本文選題：近紅外光譜 + 水果　； 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：目前,對在樹和采后的核果類水果的大部分品質(zhì)參數(shù)多采用傳統(tǒng)方法進行檢測,這些方法常常具有破壞性且時間依賴性高,而近紅外光譜分析技術(shù)具有分析速度快、綠色、無損等優(yōu)點,可以實現(xiàn)水果品質(zhì)的快速無損評價。(1)實現(xiàn)了無損檢測單顆葡萄中可溶性固形物(SSC)含量,獲得個體和群體信息,以期指導(dǎo)田間管理、葡萄儲存條件設(shè)置及滿足消費者對葡萄不同的口味需求。采用手持式NIR光譜儀MicroNIR 1700在950-1650 nm波長范圍采集葡萄的近紅外光譜,應(yīng)用偏最小二乘(PLS)回歸建立葡萄SSC預(yù)測模型。為了減少冗余無信息變量,增加模型的預(yù)測精度和穩(wěn)定性,采用無信息變量消除法(UVE)、隨機蛙算法(RF)篩選出與葡萄SSC相關(guān)的重要波長變量。研究結(jié)果表明RF篩選建立的SSC預(yù)測模型優(yōu)于全光譜PLS和UVE篩選建立的模型。RF-PLS模型的校正集、交叉驗證及預(yù)測集RC,RCV和RP分別為0.9801、0.9661和0.9646,均方根誤差RMSEC,RMSECV和RMSEP分別為0.6382°Brix、0.8299°Brix和0.8688°Brix。本研究表明,通過波長優(yōu)選后的,手持式近紅外光譜儀在預(yù)測單顆葡萄SSC的應(yīng)用上完全可行,有較高的預(yù)測精度。(2)SSC、干物質(zhì)含量(DMC)、總酚含量(TPC)以及總黃酮含量(TFC)是評價果用桑椹營養(yǎng)品質(zhì)的重要指標。利用漫反射近紅外光譜分析技術(shù)建立快速、實時無損地檢測桑椹中營養(yǎng)成分的方法。首先用便攜式近紅外光譜儀MicroNIR1700采集桑椹的近紅外光譜,光譜經(jīng)預(yù)處理后,應(yīng)用PLS建立桑椹SSC、DMC、TPC以及TFC的預(yù)測模型,并用UVE、自適應(yīng)重加權(quán)采樣(CARS)和RF三種方法篩選出最優(yōu)波長變量,提高PLS模型精度。相較于UVE和RF法,CARS方法優(yōu)選出更少的波長變量,所建PLS模型的預(yù)測效果更好。SCC、DMC、TPC以及TFC的CARS-PLS模型的校正相關(guān)系數(shù)RC分別為0.9807、0.9697、0.9491和0.9697,RMSEC分別為0.7035°Brix、0.6266%、0.3823 mg/g沒食子酸和0.2666 mg/g蘆丁,RP分別為0.9514、0.9430、0.8667和0.8771,RMSEP分別為1.2100°Brix、0.9178%、0.6352 mg/g沒食子酸和0.6939mg/g蘆丁。研究結(jié)果表明,手持式近紅外光譜儀MicroNIR 1700結(jié)合化學(xué)計量學(xué)方法,能夠用于現(xiàn)場對桑椹SCC、DMC、TPC以及TFC的快速無損檢測。(3)實現(xiàn)了在兩種相同型號儀器上蘋果中SSC近紅外模型的轉(zhuǎn)移。本文使用K/S方法探討了轉(zhuǎn)移集樣品數(shù)量對RMSEP的影響,確定樣品數(shù)為18時轉(zhuǎn)移效果最佳。采用直接校正法(DS)將從機光譜進行校正,結(jié)合CARS波長優(yōu)選算法,篩選出25個波長點建立PLS分析模型。最終,蘋果的SSC近紅外模型成功的從主機轉(zhuǎn)移到從機上,Rp為0.8603,RMSEP為0.7914°Brix。DS算法結(jié)合CARS波長優(yōu)選方法可以有效的應(yīng)用到模型轉(zhuǎn)移的研究中。
[Abstract]:At present, traditional methods are used to detect most of the quality parameters of drupe fruits in trees and postharvest fruits. These methods are often destructive and time-dependent, while the near infrared spectroscopy (NIR) analysis technology is fast and green. In order to guide field management, it can realize the fast nondestructive evaluation of fruit quality and the content of soluble solids (SSCS) in a single grape, so as to obtain individual and group information. Grape storage conditions and to meet the needs of consumers for different tastes of grapes. The near infrared spectra of grape were collected by a hand-held NIR spectrometer MicroNIR 1700 in the wavelength range of 950-1650 nm. The prediction model of grape SSC was established by partial least squares regression. In order to reduce redundant uninformed variables and increase the prediction accuracy and stability of the model, an information free variable elimination method and a random frog algorithm were used to screen the important wavelength variables associated with grape SSC. The results show that the SSC prediction model established by RF screening is superior to the calibration set of full-spectrum PLS and UVE model. The cross validation and prediction set RCU RCV and RP are 0.9801g 0.9661 and 0.9646, respectively. The RMSEC-RMSECV and RMSEP are 0.6382 擄Brix 0.8299 擄Brix and 0.8688 擄Brix, respectively. The results show that the application of hand-held near infrared spectrometer in predicting single grape SSC is feasible after wavelength optimization. The high prediction accuracy of SSCS, the content of dry matter (DMC), the content of total phenol (TPC) and the content of total flavonoids (TFCC) are important indexes for evaluating the nutritive quality of fruit mulberry. A rapid, real-time and nondestructive method for the determination of nutrients in mulberry was established by means of diffuse reflectance near infrared spectroscopy. At first, the near infrared spectra of mulberry were collected by portable near infrared spectrometer MicroNIR1700. After the spectrum was pretreated, the prediction models of mulberry SSCN DMCT TPC and TFC were established by PLS, and the optimal wavelength variables were screened out by three methods: UVE, adaptive re-weighted sampling (CARSs) and RF. Improve the precision of PLS model. Compared with the UVE and RF car methods, fewer wavelength variables are selected. The correlation coefficient RC of the model was 0.9807 / 0.9697 / 0.9491 and 0.9697, respectively. The RMSECs of 0.3823 mg/g Gallic acid and 0.2666 mg/g rutin RMSEP were 0.7035 擄Brix 0.9697 mg/g Gallic acid and 0.2666 mg/g rutin 0.8667 and 0.8771g RMSEP were 1.2100 擄Brix 0.917852 mg/g Gallic acid and 0.6352 0.6939mg/g rutin, respectively. The results show that the hand-held near infrared spectrometer MicroNIR 1700 combined with chemometrics can be used for the fast nondestructive testing of mulberry fruit SCC DMCN TPC and TFC in the field. The transfer of the near infrared model of apple on the same type of instrument is realized. In this paper, the effect of sample number on RMSEP is discussed by using the K / S method, and it is determined that the best transfer effect is at 18:00. The slave spectrum is corrected by direct correction method (DSS), and 25 wavelength points are selected to establish PLS analysis model combined with car wavelength selection algorithm. Finally, Apple's SSC near-infrared model is successfully transferred from host to machine with a RP of 0.8603 and RMSEP of 0.7914 擄Brix.DS algorithm combined with car wavelength optimization can be effectively applied to the research of model transfer.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S66

【參考文獻】

相關(guān)期刊論文 前10條

1 杜晨朝;吳志生;趙娜;周正;史新元;喬延江;;基于兩類誤差檢測理論金銀花提取過程的MEMS-NIR在線分析建模方法研究[J];中國中藥雜志;2016年19期

2 丁柯;張月敬;沈廣輝;于賢龍;楊增玲;劉賢;;蛋白飼料原料粗蛋白含量近紅外光譜模型轉(zhuǎn)移研究[J];光譜學(xué)與光譜分析;2016年05期

3 郅建軍;陳通;孫武堅;王春峰;郭麗;陳斌;;微型近紅外光譜儀檢測水果可溶性固形物[J];分析儀器;2016年01期

4 吳靜珠;董文菲;劉倩;陳巖;孫麗娟;;小麥種子發(fā)芽率近紅外定量分析模型的優(yōu)化（英文）[J];農(nóng)業(yè)工程學(xué)報;2015年S2期

5 劉嬌;李小昱;金瑞;徐森淼;庫靜;;不同品種冷鮮豬肉pH值高光譜檢測模型的傳遞方法研究[J];光譜學(xué)與光譜分析;2015年07期

6 陳辰;魯曉翔;張鵬;陳紹慧;李江闊;;玫瑰香葡萄貯藏期間糖酸品質(zhì)的近紅外檢測[J];食品與發(fā)酵工業(yè);2015年06期

7 李江波;郭志明;黃文倩;張保華;趙春江;;應(yīng)用CARS和SPA算法對草莓SSC含量NIR光譜預(yù)測模型中變量及樣本篩選[J];光譜學(xué)與光譜分析;2015年02期

8 詹白勺;倪君輝;李軍;;高光譜技術(shù)結(jié)合CARS算法的庫爾勒香梨可溶性固形物定量測定[J];光譜學(xué)與光譜分析;2014年10期

9 葉坤濤;董太源;賀文熙;李玉曉;程顯明;李廣勇;李昊宇;徐曉軒;;基于MEMS微鏡的長波近紅外光譜儀的設(shè)計與實現(xiàn)[J];光譜學(xué)與光譜分析;2014年10期

10 秦善知;陳斌;陸道禮;顏輝;;基于便攜式近紅外光譜儀檢測梨可溶性固形物[J];江蘇農(nóng)業(yè)科學(xué);2014年08期

相關(guān)會議論文 前1條

1 王滿;鄭開逸;王天一;杜一平;;葉片葉綠素含量的近紅外光譜模型轉(zhuǎn)移研究[A];第十八屆全國分子光譜學(xué)學(xué)術(shù)會議論文集[C];2014年

相關(guān)碩士學(xué)位論文 前1條

1 張曉鳳;近紅外光譜分析中模型傳遞與特征波長選擇方法研究[D];桂林電子科技大學(xué);2015年

，

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