Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to quantify fructan concentration in samples from seven grass species. Savitzky–Golay first derivative with filter width 7 and polynomial order 2 with mean centering was applied as a spectral pre-treatment method to remove unimportant baseline signals. In order to model the NIR spectroscopy data the partial least squares regression (PLSR) approach was used on the full spectra. Variable selection based on PLSR by jack-knifing within a cross-model validation (CMV) framework was applied in order to remove non-relevant spectral regions. PLSR was also used to model fructan concentrations from an augmented matrix [X|G], where X is spectra and G is correlation matrix of band specific information and X, in order to integrate the chemical band information in regression models. The present analysis showed that rapid quantification of fructans by NIR spectroscopy is possible and that jack-knifing PLSR within a CMV framework is an effective way to eliminate the wavelengths of no interest. Jack-knifing PLSR did not improve the predictive ability because the root mean square error of prediction (RMSEP) increased (1.37) compared to the full model (1.26). This was possibly due to signals from carbohydrates, which could act as cofactor in the prediction of fructans. However, jack-knifing PLSR within a CMV framework simplified the interpretation of the regression model with r² = 0.90 and RMSEP = 1.37.     

基于PCA与GA的近红外光谱建模样品选择方法

针对在利用遗传算法进行样品选择(SSGA)时,使用原光谱矩阵运算时间非常长的问题,提出了一种使用丰成分得分矩阵代替原光谱矩阵进行选样的新算法(PCA-SSCA).讨论了PCA-SSGA算法的主成分分解,染色体编码与解码,目标函数与适应度函数确定,选择算子、交叉算子、变异算子等.在Visual C 环境中开发了PCA-SSGA软件系统.通过对131份小麦籽粒样品针对其干基蛋白含量进行PCA-SSGA运算,经过39200代进化,最终找出最佳样品组合:样品数日由131减少为70,通过偏最小二乘留一法交叉验证(PLS-LOO-CV),决定系数(R2)由0.9477增加为0.9841,交叉验证预测均方差(RMSPCV)由0.3938减少为0.1934.从运算时间上看,PCA-SSGA进化一代时间是SSGA的1/2193,整个样品优选过程时间大大缩短,效率得以显著提高.试验结果表明:PCA-SSGA可以方便灵活地调整遗传算法的参数、自动地选择样品,这对优化农产品近红外光谱模型、进一步提高预测精度提供了很好的技术支持.     

Use of mid‐infrared spectroscopy in the diffuse‐reflectance mode for the prediction of the composition of organic matter in soil and litter

Mid‐infrared spectroscopy (MIRS) is assumed to be superior to near‐infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid‐infrared region including part of the near‐infrared region (7000–400 cm^–1) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C CPMAS‐NMR spectroscopy. A partial least‐square method and cross‐validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid‐infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF ≥ 0.70, 0.8 ≤ a ≤ 1.2, r ≥ 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O‐alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl–to–aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse‐reflectance mode were generally not superior to those of NIRS.     

Will the Conventional Soil-Plant Analysis Pass into Oblivion? Rapid and Low-Cost Determination Using Spectroscopy

Is it time for replacing the traditional soil-plant analysis methods by spectroscopy? Traditional analytical methods are subject to significant sources of errors that commonly result in compromise of soil characteristics and gross underestimation of chemical concentrations in soil-plant for a wide range of analyses. Given the magnitude of the decisions that are made based on these data, the technical and economic impacts of using traditional methods can be significant. Therefore, it is now time for soil-plant spectroscopy to enter an operational phase. Spectroscopy has opened a new era in which traditional analyses are being left behind. Spectroscopy can be used to accurately predict certain soil and plant properties, making it a valuable tool in precision farming. Soil spectra contain much information relevant to soil-plant properties. Multivariate regressions of spectra can accurately predict several soil properties. Spectroscopy requires only a few seconds to analyze a soil sample, but the relevant information needs to be mathematically extracted from the spectra so that it can be correlated with soil properties. Therefore, the successful application of spectroscopy to quantify and evaluate the relationships between soil reflectance and soil properties depends largely on the development of accurate and robust calibration models. These procedures appear to be sufficiently accurate and precise to enable their use in soil and plant analysis. There are many advantages to using this technique.     

锦橙叶片氮含量可见近红外光谱模型研究

以单系枳砧蓬安100号锦橙为试材,采用田间试验的方法开展利用鲜叶可见近红外光谱估测叶片氮素营养状况的研究。运用偏最小二乘法(PLS)分析叶片反射光谱与氮含量之间的关系。结果表明,在可见光350～700nm,随着氮肥用量的增加,叶片光谱反射率呈下降趋势;而在750～1075nm范围内,叶片光谱反射率随着氮肥用量增加而增加。通过对叶片反射光谱、一阶微分、二阶微分和倒数对数光谱进行变量标准化(SNV)处理,运用偏最小二乘法(PLS)与内部交叉验证建立的氮含量预测回归模型,其中反射光谱的一阶微分光谱氮含量定标模型具有最好的预测能力,其建模与预测均方根误差和标准差都较低且接近,偏差绝对值最小。因此,可以利用叶片反射光谱的一阶微分值来预测蓬安100号锦橙叶片氮含量。     

近红外光谱分析与传统方法检测豆粕中营养成分的比较

近红外光谱分析技术(NIR)能简单、快速、准确地测定有机物中的化学成分。本实验用该法测定豆粕中各种常规营养成分,并将其与化学方法进行比较。数据显示:NIR与化学方法测定的结果相比,粗蛋白质、粗脂肪、灰分、水分、中性洗涤纤维和酸性洗涤纤维测定值的差异均不显著(P>0.05),相对误差分别为0.68%、9.61%、2.23%、1.17%、6.36%和6.34%;两种方法测定结果的相关系数(r)分别为0.9561、0.9408、0.9512、0.9924、0.9632和0.9584(P<0.01)。表明NIR可以应用于豆粕中营养成分的测定。     

近红外光谱技术在木材性质预测中的应用研究进展

林木定向培育和木材资源的优化利用, 都需要对大量木材样本的性质进行快速测试.然而, 传统的测试方法成本高、效率低, 不能满足生产和科研的需要.近红外光谱技术是一种新的无损评价方法, 能够迅速、准确地对木材试样的性质进行预测.文中主要介绍了近红外分析技术的基本原理、特点以及在预测木材化学组成、物理力学性质、解剖性质等方面的研究进展.     

苹果质地品质近红外无损检测和指纹分析

为了探索近红外光谱快速无损检测苹果质地品质的方法,采集240个苹果样本的近红外光谱( 波长 8002500 nm),通过解析光谱图和进行不同的预处理,利用偏最小二乘法(PLS)和多元线性回归(MLR)建立回归模型和确定特征指纹图谱.基于波长范围为1300～2500 nm,PLS结合多元散射校正(MSC)所建模型的预测效果最好,硬度模型的预测标准偏差(RMSEP)和决定系数(R2)分别为0.226 kg/cm2、96.52%,脆度模型的 RMSEP和R2分别为0.243 kg/cm2、97.15%.用权重法基于PLS模型选择的硬度特征波长为1657、1725、1790、2455、1929、2304 nm,脆度特征波长为1613、1725、1895、2304、2058、2087、2396 nm,经MLR模型检验,特征波长与苹果的硬度和脆度有很高的相关性,硬度的RMSEP和R2分别为0.271 kg/cm2、90.30%,脆度的RMSEP和R2分别为0.304kg/cn2、91.64%.结果表明,PLS模型和特征指纹光谱均能准确预测苹果的质地品质,为苹果质地品质的评价提供了快速、直观、简便、可行的新方法.     

苹果品质动态无损感知及分级机器手系统

为了实现灵活高效的苹果多品质指标检测分级,基于机器视觉技术及可见/近红外光谱技术,开发了用于苹果内外部品质无损感知及分级的机器手系统。机器手系统采用六轴机械臂搭载自行研发的末端执行器,末端执行器上装载有光学传感器与抓取结构,可以抓取流水线上的苹果并同时采集苹果的光谱进行糖度检测。使用CMOS相机采集苹果图像,训练并使用PP-YOLO深度学习目标检测模型处理采集的苹果图像,计算苹果的坐标位置实现苹果的动态定位,并获取苹果的果径大小、着色度信息实现外部品质检测。采集苹果样本光谱,结合不同的光谱预处理方式,利用偏最小二乘（Partial Least-Square,PLS）方法进行建模分析。试验结果表明,使用PP-YOLO目标检测算法处理图像和计算苹果位置,其识别速度为38帧/s,极大地提高了检测速度。使用归一化光谱比值法（Normalized Spectral Ratio,NSR）作为预处理算法的糖度建模结果较佳。采用NSR+CARS（Competitive Adaptive Reweighted Sampling,竞争性自适应重加权算法）作为机器手的动态光谱模型效果较佳,该动态光谱模型相关系数Rv为0.958 9,验证均方根误差RMSEV（Root Mean Squared Error of Validation）为0.462 7%,与静态下建立的模型相比,机器手在动态状态下采集光谱对所建立的预测模型的预测效果影响较小。对整体机器手系统进行了试验验证,机器手在工作时能够无损伤地抓取苹果,给出果径大小、着色度、糖度3个检测指标并依据指标自动划分等级,然后依据等级信息分级。随后测定了3个指标的实测值与预测值进行分析,果径大小的预测相关系数为0.977 2,均方根误差为1.631 5 mm;着色度的预测相关系数为0.967 4,均方根误差为5.973 4%;糖度的预测相关系数为0.964 3,均方根误差为0.504 8%,预测结果与真实值均具有较强的线性关系和较低的预测误差,机器手系统分级正确率为95%,完成一颗苹果的定位、抓取、检测、分级和放置的时间约为5.2 s,具有较好的工作可靠性,研究结果可为苹果多品质指标的高效检测提供参考。