近红外光谱结合化学计量法快速无损鉴别燕麦

提出了一种基于近红外光谱技术与化学计量学的燕麦无损鉴别方法。通过近红外光谱仪测定了5个品牌与劣质燕麦的光谱曲线,利用连续小波变换方法对光谱进行预处理,然后基于标准偏差与相对标准偏差的变量筛选方法筛选出具有代表的15个波数点,最后结合主成分分析法对不同燕麦样品快速鉴别。结果表明:连续小波变换可以有效地消除光谱中的背景干扰,提取光谱有效信息,波长筛选方法可以大大提高主成分分析结果的鉴别能力。通过结合近红外光谱分析技术与化学计量学方法,可对中国国产品牌、进口品牌和劣质燕麦进行准确鉴别。     

近红外光谱法对茶叶中的咖啡碱含量的快速测定

以贵州省各地茶叶为研究对象,以近红外光谱分析技术快速检测茶叶中的咖啡碱含量为目的,将茶叶制成粉末进行光谱扫描并用化学法测定其咖啡碱含量,将偏最小二乘法与傅立叶变换近红外光谱法相结合,建立茶叶中咖啡碱含量的模型.通过多元散射校正(MSC)处理光谱,光谱范围选择4493.33cm-1～4354.48cm-1,5800.83cm-1～5696.69cm-1,主因子数为10,得到模型的内部交互验证相关系数(P)为0.991,交互验证均方差(RMSECV)为0.205;模型的预测值与化学测定值的相关系数为0.999,预测标准偏差(RMSEP)为0.242.通过稳定性试验得到的RSD都在0.30％以下.结果表明,模型的预测效果很好,具有较高的精密度和较好的稳定性,且方法简捷,能满足茶叶中咖啡碱的快速检测要求.     

近红外光谱法快速检测带鱼肉中的水分和蛋白质含量

应用傅立叶变换近红外光谱分析技术对带鱼肉中水分和蛋白质含量进行了研究。分别建立原始光谱、间隔2点一阶导数(dblg2)、3点平滑(sa3)、标准归一化(SNV)和多元散射校正(MSC)的偏最小二乘回归(PLS)模型,比较定标相关系数(Rc)、预测相关系数(Rv)、定标标准差(SEC)和预测标准差(SEP),建立了MSC预测模型,水分和蛋白质近红外检测模型的相关系数均在0.9以上。SEC分别为0.74和0.68,SEP分别为0.81和0.73。将确定的模型进行了外部验证,水分和蛋白质NIR预测值和化学分析值的配对t检验差异均不显著。说明近红外光谱法应用于带鱼肉中水分和蛋白质含量的快速检测是可行的。     

近红外与表面增强拉曼光谱融合技术快速检测花生油中黄曲霉毒素B1

目的 在近红外光谱(near infrared spectroscopy, NIR)与表面增强拉曼光谱(surface-enhanced Raman spectroscopy, SERS)特征层数据融合的基础上构建偏最小二乘回归模型(partial least squares regression, PLSR)实现花生油中黄曲霉毒素B1 (aflatoxin B1, AFB1)含量的快速检测。方法 首先,分别采集待测样本的NIR与SERS光谱。其次,将采集的NIR与SERS光谱分别进行光谱预处理。然后,采用基于希尔伯特-施密特独立准则的变量空间迭代优化算法(Hilbert-Schmidt independence criterion based variable space iterative optimization, HSIC-VSIO)分别筛选NIR与SERS光谱的特征变量。最后,将筛选的特征变量进行融合并构建PLSR模型用于定量检测花生油中AFB1含量。结果 与NIR光谱数据、SERS光谱数据以及NIR与SERS光谱直接融合数据构建的PLSR模型相比,NIR与SERS光谱特征层融合数据构建的PLSR模型具有最佳的预测性能：校正集均方根误差(root mean squared error of calibration set, RMSEC)为0.1569,校正集决定系数(coefficient of determination of calibration set, )为0.9908,预测集均方根误差(root mean squared error of prediction set, RMSEP)为0.1827,预测集决定系数(coefficient of determination of prediction set, )为0.9854,性能偏差比(ratio of performance to deviation, RPD)为8.2761。将本方法与标准方法分别检测真实含有AFB1的花生油样本,结果表明两者的检测性能无显著性差异(P=0.84>0.05)。结论 本方法可实现花生油中AFB1含量的快速、高精度定量检测,也验证了NIR与SERS光谱融合的可行性与有效性。     

Non‐destructive prediction of ‘Hass’ avocado dry matter via FT‐NIR spectroscopy

BACKGROUND: The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near‐infrared spectroscopy (NIRS) was investigated for the first time as a non‐invasive technique for estimating %DM of whole intact ‘Hass’ avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra‐seasonal variation and orchard conditions. RESULTS: It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4–34.2%. CONCLUSION: The results of the study indicate the potential of FT‐NIRS in diffuse reflectance mode to non‐invasively predict %DM of whole ‘Hass’ avocado fruit. When the FT‐NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados. Copyright © 2010 Society of Chemical Industry     

蜂蜜中羟甲基糠醛的近红外光谱快速测定方法

为实现蜂蜜中羟甲基糠醛的快速测定,利用近红外（NIR）光谱分析技术结合偏最小二乘法（PLS）建立了蜂蜜中羟甲基糠醛的定量分析模型,并进行了预测。通过光谱扫描,波数范围为7706⁴009cm-1、一阶导数、norris derivative平滑及10个因子数进行光谱预处理,偏最小二乘法（PLS）,交叉验证。结果表明,羟甲基糠醛定量模型的交叉验证相关系数（Rcv2）=0.99620、交叉验证均方差（RMSECV）=2.40;预测相关系数（Rp2）=0.99874、预测均方差（RMSEP）=2.02;预测值与测定值之间无显著差异,该方法适用于蜂蜜中羟甲基糠醛的快速测定。      

Challenges in Model Development for Meat Composition Using Multipoint NIR Spectroscopy from At‐Line to In‐Line Monitoring

This study evaluates the efficiency of multipoint near‐infrared spectroscopy (NIRS) to predict the fat and moisture content of minced beef samples both in at‐line and on‐line modes. Additionally, it aims at identifying the obstacles that can be encountered in the path of performing in‐line monitoring. Near‐infrared (NIR) reflectance spectra of minced beef samples were collected using an NIR spectrophotometer, employing a Fabry‐Perot interferometer. Partial least squares regression (PLSR) models based on reference values from proximate analysis yielded calibration coefficients of determination of 0.96 for both fat and moisture. For an independent batch of samples, fat was estimated with a prediction coefficient of determination of 0.87 and 0.82 for the samples in at‐line and on‐line modes, respectively. All the models were found to have good prediction accuracy; however, a higher bias was observed for predictions under on‐line mode. Overall results from this study illustrate that multipoint NIR systems combined with multivariate analysis has potential as a process analytical technology (PAT) tool for monitoring process parameters such as fat and moisture in the meat industry, providing real‐time spectral and spatial information.     

A Feasibility Study on Monitoring Residual Sugar and Alcohol Strength in Kiwi Wine Fermentation Using a Fiber‐Optic FT‐NIR Spectrometry and PLS Regression

This work aims to investigate the potential of fiber‐optic Fourier transform‐near‐infrared (FT‐NIR) spectrometry associated with chemometric analysis, which will be applied to monitor time‐related changes in residual sugar and alcohol strength during kiwi wine fermentation. NIR calibration models for residual sugar and alcohol strength during kiwi wine fermentation were established on the FT‐NIR spectra of 98 samples scanned in a fiber‐optic FT‐NIR spectrometer, and partial least squares regression method. The results showed that R² and root mean square error of cross‐validation could achieve 0.982 and 3.81 g/L for residual sugar, and 0.984 and 0.34% for alcohol strength, respectively. Furthermore, crucial process information on kiwi must and wine fermentations provided by fiber‐optic FT‐NIR spectrometry was found to agree with those obtained from traditional chemical methods, and therefore this fiber‐optic FT‐NIR spectrometry can be applied as an effective and suitable alternative for analyses and monitoring of those processes. The overall results suggested that fiber‐optic FT‐NIR spectrometry is a promising tool for monitoring and controlling the kiwi wine fermentation process.     

Near‐infrared reflectance spectroscopy for determining end‐point temperature of heated fish and shellfish meats

Attempts have been made to assess the end‐point temperature (EPT) of heated fish and shellfish meats by near‐infrared (NIR) reflectance spectroscopy. Since the presence of water affects NIR spectra, the influence of the water content of samples on the performance of NIR spectroscopy for determining EPT was also evaluated. Blue marlin (Makaira mazara), skipjack (Katsuwonus pelamis), red sea bream (Pagrus major), kuruma prawn (Penaeus japonicus) and scallop (Patinopecten yessoensis) meats were heat‐treated at different temperatures (5 °C intervals between 60 and 100 °C). NIR spectra were measured at 2 nm intervals between 1100 and 2500 nm. A stepwise multiple linear regression method was used to develop a calibration curve. Changes in NIR reflectance spectra upon heat treatment were related to the heating temperature. Moreover, the interference from the variation in water content on the prediction of EPT was eliminated by selecting appropriate wavelengths. Plotting of NIR‐predicted temperatures determined from d² log(1/R) at four specific wavelengths against the actual EPT revealed a promising linear relationship with correlation coefficients between 0.94 and 0.98. In the temperature range 60–100 °C, NIR reflectance spectroscopy was able to detect EPT of heated fish and shellfish meats with a prediction error of 1.9–3.1%. © 2002 Society of Chemical Industry     

近红外光谱结合偏最小二乘法快速测定糖果中水分含量

采用近红外光谱(NIR)结合偏最小二乘法(PLS)建立了一种糖果中水分含量快速准确的测定方法。在12500~3600 cm^-1光谱范围内采集116批糖果的近红外漫反射光谱,并用减压干燥失重法测定其水分含量。通过比较不同参数对建模的影响,发现用多元散射校正法进行预处理,在11682.2~9826.1、8939.0~6267.9、5378.8~4487.8 cm^-1光谱范围内,主成分数为15时,应用PLS方法建立的糖果水分的定量分析模型效果最佳。所建立模型的相关系数为0.9716,校正均方根误差和验证均方根误差分别为0.97%和1.03%。该方法结果准确可靠、操作简便,可用于糖果中水分含量的快速检测。     

应用近红外光谱技术快速测定粳稻品种的直链淀粉含量

应用近红外光谱法以稻谷、糙米、精米、糙米粉和精米粉为扫描材料分别建立了粳稻直链淀粉含量的预测模型。结果表明采用光谱预处理的校正效果比不采用预处理的好,用偏最小二乘法(PLS)获得的粳稻稻谷、糙米、精米、糙米粉、精米粉的回归模型和交叉验证结果为:最优校正决定系数(R2)和交叉检验均方误差(RMSECV)分别为0.8136、2.74,0.8864、2.56,0.8915、2.59,0.9261、2.26,0.9505、1.83,粉碎性样品的误差比整粒样品的误差小。育种实践中,低世代可选用糙米、高世代可选用糙米粉或精米粉作为扫描样本测定稻米直链淀粉含量。