小麦脂肪酸值的近红外光谱快速测定研究

采用近红外光谱(NIRS)分析技术和化学计量方法建立小麦脂肪酸值的近红外分析模型,并对模型进行预测准确性评价。结果表明:所建立模型的定标相关系数(RSQ)为0.9026,交叉验证相关系数(1-VR)为0.6278,定标标准偏差(SEC)为3.8735,交叉验证标准偏差(SECV)为7.0908。外部验证的相关系数(r)为0.948,外部验证标准偏差(SEP)为3.8709。标准方法与NIRS测定方法测定的小麦脂肪酸值含量之间的t检验值为1.345,显示两种方法测定结果无显著性差异(P<0.05),预测值与实测值的平均绝对偏差为0.25,说明所建立的稻谷脂肪酸值的NIRS数学模型预测准确性较好,可用于小麦脂肪酸值的快速预测。     

便携式近红外谷物分析仪快速测定小麦蛋白质的研究

收集中国小麦各个主产区不同品种样品104份,研究采用国产便携式近红外谷物分析仪快速测定小麦蛋白质含量,用化学计量方法建立了小麦蛋白质含量的近红外分析模型,并对模型进行了预测准确性评价。在建模过程中,分别探讨光谱散射和数学导数处理等优化对定标模型的影响。结果表明:采用偏最小二乘法(PLS)建立模型,光谱预处理最佳条件为:Savitzky-Golay平滑、Savitzky-Golay一阶导数、基线校正、均值中心化、主因子数为12。所建模型的定标标准偏差(SEC)和定标相关系数(RC)分别为0.177和0.988;外部验证的标准偏差(SEP)和相关系数(RP)分别为0.188和0.961。标准方法测定值与NIRS方法预测值之间的T检验结果为T=0.304(P<0.05),表明两种测定方法测定值之间无显著性差异,说明定标模型具有很好的预测准确性,可应用于优质小麦收购中蛋白质含量的快速测定。     

“小麦容重与出粉率”的验证及其关系的初浅探讨

一、验证目的通过分析小麦容重与出粉率的关系为我国小麦品质的定性提供科学的依据。二、验证的设备及原料1、验证设备特制一等粉的生产线。有液压磨粉机6台(其中:800mm4台,600mm2台)磨辊总长度为8800mm。筛理总面积为86m~2。     

“小麦容重与出粉率”的验证及其关系初探

<正> 分析“小麦容重与出粉率”之间的关系。能为我国小麦品质的定等提供科学的依据。是商业部粮食“七五”攻关的主要项目。我们受委托为该项目进行了生产验证。实践证明,小麦容重的高低对出粉率有着举足轻重的影响。下面是生产验证的情况: 一、验证目的: 通过验证弄清小麦容重与出粉率及其关系。二、验证的设备及工艺: 特制一等粉生产线。有液压磨粉机6台(其中:800mm 4台、600mm2台。磨辊总长度8800mm。筛理总面积为86m~2)。采用4皮3心。1、2、3皮粗细磨工艺。三、验证的小麦原料: 不同容重的87年产“宁丰”15％、“泗阳     

近红外光谱快速测定 Mortierella alpina菌丝体油脂含量

为实现玉米皮渣发酵生产微生物油脂过程中菌丝体油脂含量的实时监测,建立了菌丝体油脂含量测定的近红外定标模型。结果表明,采用偏最小二乘法(PLS)、归一化预处理方法,模型的定标集相关系数(R)及验证集相关系数分别为0.997 4和0.995 8,定标集标准偏差(SEE)为1.266 6,验证集标准偏差(SEP)为1.417 1,定标集标准偏差(SEE)与验证集标准偏差(SEP)的比值为0.893 7,这表明该方法可快速准确地测定菌丝体油脂含量。     

粮食铅和镉的测定中干法灰化温度的研究

选用稻谷、玉米、小麦三个粮食品种各5份样品,利用干法灰化法对样品进行试样消解,将灰化温度分别设定为500℃和600℃,运用原子吸收分光光度计分别测定同一样品中铅、镉含量,通过实验发现两者测定结果比较接近,同时用质控样(GBW10011 GSB-2小麦)来验证其铅、镉测定的结果符合标准值,表明灰化温度设定为600℃是可行的、合理的,且能够明显缩短实验时间。     

盐酸浸提-ICP-MS法测定小麦和大米中的10种营养元素

研究了盐酸浸提小麦和大米,应用电感耦合等离子体质谱(ICP-MS)技术测定提取液中的B、Na、Mg、P、K、Ca、Mn、Fe、Cu、Zn等10种营养元素。研究了不同浸提条件对各待测元素浸提率的影响,应用动态反应池(DRC)技术消除了样品溶液中氯化物基质、氩气、水所形成的多原子离子质谱干扰,选用由Li、Sc和Y标准溶液组成的混合内标溶液消除了基体效应。结果表明,采用盐酸浸提法各元素的检出限为2.7~30.8 ng/g之间,相对标准偏差(RSD)为1.7%~6.2%,选用国家一级标准物质小麦(GBW10011)和大米(GBW10045)验证了方法的准确度和精密度。该方法操作简单、结果准确,能满足小麦和大米中10种营养元素的分析要求。     

近红外法测定大豆脂肪酸值方法的研究

脂肪酸值是衡量大豆品质重要指标.将近红外光谱技术与化学计量方法结合,建立大豆样品脂肪酸值的定标方程,并对定标方程进行了验证,优化得到大豆脂肪酸值的定标方程,交互定标决定系数(1-VR)为0.948 2,外部验证决定系数(R2)为0.915 0,定标标准偏差(SEC)为1.205 8,交叉验证标准偏差(SECV)为1.591 2,现有数据预测标准偏差(SEP)为1.395.通过外部验证,表明该方法也可以应用于实际检测.     

QuEChERS-超高效液相色谱-串联质谱法快速测定小麦及土壤中环丙唑醇的残留

目的 用QuEChERS方法对小麦及土壤样品进行前处理后,结合超高效液相色谱-串联质谱仪(ultra performance liquid chromatography-tandem mass spectrometry, UPLC?MS/MS)建立快速检测环丙唑醇在小麦籽粒与秸秆及土壤中的残留。方法 小麦籽粒与秸秆及土壤经纯水浸泡乙腈振荡提取并离心后,上清液加乙二胺-N-丙基硅烷吸附剂[N?(n?Propyl)-Ethylenediamine, PSA]和硫酸镁混合净化,高速离心后取净化上清用流动相定容,0.22μm尼龙膜过滤后做为待测液上机检测。结果 对小麦籽粒与秸秆及土壤三种基质而言,本方法在0.001~0.40 mg/L浓度范围内线性响应均良好,相关系数分别为0.9997、0.9994及0.9996。环丙唑醇在小麦籽粒中的添加水平为 0.010~1.0 mg/kg,平均回收率为92%~98%,相对标准偏差均小于1.6% (n=5);在小麦秸秆中的添加水平为 0.010~5.0 mg/kg,平均回收率为72%~104%,相对标准偏差均小于1.7% (n=5);在土壤中的添加水平为0.010~5.0 mg/kg,平均回收率为91%~103%,相对标准偏差均小于2.1% (n=5)。结论 本方法简单快速、灵敏度高、准确度和精密度好,能满足小麦及土壤中环丙唑醇残留分析的需要。     

微波消解-电感耦合等离子体质谱法同时测定小麦中35种元素

目的 建立小麦中Li、Be、V、Cr、Mn、Co、Ni、Cu、As、Se、Y、Mo、Ag、Cd、Sb、Ba、La、Ce、Pr、Nd、Sm、Eu、Gd、Tb、Dy、Ho、Er、Tm、Yb、Lu、Hg、Tl、Pb、Th、U等35种元素同时测定的ICP-MS分析方法。方法 经微波消解处理后,用ICP-MS对样品进行测定,以Sc、Rh、In、Bi为内标元素校正基体效应和信号漂移。结果 各元素均呈良好的线性关系,相关系数在0.9993~1.0000之间。该方法用于小麦中35种元素检测的方法检出限在0.0001~0.0030 mg/kg之间,相对标准偏差(RSD)在0.36%~4.90%之间,加标回收率在89.0%~115.5%之间。分别采用一级标准物质河南小麦(GBW10046)和四川大米(GBW10044)来验证方法的精密度和准确度,结果均在标准值范围内。对60份小麦样品进行测定,所得结果满意。结论 该方法用于小麦中多元素的测定,操作简便、分析速度快、灵敏度高,为小麦的质量控制和营养评价提供依据。     

基于近红外光谱技术与BP-ANN算法的豆粕品质快速检测

应用近红外漫反射光谱技术结合误差反向传递人工神经网络(BP-ANN)算法,建立豆粕品质(包括水分、粗蛋白、残油)的定量分析模型。将豆粕漫反射吸收光谱数据进行SNV、DT、SG求导、SG平滑和均值中心化处理,然后采用偏最小二乘方法(PLS)降维获取主成分,并优化选择合适的隐含层节点数、隐含层和输出层转化函数,建立校正模型,并用验证样品对校正模型进行验证。结果显示,BP-ANN法建立的水分、粗蛋白和残油的预测相关系数(R)分别为0.981、0.988、0.982,预测标准偏差(SEP)分别为0.120、0.216、0.036,均优于PLS建模方法结果,且满足传统分析方法的重复性要求,表明BP-ANN方法可用于生产过程豆粕品质的快速监控。     

Application of near infrared spectroscopy for the rapid determination of antioxidant activity of bamboo leaf extract

This study was carried out to evaluate the feasibility of using near infrared (NIR) spectroscopy for determining three antioxidant activity indices of the extract of bamboo leaves (EBL), specifically 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing/antioxidant power (FRAP), and 2,2′-azinobis-(3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS). Four different linear and nonlinear regressions tools (i.e. partial least squares (PLS), multiple linear regression (MLR), back-propagation artificial neural network (BP-ANN), and least squares support vector machine (LS-SVM)) were systemically studied and compared in developing the model. Variable selection was first time considered in applying the NIR spectroscopic technique for the determination of antioxidant activity of food or agricultural products. On the basis of these selected optimum wavelengths, the established MLR calibration models provided the coefficients of correlation with a prediction (r_pre) of 0.863, 0.910, and 0.966 for DPPH, FARP, and ABTS determinations, respectively. The overall results of this study revealed the potential for use of NIR spectroscopy as an objective and non-destructive method to inspect the antioxidant activity of EBL.     

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools

Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486 g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.     

电子舌识别技术对腌制黄瓜调料的定量化分析

采用一种基于电子舌识别技术的方法对腌制黄瓜中盐、总酸、还原糖和乳酸钙进行定量分析。采用PLS分析和BP-ANN分析对数据进行处理,建立并对比了两种预测模型。结果表明,PLS模型和BP-ANN模型均具有较好的预测能力,相关系数(R^2)均在0.9以上。对于盐和乳酸钙,两种模型适用性相似;对于总酸,BP-ANN模型优于PLS模型;对于还原糖,PLS模型优于BP-ANN模型。该研究为电子舌识别技术在腌制黄瓜调料定量化分析中应用的可行性提供了理论依据。     

基于图像增强的高湿度环境下野生二粒小麦籽粒硬度检测方法

为有效检测高湿度环境下谷类籽粒硬度,以某谷类食品加工厂生产的野生二粒小麦为试验对象,采用高光谱图像成像系统获取野生二粒小麦高光谱图像,通过基于匹配思想的自适应消噪方法(PLS)去除野生二粒小麦高光谱图像的带状噪声,增强高湿度环境下野生二粒小麦高光谱图像质量,在此基础上,将野生二粒小麦光谱数据的平均值作为光谱数据,构建高湿度环境下野生二粒小麦籽粒硬度预测模型,实现对高湿度环境下谷类籽粒硬度的准确检测。仿真试验结果表明,当加工环境湿度为55%时,本文方法检测野生二粒小麦籽粒硬度值平均结果为1745 g,与标准红外检测方法得到的结果差值仅有3 g;当环境湿度提高到75%时,本文方法检测结果为1712 g,与标准红外检测方法相差42 g,本文方法检测野生二粒小麦籽粒硬度结果精度高,优于声振频带幅值特性法,是一种高精度的高湿度环境谷类籽粒硬度检测方法。     

Determination of 10‐Hydroxy‐2‐Decenoic Acid of Royal Jelly Using Near‐Infrared Spectroscopy Combined with Chemometrics

A rapid quantitative analysis model for determining the hydroxy‐2‐decenoic acid (10‐HDA) content of royal jelly based on near‐infrared spectroscopy combining with PLS has been developed. Firstly, near‐infrared spectra of 232 royal jelly samples with different 10‐HDA concentrations (0.35% to 2.44%) were be collected. Second‐order derivative processing of the spectra was carried out to construct a full‐spectrum PLS model. Secondly, GA‐PLS, CARS‐PLS, and Si‐PLS were used to select characteristic wavelengths from the second‐order derivative spectrum to construct a PLS calibration model. Finally, 58 samples were used to select the best predictive model for 10‐HDA content. The result show that the PLS model constructed after wavelength selection was significantly more accurate than the full spectrum model. The Si‐PLS algorithm performed best and the corresponding characteristic wavelength range were: 980 to 1038, 1220 to 1278, 1340 to 1398, and 1688 to 1746 nm. The prediction results were RMSEP = 0.1496% and R_P = 0.9380. Hence, it is feasible to employ near‐infrared spectra to analyze 10‐HDA in royal jelly.     

Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy

To address the rapid and nondestructive determination of pork storage time associated with its freshness, Fourier transform near infrared (FT-NIR) spectroscopy technique, with the help of classification algorithm, was attempted in this work. To investigate the effects of different linear and non-linear classification algorithms on the discrimination results, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and back propagation artificial neural network (BP-ANN) were used to develop the discrimination models, respectively. The number of principal components (PCs) and other parameters were optimized by cross-validation in developing discrimination models. Experimental results showed that the performance of BP-ANN model was superior to others, and the optimal BP-ANN model was achieved when 5 PCs were included. The discrimination rates of the BP-ANN model were 99.26% and 96.21% in the training and prediction sets, respectively. The overall results sufficiently demonstrate that the FT-NIR spectroscopy technique combined with BP-ANN classification algorithm has the potential to determine pork storage time associated with its freshness.     

Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine

More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ = 0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (R_p) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar.     

基于可见/近红外光谱和变量选择的脐橙可溶性固形物含量在线检测

为联合可见/近红外光谱技术和变量选择方法在线检测脐橙主要内部品质指标可溶性固形物(SSC),分别选定脐橙校正集和预测集样本141个和47个,脐橙运输速度为0.3m/s,利用USB4000微型光谱仪在线采集脐橙样本的可见/近红外光谱,先分别采用无信息变量消除(UVE)和遗传算法(GA)对650~950nm波段范围的波长变量进行预筛选,再分别利用竞争自适应重加权采样(CARS)及连续投影算法(SPA)对波长变量进一步筛选,并应用偏最小二乘(PLS)方法分别建立脐橙SSC的在线预测模型,并与原始光谱等建立的预测模型进行比较。结果表明,对于脐橙SSC,预筛选方法GA优于UVE方法,变量选择方法CARS优于SPA方法;GA-CARS及GA-SPA联合变量选择方法优于对应的单一变量选择方法CARS及SPA。在上述变量选择方法中,GA-CARS方法获得的结果最优,其所建立的脐橙SSC的PLS模型的校正集和预测集相关系数分别为0.933和0.824,校正集和预测集均方根误差分别为0.429%和0.670%,性能优于原始光谱建立的PLS模型,且建模波长变量数由1 385个下降为78个,仅占原波长变量数的5.63%。由此表明,GA-CARS联合变量选择方法可以有效筛选脐橙SSC的波长变量,提高预测模型的稳定性和预测精度。