Discrimination of Chili Powder Origin Using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES), and Near Infrared (NIR) Spectroscopy

This study investigated the content of macronutrients (Ca, K, Mg, Na, P, and S), trace elements (Ba, Be, Co, Cr, Cu, Fe, Ga, Li, Mn, Ni, Rb, Se, Sr, V, and Zn), and trace toxic elements (As, Cd, In, Pb, and Tl) in chili powder from Korea, China, and Vietnam. For the analyses, inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma-optical emission spectroscopy (ICP-OES) were used. In addition, near-infrared (NIR) spectroscopy was used to differentiate functional groups based on the chemical constituents. For the validation of the applied analytical methods, good linearity (coefficients of determination, 0.9443–0.99997), and relative standard deviations below 4% were obtained. The contents of the toxic elements were below the provisional tolerable weekly intake values. Linear discriminant analysis using elemental contents and NIR data was performed to establish and authenticate the geographical origin of chili powder, which confirmed the reliability of the obtained results.     

基于FTIR指纹光谱的牛肉产地溯源技术研究

在中国牛肉4个产区吉林省榆树市、贵州省安顺市、宁夏自治区同心县、河北省张北县屠宰场采集了58个肉牛牛肉样品,进行脱脂、干燥、粉碎。利用近红外光谱分析技术,对牛肉样品的近红外光谱进行主成分分析、聚类分析、判别分析,建立了判别牛肉产地来源的傅里叶变换红外光谱定性分析模型。分析结果显示4个产区的牛肉的平均近红外光谱存在有差异,牛肉样品的主成分空间分布也位于不同的区域。根据光谱距离,对样品进行聚类分析,初步判定了牛肉的产区,其中贵州的牛肉光谱比较复杂,与河北牛肉样品的光谱距离很近,存在一定交叉。利用来源于4个产区的18个独立样品对模型进行了验证,正确识别率为100％。说明应用近红外光谱分析技术可准确、快速、低廉地追溯牛肉的产地来源。     

基于遗传算法的近红外光谱橄榄油产地鉴别方法研究

提出了一种应用近红外光谱技术快速无损鉴别橄榄油产地的新方法。采用近红外光谱仪获取三种不同产地的橄榄油各30个样本的光谱漫反射特征曲线,利用全局搜索算法-遗传算法提取特征波长,即从光谱751个波长数据提取9个特征波长数据,并将其作为主成分分析法的输入变量,运用主成分分析法建立分析校正模型。结果表明,主成分1和2累计可信度已达99.130%,对不同产地的橄榄油有很好的聚类作用,同时也说明遗传算法抽取特征波长方法正确。将提取到的六种主成分作为BP神经网络的输入变量,品种类型作为神经网络的输出变量,建立3层人工神经网络模型,对30个未知橄榄油产地进行预测,预测结果准确率达100％。该方法能快速无损地检测橄榄油产地,同时也为其他油类产地鉴别提供了一种新方法。     

近红外光谱技术(NIRS)在干草品质检测中的研究与应用

干草品质对动物的生产性能、牧草价值和最终盈利有着最直接的影响,检测干草品质的方法也在不断改进,近红外光谱技术(NIRS)这种快速、 高效而无破坏性的现代检测技术已在饲草料中广泛应用。文章阐述了应用NIRS分析干草品质的可行性和高效性,详细介绍了NIRS用于检测牧草营养成分、真菌侵染程度、内生真菌产生的有毒物质以及一些难于用化学方法检测的微量成分等重要指标的研究成果。指出NIRS用于分析干草品质突破了过去常规化学方法的复杂、耗时、成本高等缺陷,是干草品质分析方法的一个重大进步,并期待着NIRS在牧草品质中的应用研究工作继续完善和发展,建立更精确的定标模型以及更系统的分析软件,促进畜牧业的快速发展。     

NIRS法对栀子不同炮制品栀子苷含量的快速检测

以96批栀子不同炮制品为研究对象,高效液相色谱测定栀子苷含量为参考值,利用近红外光谱仪积分球漫反射测定其光谱图,建模波段取8 660~7 500,6 650~5 600和4 900~4 000 cm-1,以标准正态变换(SNV)和二阶导数法(2nd derivative)为预处理方法,主成分数为8,采用偏最小二乘法(PLS)对83批栀子样品建立栀子苷的定量校正模型,最终以13批栀子不同炮制品对模型进行验证。结果,定量模型的内部交叉验证决定系数(R2)为0.992 85,校正均方差(RMSEC)为0.240,预测均方差(RMSEP)为0.254,内部交叉验证均方差(RMSECV)为0.386 91,RMSEP/RMSEC=1.06。模型验证得到的相对分析误差(RPD)为8.81,绝对偏差范围-0.39%~0.23%,说明模型预测性较好。通过相关系数法,优选样品装样量、扫描次数、重复次数、分辨率实验条件;并由近红外一阶导和二阶导图,除去温湿度和样品水分影响波段,结合栀子苷对照品近红外光谱图,确定建模波段。首次利用NIRS法建立栀子不同炮制品栀子苷定量校正模型,方法简单快速,模型稳定可靠、准确性高,可同时应用于不同炮制品栀子中栀子苷含量的预测。     

近红外光谱法检测乙醇柴油主要性能指标

乙醇柴油是柴油替代品的一种,它的使用越来越广泛,乙醇柴油品质由许多指标决定,采用传统方法检测这些指标不仅价格昂贵而且耗时长。近红外光谱技术是一种廉价、快速实时在线检测乙醇柴油品质的有效方法。本文采用近红外光谱技术结合最小二乘支持向量机检测了乙醇柴油的密度、粘度和乙醇含量,比较了线性和非线性校正技术(主成分回归、偏最小二乘回归和最小二乘支持向量机)对乙醇柴油品质的分析效果,同时也比较了不同预处理方法对预测模型能力的影响。实验结果表明,最小二乘支持向量机优于主成分回归和偏最小二乘回归模型,其对乙醇柴油密度、粘度、乙醇含量建模效果最优,相关系数分别是0.995 8、0.995 7和0.995 3;预测均方根误差分别为0.000 68、0.011 3和0.5714。     

血流动力学参数的指示剂光密度检测方法研究

血流动力学参数检测一直是临床医学研究的热点。针对临床上测量血流动力学参数的方法存在有创、操作复杂、不适合重复测量的问题,研究了一种结合指示剂稀释理论与近红外光谱技术的指示剂光密度测量法,实现了血流动力学参数的无创检测。通过体外注射吲哚氰绿(ICG)色素指示剂,建立其在血液循环系统中稀释代谢的动力学模型,利用近红外发光探头在指端分时发送735,805和940 nm三个波长的近红外光, 同时在手指对侧实时接收携带脉搏波信息的透射光信号,将测得的信号上传至计算机进行分析处理得到随时间变化的ICG浓度并将其绘制成连续的色素浓度曲线,根据该曲线确定色素平均传输时间MTT及初始色素含量C_t0等中间变量,进一步推算出心排出量CO及循环血容量CBV两项血流动力学参数。将该方法与临床上测量上述两种参数的“金标准”—热稀释法、碘-131同位素标记法进行临床试验对比,测得10组CO及CBV的试验对比数据,经误差分析得到两项参数的相对误差最大值分别为8.88%和4.28%,平均相对误差值均低于5%,满足临床检测的精度要求,为临床上血流动力学参数的测量提供了一种安全性强、连续性好、适应范围更为广泛的方法。     

基于近红外光谱的商品玉米品种快速鉴别方法

现有的玉米种子品种鉴别方法检测时间长,费用高,不易大批量快速鉴别。提出了一种基于近红外光谱数据快速鉴别商品玉米品种的新方法。先使用傅里叶变换近红外光谱仪获得从4 000到12 000 cm-1波段范围的37个商品玉米品种籽粒的漫反射光谱数据。对原始光谱进行矢量归一化预处理以消除噪声干扰,为了找到玉米品种籽粒的光谱特征波段,提出一种基于标准差的方法,进而对寻找到的玉米籽粒特征波段光谱做主成分分析(PCA),取能反映玉米品种 99.98％ 光谱信息的前10个主成分。最后使用仿生模式识别 (BPR)方法建立了37个玉米品种鉴别模型,对于每个品种的25个样本,随机挑选15个样本作为训练样本,其余10个样本作为第一测试集,其他品种共900个样本作为第二测试集。该鉴别模型对于37个玉米品种的平均正确识别率为94.3%。该方法的进一步研究有利于建立以近红外光谱为基础的物理指纹品种鉴别技术。     

Revealing the Effect of Heat Treatment on the Spectral Pattern of Unifloral Honeys Using Aquaphotomics

In this study we aimed to investigate the effect of heat treatment on the spectral pattern of honey using near infrared spectroscopy (NIRS). For the research, sunflower, bastard indigo, and acacia honeys were collected from entrusted beekeepers. The honeys were not subject to any treatment before. Samples were treated at 40 °C, 60 °C, 80 °C, and 100 °C for 60, 120, 180, and 240 min. This resulted in 17 levels, including the untreated control samples. The 5-hydroxymethylfurfural (HMF) content of the honeys was determined using the Winkler method. NIRS spectra were recorded using a handheld instrument. Data analysis was performed using ANOVA for the HMF content and multivariate analysis for the NIRS data. For the latter, PCA, PCA-LDA, and PLSR models were built (using the 1300–1600 nm spectral range) and the wavelengths presenting the greatest change induced by the perturbations of temperature and time intervals were collected systematically, based on the difference spectra and the weights of the models. The most contributing wavelengths were used to visualize the spectral pattern changes on the aquagrams in the specific water matrix coordinates. Our results showed that the heat treatment highly contributed to the formation of free or less bonded water, however, the changes in the spectral pattern highly depended on the crystallization phase and the honey type.     

Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy

The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data.