本文選題：近紅外光譜 + 馬鈴薯�。� 參考：《黑龍江八一農(nóng)墾大學(xué)》2016年碩士論文

【摘要】：立足糧食的供求形勢,繼稻米、小麥、玉米之后,馬鈴薯成為我國新興的第四大主糧。近幾年,我國深入研討了馬鈴薯主糧化的戰(zhàn)略意義,積極推進(jìn)馬鈴薯產(chǎn)業(yè)化發(fā)展,因此,需要更科學(xué)、更高效的方法來嚴(yán)控生產(chǎn)中的每一環(huán)節(jié)。就馬鈴薯品種鑒別和品質(zhì)檢測而言,多數(shù)實驗室依舊采用傳統(tǒng)方法進(jìn)行,這些方法不適合生產(chǎn)過程中大批量樣品的實時分析,并對樣品存在破壞性。因此,研究新方法來提高馬鈴薯各指標(biāo)檢測效率,意義十分重大。近紅外光譜分析技術(shù)是我國近二十年逐步發(fā)展起來的一項快速分析技術(shù),它高效、無損的分析特點被很多領(lǐng)域廣泛應(yīng)用,結(jié)合馬鈴薯當(dāng)前的產(chǎn)業(yè)需求,本文開展了基于近紅外光譜分析的馬鈴薯品種鑒別及干物質(zhì)含量檢測方法研究,主要內(nèi)容如下:1、基于近紅外光譜的馬鈴薯品種快速鑒別方法的研究。試驗以3種不同品種共計352個樣本的馬鈴薯作為主要研究對象,并隨機將其分為建模集(307個樣本)和預(yù)測集(45個樣本)。使用可見-近紅外光譜儀獲取建模集和預(yù)測集樣品的光譜圖,將獲取的光譜圖通過多元散射校正(multiplicative scatter correction,MSC)和窗口大小為9的Savitzky-Golay(S-G)一階卷積求導(dǎo)方法預(yù)處理,消除顆粒大小、表面散射及光程變化對漫反射光譜的影響,降低原始光譜曲線的隨機噪聲影響。然后用偏最小二乘法(partial least square,PLS)對數(shù)據(jù)進(jìn)行降維、壓縮,使用主成分分析方法(principal component analysis,PCA)獲得的前4個主成分累計貢獻(xiàn)率達(dá)到96%以上,并從前4個主成分圖譜中提取20個吸收峰作為輸入變量,經(jīng)過試驗,得到一個20(輸入)-12(隱含)-3(輸出)結(jié)構(gòu)的3層BP神經(jīng)網(wǎng)絡(luò)。最后利用該模型對預(yù)測集樣本進(jìn)行品種鑒別,識別正確率達(dá)到100%。此方法能較為快速、準(zhǔn)確地鑒別馬鈴薯的品種,為馬鈴薯品種的快速鑒別提供了新思路。2、基于近紅外光譜技術(shù)的馬鈴薯干物質(zhì)含量檢測研究。以207個具有代表性的馬鈴薯樣本作為研究對象,其中115個用于馬鈴薯切片樣本的研究,92個用于完整馬鈴薯的研究,通過對比兩種樣本的模型預(yù)測效果,探討采用可見-短波近紅外光譜進(jìn)行馬鈴薯干物質(zhì)含量的完全無損檢測。切片樣本光譜數(shù)據(jù)用Savitzky-Golay(S-G)一階卷積求導(dǎo)方法預(yù)處理,根據(jù)局部最大值最小值原則和含氫基團(C-H、O-H)伸縮振動的敏感波段選定了5段特征波長參與建模,模型外部檢驗決定系數(shù)R2=0.9416,標(biāo)準(zhǔn)誤差RMSE=3.91。完整馬鈴薯樣本光譜數(shù)據(jù)經(jīng)過多元散射校正處理的基礎(chǔ)上使用S-G一階卷積求導(dǎo)方法預(yù)處理,選取了線性關(guān)系較好的5段波長參與建模。模型外部檢驗決定系數(shù)R2=0.8475,標(biāo)準(zhǔn)誤差RMSE=4.07。結(jié)果表明,完整馬鈴薯樣本模型的檢測效果雖然沒有切片樣本效果理想,但仍可以作為實際生產(chǎn)中進(jìn)行馬鈴薯干物質(zhì)含量檢測的有效手段。
[Abstract]:Based on the supply and demand situation of grain, potato has become the fourth main grain after rice, wheat and corn. In recent years, China has deeply discussed the strategic significance of potato grain production and actively promoted the development of potato industrialization. Therefore, more scientific and efficient methods are needed to strictly control every link in production. As far as potato variety identification and quality detection are concerned, traditional methods are still used in most laboratories. These methods are not suitable for real-time analysis of large quantities of samples in the production process and are destructive to the samples. Therefore, it is of great significance to study new methods to improve the detection efficiency of potato indexes. Near-infrared spectroscopy (NIR) analysis technology is a rapid analysis technology developed gradually in China in the past two decades. It has been widely used in many fields because of its high efficiency and nondestructive characteristics, combining with the current demand of potato industry. In this paper, the identification of potato varieties based on near infrared spectroscopy and the determination of dry matter content were studied. The main contents are as follows: 1. The rapid identification method of potato varieties based on near infrared spectroscopy. In the experiment, 352 samples of 3 different varieties of potato were selected as the main research objects, which were randomly divided into two groups: the modeling set (307 samples) and the prediction set (45 samples). The spectral images of the modeling and prediction samples were obtained by using the visible-near infrared spectrometer. The obtained spectra were preprocessed by multiple scattering correction multiple scatter correction MSCs and Savitzky-Golayay S-GG convolution method with window size 9 to eliminate the particle size. The effect of surface scattering and optical path change on diffuse reflectance spectrum is reduced, and the random noise effect of the original spectral curve is reduced. Then the data are reduced and compressed by partial least square (PLS). The cumulative contribution rate of the first four principal components obtained by principal component analysis (PCA) is over 96%. Twenty absorption peaks were extracted from the first four principal component maps as input variables, and a three-layer BP neural network with 20 (input) -12 (implicit) (output) structure was obtained. Finally, the model is used to identify the samples of the prediction set, and the recognition accuracy is 100%. This method can identify potato varieties quickly and accurately, and provide a new way of thinking for rapid identification of potato varieties. 2. The determination of dry matter content in potato based on near infrared spectroscopy is studied. Among 207 representative potato samples, 115 were used for potato slicing and 92 for intact potato. A complete nondestructive test of dry matter content in potato by visible-short-wave near-infrared spectroscopy was studied. The spectral data of slice samples were preprocessed by Savitzky-Golay-S-G) first order convolution method. According to the principle of local maximum and minimum and the sensitive band of H-containing group C-HO-H) stretching vibration, five sections of characteristic wavelengths were selected to participate in modeling. The determination coefficient of external test is 0.9416, and the standard error is RMSE 3.91. On the basis of multivariate scattering correction of the complete potato sample spectral data, S-G first order convolution method was used to preprocess, and 5 wavelengths with good linear relationship were selected to model the model. The determination coefficient of external test of the model is 0.8475, and the standard error RMSE is 4.07. The results showed that the detection effect of intact potato sample model was not satisfactory, but it could be used as an effective method to detect the dry matter content of potato in practical production.
【學(xué)位授予單位】：黑龍江八一農(nóng)墾大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：S532;O657.33

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