• ISSN 1008-505X
  • CN 11-3996/S
傅嘉敏, 耿琦, 王梦荷, 张丽霞, 黄晓琴, 韩晓阳. 基于电子鼻和分光测色仪技术的茶树叶片氮营养诊断[J]. 植物营养与肥料学报, 2019, 25(8): 1413-1421. DOI: 10.11674/zwyf.18338
引用本文: 傅嘉敏, 耿琦, 王梦荷, 张丽霞, 黄晓琴, 韩晓阳. 基于电子鼻和分光测色仪技术的茶树叶片氮营养诊断[J]. 植物营养与肥料学报, 2019, 25(8): 1413-1421. DOI: 10.11674/zwyf.18338
FU Jia-min, GENG Qi, WANG Meng-he, ZHANG Li-xia, HUANG Xiao-qin, HAN Xiao-yang. Diagnosis of nitrogen nutrition in fresh tea leaves with electronic nose and spectrophotometer[J]. Journal of Plant Nutrition and Fertilizers, 2019, 25(8): 1413-1421. DOI: 10.11674/zwyf.18338
Citation: FU Jia-min, GENG Qi, WANG Meng-he, ZHANG Li-xia, HUANG Xiao-qin, HAN Xiao-yang. Diagnosis of nitrogen nutrition in fresh tea leaves with electronic nose and spectrophotometer[J]. Journal of Plant Nutrition and Fertilizers, 2019, 25(8): 1413-1421. DOI: 10.11674/zwyf.18338

基于电子鼻和分光测色仪技术的茶树叶片氮营养诊断

Diagnosis of nitrogen nutrition in fresh tea leaves with electronic nose and spectrophotometer

  • 摘要:
    目的 利用电子鼻和分光测色仪建立一套快速检测茶树叶片氮含量的无损伤检测方法。
    方法 供试样品为茶树顶芽向下第3~4片无损伤叶片。在预实验中优化了气体收集瓶体积、顶空预热温度和顶空时间等参数。采用电子鼻自带Winmuster软件将经过优化后的传感器响应特征值进行主成分分析 (principal component analysis,PCA)、线性判别法分析 (linear discriminant analysis,LDA) 和负荷加载分析 (loadings analysis,LA),筛选出灵敏性最好的传感器。同时用分光色差仪对茶树叶片色度值进行测定。样品的测量部位是叶肉区,每组20次重复。色度值主要包括L (表示黑白或者亮暗)、a (表示红绿)、b (表示黄蓝) 值。采用Origin 8.0软件对测色仪L、a、b值分别进行一元线性回归分析。利用SPSS 16.0软件采用LSD法进行单因素方差分析 (one-way Anova),并进行t检验。对分光测色仪中色差指标进行筛选,以获得相关系数最高的参数。采用凯氏定氮法测定茶叶总氮含量。正式试验第二步是以不同氮含量下的电子鼻和分光测色检测数据为基础,分别建立气味、颜色、气味结合颜色的3种氮含量预测模型,并进行比较分析。
    结果 通过预备试验,建立了气体收集器体积为50 mL、顶空预热温度为30℃、顶空时间为30 min的电子鼻检测体系。正式试验第一步确定了以对氮氧化合物灵敏 (S2),对甲烷灵敏 (S6),对无机硫化物灵敏 (S7),对醇类、醛类、酮类物质灵敏 (S8),对有机硫化物灵敏 (S9) 的传感器为主要传感器。根据L、a、b表色系统,b值与叶片缺氮程度呈线性相关。正式试验第二步利用气味、颜色、气味结合颜色建立的3个氮含量预测模型都具有可行性,其中气味结合颜色建立的预测模型准确率最高,达到90%。
    结论 用气味结合颜色的预测模型预测茶树叶片氮含量准确度较高,可在实际工作中进行运用。

     

    Abstract:
    Objectives This study was to establish a non-destructive method for rapid detection of nitrogen contents in tea leaves using electronic nose and spectrophotometer.
    Methods The 3rd or 4th healthy leaves below the top bud of tea tree were used as the tested materials. In preliminary experiment, the parameters of gas collector volume, headspace preheating temperature and headspace time were optimized. The first step of the main experiment used the built-in Winmuster software of electronic nose, and principal component analysis (PCA), linear discriminant analysis (LDA) and loadings analysis (LA) were carried out with characteristic values responded to sensors after optimization, thus the most sensitive sensors were chosen. Meanwhile, the chromatic values were determined by spectrophotometer. The measured area was mesophyll with 20 replications. The chromatic values included L (represents black or white), a (represents red or green) and b (represents yellow or blue). The one variant linear regression analysis to L, a and b values was conducted by Origin 8.0 software, and one way Anova analysis was made with SPSS 16.0 software and eventually the t test was done. Then chromatic aberration values were screened and parameter with the highest correlation coefficient was acquired. And nitrogen content was detected with Kjeldahl method. In the second step of main experiment based on the electronic nose and spectrophotometric colorimeter data under different nitrogen contents, the prediction models of nitrogen content by odor, by color and by odor combining with color were established and compared.
    Results In preliminary experiment, the electronic nose detection system was established: with 50 mL gas collector volume, 30℃ headspace temperature and 30 min headspace time. The nitrogen contents of tea leaves could be distinguished by electronic nose. In the first step of main experiment, the sensors sensitive to oxynitride (S2), methane (S6), sulfur compounds (S7), alcohols, aldehydes and ketones (S8) and organic sulfur compounds (S9) were selected in the process of parameter optimization. According to L, a, b chromametric system, the b value showed a significant linear correlation with the total nitrogen content, so that it could be used as a marker to judge leaf nitrogen contents. In the second step of main experiment based on the odor, color and odor combined with color data, the prediction models for nitrogen contents were established. Among them, the prediction model based on odor combined with color had the highest accuracy, reaching 90%.
    Conclusions The prediction model in predicting nitrogen contents in tea leaves by odor combining with color had high accuracy, hence could be used in practical operation.

     

/

返回文章
返回