基于高光譜成像技術(shù)和近紅外光譜技術(shù)測定花生種子及花生油中油酸和亞油酸含量的方法研究
發(fā)布時間：2018-08-17 18:24
【摘要】：本研究的目的在于測定不同品種花生仁和花生油中油酸和亞油酸的含量。食用油的營養(yǎng)很大程度上取決于脂肪酸的含量,而在不同植物品種的油中脂肪酸的含量差別很大。花生油是一種很好的油酸和亞油酸的來源,通常被稱為“經(jīng)濟型”橄欖油。近年來,花生已廣泛種植于世界大多數(shù)熱帶和亞熱帶地區(qū)的國家,其中中國花生產(chǎn)量最大�；ㄉ诓煌念I(lǐng)域,包括健康、食品、農(nóng)業(yè)、工業(yè)、環(huán)境和經(jīng)濟都是非常重要的�；ㄉ歉郀I養(yǎng)的食物,它的消費量一直與降低患冠心病風險水平有直接的關(guān)系�；ㄉ臓I養(yǎng)價值主要歸因于高含量的不飽和脂肪酸,比如油酸(ω9)和亞油酸(ω6)。不飽和脂肪酸的存在可以提高血液中高密度脂蛋白、降低低密度脂蛋白(劣質(zhì)膽固醇)的水平,進而達到預(yù)防疾病(心臟病、糖尿病和癌癥)、調(diào)節(jié)體重、降低血糖、血壓的作用。本研究利用無損光譜技術(shù)測定花生中油酸和亞油酸含量。傳統(tǒng)標準的氣相色譜法(GC)也被采用為模型建立提供化學值。氣相色譜法(濕化學方法)可以獲得準確的參考值,但是速度慢,耗費時間,步驟繁瑣并且需要大量樣品。利用無損分析方法獲得標準脂肪酸的校正集光譜數(shù)據(jù)。96品種的花生仁和83品種的花生油用于實驗分析。利用高光譜成像系統(tǒng)(Sisu CHEMA)和近紅外光譜設(shè)備(DA 7200)獲得花生仁的光譜數(shù)據(jù),利用Micro NIR 1700獲得花生油的光譜數(shù)據(jù)。刪除異常值并選擇顯著波長,采用PCA和PLS等化學計量學方法提取有用的光譜信息并建立模型。校正模型和預(yù)測模型都有良好的回歸系數(shù),表明結(jié)果良好。例如,從近紅外光譜區(qū)間(900.82-1647.7 nm)的239個光譜中得到的10個有效波長建立PLS模型,模型的回歸系數(shù)為0.97,誤差分別為2.4和0.5,該模型預(yù)測油酸含量潛力巨大。研究表明光譜檢測技術(shù)可以實時測定食品中的組分(如油酸、亞油酸),采用該技術(shù)可以實現(xiàn)持續(xù)監(jiān)測食品質(zhì)量安全并建立控制體系,這滿足了消費者對食品健康品質(zhì)的日益關(guān)注。采用適宜、高效的光譜技術(shù)需要考慮不同技術(shù)的差異性。例如,在花生仁檢測中,與NIR方法相比,高光譜成像呈現(xiàn)出更多的信息。由于高光譜成像能夠獲得光譜和空間數(shù)據(jù),采用少量的檢測樣品能夠預(yù)測油酸和亞油酸的含量,油酸和亞油酸含量分別為18.8~20.2 mg/100 g和15~18 mg/100 g。傳統(tǒng)的近紅外光譜(NIRS)不能提供食品組分(油酸和亞油酸的脂肪酸)空間信息,而高光譜成像可以檢測組分的三維信息,從而得到全面結(jié)果。此外,研究結(jié)果表明三種光譜技術(shù)在最適波長檢測同一脂肪酸含量相關(guān)性較差。與此同時,Micro NIR可以用于采集花生油的光譜數(shù)據(jù),而DA7200 NIR及HSI設(shè)備沒有相應(yīng)附件暫時不能用于油樣測定。微型NIR采集的花生油光譜數(shù)據(jù)可按照HSI及NIRS采集花生仁數(shù)據(jù)的處理方式進行類似分析,進而建立預(yù)測模型。除了需要提取油脂之外,使用Micro NIR采建模效果與NIRS及HSI相當。本研究通過使用以上三種設(shè)備采集光譜數(shù)據(jù)建立了6種數(shù)據(jù)模型:三種用于檢測油酸;三種用于檢測亞油酸�；谧顑�(yōu)波長及相應(yīng)的回歸系數(shù)建立數(shù)學模型,并且預(yù)測了模型的偏移量。本研究建立的模型需要進一步在多個大型實驗室進行驗證與確認,從而得出以上模型是否適用于未來食品工業(yè)化的檢測與控制。本研究相比較傳統(tǒng)方法取得了重大突破,提供一種快速無損的方法來預(yù)測未知花生樣品。該方法步驟簡單、不破壞環(huán)境、使用少量樣品。但是該技術(shù)也面臨一些挑戰(zhàn),例如大量的數(shù)據(jù)計算、無關(guān)信息刪除和模型穩(wěn)定性問題。
[Abstract]:The purpose of this study was to determine the content of oleic acid and linoleic acid in different varieties of peanut kernel and peanut oil. Nutrition of edible oil depends largely on the content of fatty acids, but the content of fatty acids varies greatly in different plant varieties. In recent years, peanuts have been widely grown in most tropical and subtropical countries in the world, of which China has the largest peanut production. Peanuts are very important in different fields, including health, food, agriculture, industry, environment and economy. Peanuts are high nutrient foods, and their consumption has been associated with a reduction in coronary heart disease. The nutritional value of peanuts is mainly attributed to high levels of unsaturated fatty acids, such as oleic acid (_9) and linoleic acid (_6). The presence of unsaturated fatty acids can increase high-density lipoprotein in the blood, reduce low-density lipoprotein (low-quality cholesterol) levels, and thus achieve disease prevention (heart disease, diabetes and cancer). In this study, nondestructive spectroscopy was used to determine oleic acid and linoleic acid content in peanuts. Traditional standard gas chromatography (GC) was also used to provide chemical values for modeling. Gas chromatography (wet chemical method) can obtain accurate reference values, but it is slow, time-consuming and complicated. Nondestructive analysis was used to obtain calibration set spectral data of standard fatty acids. 96 varieties of peanut kernel and 83 varieties of peanut oil were used for experimental analysis. Spectral data of peanut kernel were obtained by hyperspectral imaging system (Sisu CHEMA) and near infrared spectroscopy equipment (DA 7200), and peanut oil was obtained by Micro NIR 1700. Spectral data. Abnormal values are deleted and significant wavelengths are selected. Useful spectral information is extracted and modeled using chemometrics methods such as PCA and PLS. Both correction and prediction models have good regression coefficients and show good results. For example, 10 effective waves are obtained from 239 spectra of the near infrared spectrum (900.82-1647.7 nm). A long-term PLS model with regression coefficients of 0.97 and errors of 2.4 and 0.5 was established. The model has great potential for predicting oleic acid content. For example, in peanut kernel detection, hyperspectral imaging presents more information than NIR. Because hyperspectral imaging can obtain spectral and spatial data, oleic acid can be predicted with a small number of detection samples. The contents of oleic acid and linoleic acid were 18.8-20.2 mg/100 g and 15-18 mg/100 g, respectively. The traditional near infrared spectroscopy (NIRS) could not provide spatial information of food components (oleic acid and linoleic acid fatty acids), but hyperspectral imaging could detect the three-dimensional information of the components and obtain comprehensive results. At the same time, micro-NIR can be used to collect the spectral data of peanut oil, but DA7200 NIR and HSI equipment can not be used to determine the oil samples temporarily without corresponding accessories. In addition to the need to extract oils and fats, the use of Micro NIR for modeling is equivalent to NIRS and HSI. Six data models were established by using the above three devices to collect spectral data: three for oleic acid detection; three for linoleic acid detection. The regression coefficient establishes a mathematical model and predicts the offset of the model. The model established in this study needs to be further validated and validated in several large-scale laboratories to determine whether the above model is suitable for the detection and control of food industrialization in the future. Nondestructive methods for predicting unknown peanut samples are simple, environmentally friendly, and use a small number of samples. However, this technique also faces some challenges, such as large amounts of data computation, irrelevant information deletion and model stability.
【學位授予單位】：中國農(nóng)業(yè)科學院
【學位級別】：博士
【學位授予年份】：2016
【分類號】：S565.2;O657.3;TS225.12
，
本文編號：2188505
[Abstract]:The purpose of this study was to determine the content of oleic acid and linoleic acid in different varieties of peanut kernel and peanut oil. Nutrition of edible oil depends largely on the content of fatty acids, but the content of fatty acids varies greatly in different plant varieties. In recent years, peanuts have been widely grown in most tropical and subtropical countries in the world, of which China has the largest peanut production. Peanuts are very important in different fields, including health, food, agriculture, industry, environment and economy. Peanuts are high nutrient foods, and their consumption has been associated with a reduction in coronary heart disease. The nutritional value of peanuts is mainly attributed to high levels of unsaturated fatty acids, such as oleic acid (_9) and linoleic acid (_6). The presence of unsaturated fatty acids can increase high-density lipoprotein in the blood, reduce low-density lipoprotein (low-quality cholesterol) levels, and thus achieve disease prevention (heart disease, diabetes and cancer). In this study, nondestructive spectroscopy was used to determine oleic acid and linoleic acid content in peanuts. Traditional standard gas chromatography (GC) was also used to provide chemical values for modeling. Gas chromatography (wet chemical method) can obtain accurate reference values, but it is slow, time-consuming and complicated. Nondestructive analysis was used to obtain calibration set spectral data of standard fatty acids. 96 varieties of peanut kernel and 83 varieties of peanut oil were used for experimental analysis. Spectral data of peanut kernel were obtained by hyperspectral imaging system (Sisu CHEMA) and near infrared spectroscopy equipment (DA 7200), and peanut oil was obtained by Micro NIR 1700. Spectral data. Abnormal values are deleted and significant wavelengths are selected. Useful spectral information is extracted and modeled using chemometrics methods such as PCA and PLS. Both correction and prediction models have good regression coefficients and show good results. For example, 10 effective waves are obtained from 239 spectra of the near infrared spectrum (900.82-1647.7 nm). A long-term PLS model with regression coefficients of 0.97 and errors of 2.4 and 0.5 was established. The model has great potential for predicting oleic acid content. For example, in peanut kernel detection, hyperspectral imaging presents more information than NIR. Because hyperspectral imaging can obtain spectral and spatial data, oleic acid can be predicted with a small number of detection samples. The contents of oleic acid and linoleic acid were 18.8-20.2 mg/100 g and 15-18 mg/100 g, respectively. The traditional near infrared spectroscopy (NIRS) could not provide spatial information of food components (oleic acid and linoleic acid fatty acids), but hyperspectral imaging could detect the three-dimensional information of the components and obtain comprehensive results. At the same time, micro-NIR can be used to collect the spectral data of peanut oil, but DA7200 NIR and HSI equipment can not be used to determine the oil samples temporarily without corresponding accessories. In addition to the need to extract oils and fats, the use of Micro NIR for modeling is equivalent to NIRS and HSI. Six data models were established by using the above three devices to collect spectral data: three for oleic acid detection; three for linoleic acid detection. The regression coefficient establishes a mathematical model and predicts the offset of the model. The model established in this study needs to be further validated and validated in several large-scale laboratories to determine whether the above model is suitable for the detection and control of food industrialization in the future. Nondestructive methods for predicting unknown peanut samples are simple, environmentally friendly, and use a small number of samples. However, this technique also faces some challenges, such as large amounts of data computation, irrelevant information deletion and model stability.
【學位授予單位】：中國農(nóng)業(yè)科學院
【學位級別】：博士
【學位授予年份】：2016
【分類號】：S565.2;O657.3;TS225.12
，
本文編號：2188505
本文鏈接：http://sikaile.net/shoufeilunwen/nykjbs/2188505.html
最近更新
教材專著
