本文選題：在線檢測 + 可溶性固形物　； 參考：《光譜學與光譜分析》2017年07期

【摘要】：采用可見/近紅外光譜技術在線檢測水果糖度,每個水果品種要單獨建模,模型升級維護耗時費力。探討建立蘋果、梨等薄皮水果可溶性固形物(SSC)在線檢測通用數(shù)學模型的可行性。利用自行設計的可見/近紅外漫透射光譜在線檢測系統(tǒng),在積分時間80ms、單線速度5個/s的條件下,采集新梨7號、碭山酥梨、玉露香梨和富士蘋果四種水果的可見/近紅外漫透射光譜。分析了四種水果的可見/近紅外漫透射光譜響應特性,采用變異系數(shù)法和連續(xù)投影算法,篩選通用數(shù)學模型建模用光譜變量,并建立了偏最小二乘和最小二乘支持向量機梨與蘋果梨通用數(shù)學模型。采用新樣品評價模型的預測能力,變異系數(shù)法篩選光譜波段建立的偏最小二乘通用數(shù)學模型預測精度最高,通用模型預測梨和蘋果梨模型預測均方根誤差分別為0.49%和0.55%,通用模型預測相關系數(shù)分別為0.88和0.93;獨立模型預測新梨7號、玉露香梨、碭山酥梨和富士蘋果的預測相關系數(shù)分別為0.93,0.91,0.88和0.95,預測均方根誤差分別為0.40%,0.42%,0.41%和0.46%。通用數(shù)學模型的預測精度略低于每個品種的獨立數(shù)學模型,但是通用模型的通用性高于單一模型。實驗結果說明采用變異系數(shù)法結合偏最小二乘法建立薄皮水果在線檢測通用數(shù)學模型,實現(xiàn)四種水果糖度在線檢測是可行的。
[Abstract]:Using visible / near infrared spectroscopy (NIR) technology to measure the degree of fructose on line, each fruit variety needs to be modeled separately, and the model upgrading and maintenance takes time and effort. To explore the feasibility of establishing a general mathematical model for on-line detection of soluble solids in apple, pear and other thin-skinned fruits. Using the self-designed on-line detection system of visible / near infrared diffuse transmission spectrum, under the condition of integrating time 80 Ms and single line velocity 5 / s, Xinli 7 and Dangshan pear were collected, Dangshan pear, Dangshan pear, The visible / near infrared diffuse transmission spectra of four kinds of fruits, Julu pear and Fuji apple. The response characteristics of visible / near infrared diffuse transmission spectra of four kinds of fruits are analyzed. The spectral variables used in the modeling of general mathematical models are selected by means of variation coefficient method and continuous projection algorithm. The general mathematical models of pear and apple pear based on partial least squares and least squares support vector machine are established. Based on the prediction ability of the new sample evaluation model, the partial least squares general mathematical model established by the coefficient of variation method for spectral band selection has the highest prediction accuracy. The root-mean-square error (RMS) of the two models was 0.49% and 0.55%, respectively, and the correlation coefficient was 0.88 and 0.93.The independent model was used to predict Xinli 7, Yulu fragrant pear. The predictive correlation coefficients of Dangshan pear and Fuji apple were 0.93 ~ 0.91 ~ 0.88 and 0.95, respectively. The root mean square error was 0.400.42% and 0.46%, respectively. The prediction accuracy of the general mathematical model is slightly lower than that of the independent mathematical model of each variety, but the general-purpose model is more general than the single model. The experimental results show that it is feasible to establish a general mathematical model for on-line detection of thin skin fruits by means of coefficient of variation method combined with partial least square method and to realize the on-line detection of sugar content of four kinds of fruits.
【作者單位】： 華東交通大學機電與車輛工程學院;
【基金】：國家自然科學基金項目(61640417) 南方山地果園智能化管理技術與裝備協(xié)同創(chuàng)新中心(贛教高字[2014]60號) 江西省優(yōu)勢科技創(chuàng)新團隊(20153BCB24002)資助
【分類號】：O657.3;TS255.7

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