Application of SIMCA algorithm based on near-infrared spectroscopy in blended tobacco module threshing
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Abstract
To rapidly and nondestructively presort the cured tobacco leaves of blended modules for threshing, 172 samples of tobacco leaves harvested in 2011—2014 were collected from Liangshan and Panzhihua in Sichuan and their NIR （near-infrared spectroscopy） data were recorded. The presorting models for the flavor, smoke, taste, expansion, middle-upper, and lower modules of flue-cured tobacco leaves were established by adopting SIMCA （Softing Dependent Modeling of Class Analogy） algorithm based on the NIR data. The results of verification showed that the accuracy rate between the predicted attribution and the actual entered blended module of the samples reached 72.2%, which suggested that the models could be used to presort cured tobacco leaves of blended modules for threshing and reduce the blending workload of workers.
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