近红外光谱技术快速检测猪肉糜中的掺杂鸭肉

为快速、准确利用近红外定性分析混入鸭肉的掺假猪肉糜。试验在猪肉糜中添加不同比例的鸭肉肉糜,制备不同掺假鸭肉的样品90个,其中76个为掺有不同鸭肉质量分数的样品,14个为纯猪里脊肉,并独立制备44个验证集样品,其中37个为掺有不同鸭肉肉糜质量分数的样品,7个为纯猪肉。在10 000 cm-1~4 000 cm-1波数范围内进行光谱扫描,采用不同光谱预处理方法结合主成分分析,建立PLS-DA模型。建立的判别分析模型:在全波段范围(10 000 cm-1~4 000 cm-1)内,对校正集正确判别率可达100%,验证集的正确判别率可达88.6%。研究结果表明近红外漫反射光谱可以快速检测猪肉中的掺杂鸭肉。     

猪肉糜中掺鸡肉的近红外光谱快速定性判别方法研究

为快速准确地定性判别猪肉糜中是否掺有鸡肉糜,在猪肉糜中添加不同比例的鸡肉糜,制备不同掺假样品74个,其中60个为掺有不同质量分数鸡肉的样品,14个为纯猪肉糜,并独立制备39个验证集样品,其中31个为掺有不同质量分数的鸡肉糜样品,8个为纯猪肉糜。在10000~4000 cm-1波数范围内进行光谱扫描,采用PLS-DA方法建立判别分析模型。结果所建判别模型对校正集和预测集的正确判别率均可达100%。表明近红外漫反射光谱可以提供一种快速鉴别猪肉糜中是否掺有鸡肉糜的方法。     

基于支持向量机的近红外光谱技术快速鉴别掺假羊肉

为实现掺假羊肉的无损鉴别,利用傅里叶变换近红外光谱分析技术建立混入鸭肉的掺假羊肉糜的快速检测方法。实验通过在羊肉糜中添加不同比例的鸭肉糜来制备掺假羊肉,采用近红外漫反射方式在全波段范围(10000~4000 cm-1)内采集羊肉、掺假羊肉和鸭肉的近红外光谱图,分别考察多元散射校正(Multiplicative scatter correction,MSC)、标准正态变量变换(Standard normal variate correction,SNV)、面积归一化(Area normalization)、标准化(Autoscale)、15点平滑处理(Smoothing)、一阶导数处理(1stderivative)的光谱预处理方法对所建支持向量机(nuSVM)判别模型的预测效果。结果显示,不同光谱预处理所建nu-SVM判别模型预测效果不同。其中,经标准化处理后所建的nu-SVM模型的预测能力最差为90.38%;15点平滑处理后所建nu-SVM模型的预测效果最好(96.15%),对建模集正确判别率为99.07%,对检验集正确判别率为96.15%;其余处理所建nu-SVM模型的判别能力介于二者之间。结果表明,采用近红外光谱技术结合15点平滑预处理后所建nu-SVM模型可以实现羊肉中的掺杂鸭肉的鉴别。     

基于近红外漫反射光谱技术判别柿子品种和贮藏期的研究

目的通过近红外漫反射光谱技术建立了柿子不同品种和贮藏期的快速判别方法。方法实验对贮藏冷库(0±0.5℃)20 d、40 d、60 d的磨盘柿和阳丰甜柿的近红外光谱(400~2500 nm)进行平滑、一阶倒数和标准正常化处理(SNV)处理,采用主成分分析法(PCA)建立判别模型。结果在全波长范围内,不同品种定标模型的正确分类率达到100%;阳丰甜柿不同贮藏期的正确分类率达到97.78%;磨盘柿不同贮藏期的正确分类率达到98.89%。3个预测模型的累积准确率达到96.67%。结论通过近红外漫反射光谱技术,判别不同品种的柿子并预测其贮藏期具有应用价值。     

基于近红外漫反射光谱技术判别柿子品种和 贮藏期的研究

目的 通过近红外漫反射光谱技术建立了柿子不同品种和贮藏期的快速判别方法。方法 实验对置于冷库(0±0.5℃)贮藏20d、40d、60d的磨盘柿和阳丰甜柿的近红外光谱(400～2500nm)进行平滑处理、一阶倒数和标准正常化处理(SNV)处理,采用主成分分析法(PCA)建立判别模型。结果 在全波长范围内,不同品种定标模型的正确分类率达到100%;阳丰甜柿不同贮藏期的正确分类率达到97.78%;磨盘柿不同贮藏期的正确分类率达到98.89%。3个预测模型的累积准确率达到96.67%。结论 通过近红外漫反射光谱技术,判别不同品种的柿子并预测其贮藏期具有应用价值。     

苹果品种及损伤苹果的FT-NIR鉴别研究

用傅里叶近红外光谱技术(FT-NIR)对不同品种的苹果以及损伤嘎啦和完好嘎啦进行快速、无损检测,比较不同判别方法对所建立的区分苹果品种及苹果损伤模型的影响。结果表明:损伤嘎啦和完好嘎啦的近红外图谱经小波分析预处理后,用12000～4000cm-1波数范围的前5个主成分分别结合多层感知神经网络、径向基神经网络、Fisher判别3种方法所建立的判别模型对未知样本的正确判别率分别为97.8%、87.2%和84.8%,基于权重法用多元线性回归(MLR)所选择的特征波长所建立的Fisher判别模型对未知样本的正确判别率为89.1%;用偏最小二乘判别(PLS-DA)所建立的判别模型对未知样本的正确判别率为100%,由于PLS-DA模型对训练集和验证集的正确判别率均为100%,因此PLS-DA模型优于其他模型。不同品种苹果的光谱经平滑预处理后,用全波数范围12000～4000cm-1的前6个主成分所建立的判别模型优于经验波数范围8000～4500cm-1所建立的判别模型,其较优模型对建模集和验证集的正确判别率分别为90.9%和92.1%。近红外光谱技术结合化学计量学可以快速、无损鉴别苹果是否有损伤以及不同品种的苹果。     

原料肉中掺大豆蛋白的近红外检测技术的研究

探讨了利用近红外光谱技术快速、准确地进行原料肉中是否掺大豆蛋白鉴别的可行性。首先以原料肉和掺假肉为原料,利用近红外光谱仪测定样品的漫反射光谱曲线,再应用Fisher线性判别分析和偏最小二乘法对试验数据进行了多元统计分析。分析结果表明,利用Fisher判别分析建立的判别模型的正确判别率达到100%,对未知样进行检验,正确判别率达到86.1%;最后利用偏最小二乘法(PLS)建立大豆蛋白掺入量的定量检测模型,校正模型决定系数(R2)为93.4%,内部交叉验证均方根差(RMSECV)为2.45%,对此模型进行验证,预测集相关系数(R2)为84.1%,预测标准偏差(RMSEP)为3.59%。说明应用近红外光谱技术对原料肉中掺大豆蛋白进行快速、准确的的鉴别是可行的。     

近红外光谱鉴别滇南小耳猪和DLY商品猪

应用近红外漫反射光谱技术(near infrared reflectance spectroscopy,NIRS),对滇南小耳猪和DLY商品猪的整块、匀质肉糜、烘干粉、冻干粉的光谱建模分析,进行品种鉴别,并筛选出最优的光谱建模方法。采集4 300~11 000 cm-1范围内53头滇南小耳猪、58头DLY商品猪背最长肌整块、匀质肉糜、烘干粉、冻干粉光谱数据。分别采用原始光谱、一阶导数、二阶导数、变量标准化、多元散射校正等方法进行光谱预处理,应用簇类独立软模式方法建立品种鉴别模型。其中80个样品作为校正集,余下31个样品作为验证集。结果表明:DLY商品猪和滇南小耳猪整块肉光谱模型对验证集DLY商品猪识别率为100%,拒绝率为0%,对验证集滇南小耳猪识别率为100%,拒绝率50%,效果不理想;匀质肉糜光谱模型对验证集DLY商品猪识别率为100%,拒绝率为0%,对验证集滇南小耳猪识别率为100%,拒绝率0%,效果不理想;烘干粉、冻干粉的近红外光谱模型鉴别效果较好,验证集的识别率和拒绝率均为100%。综上,近红外光谱分析方法可用于鉴别DLY商品猪和滇南小耳猪两个品种。     

基于傅里叶近红外光谱和电子鼻技术的苹果霉心病无损检测

为研究傅里叶近红外光谱技术(Fourier transform near infrared spectroscopy,FT-NIRS)和电子鼻技术分别结合化学计量学方法对苹果霉心病的判别效果,以“红富士”霉心病苹果和健康苹果为试材,利用近红外光谱技术,基于主成分分析建立Fisher判别和多层感知器(multi-layer perceptron,MLP)神经网络模型;同时利用电子鼻技术分别结合Fisher判别、MLP神经网络和径向基函数神经网络3种化学计量学的方法建立判别模型。根据建模集和验证集的预测准确率综合考虑,基于主成分分析建立的MLP神经网络模型和电子鼻结合MLP神经网络模型对苹果霉心病的判别效果最好,验证集中的正确判别率分别达到87.7%和86.2%。说明电子鼻和近红外光谱技术均可以较好地判别苹果霉心病。     

基于近红外光谱的豆酱鉴别技术研究

针对葱伴侣、凤彩桥、海天和金菜地四种品牌的豆酱,利用近红外光谱分析技术,对其进行预处理、主成分分析(PCA)、聚类分析(CA)和判别分析(DA),以建立识别不同豆酱品牌的近红外光谱定性判别模型.分析结果显示4种不同品牌的平均近红外光谱存在差异,其主成分空间分布也处于不同区域.对样品进行聚类分析,凤彩桥和海天存在小部分交...     

基于近红外光谱的冷鲜肉--解冻肉的判别研究

目的 利用近红外光谱对冷鲜肉和解冻肉进行判别研究。方法 利用385～935nm的近红外光谱系统,采集冷鲜肉与解冻肉表面的反射光谱数据,采用Savitzky-Golay(S-G)平滑和标准变量正态变换(SNVT)方法对光谱数据进行预处理。然后利用主成分分析法(PCA)实现数据降维,提取主成分后结合两种状态肉的理化指标(L*值,a*值,pH,蒸煮损失和嫩度),分别利用Fisher判别法、贝叶斯判别法两种判别分析方法对冷鲜肉和解冻肉进行判别研究。结果 两种判别分析方法,均取得较好的分类效果,尤其是Fisher判别法,校正集的回判正确率为96.67%,验证集的正确率为100%。结论 近红外光谱技术应用于冷鲜肉和解冻肉的鉴别是可行的。     

Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef

This study aimed to evaluate the potential of near infrared spectroscopy (NIRS) as a fast and non-destructive tool for detecting and quantifying different adulterants in fresh and frozen-thawed minced beef. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R_P²) of 0.96, 0.94 and 0.95 with standard error of prediction (SEP) of 5.39, 5.12 and 2.08% (w/w) for minced beef adulterated by pork, fat trimming and offal, respectively. The performance of the developed models declined when the samples were in a frozen-thawed condition, yielding R_P² of 0.93, 0.82 and 0.95 with simultaneous augments in the SEP of 7.11, 9.10 and 2.38% (w/w), respectively. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and non-linear regression models (logistic, probit and exponential regression) were developed at the most relevant wavelengths to discriminate between the pure (unadulterated) and adulterated minced beef. The classification accuracy resulting from both types of models was quite high, especially the LDA, PLS-DA and exponential regression models which yielded 100% accuracy. The current study demonstrated that the VIS-NIR spectroscopy can be utilized securely to detect and quantify the amount of adulterants added to the minced beef with acceptable precision and accuracy.     

Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors

The aims were to detect the adulteration of mutton by applying traditional methods (pH and color evaluation) and the E-nose, to build a model for prediction of the content of pork in minced mutton. An E-nose of metal oxide sensors was used for the collection of volatiles presented in the samples. Feature extraction methods, Principle component analysis (PCA), loading analysis and Stepwise linear discriminant analysis (step-LDA) were employed to optimize the data matrix. The results were evaluated by discriminant analysis methods, finding that step-LDA was the most effective method. Then Canonical discriminant analysis (CDA) was used as pattern recognition techniques for the authentication of meat. Partial least square analysis (PLS), Multiple Linear Regression (MLR) and Back propagation neural network (BPNN) were used to build a predictive model for the pork content in minced mutton. The model built by BPNN could predict the adulteration more precisely than PLS and MLR do.     

近红外光谱定性定量检测牛肉汉堡饼中猪肉掺假

利用近红外光谱技术结合化学计量学方法,对不同肥肉占比的解冻牛肉汉堡饼中的猪肉掺假进行定性判别建模,并建立猪肉掺假比例的定量检测模型。结果表明：对不同掺假比例样品的判别,应用偏最小二乘判别分析方法效果优于主成分分析-支持向量机方法,最优模型校正集和验证集判别正确率均为100%。应用偏最小二乘方回归法定量检测不同肥瘦比解冻牛肉汉堡饼中的猪肉掺假比例,模型校正集和验证集的相关系数Rc和Rp、验证集均方根误差分别为0.968 9、0.861 1、7.221%。因此,应用近红外光谱技术可以实现对不同肥肉占比的解冻牛肉汉堡饼中的猪肉掺假进行定性判别和定量检测。     

基于电子鼻和可见/近红外光谱技术的羊肉真实性鉴别

为快速、准确鉴别市面上羊肉中掺入鸭肉的商品,本研究应用电子鼻结合可见/近红外光谱技术,实现了羊肉中掺入不同比例鸭肉样品的有效鉴别。试验制备了174个羊肉中掺入不同比例鸭肉样品,分别采集了样品电子鼻数据和200~1 100 nm、900~1 700 nm波长范围内的反射光谱数据,利用2分类定性判别和6分类定量检测法分别构建了支持向量机（Support Vector Machine,SVM）和偏最小二乘法（Partial Least Squares,PLS）定性定量判别模型,并用6分类最优模型进行预测。结果表明：电子鼻可以利用不同比例羊肉鸭肉样品间的气味差异对不同组进行判别,羊肉中含有的挥发性香气成分如萜烯类、芳香类、有机硫化物等物质的含量高于鸭肉。基于两个波段数据、两种分类方法构建的PLS模型判别效果优于SVM模型,总的判别正确率均达到96%以上,光谱数据经过多元散射校正处理的效果最佳,且最优模型预测效果良好。电子鼻结合可见/近红外光谱分析技术可有效鉴别羊肉中掺入不同比例鸭肉样品,为羊肉真实性的快速无损鉴别提供技术支撑。     

Mid-infrared spectroscopy and authenticity problems in selected meats: a feasibility study

This paper describes the results of a feasibility study into the use of mid-infrared spectroscopy for addressing certain authenticity problems with selected fresh meats. Preliminary analyses for meat speciation, the detection of ‘frozen-thawed’ meat, and semi-quantitative analysis of meat mixtures are reported. Fourier transform mid-infrared spectroscopy, attenuated total reflectance sample presentation, principal component analysis and partial least squares regression were used. It was possible to distinguish minced chicken, pork and turkey meats from their infrared spectra, and for each meat species it was possible to differentiate between fresh and frozen-thawed samples. Mid-infrared spectroscopy was also able to semi-quantitatively measure the levels of turkey and pork mixed with chicken meat. The method, which is rapid and easy to use, could with further development have the potential for authentication and quality control of meat products.     

SERS结合PCA-LDA分析鉴别鸡肉中硝基呋喃类代谢物的混合残留

以纳米Au溶胶和NaCl溶液为活性增强基底,对鸡肉中残留的两种呋喃它酮代谢物(AMOZ)和呋喃妥因代谢物(AHD)进行表面增强拉曼光谱(SERS)快速检测技术研究。采用自适应迭代惩罚最小二乘法消除原始数据中的背景干扰,应用标准归一化进行光谱预处理,并结合主成分—线性判别方法(PCA-LDA)建立识别模型,得出模型校正集的判别正确率为90.48%,预测集的判别正确率为94.29%。研究表明,SERS技术与PCA-LDA相结合可以有效地鉴别出鸡肉样本中残留的AMOZ和AHD。     

Application of Pca and Slda Methods for the Classification and Differentiation of Cooked Pork From Chinese Indigenous Pig Breeds and A Hybrid Pig Breed

In order to differentiate and characterize the cooked pork samples from three Chinese indigenous pig breeds (Anqingliubai, Wannanhua Pig, and Dingyuan Pig) and a typical hybrid pig breed Yorkshire×Large White×Duroc, the volatile flavor composition was determined for 32 monovarietal samples from these four pig varieties. A headspace solid-phase microextraction gas chromatography–mass spectrometry (headspace–solid-phase microextraction–gas chromatography–mass spectrometry) procedure was used to identify the volatile flavor compounds and detect the concentration of these compounds. One way analysis of variance, principal component analysis, and stepwise linear discriminant analysis were used to process data and to develop discriminant models. Principal component analysis described the correlation between samples (objects) and volatile flavor compounds (variables). The results of the principal component analysis show that the most important contributions to the differentiation of Anqingliubai cooked pork samples are undecanal and 2-undecenal. Hexadecanal and 2-decene-1-ol are the major contributions in Yorkshire×Large White×Duroc cooked pork samples. Benzaldehyde, hexanal, dodecanal, 1-octen-3-ol, and 1-octanolare strongly associated to Wannanhua pig cooked pork samples. Dingyuan pig cooked pork samples are most correlated with heptanal, octanal, 14-octadecenal, (E)-2-octenal, N-hexadcanoic acid, (E)-13-octadecenic acid 3-hydroxy-2-butanone, 2-ethylfuran and 2-butanone. Stepwise linear discriminant analysis was used for differentiation of cooked pork samples according to the varieties, resulting in a correct classification of 100%.     

基于近红外光谱技术快速识别不同动物源肉品

利用傅里叶变换近红外光谱技术建立猪肉、牛肉和羊肉的定性识别模型。用主成分分析法将原始数据压缩为10 个主成分,在全波段9 881.46～4 119.20 cm－1范围内,利用附加散射校正技术结合Savitzky-Golay滤波法对光谱数据进行预处理,采用判别分析法建立模型。模型对训练集的识别准确率为100%,对预测集猪肉、牛肉和羊肉的识别准确率分别为99.28%、97.42%和100%。结果表明：近红外光谱结合模式识别方法能快速无损地识别猪肉、牛肉和羊肉。