基于叶片光谱分析的小麦白粉病与条锈病区分及病情反演研究

小麦条锈病和白粉病作为我国麦区两种重要病害,在田间常同时发生,为病害防治管理带来困难。基于实验测试获得白粉病、 条锈病叶片光谱数据,探讨采用光谱分析对两种病害进行区分识别及严重度监测的可行性。通过相关分析和独立T检验,筛选出对白粉病和条锈病敏感度差异较显著的波段及光谱特征,包括665～684,718～726 nm等6个波段范围,以及DEP_550-770,SIWSI等11个光谱特征。基于这些波段和特征,采用FLDA构建病害判别模型;借助PLSR分析构建病情严重度反演模型。研究结果表明,筛选得到的反射率波段和光谱特征能够较好地区分两种病害,判别模型总体精度达到80%以上,准确度较高。其中,染病比率超过20%的病叶区分和识别精度可达95%。同时,分别基于两种病害敏感光谱特征构建的病情严重度反演模型能够较好地估测病情严重度,两种病害估测均方根误差均低于15%。上述叶片尺度小麦白粉病和条锈病区分和严重度反演模型为进一步研究两种病害冠层尺度的区分和监测提供基础。     

Simultaneous Determination of Oil and Water in Soybean by LF-NMR Relaxometry and Chemometrics

A fast, non-destructive and eco-friendly method was developed to simultaneously determine the oil and water contents of soybean based on low field nuclear magnetic resonance(LF-NMR) relaxometry combined with chemometrics, such as partial least squares regression(PLSR). The Carr-Purcell-Meiboom-Gill(CPMG) magnetization decay data of ten soybean samples were acquired by LF-NMR and directly applied to the PLSR analysis. Calibration models were established via PLSR with full cross-validation based on the reference values obtained by the Soxhlet extraction method for measuring oil and oven-drying method for measuring water. The results indicate that the calibration models are satisfactory for both oil and water determinations; the root mean squared errors of cross-validation(RMSECV) for oil and water are 0.2285% and 0.0178%, respectively. Furthermore, the oil and water contents in unknown soybean samples were predicted by the PLSR models and the results were compared with the reference values. The relative errors of the predicted oil and water contents were in ranges of 1.25%-4.96% and 0.44%-2.49%, respectively. These results demonstrate that the combination of LF-NMR relaxometry with chemometrics shows great potential for the simultaneous determination of contents of oil and water in soybean with high accuracy.     

利用Far-IR和THz-TDS光谱法测定大米中西维因

利用太赫兹时域光谱（THz-TDS）和傅里叶变换远红外光谱（FT-Far-IR）技术研究了大米中西维因在太赫兹频段的吸收光谱特征,并结合化学计量学方法对大米中西维因进行了测定。样品的制备采取待测农药西维因与大米粉末混合压片的方法模拟真实检测情景,无需样品的分离富集。分别将样品在1.8～6.3 THz特征波段内的Far-IR吸收光谱数据和在0.5～1.5 THz特征波段内的THz-TDS吸收光谱数据随机划分为训练集和验证集。采用偏最小二乘回归（PLSR）方法建立定量分析模型,将校正均方根误差（RMSECV）、预测均方根误差（RMSEP）、预测相关系数（R_v）作为模型性能评判的依据,RMSECV,RMSEP越小,R_v越大,则所建立的模型越好。两种检测技术均得到较好的结果。其中,运用Far-IR技术所得数据建立的定量分析模型预测相关系数(R_v)为0.99,校正均方根误差（RMSECV）为0.007 7, 预测均方根误差（RMSEP）为0.008 6; 运用THz-TDS技术所得数据建立的定量分析模型预测相关系数为0.98,校正均方根误差（RMSECV）为0.002 5、预测均方根误差（RMSEP）为0.004 4。该研究为定量检测粮食中的农残提供了一种新方法。     

土壤全氮反射光谱估算机理研究

反射光谱技术具有快速、便捷等特点,过去几十年中将其应用于土壤科学的研究呈指数增长,且广泛用于土壤属性估算。土壤全氮含量是一项非常重要的肥力指标,光谱估算全氮含量可以为实现精准农业提供重要支持。但反射光谱估算土壤全氮含量是基于全氮与有机碳的相关性还是基于氮本身的吸收特征仍然存在争议。本文以江苏滨海土壤为研究对象,利用偏最小二乘法分别构建全氮和有机碳在相同建模样本量、不同全氮含量及变异程度情况下的估算模型,通过分析模型精度变化规律及全氮与有机碳估算模型系数的相关性,探讨土壤全氮反射光谱估算机理。结果表明,该地区土壤为1 000年来滨海滩涂经人为耕作发育形成,全氮含量不高,有机碳含量偏低。全氮与有机碳之间存在较强的相关性,相关系数高达0.98。土壤全氮含量估算精度随样本集全氮含量的平均值、标准差增大出现先增加后略有减小的变化规律,与变异系数的变化规律相一致。当全氮含量较低时(样本平均值小于0.27 g·kg-1),土壤全氮与有机碳相关系数也较小,实现反射光谱估算全氮是基于氮的吸收特征;当全氮含量较高时(样本平均值大于0.29 g·kg-1),全氮与有机碳相关性较强且有机碳模型精度高于全氮,说明有机碳对光谱曲线的影响随其含量增加而增大,并掩盖了氮的吸收特征,实现反射光谱估算全氮是基于其与有机碳的相关性。该研究揭示了土壤反射光谱估算全氮含量的机理,从而为反射光谱快速估算土壤全氮含量提供理论依据。     

PROSPECT模型的特征波长优化与作物叶绿素含量检测

叶绿素是作物生长诊断的重要参数,对其进行高效检测是农田精细化管理的基础.PROSPECT模型是作物光谱学检测研究的重要工具,可为建立高精度叶绿素诊断模型提供数据集基础.为了建立具有普适性的田间玉米作物叶绿素含量检测模型,使用PROSPECT模型输入叶片结构参数和生化参数模拟叶片400～2500 nm波段反射率曲线106...     

不同贮藏温度下滩羊肉pH值的近红外高光谱动力学模型建立

为了比较4℃、15℃两种贮藏温度下滩羊肉pH值的变化,优选出滩羊肉贮藏期间的最优模型,采用pH酸度计测量样本pH值,建立两种贮藏温度下传统动力学的零级和一级模型;应用近红外(900～1 700 nm)高光谱成像采集两种贮藏温度下滩羊肉的光谱数据,剔除异常值后进行光谱预处理;使用连续投影算法(SPA)提取特征波长,建立全波段和特征波长的偏最小二乘(PLSR)预测模型;对比分析得到的最优光谱模型与动力学模型相结合,确定滩羊肉光谱动力学模型。结果表明,4℃和15℃的传统动力学模型的相关系数分别为0.502和0.912;4℃下原始光谱经PLSR建模后效果最优,相关系数R_c为0.821,R_p为0.863,15℃经SG-S(3,7)+De-trending(4)预处理后经PLSR建模效果最优,相关系数R_c为0.876,R_p为0.819。因此,高光谱结合传统动力学的模型检测羊肉pH值的方法是可行的,该模型可以预测15℃下滩羊肉的贮藏期。     

基于二次迭代Monte Carlo的羊肉硬度定量检测研究

随着居民生活水平的提高和对健康饮食结构的重视,羊肉作为一种高蛋白且低脂肪和胆固醇的畜肉,需求量逐年上涨.根据国家统计局统计,2012年—2019年我国畜肉产业中羊肉产量占比从6.27％ 上升到9.02％.研究提出了一种基于二次迭代Monte Carlo(MC)算法剔除异常样本的羊肉硬度定量检测PLSR模型.采用Gaia...     

铁皮石斛多糖含量的高光谱反演研究

铁皮石斛是我国传统的中草药,多糖含量是衡量其品质优劣的一个重要指标。主要研究铁皮石斛多糖含量与光谱间的关系,旨在寻找铁皮石斛多糖含量的无损检测方法。研究方法:实验材料选自不同处理铁皮石斛组培苗和不同生长阶段的驯化苗。其中36个样本作为建模样本建立模型,11个样本作为检验样本对模型进行检验。分别采用偏最小二乘回归法和因子分析法建立样本光谱与多糖含量间的关系。结果表明:(1)光谱一阶微分与多糖含量的相关性优于光谱反射率,其中对多糖含量较敏感的波段主要集中在可见光区域。(2)偏最小二乘回归法的模型决定系数最高,但其预测能力较差。因子分析法模型具有较好预测能力,基于光谱反射率因子分析模型和基于光谱一阶微分因子分析模型的相对分析误差分别为2.269和2.305。     

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).