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首页> 外文会议>Proceedings of joint international agricultural conference (JIAC 2009) >EFFECTS OF SPECTRAL PREPROCESSING AND WAVELENGTH SELECTION ON DISCRIMINATION OF MAIZE VARIETIES BY NIR SPECTROSCOPY
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EFFECTS OF SPECTRAL PREPROCESSING AND WAVELENGTH SELECTION ON DISCRIMINATION OF MAIZE VARIETIES BY NIR SPECTROSCOPY

机译：光谱预处理和波长选择对近红外光谱鉴别玉米品种的影响

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摘要

The discrimination of crop seed varieties is now one of the important subjects in the agricultural product testing. The near-infrared (NIR) spectroscopy as a powerful tool to make the fast, non-destructive testing has recently been applied in the discrimination of seed varieties, e.g. maize, wheat and rice. However, the analysis of NIR spectral data has to depend on chemometrics, in which the data preprocessing and wavelength selection are the foundation of building a model. Therefore, it is needed to investigate the effects of pre-processing and wavelength selection on the qualitative discrimination of seed varieties.In this work we study the effects of data preprocessing (no preprocessing, first derivative and second derivative transformation, standard normal variate transformation (SNV), vector normalization, smoothing,) and wavelength selection on the discrimination of maize seed varieties. Totally 140 samples were obtained from seven varieties of maize seeds (20 samples per variety, 10 for training the model, another 10 for testing the model). NIR spectra were recorded from 4000 cm-1~12000cm-1 at 3.9 cm-1 interval using a FT-NIR spectrometer (resolution 8 cm-1). The raw spectral data are pretreated by the above-referenced methods. The performance of the six preprocessing methods is evaluated on basis of the two data sets: all of the spectral data and the data from the characteristic regions selected by a standard deviation-based feature selection method, respectively. Furthermore, based on principal component analysis (PCA) scores of the processed data, the discrimination models for every variety are built using Biomimetic Pattern Recognition (BPR) method, which has been successfully used to build the discrimination models for maize and wheat seeds. The correct acceptance rate (CAR) (to measure the ability of recognizing the samples of the same variety) and correct rejection rate (CRR) (to measure ability of distinguishing other varieties) for the testing samples were finally calculated.The average CAR and CRR for seven varieties of each kind of model using one combination (one preprocessing method with or without wavelength selection) attain above 80%. The best discrimination model uses first derivative and all spectral data, by which both CAR and CRR for five varieties reach 100%, and the average CAR and CRR attains 98.6% and 98%, respectively. Except smoothing, other preprocessing methods can get the better CAR and CRR than non-preprocessing. The wavelength selection can only improve CAR of SNV and vector normalization models, which rise CAR to 100%. Because the seeds of different cereal crops share similarity in compositions and traits, our results may be helpful for building the qualitative discrimination models of seed varieties of other cereal crops by NIR spectroscopy.
机译：现在，对农作物种子品种的歧视是农产品测试中的重要主题之一。近红外（NIR）光谱作为进行快速，无损检测的有力工具，最近已被用于区分种子品种，例如玉米，小麦和大米。但是，近红外光谱数据的分析必须依靠化学计量学，其中数据预处理和波长选择是建立模型的基础。因此，需要研究预处理和波长选择对种子品种定性判别的影响。在这项工作中，我们研究了数据预处理（无预处理，一阶和二阶导数变换，标准正态变量变换（ SNV），向量归一化，平滑化和波长选择对玉米种子品种的区分。从七个玉米种子品种中总共获得了140个样品（每个品种20个样品，用于模型训练的10个，用于测试模型的另外10个）。使用FT-NIR光谱仪（分辨率8 cm-1）以3.9 cm-1的间隔记录4000 cm-1〜12000cm-1的NIR光谱。原始光谱数据通过上述方法进行预处理。基于两个数据集评估六种预处理方法的性能：分别通过基于标准偏差的特征选择方法选择所有光谱数据和来自特征区域的数据。此外，基于处理数据的主成分分析（PCA）分数，使用仿生模式识别（BPR）方法建立了每个品种的鉴别模型，该方法已成功地用于建立玉米和小麦种子的鉴别模型。最终计算出测试样品的正确接受率（CAR）（用于衡量识别同一品种的样品的能力）和正确拒绝率（CRR）（用于测量对其他品种的识别的能力）。平均CAR和CRR使用一种组合（一种带有或不带有波长选择的预处理方法）对每种模型的七个变体的结果达到80％以上。最佳判别模型使用一阶导数和所有光谱数据，五个品种的CAR和CRR均达到100％，平均CAR和CRR分别达到98.6％和98％。除了平滑之外，其他预处理方法比非预处理可以获得更好的CAR和CRR。波长选择只能提高SNV和矢量归一化模型的CAR，从而使CAR达到100％。由于不同谷物作物的种子在成分和性状上具有相似性，因此我们的研究结果可能有助于通过近红外光谱技术建立其他谷物作物种子品种的定性判别模型。

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