基于高光谱成像技术和近红外光谱技术测定花生种子及花生油中油酸和亚油酸含量的方法研究
发布时间：2018-08-17 18:24
【摘要】：本研究的目的在于测定不同品种花生仁和花生油中油酸和亚油酸的含量。食用油的营养很大程度上取决于脂肪酸的含量,而在不同植物品种的油中脂肪酸的含量差别很大。花生油是一种很好的油酸和亚油酸的来源,通常被称为“经济型”橄榄油。近年来,花生已广泛种植于世界大多数热带和亚热带地区的国家,其中中国花生产量最大。花生在不同的领域,包括健康、食品、农业、工业、环境和经济都是非常重要的。花生是高营养的食物,它的消费量一直与降低患冠心病风险水平有直接的关系。花生的营养价值主要归因于高含量的不饱和脂肪酸,比如油酸(ω9)和亚油酸(ω6)。不饱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(劣质胆固醇)的水平,进而达到预防疾病(心脏病、糖尿病和癌症)、调节体重、降低血糖、血压的作用。本研究利用无损光谱技术测定花生中油酸和亚油酸含量。传统标准的气相色谱法(GC)也被采用为模型建立提供化学值。气相色谱法(湿化学方法)可以获得准确的参考值,但是速度慢,耗费时间,步骤繁琐并且需要大量样品。利用无损分析方法获得标准脂肪酸的校正集光谱数据。96品种的花生仁和83品种的花生油用于实验分析。利用高光谱成像系统(Sisu CHEMA)和近红外光谱设备(DA 7200)获得花生仁的光谱数据,利用Micro NIR 1700获得花生油的光谱数据。删除异常值并选择显著波长,采用PCA和PLS等化学计量学方法提取有用的光谱信息并建立模型。校正模型和预测模型都有良好的回归系数,表明结果良好。例如,从近红外光谱区间(900.82-1647.7 nm)的239个光谱中得到的10个有效波长建立PLS模型,模型的回归系数为0.97,误差分别为2.4和0.5,该模型预测油酸含量潜力巨大。研究表明光谱检测技术可以实时测定食品中的组分(如油酸、亚油酸),采用该技术可以实现持续监测食品质量安全并建立控制体系,这满足了消费者对食品健康品质的日益关注。采用适宜、高效的光谱技术需要考虑不同技术的差异性。例如,在花生仁检测中,与NIR方法相比,高光谱成像呈现出更多的信息。由于高光谱成像能够获得光谱和空间数据,采用少量的检测样品能够预测油酸和亚油酸的含量,油酸和亚油酸含量分别为18.8~20.2 mg/100 g和15~18 mg/100 g。传统的近红外光谱(NIRS)不能提供食品组分(油酸和亚油酸的脂肪酸)空间信息,而高光谱成像可以检测组分的三维信息,从而得到全面结果。此外,研究结果表明三种光谱技术在最适波长检测同一脂肪酸含量相关性较差。与此同时,Micro NIR可以用于采集花生油的光谱数据,而DA7200 NIR及HSI设备没有相应附件暂时不能用于油样测定。微型NIR采集的花生油光谱数据可按照HSI及NIRS采集花生仁数据的处理方式进行类似分析,进而建立预测模型。除了需要提取油脂之外,使用Micro NIR采建模效果与NIRS及HSI相当。本研究通过使用以上三种设备采集光谱数据建立了6种数据模型:三种用于检测油酸;三种用于检测亚油酸。基于最优波长及相应的回归系数建立数学模型,并且预测了模型的偏移量。本研究建立的模型需要进一步在多个大型实验室进行验证与确认,从而得出以上模型是否适用于未来食品工业化的检测与控制。本研究相比较传统方法取得了重大突破,提供一种快速无损的方法来预测未知花生样品。该方法步骤简单、不破坏环境、使用少量样品。但是该技术也面临一些挑战,例如大量的数据计算、无关信息删除和模型稳定性问题。
[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.
【学位授予单位】：中国农业科学院
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S565.2;O657.3;TS225.12
，
本文编号：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.
【学位授予单位】：中国农业科学院
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S565.2;O657.3;TS225.12
，
本文编号：2188505
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