發(fā)布時間：2018-04-26 14:48

  本文選題：近紅外 + 特征波長��； 參考：《光譜學(xué)與光譜分析》2017年10期

【摘要】：為實現(xiàn)近紅外光譜進(jìn)行勾兌梨汁中原汁含量的快速檢測,采用相同可溶性固形物含量的新鮮梨汁和果汁粉沖劑按照原汁含量為0%~100%進(jìn)行勾兌,并結(jié)合遺傳算法(GA)、粒子群算法(PSO)以及螢火蟲算法(GSOFA)進(jìn)行特征波長篩選,比較分析四種算法分別建立的偏最小二乘(PLS)模型。結(jié)果表明,GA-PLS,PSO-PLS,GSO-PLS,FA-PLS四種模型均能夠剔除大部分波長變量,其中以FA-PLS模型效果最佳,不僅保證模型的穩(wěn)健性,而且簡化了模型,提高了預(yù)測的精度。為了進(jìn)一步優(yōu)選特征波長,利用連續(xù)投影算法(SPA)在FA基礎(chǔ)上做進(jìn)一步波長篩選,并比較全波段PLS,SPA-PLS,FA-PLS,FA-SPA-PLS模型,四種模型泛化能力為:FA-PLSPLSFA-SPA-PLSSPA-PLS,其預(yù)測均方根誤差分別為0.029 1,0.033 3,0.033 9和0.137 0,相應(yīng)的波長變量數(shù)量依次367,765,20和18。其中SPA-PLS波長變量最少,但預(yù)測誤差遠(yuǎn)遠(yuǎn)高于其他三種模型,綜合考慮預(yù)測精度與波長變量數(shù)目,FA-SPA-PLS模型不僅波長變量較少而且預(yù)測精度較高,能夠有效鑒別勾兌梨汁中原汁含量。研究利用近紅外光譜技術(shù)為快速鑒別勾兌果汁提供一種有益思路,并通過波長變量篩選簡化定量分析模型。
[Abstract]:In order to realize the rapid detection of the content of the juice in the pear juice by near infrared spectrum, the fresh pear juice and the juice powder powder with the same soluble solid content were used in accordance with the content of 0%~100%, and the characteristic wavelength was selected by combining the genetic algorithm (GA), particle swarm optimization (PSO) and the firefly algorithm (GSOFA), and the comparison and analysis of four was compared. The partial least squares (PLS) model is established respectively. The results show that the four models of GA-PLS, PSO-PLS, GSO-PLS and FA-PLS can eliminate most of the wavelength variables, and the FA-PLS model is the best, not only to guarantee the robustness of the model, but also to simplify the model and improve the accuracy of the prediction. The continuation projection algorithm (SPA) performs further wavelength screening on the basis of FA, and compares the full band PLS, SPA-PLS, FA-PLS, and FA-SPA-PLS models. The generalization ability of the four models is: FA-PLSPLSFA-SPA-PLSSPA-PLS, its predicted root mean square error is 0.029 1,0.033 3,0.033 9 and 0.1370 respectively, the number of corresponding wavelength variables in turn 367765,20 and 18. SPA-PLS among them The wavelength variable is the least, but the prediction error is far higher than the other three models. Considering the prediction accuracy and the number of wavelength variables, the FA-SPA-PLS model not only has less wavelength variable and higher prediction precision, and can effectively identify the content of the juice in the pear juice. The quantitative analysis model is simplified by wavelength variable screening.

【作者單位】： 福州大學(xué)電氣工程與自動化學(xué)院;福建省醫(yī)療器械和醫(yī)藥技術(shù)重點實驗室;福州大學(xué)生物科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(61403319) 福建省科技廳國際合作項目(2015I003) 福建省教育廳科技項目(JK2014001)資助
【分類號】：O657.33;TS255.44

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本文編號：1806486