【摘要】：隨著人們生活質(zhì)量的不斷提高以及對身體健康的重視程度越來越強,消費者對大米的適口性、營養(yǎng)等品質(zhì)方面的需求也隨之提高。大米的品質(zhì)不但能讓消費者在感官上特別享受,直接關(guān)系著人們身體對大米的消化以及吸收,而且在大米的貿(mào)易和相關(guān)科學(xué)領(lǐng)域的研究方面也至關(guān)重要。在大米的品質(zhì)評定中,水分和蛋白質(zhì)是評價大米價值的關(guān)鍵因素。大米的水分含量不單影響著大米的品質(zhì),更重要的是關(guān)系到公眾的食品安全問題。大米以及它的副產(chǎn)品同時提供人們每日所需要的能量和蛋白質(zhì),蛋白質(zhì)含量的高低在大米的食味品質(zhì)方面起著重要的作用。因此本文將以此作為著手點,采用大米作為研究對象并利用近紅外光譜技術(shù)結(jié)合其水分和蛋白質(zhì)的含量進行圖譜擬合,以求探索快速檢測實現(xiàn)的可能性。主要進行了如下研究工作:(1)從黑龍江各地收集109個不同種類的大米樣品,利用Thermo Fisher公司Antaris II近紅外光譜儀對其進行光譜掃描。然后采用傳統(tǒng)的國家標準化學(xué)方法對主要組成成分水分和蛋白質(zhì)的化學(xué)值進行測定,為后續(xù)的數(shù)據(jù)處理以及模型的建立做好準備工作。(2)針對大米蛋白質(zhì)的定量分析模型分別采用霍特林T2統(tǒng)計、X-Y殘差以及3D視圖分析三種方法進行異常樣品的剔除。經(jīng)對比發(fā)現(xiàn)通過X-Y殘差分析方法剔除異常點后建立模型的RMSECV和R2分別從最初的0.2428和0.7626提升到了0.2060和0.8364。(3)采用The DUPLEX Method將水分和蛋白質(zhì)的樣品劃分為校正集和預(yù)測集,結(jié)果表明二者不單單在含量的范圍方面十分相近,在對樣品求得的平均值以及標準差上也十分相似,從而獲得符合實驗要求且均勻分布的校正集和預(yù)測集,達到了本研究的預(yù)期效果。(4)對大米水分的原始光譜圖分別采用導(dǎo)數(shù)、歸一化以及平滑三種方法進行去噪處理,經(jīng)對比發(fā)現(xiàn)平滑點數(shù)采用15點時去除噪聲后的建模效果最佳。對大米蛋白質(zhì)的原始光譜圖分別采用一階導(dǎo)數(shù)+平滑、二階導(dǎo)數(shù)+平滑以及正交信號校正三種去噪方法對光譜中所含的噪聲進行消除,研究表明二階導(dǎo)數(shù)+平滑處理具有最好的去噪效果,其所建立模型的校正集決定系數(shù)R2更趨向于1,同時有著較低的校正集均方誤差根RMSECV。(5)對大米水分光譜采用MWPLS和IPLS波長選擇方法進行模型驗證,通過對比發(fā)現(xiàn),MW-IPLS對水分光譜特征吸收波長的選取是一種有效的方法。結(jié)合MWPLS和IPLS方法進行波長選擇,確定二者具有交叉的最優(yōu)波長范圍4108-4386cm-1。在此波段采用PLSR建立大米水分的定量模型,預(yù)測集均方誤差根RMSEP為0.2753,其決定系數(shù)R2達到0.8597。(6)分別對80個校正集和24個預(yù)測集大米樣品的蛋白質(zhì)光譜建立PLSR和PCR模型。通過對比預(yù)測集均方根誤差根RMSEP和決定系數(shù)R2可以得出,PCR模型對大米蛋白質(zhì)含量的預(yù)測能力上表現(xiàn)更優(yōu),預(yù)測集均方根誤差根RMSEP和其決定系數(shù)R2達到0.1288和0.8865。綜上所述,本文利用近紅外光譜技術(shù)針對大米中水分和蛋白質(zhì)含量所建立的模型精度較高,具有一定的可行性,為今后大米成分的快速檢測提供新方法。
[Abstract]:With the continuous improvement of people's quality of life and the increasing emphasis on health, consumers'demand for the quality of rice, such as palatability, nutrition and so on, has also increased. Water and protein are the key factors in evaluating the value of rice. The water content of rice not only affects the quality of rice, but also the food safety of the public. The energy and protein needed and the protein content of rice play an important role in the food quality. Therefore, this paper will take this as the starting point, take rice as the research object, and use near infrared spectroscopy to fit the water and protein content of rice, in order to explore the possibility of rapid detection. The main research work is as follows: (1) 109 different kinds of rice samples were collected from Heilongjiang Province and scanned by Antaris II near infrared spectrometer of Thermo Fisher Company. Then the chemical values of water and protein were determined by traditional national standard chemical method for the follow-up. (2) Hotelling T2 statistics, X-Y residuals and 3D view analysis were used to eliminate the abnormal samples for the quantitative analysis model of rice protein. By comparison, the RMSECV and R2 of the model were established after removing the abnormal points by X-Y residuals analysis. (3) The DUPLEX Method was used to divide the water and protein samples into correction set and prediction set. The results showed that the two samples were not only very similar in the content range, but also very similar in the average value and standard deviation obtained from the samples, which accorded with the experimental requirements. (4) Derivative, normalization and smoothing were used to denoise the original spectrogram of rice moisture, and the smoothing point was found to be the best when the smoothing point was 15. The original spectrogram of rice protein was collected separately. Three denoising methods, first derivative + smoothing, second derivative + smoothing and orthogonal signal correction, are used to eliminate the noise contained in the spectrum. The results show that the second derivative + smoothing method has the best denoising effect. The calibration set determinant R2 of the model tends to be 1, and has a lower root mean square error of the calibration set RMSECV. (5) The MWPLS and IPLS wavelength selection methods were used to validate the model of water spectrum of rice. It was found that MW-IPLS was an effective method to select the characteristic absorption wavelength of water spectrum. The root mean square error (RMSEP) of prediction set was 0.2753, and the determination coefficient R2 was 0.8597. (6) PLSR and PCR models were established for protein spectra of 80 correction sets and 24 prediction sets respectively. The RMSEP and its determinant R2 are 0.1288 and 0.8865. In summary, the model established by near infrared spectroscopy for moisture and protein content in rice is more accurate and feasible, which provides a new method for rapid detection of rice components in the future. Method.
【學(xué)位授予單位】：東北農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS210.7;O657.33

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