摘要: 无土栽培具有省地、省时等诸多优点,是农业生产中发展最快的技术之一,基质选择是无土栽培成功与否的关键之一。因此,对基质参数的快速测定具有重要意义。文章采用近红外光谱技术定量测定基质中含水率、电导率和pH值,利用基线校正和微分方法对光谱进行预处理,在不同的波段范围建立不同的偏最小二乘(partial least squares,PLS)回归模型。研究发现:光谱经基线校正后,漂移现象得到明显改善;近红外光谱可用于基质中电导率的定量检测,用于预测基质电导率的最优PLS模型的相关系数r、交互验证均方根误差(root mean square error of cross-validation,RMSECV)、相对分析误差(relative percent deviation,RPD)和检验偏差(Bias)值分别是0.923 6,634 μs·cm-1,3.11和19.8 μs·cm-1,使用的最佳回归因子是7,建模的最优波段范围是4 246.7~7 502.2 cm-1。近红外光谱技术对于含水率的预测也是可行的,但模型精度有待进一步提高,而近红外光谱无法预测基质中的pH值。
关键词:近红外光谱;基质;含水率;电导率;pH
Abstract:Soilless culture has many virtues, such as space saving, time saving, etc.. It has become one of the technologies which developed fastest in agricultural products. The selection of substrate is one of the keys to determining the success of soilless culture. Therefore, it is important to rapidly determine the parameters of substrate. In the present paper, moisture, electronic conductivity and pH values of substrate were tested by near-infrared (NIR) spectroscopy. The spectra were preprocessed by baseline correction and derivative. Partial least squares (PLS) regression model was built using different wave bands. It was found that baseline drift was improved after correction. NIR spectroscopy can be used to determine EC value of substrate. The correlation coefficient r, root-mean-square error of the cross validation (RMSECV), relative percent difference (RPD) and bias of the optimum PLS was 0.923 6, 634 μs·cm-1, 3.11 and 19.8 μs·cm-1, respectively, when the best wave band was 4 246.7~7 502.2 cm-1 and the best factor was 7. NIR spectroscopy technique can also be used to predict moisture of substrate although the accuracy of model should be improved. However, it can not be used to predict pH value of substrate.
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