温度对叶片近红外光谱的影响

近红外光谱分析技术是发展最快的定性和定量分析技术之一,在各个领域得到了广泛应用。但近红外谱区自身信息的特点决定了其吸收强度弱、信噪比低、谱峰严重重叠等缺点,这使得近红外光谱易受样品来源、样品种类、样品状态、装样条件和样品温度等的影响,造成光谱的不确定性。文章以温度对叶片近红外光谱的影响为研究内容,详细考察了不同温度下样品的叶绿素的校正和预测模型。结果发现温度对模型精度有一定的影响,样品的温度在10和20℃下模型的精度较高,10℃下模型精度效果最好,但是所用的主成分也较多。当实验温度达到25℃时,模型的校正和预测精度都相对较差。利用判别分析对各个温度下的光谱进行分类。发现20℃下采集的光谱没有分到其他温度区外,其他的都有不同程度的跨区,这说明除20℃外,其他温度下测得的光谱差异不明显。试验对叶片近红外检测的条件和应用做了初步的探索性工作,为今后提出温度修正模型提供理论基础。

Study on the Influence of Temperature on Near Infrared Spectra

Near-infrared (NIR) spectroscopy technique is one of qualitative and quantitative analysis techniques that were developed quickly in recent years. However, the characteristic of NIR spectral region determines its low absorbance, low sign-to-noise and peak overlapping, which results in the phenomenon that th NIR spectrum is affected by sample source, sample varieties, sample state, location and sample temperature and uncertainty of spectrum. In this paper, the influence of temperature on NIR spectra was studied and calibration and validation models of chlorophyll at different temperatures were built. The results show that temperature does have effect on the model precision. High precision could be achieved at 10 and 20 degrees C and the former was the best though using more principal components. Calibration and validation precisions of models were poor when it was 25 degrees C. At the same time, spectra obtained at different temperatures were classified using discriminate analysis. It was found that the spectra were misclassified exception ones obtained at 20 degrees C which meant that there were not clear differences between the spectra exception the ones at 20 degrees C. The primary research work about the condition and application of detecting leaves was studied which helps to provide theory foundation for proposing temperature modification model in the future.