基于温度修正和可见/近红外光谱的油茶籽含水率检测

目的:解决干燥时温度变化对可见/近红外光谱检测油茶籽含水率的影响,并提出一种基于温度修正的油茶籽含水率检测模型。方法:在不同温度下(50,60,70℃)进行干燥试验,采集光谱数据。通过获取不同温度下采集的光谱数据,分析温度对光谱影响的原因。对比3种光谱预处理方式,运用竞争性自适应重加权算法(CARS)和偏最小二乘回归算法(PLSR),建立60℃下的基准PLSR模型。并采用斜率/偏差法对50,70℃下的外部样本预测值进行修正。结果:50,70℃下,修正前和修正后的决定系数分别为0.729和0.848,0.763和0.862;相对分析误差RPD值分别为1.921和2.565,2.054和2.692。结论:修正模型可以有效提高预测精度,达到良好的预测效果,克服了温度的影响。     

基于可见/近红外光谱和变量优选的南水梨糖度在线检测

利用可见/近红外光谱技术对南水梨糖度进行在线检测研究。南水梨样本以0.3m/s速度传输,并采用USB4000光谱仪在470~1 150nm波段范围内采集南水梨样本的光谱。然后,利用3种变量选择方法对波长变量进行筛选,应用偏最小二乘(PLS)方法分别建立南水梨糖度的在线预测模型,并分析预测模型性能的优劣。结果表明:可见/近红外光谱技术结合变量选择方法在线检测南水梨的糖度是可行的;竞争自适应重加权采样(CARS)方法优于无信息变量消除(UVE)及连续投影算法(SPA);CARS方法可以有效简化预测模型并提高预测模型的性能;南水梨全光谱PLS及CARS—PLS糖度预测模型的预测集相关系数和预测均方根误差(RMSEP)分别为0.940,0.951和0.467%,0.420%。     

可见/近红外光谱技术无损检测新鲜鸡蛋pH及蛋白质的研究

本文利用可见/近红外光谱技术检测新鲜鸡蛋p H和蛋白质。分别采集新鲜鸡蛋在400~1000 nm和900~1700 nm波长范围的漫反射光谱,使用多元散射矫正(MSC)、标准正态变量变换(SNV)等光谱预处理技术,选择最佳的预处理方法,使用偏最小二乘法(PLS)建立p H和蛋白质模型并对其进行评价。结果表明,基于900~1700 nm波长范围的光谱获得的p H模型较好,其校正集相关系数为0.948,预测集相关系数为0.855;基于400~1000 nm波长范围的光谱获得的蛋白质模型较好,其校正集相关系数为0.927,预测集相关系数为0.906。研究表明,可见/近红外光谱技术可以较好的预测新鲜鸡蛋的p H和蛋白质,为鸡蛋营养成分的快速无损检测提供新的思路和方法。      

基于可见/近红外高光谱成像技术的鸡蛋新鲜度无损检测

以400~1 000nm高光谱系统获得鸡蛋样本的高光谱图像,利用蒙特卡洛法检测异常样本,采用不同预处理方法处理原始光谱;应用竞争性正自适应加权算法(Competitive Adaptive Reweighted Sampling,CARS)、遗传偏最小二乘法(Genetic Algorithms PLS,GAPLS)和间隔蛙跳法(Interval Random Frog,IRF)对预处理后光谱数据提取特征波长;分别建立基于全光谱和特征波长的偏最小二乘回归(Partial Least Squares Regression,PLSR)和最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)鸡蛋新鲜度预测模型。结果表明:标准正态变量变换(Standardized Normal Variate,SNV)法为最优预处理方法;利用CARS、GAPLS和IRF分别选出8,35,74个特征波长;基于GAPLS提取的特征波长的LS-SVM模型最优,其校正相关系数(Rc)为0.899,预测相关系数(Rp)为0.832。表明基于高光谱成像技术的鸡蛋新鲜度无损检测是可行的。     

基于CARS-SPA的苹果可溶性固形物可见/近红外光谱在线检测

采用CARS（competitive adaptive reweighted sampling）联合连续投影算法（SPA）方法筛选苹果可见/近红外光谱的特征变量,继而联合多种不同建模方法建立苹果可溶性固形物（SSC）预测模型,并对预测模型进行对比研究。研究结果显示,采用CARS-SPA联合筛选出的31个变量,通过采用PLS建立苹果SSC的可见/近红外光谱在线检测模型性能最稳定,其变量数仅为原始光谱的1.69%,预测集的相关系数和均方根误差分别为0.936和0.351%。研究表明采用CARS-SPA能有效提取苹果SSC的光谱特征变量,能有效简化模型并提高模型精度。      

可见/近红外漫反射光谱无损检测甜柿果实硬度

该研究的目的是建立可见/近红外漫反射光谱无损检测甜柿果实硬度的数学模型,评价可见/近红外漫反射光谱无损检测甜柿果实硬度的应用价值。果实硬度采用果皮脆性、果皮强度和果肉平均硬度作为评价指标。在可见/近红外光谱区域(400～2 500 nm),采用改进偏最小二乘法,对比分析了不同导数处理、不同散射及标准化处理的甜柿果实硬度定标模型。结果表明,对于果皮强度和果皮脆性,采用最小偏二乘法、一阶导数处理和标准多元离散校正处理建立的定标模型预测效果较好,RP2分别为0.858和0.862,SEP分别为0.094和0.157,RPD分别为2.47和2.63。对于果肉平均硬度,采用改进偏最小二乘法、一阶导数处理和标准正常化和去散射处理建立的定标模型预测效果较好,RP2为0.82,SEP为0.063,RPD为2.35。因此,可见/近红外漫反射光谱无损检测技术可用于甜柿果实硬度的无损检测。     

利用可见/近红外光谱判别干枣品种

目的利用可见/近红外反射光谱技术快速判别干枣的品种。方法使用光谱仪获取山西永和枣、山西板枣和新疆和田枣3种干枣在345~1100 nm波段范围内的漫反射光谱;分别使用多元散射校正(MSC)法和一阶导数法(1~(st)-D)和二阶导数法(2~(st)-D)对反射光谱进行预处理;对预处理光谱进行主成分分析,全交差验证法确定最佳主成分数量,提取主成分,结合马氏距离法和线性判别法建立品种判别模型,建立模型过程中使用全交叉验证法确定最佳主成分数,将模型应用于干枣的品种判别。结果可见/近红外反射光谱经过MSC处理后提取主成分建立品种预测模型对枣的品种判别结果最好,利用前4个主成分结合马氏距离法建立的判别模型和利用前5个主成分结合线性判别法建立判别模型,对于3个品种的枣的校正和验证判别准确率都达到了100%。结论可见/近红外反射光谱技术可以较好地判别干枣品种,本研究可为可见/近红外光谱技术在于枣品种和产地的快速鉴别和溯源中的应用提供一定的技术基础。     

基于可见/近红外光谱技术的便携分析仪的应用

目的为解决水果内部品质信息的快速无损检测,自主研制了一台基于可见/近红外光谱技术的便携式分析仪,通过试验验证其可行性及所建模型的鲁棒性。方法以红富士苹果为检测对象,采集透射光谱曲线,与化学指标可溶性固形物含量(soluble solid content,SSC)分别建立基于平均光谱、基于各采样光谱的偏最小二乘(partial least squares,PLS)回归模型,比较预测精度并对非同批次样本进行预测。结果试验表明该分析仪对苹果SSC具有较高的测量精度,特别是基于各采样光谱的PLS模型,对同批次样本预测相关系数(Rp)达到0.924,预测均方根误差低至0.429%Brix,预测精密度(平均偏差)低至0.136%Brix,对非同批次样本SSC表现出较强的鲁棒性能,预测均方根误差为0.531%Brix。结论通过此项研究,表明该便携分析仪可用于水果内部品质信息的定量分析,并建议采用基于各采样光谱建立的回归模型用于外来样本的预测。     

基于近红外光谱技术的生鲜猪肉质量检测研究进展

近红外光谱技术(Near-Infrared Spectroscopy,NIR)是一种根据被检测物质的光谱信息,运用统计学的方法,构建被测物质某种属性值和光谱信息之间最优预测模型的一种间接分析技术。近红外光谱分析集光谱测量技术、计算机技术、化学计量学技术和基础测试技术于一体,通过选择合适的化学计量学方法,将样品光谱信息和指标参考值相关联,构建高精度、高稳定性的数学模型预测未知样品参考值,具有无损、快捷和环保等特点。相比传统理化及生物方法反复试验且破坏原料获取数据,近红外光谱信息更容易获取、信息量更丰富、数据计算速度更快,在猪肉质量的分析检测方面获得广泛研究。本文主要综述了2010年至今近红外光谱用于研究猪肉的物理属性、化学组成、新鲜度预测和肉品掺假等方面的最新研究进展和成果,为研发肉品无损分析检测设备提供相关信息和参考。     

基于可见/近红外光谱技术的红提成熟度判别模

成熟度是水果评价的重要标准,直接影响水果的品质和经济价值。针对红提采摘成熟度评判困难,果肉营养价值参差不齐、产品竞争力低等问题,建立基于可见/近红外光谱技术的红提成熟度判别模型。该研究选取红提生长过程的4个阶段（分别为：未成熟、半成熟、成熟、过熟）的样本并进行光谱信息采集。选择550 nm~1 000 nm的光谱波段建模,分别将经过预处理的光谱用竞争性自适应加权算法（Competitive Adaptive Reweighted Sampling,CARS）、无信息变量消除算法（Uniformative Variable Elimination,UVE）和连续投影算法（Successive Projection Algorithm,SPA）进行特征波长提取,建立支持向量机（Support Vector Machines,SVM）、极限学习机（Extreme Learning Machine,ELM）和偏最小二乘判别分析（Partial Least Squares Discriminant Analysis,PLS-DA）的判别模型,最终建立可见/近红外光谱技术的红提成熟度的最佳判别分类模型。研究结果表明,在Savitzky-Golay（SG）卷积平滑处理算法光谱预处理后运用SPA算法进行特征波段提取建立的ELM模型成熟度判别分类效果最佳,SVM模型次之,PLS-DA模型最差。因此,红提成熟度的最佳判别分类模型为SG-SPA-ELM,该模型的训练集和测试集的准确率分别为97.50%和96.67%。利用可见/近红外光谱技术对红提成熟度进行判别是可行的,该研究为红提成熟度的判别找到了一种新的无损检测方法。     

Moisture content detection of maize seed based on visible/near-infrared and near-infrared hyperspectral imaging technology

To realise accurate and nondestructive detection on moisture content of maize seed based on visible/near-infrared (Vis/NIR) and near-infrared (NIR) hyperspectral imaging technology, the hyperspectral images on two sides (embryo and endosperm sides) of each maize seed of four varieties were collected. The effects of average spectra extraction regions, that is centroid region and whole seed region, and different spectral preprocessing methods, were investigated. Uninformative variable elimination (UVE) was used to extract the feature wavelengths, and the partial least squares regression (PLSR) prediction models were established. The results showed that extracting the average spectra from the centroid region did better than from the whole seed region, and S-G smoothing was prior to other preprocessing methods. The PLSR models established with NIR spectra had better performance than that with Vis/NIR spectra. The model developed for a single variety was superior to that for all varieties together.     

On-line prediction of pH values in fresh pork using visible/near-infrared spectroscopy with wavelet de-noising and variable selection methods

The visible/near-infrared (Vis/NIR) reflectance spectroscopy as an on-line approach to assess the pH value in fresh pork was investigated. Multivariate calibration was carried out by using chemometrics. Discrete wavelet transform was applied to de-noise the spectra scanned on-line, and several variable selection methods were proposed to simplify the calibration models. The study found that the model based on the spectra de-noised by Daubechies 6 wavelet (db6) at decomposition level 6, soft thresholding strategy and minimaxi threshold estimator gave reasonable performance (r > 0.900, root mean square error of calibration (RMSEC) = 0.100, cross validation (RMSECV) = 0.139 and prediction (RMSEP) = 0.125). Then, only 15% variables from this model were selected via the method of uninformative variable elimination to develop a simpler model, of which the performance deterioration could be ignored. The results showed that Vis/NIR can be used to predict pH value in fresh pork on-line, and variable selection can provide a simpler, more cost-effective calibration model.     

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350–1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6–6) showed high de-noising ability with good information preservation. The first derivative of db6–6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R²) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.     

手持式肉品品质无损检测系统设计

目的开发设计生鲜猪肉品质无损在线检测系统。方法通过用可见近红外光谱技术,基于VC++和Matlab语言,开发与计算机硬件和Windows XP软件环境兼容的手持式猪肉品质无损实时检测系统。结果设计出开放式、模块化、集成化的检测系统,可实现光谱数据的多点自动采集、光谱曲线的动态显示、数据的实时处理、样品品质的自动预测等功能,通过一键触发开关可完成从样本多点信息的采集到结果预测与保存的全过程。用该系统对猪肉水分含量进行检测的实验结果表明,系统对参数的检测精度能满足检测要求。结论该系统软件界面友好,操作方便,后续可扩展检测其他品质参数,进一步推广用于企业生产线。     

Irradiation dose detection of irradiated milk powder using visible and near-infrared spectroscopy and chemometrics

The objective of this study was to examine the possibility of applying visible and near-infrared spectroscopy to the quantitative detection of irradiation dose of irradiated milk powder. A total of 150 samples were used: 100 for the calibration set and 50 for the validation set. The samples were irradiated at 5 different dose levels in the dose range 0 to 6.0 kGy. Six different pretreatment methods were compared. The prediction results of full spectra given by linear and nonlinear calibration methods suggested that Savitzky-Golay smoothing and first derivative were suitable pretreatment methods in this study. Regression coefficient analysis was applied to select effective wavelengths (EW). Less than 10 EW were selected and they were useful for portable detection instrument or sensor development. Partial least squares, extreme learning machine, and least squares support vector machine were used. The best prediction performance was achieved by the EW-extreme learning machine model with first-derivative spectra, and correlation coefficients = 0.97 and root mean square error of prediction = 0.844. This study provided a new approach for the fast detection of irradiation dose of milk powder. The results could be helpful for quality detection and safety monitoring of milk powder.     

便携近红外检测仪测定鲜猪肉水分含量的研究

采用课题自主研发的便携近红外检测仪,对市售15份猪背最长肌进行测定,通过试验,研究不同波长下测定的电压值与猪肉水分含量测定值之间的关系。结果表明,市售猪肉在室温下(25℃)放置2d,水分含量逐渐减少,便携近红外检测仪测定的电压值逐渐升高,在810、850、880nm的波长下水分含量和电压值相关性检验显著(0.01相似文献   

Non-destructive detection of pesticide residues in cucumber using visible/near-infrared spectroscopy

The feasibility of using visible/near-infrared (Vis/NIR) spectroscopy was assessed for non-destructive detection of diazinon residues in intact cucumbers. Vis/NIR spectra of diazinon solution and cucumber samples without and with different concentrations of diazinon residue were analysed at the range of 450–1000 nm. Partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the MRL as safe and unsafe samples, respectively. The best model was obtained using a first-derivative method with the lowest standard error of cross-validation (SECV = 0.366). Moreover, total percentages of correctly classified samples in calibration and prediction sets were 97.5% and 92.31%, respectively. It was concluded that Vis/NIR spectroscopy could be an appropriate, fast and non-destructive technology for safety control of intact cucumbers by the absence/presence of diazinon residues.     

基于近红外光谱的生鲜猪肉新鲜度实时评估

目的 运用近红外光谱对生鲜猪肉新鲜度进行实时评估。方法 利用多通道可见近红外光谱系统, 获取了猪肉表面380~1080 nm波长范围内的漫反射光谱数据, 采用多元散射校正(MSC)和变量标准化(SNV)的预处理方法, 然后使用偏最小二乘回归建立猪肉新鲜度的预测模型, 进而对猪肉新鲜度进行评价。结果 采用变量标准化处理后的偏最小二乘回归模型相对比较稳定, 建模效果比较好。对挥发性盐基氮 (TVB-N)的验证集的相关系数达到0.91, 对pH值的验证集的相关系数达到0.93。最后利用该模型对猪肉新鲜度进行评定, 评定准确率达92.9%。结论 实验中运用多点的测量方式提高了近红外检测的精度和稳定性, 对于实时检测评估生鲜猪肉的新鲜度有很大的潜力。     

基于DSP的便携式猪肉品质无损快速检测装置的研发

本研究针对猪肉生产及监管部门的实际需求,以猪肉新鲜度品质指标为目标,利用数字信号处理器（DSP）TMS320DM6437作为核心处理器,以TMS320F28015为辅助处理器,开发一种能够脱机使用的轻简便携式猪肉品质检测装置系统,包括硬件和软件系统:硬件系统由大功率LED点光源单元、图像采集单元、DSP数据处理单元和液晶显示单元组成;软件系统采用C语言编程工具开发的信号采集、分析处理、结果显示保存模块等。利用洛伦兹函数拟合猪肉的扩散图像,获取了猪肉品质的光学特征参数,基于光学特征参数建立猪肉的挥发性盐基氮（TVB-N）值、pH和颜色（L*、a*、b*）的预测模型。通过建模分析和独立样品验证,挥发性盐基氮的预测相关系数（RV）为0.92,预测标准误差（SE_P）为2.03,pH的预测相关系数（R_V）为0.90,预测标准误差（SE_P）为0.04;L*的R_V为0.94,SEP为1.02,a*的R_V为0.90,SE_P为0.80,b*的R_V为0.91,SE_P为0.58。通过软件与硬件系统调试和实验,该系统装置的检测速度为每个样品检测仅需4 s,符合猪肉现场快速检测的实际要求。      

便捷式智能化大米新鲜度检测系统设计与应用

目的:实现大米新鲜度的快速无损检测。方法:研制一套基于纳米色敏传感器结合近红外光谱分析原理的便携式装置系统。对所采集到的不同掺陈度大米样品所对应色敏传感器的光谱数据,进行多梯度掺陈大米的鉴别与跨批次大米新鲜度的预测。结果:使用Si-CARS-PLS提取光谱特征变量,经LDA算法建模后判别模型的识别率最高,训练集和预测集的识别率分别为97.22%和95.83%。同时,PLSR模型预测跨批次数据具有更强的稳定性,不同批次大米样品数据训练集和预测集的相关系数(R_c、R_p)均稳定在0.95左右,均方根误差(RMSEC、RMSEP)均低于0.2,相对分析误差(RPD)均大于3。结论:该系统具有准确率高、便捷和预测模型鲁棒性好等特点,在大米新鲜度的现场检测中有很好的应用前景。