Rapid nondestructive evaluation of duck meat pH and color using visible and near-infrared reflectance spectroscopy

In the present study, visible and near-infrared reflectance spectroscopy were applied to predict quality attributes of duck breast meat. The real color (expressed as lightness, redness, and yellowness) and pH of duck samples were recorded using traditional contact methods and then modeled with their corresponding spectral data by partial least square regression. Before the establishment of prediction models, three different spectral preprocessing methods including first derivative, standard normalized variate, and Savitzky–Golay smoothing were used. Compared to the models obtained from original spectral data set, the predictive capabilities of models based on the spectra after preprocessing were improved effectively. As a result, the determination coefficient of calibration set and prediction set of the best models for lightness, redness, yellowness, and pH parameters were 0.96 and 0.85, 0.94 and 0.94, 0.96 and 0.94, 0.81 and 0.76, respectively. Results demonstrated that visible and near-infrared spectroscopy can become a useful tool for rapid prediction of duck color and pH quality attributes.     

基于三维荧光导数光谱的水体有机污染物浓度检测

提出了应用三维荧光一阶导数光谱检测水体中有机污染物浓度的分析方法。在计算三维荧光导数光谱时,采用Savitzky-Golay多项式拟合微分法对激发、发射光谱分别求导。由于在计算导数光谱时所采用的多项式拟合本身具有平滑功能,因此不需要单独进行平滑处理以去除噪声。针对所构成的四维荧光导数光谱(激发波长,发射波长,激发波长的一阶导数,发射波长的一阶导数),采用偏最小二乘方法建立回归模型。实验结果表明,在对水体中总有机碳的检测中,与常规的三维荧光光谱分析方法相比,文章提出的分析方法可较好地提高分析精度。     

Using Short Wave Visible–Near Infrared Reflectance Spectroscopy to Predict Soil Properties and Content

In this study, short wave visible–near infrared reflectance spectroscopy was evaluated for prediction of diverse soil properties related to four different soil series of several regions in Jiangxi, China. A total of 240 soil samples were collected for the calibration (n = 168) and prediction (n = 72) sets. The used wavelength range of short wave visible–near infrared reflectance spectroscopy is 325–1075 nm. Partial least squares regression and back propagation neural network were used to develop models for soil properties such as organic matter and extractable forms of calcium, magnesium, and potassium. Performance of these models was also compared and analyzed. The input of back propagation neural network was the first six principal components resulted from the principal component analysis and the optimal number of latent variables obtained from partial least squares regression. The overall results showed that the performance of partial least squares regression model was inferior to all back propagation neural network models. The best prediction was obtained with latent variables as input of back propagation neural network model for organic matter (determination coefficient = 0.84 and relative predictive determinant = 2.38), which was classified as very good model predictions. The prediction of calcium, magnesium, and potassium was classified as fair (determination coefficient = 0.56–0.68 and relative predictive determinant = 1.51–1.61), where quantitative predictions were considered possible. It is recommended to adopt latent variables as input for back propagation neural network model predicting soil properties with short wave visible–near infrared reflectance spectroscopy. In conclusion, short wave visible–near infrared reflectance spectroscopy was variably successful in estimating soil properties and showed potential for substituting laboratory analyses.     

便携式短波近红外光谱仪器检测土壤总氮含量研究

基于便携式短波近红外光谱技术检测了土壤总氮含量。采集浙江省文城地区农田土壤样本243个,将土壤样本分为三组,一组未经过粉碎、过筛等处理,一组做过2 mm筛处理,一组过0.5 mm筛过处理,采用usb4000便携式光谱获取土壤光谱数据,结合(savitzky-golay, SG)平滑算法,波长压缩算法和小波变换对原始数据进行预处理,然后采用竞争性自适应重加权、随机青蛙和连续投影算法进行特征波长选择。基于全光谱建立了偏最小二乘回归和基于特征波长建立了极限学习机和LS-SVM模型。结果表明过筛处理后的样本模型结果优于未过筛的样本模型结果,过0.5 mm筛处理的土壤样本模型预测结果略优于过2 mm筛处理的土壤样本模型预测结果,最优预测集的决定系数为0.63,预测均方根误差为0.007 9,剩余预测偏差为1.58。表明便携式仪器检测土壤总氮含量,经过过筛处理的土壤样品检测结果优于未过筛土壤样品检测结果,建议土壤样品检测总氮含量时需经过过筛处理,这样得到的结果较为理想,在此基础上采用性能较好的光谱仪器采集数据,以减小原始光谱噪声。     

土壤碱解氮含量可见/近红外光谱预测模型优化

可见/近红外光谱技术是土壤成分检测的有效工具。波长筛选对可见/近红外模型土壤属性的预测精度有重要影响。以宁夏吴忠地区75个水稻土样为研究对象,利用可见/近红外光谱技术采集土壤样品光谱,采用SPXY （Sample set partitioning based on joint X-Y distance）方法选取了校正集和预测集样本,比较了分别采用Savitzky Golay平滑（SG smoothing）、多元散射校正（Multiple scatter correction,MSC）、标准正态变量变换（Standard normal variate,SNV）3种预处理方法对光谱数据处理后建立土壤碱解氮偏最小二乘法模型和原始光谱数据建模的效果。在此基础上,分别采用遗传算法（Genetic gorithms,GA）、连续投影算法（Successive projections algorithm,SPA）、竞争性自适应重加权算法（Competitive adaptive reweighted Sampling,CARS）、随机蛙跳（Random frog,RF）进行波长筛选,最后应用偏最小二乘法建立基于不同波长筛选方法的土壤碱解氮含量预测模型。研究表明,由于仪器性能稳定,样品的颗粒度比较小和均匀,本次实验原始光谱数据建模效果最好;各种波长筛选方法均可有效减少参与建模的波长数,且连续投影算法优于全谱建模,所选波长数仅为全谱波长数的1%,其预测决定系数（R²）、预测均方根误差和相对分析误差值分别为0.726,3.616,1.906。这表明连续投影算法可以有效筛选水稻土碱解氮敏感波段,为土壤碱解氮传感器开发提供技术支持。     

Evaluation of spectral pretreatments,spectral range,and regression methods for quantitative spectroscopic analysis of soil organic carbon composition

Although there is an increasing interest in using infrared spectroscopy for the simple, rapid, and inexpensive prediction of soil organic carbon content, few studies have used this technique to measure organic carbon chemistry. In this paper, based on both near-infrared and mid-infrared diffuse reflectance spectroscopy, we compared the use of instrumentation, spectral pretreatment, and regression method for the prediction of three parameters related to organic carbon content, one related to isotopic composition, and five related to organic carbon chemistry. A total of 140 soil samples collected from seven oriental oak forest sites across East China were used as the data set for the calibration-validation procedure. Calibrations using sample set partitioning based on joint x-y distances method significantly outperformed those using Kennard-Stone method. Compared to models using linear method (i.e., partial least squares), those using non-linear regression method (i.e., support vector machines) greatly improved the prediction precision of the alkyl-to-O-alkyl ratio and performed slightly better for the other organic carbon chemical compositions. Instrumentation had a large effect as mid-infrared models had higher average prediction accuracies than near-infrared models. We finally proposed a model using second derivative preprocessing, joint x-y distances based sample set partitioning, mid-infrared spectra, and support vector machines regression to quantify organic carbon chemistry in this study. The results are helpful for the further study of soil composition measurement.     

Optimized prediction of sugar content in ‘Snow’ pear using near-infrared diffuse reflectance spectroscopy combined with chemometrics

In near-infrared spectroscopy applications, the original spectra often contain redundant information, which will seriously affect the performance of chemometric models. Therefore, preprocessing, effective wavelengths selection, and appropriate regression models are essential. The objective of this study was to optimize the nondestructive determination multivariate calibration model of sugar content in ‘Snow’ pears, using near-infrared diffuse reflectance spectroscopy combined with chemometrics. All data (sugar content reference values and spectra data) from three measuring positions (P1, P2, and P3, marked around the pear’s equator at angular distances of approximately 120°) were divided into four grouped datasets, namely Set-1 (P1), Set-2 (P2), Set-3 (P3), and Set-4 (average of the three measuring positions). All subsequent optimized processes were performed based on each grouped dataset. First, different preprocessing methods were tested and an optimal method was determined. Then, synergy interval partial least squares and synergy interval partial least squares-competitive adaptive reweighted sampling were applied to select effective regions and effective wavelengths from all wavelengths, respectively, and partial least squares regression models were established and analyzed. In addition, support vector regression models were also established for comparative study. After comprehensive analysis of prediction accuracy and model complexity, the partial least squares regression model based on the 16 selected effective wavelengths for Set-4 was optimal, with the correlation coefficient for prediction, root-mean-square error of prediction, and residual predictive deviation of 0.9701, 0.2311, and 4.12, respectively. The results indicated that with these optimized processes, the multivariate calibration model of sugar content in ‘Snow’ pears was effectively optimized for each dataset. In addition, it is concluded that partial least squares regression was superior to support vector regression in this study, although some other researches had found different results in related fields.     

土壤水分去除算法的田间原位光谱反演棉田有机质

田间原位可见-近红外光谱(VIS-NIR)能够有效的提高土壤属性的检测效率,但由于原位土壤中水分因素的影响,土壤属性的预测精度很难达到预期。如何有效去除土壤中的水分对土壤其他属性光谱预测的影响,是利用田间原位光谱高精度预测土壤属性所面临的难题,也是土壤光谱技术由室内转向田间的突破口。该问题的有效解决,可减除土壤样品的采集与室内预处理等过程,实现土壤属性的田间原位光谱测定。以新疆南部地区阿拉尔垦区十二团棉田为研究区,采用网格采样法共采集了116个0～20 cm深度的表层土壤样品,剔除1个异常值样品,得到115个有用样品,利用SR-3500型便携式地物光谱仪采集了231个样点的田间原位光谱数据,土样经风干、研磨和过筛等处理后测定其室内光谱和有机质含量。利用Kennard-Stone算法将115个土样分为69个转换子集及46个预测集,采用外部参数正交化法(EPO)、光谱直接转换法(DS)及光谱间接转换法(PDS)三种去除水分算法结合原位光谱反射率(R)、反射率一阶微分(R′)、反射率对数(LOG(R))以及反射率倒数(1/R)四种数学变换方式,运用随机森林(RF)模型进行不同组合模型的构建及精度评价。结果表明：(1)土壤有机质含量越高,土壤光谱反射率越低。土壤田间原位光谱反射率低于土壤室内光谱反射率;(2)室内光谱反射率与土壤有机质含量之间的相关性大于田间原位光谱,室内光谱经一阶微分变换后与土壤有机质含量之间的相关性显著提升。(3)土壤室内光谱反射率模型预测精度(R2=0.86, RPD=2.08, RMSE=1.55 g·kg-1, MAPE= 0.14)高于田间原位光谱反射率模型(R2=0.71, RPD=1.49, RMSE=2.17 g·kg-1, MAPE=0.20)。在去除水分算法模型中,以EPO一阶微分模型去除水分效果最好,决定系数R2由0.71提高到0.83,RPD由1.49提高到2.04,RMSE由2.17 g·kg-1降低至1.58 g·kg-1,MAPE由0.20降低至0.14。本研究实现了去除土壤水分因素的影响,提高了田间原位光谱预测土壤有机质的精度,为南疆棉田大尺度土壤有机质的预测及土壤肥力的评价提供了重要的参考。     

水分对土壤有机质检测影响的光谱特性分析及抗水分干扰模型建立

土壤水分对光谱表现出很强的吸收性,且土壤水分与土壤有机质的吸收波段有重叠,因此土壤水分对土壤有机质的检测造成一定的干扰。为此做了以下工作：(1)采用可见近红外光谱仪在室内获取相同含水率下不同土壤动态光谱图;(2)通过对相同含水率下不同有机质含量的二维同步相关光谱图分析得出：当土壤为烘干土样时,600和1 660 nm左右表征土壤有机质的波段出现强的自相关峰,但随着含水率的增加,这两个波段逐渐消失,由于受水分的影响,1 931,2 200和1 480 nm均形成了强的自相关峰。说明水分会掩盖表征土壤有机质信息的波段,对土壤有机质检测造成干扰。(3)为了消除水分影响,提高模型对不同含水率下土壤有机质的预测精度,将田间近似最大含水率样本参与建模,采用偏最小二乘定量分析方法在550～650和1 610～1 710 nm波段内建立了抗水分干扰土壤有机质预测模型,并对不同含水率的土壤有机质进行预测,结果表明：预测样本的相关系数为0.954,标准偏差为0.744%,标准差为0.844%,预测效果明显提高,说明此方法可减少水分对土壤有机质检测的影响。     

Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing

Application of the finite impulse response (FIR) filtration technique for the removal of spectral noise and background broadband deformations from the Raman spectra is tested. Optimal parameters of FIR filters are found and their effectiveness is compared with the Savitzky–Golay (SG) smoothing procedure. The FIR filtration is found to be an effective procedure to treat the whole Raman spectra, but high computing power is needed. Copyright © 2007 John Wiley & Sons, Ltd.     

可见光-近红外、中红外光谱的土壤有机质组分反演

土壤有机质是土壤肥力的物质基础,其含量的高低是评价土壤肥力的重要标志。土壤有机质组分根据其溶解性可分为胡敏素(HM)、胡敏酸(HA)、富里酸(FA),不同组分的肥力特性差异显著,因此,土壤有机质组分数据可更加全面、客观的反映土壤肥力状况。传统土壤土壤有机质及组分的测定工序繁杂,效率低下且时效性差,大量研究表明高光谱技术能有效提高土壤属性的检测效率并降低测试成本,但关于可见光-近红外、中红外光谱检测土壤有机质组分的报道鲜见。为了探索中红外光谱及可见光-近红外-中红外组合光谱对土壤有机质组分检测的可行性,并对比有机质单一光谱模型与有机质不同组分的组合光谱模型的预测精度,以南疆地区农田土壤为例,在阿克苏及和田地区共采集93个土样,进行有机质、胡敏素、胡敏酸、富里酸含量及光谱数据的测定。其次,利用可见-近红外(VNIR)、中红外(MIR)及其组合光谱(VNIR-MIR)三种光谱数据集,采用偏最小二乘(PLSR)、支持向量机(SVM)、随机森林(RF)三种建模方式对土壤有机质、胡敏素、胡敏酸、富里酸含量进行组合模型分析预测。结果表明：(1)土壤有机质及各组分均与光谱反射率有较好的相关性,土壤有机质及组分在MIR谱段的特征波段数量明显多于VNIR谱段。(2)有机质最优预测模型的模式为VNIR-MIR-RF,该模型的决定系数R2为0.90;胡敏素与胡敏酸最优预测模型的模式均为VNIR-RF模型,R2均为0.92;富里酸最优预测模型的模式为MIR-RF模型,R2为0.94。(3) 基于胡敏素、胡敏酸和富里酸的有机质组合光谱模型的预测精度明显高于有机质单一光谱模型,两种模型的R2分别为0.93和0.90。实现了土壤有机质组分的高效快速反演,且基于有机质组分的组合模型提高了土壤有机质预测精度,为南疆地区大尺度土壤肥力的鉴定与精准施肥提供重要的参考价值。     

Selection of optimal combinations of inputs in a partial least squares model for prediction of soil organic matter

Abstract

Partial least squares model is widely used in estimation of soil physical and chemical parameters such as soil organic matter and moisture content, due to its advantages in dealing with collinearity of variables like hyperspectral reflectance. However, it is hard to determine optimal combination of partial least squares model input for soil organic matter prediction since there are lots of possibilities such as, different mathematical transformation of spectral reflectance, wavelength ranges, and spectral resolution. Laboratory hyperspectral reflectance of soils in Songnen plain were analyzed in this study, and the orthogonal experimental design method for deriving optimal combination of input variables for soil organic matter prediction models was introduced. For intercalating orthogonal experimental design table, five different levels which commonly used by researchers were assigned to factors. Results show that the optimal combination input for single black soil is using the derivative logarithmic reciprocal reflectance in the wavelength range selected by multiple stepwise regression at a spectral resolution of 5?nm (R²=?0.95, RMSE?=?0.21, and RPD?=?4.49), and different soils is using continuum removed in the wavelength range selected by MSR at a spectral resolution of 5?nm (R²?=?0.77, RMSE?=?0.74, and RPD?=?2.08). With optimal combination input, the partial least squares model prediction ability was evaluated as excellent for single black soil, possible for different soils. This study illustrates the orthogonal experimental design method can be an effective way to identify the optimal input variables of a partial least squares model for soil organic matter prediction, and multiple stepwise regression can be a preprocessing step to reduce hyperspectral data redundancy before using partial least squares to predict soil organic matter. Overall, this study provides a new approach for determining optimal input of partial least squares predicting model.     

脐橙糖度近红外光谱在线检测数学模型优化研究

目的是优化脐橙糖度近红外光谱在线检测数学模型,提高检测精度.在700.28～933.79 nm光谱范围内,根据建模集样品在不同波长处的变异系数,选择基准波长点,计算样品的反射比光谱.吸光度和反射比光谱,经不同光谱预处理后,分别采用偏最小二乘法(PLS)和最小二乘支持向量回归法(LSSVR),建立脐橙糖度近红外光谱在线检...     

Adaptive signal enhancement for overlapped peaks based on weighting factor selection

Peak-sharpening is an effective method for the peak position detection of overlapped spectra. However, the weighing factor parameter strongly affects the sharpening performance, and the derivative adopted in the peak-sharpening method is sensitive to noise. In this paper, an adaptive peak-sharpening method based on weighting factor selection is proposed. The relationship between the sharpening ratio and weighting factor is studied. In addition, the Savitzky–Golay filter is adopted due to its excellent noise reduction and peak shape retention abilities. First, the smoothed signal and second-order derivative signal are obtained by the Savitzky–Golay filter. Then, the parameters of the overlapped peaks are estimated for the weighting factor selection. Next, the peak position is detected by the peak-sharpening method. After that step, the estimated parameters are updated, and the above steps are iterated until the detection of the peak position converges. Finally, the converged results are considered to be the final detection results. The experimental results using a simulated dataset, a virtual mass spectra dataset and a polarography dataset show that the proposed method is effective for peak position detection.     

西范坪矿区土壤铜元素的高光谱响应与反演模型研究

为解决传统的土壤地球化学测量方法成本高、效率低等问题,研究了利用可见-近红外光谱技术检测土壤重金属含量的简易方法。研究对西范坪矿区土壤反射光谱进行微分、连续统去除等六种变换,利用逐步回归法和皮尔逊相关系数选出对土壤铜含量敏感的特征波段,组成综合特征变量集,再应用不同的特征变量选取方法和参数建立估算模型并检验。结果表明：不同的光谱变换方法对土壤铜含量信息提取能力不同,每种光谱变换都对应特定的敏感波谱区间;基于综合光谱变换信息建立的土壤铜含量反演模型精度优于基于单种光谱变换信息建立的模型;利用综合光谱变换信息建立土壤铜含量反演模型,后向剔除法优于前向引入法和逐步回归法,当Removal取0.20时得到最优回归模型,其模型决定系数R2和预测模型决定系数R2_pre分别达到了0.851和0.830,建模均方根误差RMSEC和预测均方根误差RMSEP分别为0.349和0.468 mg·kg-1,能较好地检测土壤铜含量,同时为其他土壤重金属元素的光谱检测提供了思路。     

矿区复垦农田土壤重金属含量的高光谱反演分析

以矿区复垦农田土壤为研究对象,利用实验室获取的土壤重金属元素砷(As)、锌(Zn)、铜(Cu)、铬(Cr)和铅(Pb)的含量与土壤可见近红外高光谱数据建立重金属元素含量的定量估算模型。为了保证模型预测的精度和稳定性,首先,对原始光谱数据进行平滑处理,并进行光谱变换,即：一阶导数,标准正态变量变换及连续统去除变换;然后,通过相关性分析提取不同变换光谱的特征波段;最后,将最小二乘支持向量机与传统的多元线性回归和偏最小二乘回归方法的结果相比较。研究表明：(1)以不同变换光谱数据建立反演模型均有较好的稳定性并达到一定精度,其中以最小二乘支持向量机方法优于偏最小二乘回归优于多元线性回归模型(除少数几个情况外);(2)从不同光谱变换数据中提取的光谱特征对反演模型结果有一定影响,其中以连续统去除和标准正态变量变换建模结果较好,一阶导数变换稍差。因此,利用高光谱遥感技术来定量估算土壤重金属含量是可行的,而且,必要的光谱预处理对提高估算模型的精度很有帮助。     

土壤主要养分近红外光谱分析及其测量系统

土壤是农业生产的基础,采用近红外光谱技术实现对土壤养分的快速分析,研制分立波长型近红外土壤养分测量系统,指导农业生产过程,有助于改变现有农业生产的粗放经营状态.首先,使用FOSSXDS近红外光谱分析仪对85份东北土壤样品采集光谱,采用相关系数谱及连续投影法等化学计量学算法分析土壤的近红外光谱,并优选出总氮和有机质的特征...     

Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Abstract

During harvest and transport, defects are most likely to affect the interior of jujubes and thus shorten their storage period. This study applied visible and near-infrared transmission spectroscopy to detect such internal defects. Spectra were acquired on the equator area at 0, 90, 180, and 270 degrees of each sample, and a model was constructed to obtain three-dimensional damage and defect detection model. The first derivative, multiplicative scatter correction, standard normal variate, and median filtering were used for preprocessing. Modeling by mean spectra achieved a better effect than using unidirectional spectra. Then, naive Bayes classifier and support vector machine were employed for the model establishment at 600–950?nm and 680–950?nm bands, respectively, using mean spectra. Median filtering effectively improved the signal to noise ratio and the discrimination accuracy of the support vector machine model at 600–950?nm reached 96.77%, which was the best value among all models. This result indicates that the support vector machine model was the optimum model and 600–950?nm was a suitable data range for the detection of internal defects. This research confirms the feasibility of implementing visible and near-infrared spectroscopy for the detection of internal defects in jujubes.     

南疆沙尘区骏枣叶片水分含量检测的近红外光谱预处理方法对比

南疆地区沙尘多、灰尘大,枣树叶片表面经常覆盖一定程度的粗颗粒度沙尘,为了有效去除沙尘、灰尘在枣树叶片水分光谱测量过程中产生的散射噪声和基线漂移,研究一种适用于风沙较大地区的枣树叶片水分含量的快速检测方法,以不同灌溉梯度下的枣树叶片为研究对象,通过近红外光谱仪获取120个叶片样本的1 000～1 800 nm的光谱数据,并同步测量叶片水分含量,采用归一化、移动窗口平滑、SavitZky-Golay（SG）卷积平滑、SG求导、标准正态变量校正(SNV)和多元散射校正(MSC)等方法对原始光谱进行预处理,分析对比不同方法对散射噪声的处理能力,采用偏最小二乘回归分析方法筛选了敏感波段和建立预测模型。实验结果表明,枣树叶片水分含量强吸收峰为1 443 nm,波谷为1 661 nm;归一化光谱并未消除1 000～1 400 nm波段的散射噪声;移动窗口平滑和SG卷积平滑并未改进光谱曲线,散射噪声仍然存在;SG导数光谱的光谱特征峰和特征谷明显左移,光谱曲线不够平滑,噪声明显;SNV和MSC方法具有较好的散射噪声消除能力。偏最小回归分析方法筛选特征波长的结果表明（设置筛选波长数量为5）,基于原始光谱未筛选到1 443 nm的强波峰和1 661 nm的波谷附近的波段;基于归一化光谱在1 450 nm波峰附近筛选的波长有一定的偏差,在1 661 nm波谷附近的筛选的波长明显高于1 700 nm;基于移动窗口和SG卷积平滑光谱在1 443 nm具有一定的筛选能力,但并未筛选到1 661 nm附近的波长;导数光谱并未筛选到1 443和1 661 nm波段;SNV和MSC在波峰和波谷位置附近均筛选了敏感的光谱波段,其中MSC略优于SNV方法恰好在波峰和波谷位置,共筛选了1 002, 1 383, 1 411, 1 443和1 661 nm五个特征波段,也证明了MSC方法散射噪声和基线漂移处理能力最优,提高了敏感波长的筛选能力。偏最小二乘回归模型结果表明,不同预处理方法的RMSE值均较低,SNV和MSC方法改进了模型的预测结果,R2高于0.7,其中基于MSC方法的模型具有最高的R2和最低的RMSEP和RMSEPCV,R2=0.750 4,RMSEP=0.034 3,RMSECV=0.021 5,预测结果较优。证明MSC方法对沙尘和颗粒度引入的散射噪声具有较好的去除能力,可改进波长的筛选、提高预测模型精度,为南疆沙尘区的枣树叶片水分含量的无损检测提供了有效方法。     

近红外特征光谱的羊肉TVB-N浓度预测模型

为提高生鲜羊肉储存期内（4,8和20 ℃环境）挥发性盐基氮（TVB-N）的近红外光谱（NIR）检测的稳定性和准确性,选取特征光谱和预测模型是关键步骤。以121个羊肉样品为实验对象,采集生鲜羊肉680~2 600 nm波段的近红外光谱。以多元散射校正（MSC）、标准正态变换（SNV）等散射校正方法,Savitzky-Golay卷积平滑（SGS）、移动平均平滑（MAS）等平滑处理方法,以及归一化（Normalization）、中心化（Centering）、标准化（Autoscaling）等尺度缩放方法分别预处理光谱数据后建立偏最小二乘法（PLS）预测模型。比较发现SGS处理的光谱建模效果最好。利用蒙特卡洛采样（MCS）法及马氏距离法（MD）消除了羊肉光谱的5个异常数据。运用光谱-理化值共生距离（SPXY）算法划分总样本的75%（87个）为校正集样本,剩余29个为验证集样本,利用竞争性自适应重加权法（CARS）、无信息变量消除法（UVE）、改进的无信息变量消除法（IUVE）和连续投影算法（SPA）提取特征光谱得到的波长个数分别为14,713,144和15。将全光谱和4种方法提取的特征波长作为输入变量建立预测模型,CARS提取的波长所建立模型的性能优于UVE、IUVE和SPA提取的波长所建立模型的性能,表明CARS方法可以有效简化输入变量并提高预测模型的性能。改进后得到的IUVE法相比于UVE法,筛选出的波长数更少且模型性能有所提升。以提取的特征波长建立PLS,支持向量机（SVM）和最小二乘支持向量机（LS-SVM）预测模型,SVM模型得到最优的校正集预测结果,其中CARS-SVM预测模型的校正决定系数（R2_C）和校正均方根误差（RMSEC）分别为0.939 1和1.426 7,最优的验证集预测效果为LS-SVM预测模型得到,其中IUVE-LS-SVM预测模型的验证决定系数（R2_V）和验证均方根误差（RMSEV）分别为0.856 8和1.886 2。基于近红外特征光谱建立简化、优化的生鲜羊肉储存期TVB-N预测模型,为实现快速无损检测生鲜羊肉中的TVB-N浓度提供技术支持。