| 刘洪剑 1,2，金红岗 3，黄晓明 3，肖旭斌 3，周乐群 3，刘 涛 1,2，字淑慧 1,2，李枝桦 3.茄衣烟叶 7 种化学成分近红外预测模型的建立[J].广东农业科学,2023,50(7):64-73 |
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茄衣烟叶 7 种化学成分近红外预测模型的建立 |
| Establishment of Near-infrared Prediction Model for Seven Chemical Components of Wrapper Tobacco |
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| DOI：10.16768/j.issn.1004-874X.2023.07.007 |
| 中文关键词: 茄衣烟叶 化学成分 近红外光谱 预处理方法 偏最小二乘法 定量分析 |
| 英文关键词: wrapper tobacco chemical components near infrared spectrum preprocessing method partial least squares quantitative analysis |
| 基金项目:红云红河集团项目（HYHH2021YL01） |
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| 中文摘要: |
| 【目的】常规化学检测方法检测茄衣烟叶内总氮、钾、总糖、还原糖、总碱、氯和镁等 7 种化学成分含量的过程复杂、费时费力，而近红外光谱技术操作简单、检测快速。旨在建立一种近红外光谱检测模型，对茄衣烟叶内 7 种化学成分的含量实现快速定量分析。【方法】以云南雪茄茄衣烟叶为试材，采用常规化学方法检测茄衣烟叶 7 种化学成分含量，再利用近红外光谱技术结合偏最小二乘法对茄衣烟叶中 7 种化学成分含量及其光谱数据进行近红外模型分析，通过比较模型均方根误差和相关系数确定预测性能最佳的模型。【结果】7种化学成分分别采用原始光谱、一阶导数、一阶导数、原始光谱、原始光谱、一阶导数 + 中值滤波和一阶导数+ 中值滤波预处理方法建立的模型预测效果最佳，最佳主成分数分别为 20、7、4、24、21、9 和 7。7 种模型的训练集相关系数分别为 0.9441、0.8589、0.7664、0.9511、0.9547、0.9031 和 0.8620，交叉验证均方差分别为 0.1288、0.2846、0.0280、0.0096、0.1894、0.2965 和 0.0795；验证集相关系数分别为 0.8958、0.7675、0.7181、0.7928、0.7282、0.8062 和 0.7980，验证集均方差分别为 0.1789、0.3011、0.0324、0.0193、0.3855、0.3990 和 0.0999。模型外部验证结果表明，7 种化学成分预测值与化学值的平均相对标准偏差值皆小于 32%。【结论】利用近红外光谱技术对茄衣烟叶 7 种化学成分含量进行快速定量分析是可行的，该模型对 7 种化学成分含量具有良好的预测效果，可为茄衣烟叶 7 种化学成分含量快速定量分析提供参考。 |
| 英文摘要: |
| 【Objective】It is complex, time-consuming and laborious for conventional chemical detection method to detect the content of total nitrogen, potassium, total sugars, reducing sugars, total alkali, chlorine and magnesium in wrapper tobacco, while near-infrared spectroscopy technology is simple to operate and rapid to detect. The aims is to establish a near infrared spectroscopy detection model to achieve rapid quantitative analysis of the content of the seven chemical components in wrapper tobacco.【Method】Taking Yunnan cigar wrapper tobacco as the material, conventional chemical method was used to detect the content of seven chemical components in wrapper tobacco, and then near infrared spectroscopy was used to analyze the content of seven chemical components and their spectruml data of wrapper tobacco by using near infrared spectroscopy combined with partial least squares method, and the model with the optimal predictive performance was determined by comparing the root mean square error and correlation coefficient of the model.【Result】The optimal prediction results of the seven chemical components were established by the preprocessing methods of original spectrum, first derivative, first derivative, original spectrum, original spectrum, first derivative + median filtering and first derivative + median filtering, and the optimal principal component numbers were 20, 7, 4, 24, 21, 9 and 7, respectively; The training set correlation coefficients (r) of the seven models were 0.9441, 0.8589, 0.7664, 0.9511, 0.9547, 0.9031 and 0.8620, respectively. The root mean square error of cross validation (RMSECV) were 0.1288, 0.2846, 0.0280, 0.0096, 0.1894, 0.2965 and 0.0795; The correlation coefficients (r) of the validation set were 0.8958, 0.7675, 0.7181, 0.7928, 0.7282, 0.8062 and 0.7980, and the root mean square error of prediction (RMSEP) were 0.1789, 0.3011, 0.0324, 0.0193, 0.3855, 0.3990 and 0.0999. Moreover, the models were externally verified, and the results indicated that the average RSD of the predicted values and chemical values of the seven chemical components were less than 32%.【Conclusion】It was feasible to use near infrared spectroscopy to rapidly and quantitatively analyze the contents of seven chemical components in wrapper tobacco. The models had good prediction effect on the contents of seven chemical components, and could provide a reference for the rapidly and quantitatively analysis of seven chemical components in wrapper tobacco. |
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