoa W.) usando la espectroscopía de trasmitancia en el infrarrojo cercano [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/2606
Pereda, J., Calibración para determinar composición proximal de la quinua (Chenopodium quinoa W.) usando la espectroscopía de trasmitancia en el infrarrojo cercano [Tesis]. : Universidad Nacional Agraria La Molina; 2016. https://hdl.handle.net/20.500.12996/2606
@misc{sunedu/3013821,
title = "Calibración para determinar composición proximal de la quinua (Chenopodium quinoa W.) usando la espectroscopía de trasmitancia en el infrarrojo cercano",
author = "Pereda Ibañez, Jorge Miguel",
publisher = "Universidad Nacional Agraria La Molina",
year = "2016"
}
The quinoa grain is consumed in the Andean region since pre-Inca times. In those days, they chose the plants that have better resistance to the climatic conditions of the region; this resulted in more than 2000 varieties. Currently, plant breeding programs develops crops that have higher field yields, pest resistance and adaptability to new conditions. In this context, it is necessary to characterize large amounts of seeds in a short time with reliable methods. The present study was aimed at developing a multivariate calibration in the Infratec 1241 (850-1048 nm) equipment, in order to predict the content of moisture, protein, fat and ash from quinoa; evaluating grain and flour. Near Infrared Transmittance Spectroscopy (NIT) was used in the study. A total of 120 quinoa varieties were used, they were cultivated in the Lima’s coast and the Ancash’s andes by the Plant Breeding Program Cereals and Pulses of the National Agrarian University La Molina. Once the 120 samples were cleaned, the NIT reading of the grains was performed, then the samples were grounded and the NIT reading of the flour was performed; finally the chemical analysis were performed in triplicate using methods approved by the AOAC. The calibration model was obtained after purging of abnormal spectra, applying "PLS" regressions, "SNV and Detrend" and "None" dispersions, as well as different mathematical treatments. The best model was obtained from the readings in the form of grain, but just valid for light colors; RSQ values of 0,4648, 0,8084, 0,9313 and 0,8517 for moisture, protein, fat and ash were obtained respectively. The moisture component presented the lowest statistical of calibration, due to factors such as control over the performance of the reference methods, storage and time differential between the reference method analysis and reading of spectra.
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