基于近红外光谱的小麦成分检测仪

Development of Wheat Component Detector Based on Near Infrared Spectrum

当前较为传统和普遍的谷物成分和品质检测方法主要是传统分离式、人工检测,传统检测方法的主要问题是耗费时间长效率低,无法实现快速检测。近红外(NIR,波长范围:780~2500 nm)光谱分析技术具有适用样品范围广、定量测量精度高、检测时间极短,分析效率高,无损检测,不污染环境、操作简单、可以实现现场快检或在线检测等优点,广泛的应用于谷物和粮食品质的在线或快速检测。目前现有的近红外谷物检测仪器一般能检测谷物少数成分的结果,但结构复杂、价格昂贵。且由于不同季节不同地区谷物的差异导致模型适用性差,难以在基层谷物收购、加工和流通环节推广应用。针对这些问题,设计了一款基于近红外光谱分析的小麦品质检测仪。采用Python上位机来控制近红外光谱仪,通过设定和修改采集参数集成控制检测仪三个舵机以及重量传感器,实现光谱采集,并对光谱数据进行预处理,代入模型计算得到目标样品的理化指标。通过主成分分析(PCA)处理去除异常值,后经过递推平均滤波、标准正态变换(SNV)等预处理,再经过竞争自适应重加权采样(CARS)特征筛选后利用偏最小二乘回归(PLS)得到最优模型。测试结果表明,该系统能够长时间稳定运行,并有效降低了杂散光、样品均匀性等因素带来的误差。并可实现一台机器对不同地区不同季节的小麦的水分、湿面筋、白度和容重指标的检测,可以满足谷物收购与储存等方面的需求。

Currently,the traditional measuring methods of grain quality are mainly the traditional separation and manual inspection,which take a long time and have low efficiency.Near Infrared(NIR,780~2500 nm)spectral analysis technology has the advantages of a wide range of applicable samples,high accuracy of quantitative measurement,high measurement efficiency,and non-destructive testing,which is widely used in agriculture online or rapid measurement.Currently,the existing NIR instruments measuring grain quality are expensive,which prevents a wider application of this kind of device.Moreover,the predicting model is limited in applicability due to the differences ingrains in different seasons and regions.To solve these problems,in this study,a new type of NIR spectrometer system is developed to measure wheat quality.The system uses a control system developed with Python.By setting and modifying the acquisition parameters,the three steering gears and weight sensors are integrated to control the spectra data acquisition.The spectral data are preprocessed and substituted into the model to calculate the quality parameters of the target wheat samples.The principal component analysis(PCA)method removes the outlier's spectral data.Then,the selected spectral data are preprocessed by recursive mean filtering and standard normal transformation(SNV).Finally,the optimized model is obtained with the partial least squares regression(PLS)method after competitive adaptive reweighting sampling(CARS)wavelength selection.The prediction model is currently developed for moisture,wet gluten,and whiteness of wheat.The results show that this model can effectively reduce the error caused by stray light,sample uniformity,and other effective factors.The developed NIR spectrometer system can satisfy the requirements of grain acquisition and storage.