近红外光谱对石榴品种的判别及品质的无损检测
发布时间：2019-06-06 12:45
【摘要】：石榴在中国已有2000多年的种植历史,目前中国石榴的栽种面积约为175万余亩,位居世界第一。但在石榴的栽种及贮藏过程中,因为石榴品种繁多且在贮藏保鲜方面缺少有效快速的监控方法,仅依靠人力还无法使得石榴的生产贮藏达到标准化要求。近红外光谱技术以其简单、快速、无损、无公害等优点,被广泛应用于水果品种的定性判别及品质的定量检测中。目前近红外技术分析对象主要为薄皮水果,而对于石榴等厚皮水果的研究极少。本次研究以陕西临潼石榴为研究对象,采用漫反射光谱,结合化学计量学,建立不同品种石榴和石榴理化品质、酚类物质含量的近红外模型,为石榴品种、品质检测提供软件支持,促进石榴产业的快速发展。主要研究内容和结果如下:(1)采集“一串铃”、“大净皮甜”、“黄皮甜”石榴的近红外光谱,经MSC预处理后,分别采用PCA+MLP神经网络法、PCA+Fisher线性判别法、PLS-DA判别法进行模型的建立。通过对三种判别模型的比较,PLS-DA判别结果优于其他两种,PLS-DA判别分析法在全波段内对验证集三个石榴品种的正确识别率分别为97.30%、96.55%、96.77%。为进一步提高PLS-DA模型的判别率,采用“载重法”选取特征波段,优化后模型对验证集的正确识别率分别达到97.30%、100.00%、100.00%。表明经波段优化后的PLS-DA模型可以实现对石榴品种的鉴别且效果令人满意。(2)为减小品种差异对近红外光谱质量品质检测模型的影响,以三个不同品种的石榴为研究对象,采用PLS法,建立单一品种和混合品种的石榴pH值的近红外检测模型。通过比较可知混合三个品种所建立的模型取得较好的预测效果。校正集和验证集的相关系数均大于0.900。因此,采用三个混合品种的校正集所建立的模型可以实现对石榴pH值的快速、无损、准确测定。这一结论进一步推广到其他质量指标的测定。三个品种混合作为校正集,结合CARS-PLS波段筛选,TA、SSC和成熟度模型验证集的R~2分别为0.903、0.930、0.853;RMSEP分别为0.019%、0.244°Brix、2.142。(3)采集整个石榴的近红外光谱,采用PLS法建立籽粒中多酚和黄酮含量的近红外检测模型,所建立的PLS模型校正集和验证集的相关系数均小于0.850,所建立的模型拟合和预测能力较差,预测精度有待于进一步研究。利用整个果实所采集的光谱进行籽粒花色苷含量检测时,所建立的PLS模型的预测效果良好,校正集R~2为0.881,RMSEC为1.318 mg/100g;验证集R~2为0.863,RMSEP为1.266 mg/100g。
[Abstract]:Pomegranate has been planted in China for more than 2000 years. At present, the planting area of pomegranate in China is about 1.75 million mu, ranking first in the world. However, in the process of pomegranate planting and storage, because of the variety of pomegranate and the lack of effective and rapid monitoring methods in storage and preservation, the production and storage of pomegranate can not meet the requirements of standardization by manpower alone. Near infrared spectroscopy (NIR) has been widely used in qualitative discrimination and quantitative quality detection of fruit varieties because of its simple, rapid, nondestructive, pollution-free and other advantages. At present, the analysis object of near-infrared technology is mainly thin-skinned fruit, but the research on pomegranate and other thick-skinned fruit is very few. In this study, the near infrared model of physical and chemical quality and phenolic content of different varieties of pomegranate and pomegranate was established by using diffused reflectance spectroscopy and chemometrics to establish the near infrared model of pomegranate and pomegranate content in Lintong, Shaanxi Province. Quality testing provides software support to promote the rapid development of pomegranate industry. The main research contents and results are as follows: (1) the near infrared spectra of "a string of bells", "big skin sweet" and "yellow skin sweet" pomegranate were collected. After pretreatment with MSC, PCA MLP neural network method and PCA Fisher linear discriminant method were used respectively. PLS-DA discriminant method is used to establish the model. By comparing the three discriminant models, the PLS-DA discriminant results are better than the other two. The correct recognition rates of the three pomegranate varieties in the whole band by PLS-DA discriminant analysis are 9730%, 96.55% and 96.77%, respectively. In order to further improve the discrimination rate of PLS-DA model, the "load method" is used to select the feature band. The correct recognition rate of the optimized model to the verification set is 97.30%, 100.00% and 100.00%, respectively. The results show that the optimized PLS-DA model can identify pomegranate varieties with satisfactory results. (2) in order to reduce the influence of variety differences on the quality detection model of near infrared spectroscopy, Taking three different pomegranate varieties as the research object, the near infrared detection model of pomegranate pH value of single variety and mixed variety was established by PLS method. Through the comparison, it can be seen that the model established by mixing the three varieties has achieved better prediction results. The correlation coefficients of correction set and verification set are both greater than 0.900. Therefore, the model established by using the correction set of three mixed varieties can realize the rapid, nondestructive and accurate determination of the pH value of pomegranate. This conclusion is further extended to the determination of other quality indexes. Combined with CARS-PLS band screening, the R 鈮，
本文编号：2494371
[Abstract]:Pomegranate has been planted in China for more than 2000 years. At present, the planting area of pomegranate in China is about 1.75 million mu, ranking first in the world. However, in the process of pomegranate planting and storage, because of the variety of pomegranate and the lack of effective and rapid monitoring methods in storage and preservation, the production and storage of pomegranate can not meet the requirements of standardization by manpower alone. Near infrared spectroscopy (NIR) has been widely used in qualitative discrimination and quantitative quality detection of fruit varieties because of its simple, rapid, nondestructive, pollution-free and other advantages. At present, the analysis object of near-infrared technology is mainly thin-skinned fruit, but the research on pomegranate and other thick-skinned fruit is very few. In this study, the near infrared model of physical and chemical quality and phenolic content of different varieties of pomegranate and pomegranate was established by using diffused reflectance spectroscopy and chemometrics to establish the near infrared model of pomegranate and pomegranate content in Lintong, Shaanxi Province. Quality testing provides software support to promote the rapid development of pomegranate industry. The main research contents and results are as follows: (1) the near infrared spectra of "a string of bells", "big skin sweet" and "yellow skin sweet" pomegranate were collected. After pretreatment with MSC, PCA MLP neural network method and PCA Fisher linear discriminant method were used respectively. PLS-DA discriminant method is used to establish the model. By comparing the three discriminant models, the PLS-DA discriminant results are better than the other two. The correct recognition rates of the three pomegranate varieties in the whole band by PLS-DA discriminant analysis are 9730%, 96.55% and 96.77%, respectively. In order to further improve the discrimination rate of PLS-DA model, the "load method" is used to select the feature band. The correct recognition rate of the optimized model to the verification set is 97.30%, 100.00% and 100.00%, respectively. The results show that the optimized PLS-DA model can identify pomegranate varieties with satisfactory results. (2) in order to reduce the influence of variety differences on the quality detection model of near infrared spectroscopy, Taking three different pomegranate varieties as the research object, the near infrared detection model of pomegranate pH value of single variety and mixed variety was established by PLS method. Through the comparison, it can be seen that the model established by mixing the three varieties has achieved better prediction results. The correlation coefficients of correction set and verification set are both greater than 0.900. Therefore, the model established by using the correction set of three mixed varieties can realize the rapid, nondestructive and accurate determination of the pH value of pomegranate. This conclusion is further extended to the determination of other quality indexes. Combined with CARS-PLS band screening, the R 鈮，
本文编号：2494371
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