Study of Tobacco Sensory Evaluation Model in Near Infrared Spectroscopy by Semi Supervised-Partial Least Squares / 分析化学
Chinese Journal of Analytical Chemistry
;
(12): 1687-1691, 2014.
Artigo
em Chinês
| WPRIM
| ID: wpr-460055
ABSTRACT
Semisupervisedmakesfulluseoflargeamountsofunlabeledsamplestomakeuptheinsufficiency of labeled samples. Since it is difficult to obtain a large number of accurate labeled samples and it is a good way for modeling by using a small amount of labeled samples or a large number of inaccurate samples, we proposed a new method named as semi-supervised partial least squares ( SS-PLS) to optimize model based on semi supervised learning. We used 211 samples of tobacco near infrared spectrum and sensory evaluation for modeling and used SS-PLS method to optimize tobacco sensory evaluation model. In the optimized model, the coefficient of determination ( R2 ) can reach up to 90%, the ratio of performance to deviation ( RPD) can reach up to 3 . 0 , and the standard error of cross validation and the standard error of prediction ( SECV and SEP) are below 1. 0. We divided the original sensory evaluation and SS-PLS optimized data into three grades of excellent, medium and poor in accordance with the fixed threshold, the result using projection model of based on principal component and Fisher criterion ( PPF ) shows that the classification of SS-PLS optimized data is better than the original sensory evaluation data. The SS-PLS method can solve the data representation problem of using small sample set for modeling and provides a new chemometrics method for near infrared spectroscopy modeling in case of obtaining a large number of accurately labeled samples is difficult.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Tipo de estudo:
Estudo prognóstico
Idioma:
Chinês
Revista:
Chinese Journal of Analytical Chemistry
Ano de publicação:
2014
Tipo de documento:
Artigo
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