近红外光谱技术在南极磷虾粉水分、脂肪和蛋白质含量快速检测中的应用

利用傅里叶近红外光谱分析技术,以磷虾粉样品的实测值与模型预测值为基础,研究了采用最小二乘法建立磷虾粉原始样品与磷虾粉混合样品中水分、脂肪和蛋白含量近红外定标模型的可行性和准确性.结果表明,磷虾粉近红外图谱最佳预处理方式为:标准正态变换预处理+一阶导数+Norris导数滤波;以磷虾粉混合样品构建的近红外模型较磷虾粉原始样...     

Quantification of soluble solids in reconstituted açaí (Euterpe oleracea Mart.) pulp using near‐infrared spectroscopy

Açaí consumption is increasing worldwide because of the growing recognition of its nutritional and therapeutic properties. This product is classified based on its soluble solids content (SS), but the determination of SS in pulp is time consuming, tedious and not suitable for modern food processing plants. As near‐infrared (NIR) systems have been implemented to measure various quality attributes of food products, the objective of this study was to evaluate the feasibility of NIR diffuse reflectance spectroscopy to quantify the SS content of açaí pulp. Partial least squares (PLS) regression models were constructed to predict the SS. An optimum PLS model required one latent variable [principal component (PC)1 = 97%] with a root‐mean‐square error of calibration (RMSEC) of 1.06% for the calibration data set and the root‐mean‐square error of prediction (RMSEP) of 1.03% for internal cross‐validation. External validation using an independent data set showed good performance (RMSEP = 1.33% and Rp² = 0.82). NIR spectroscopy is a reliable method with which to determine SS in açaí pulp and thereby to classify açaí pulp according to established minimum quality standards.     

基于近红外光谱建立纸张表面施胶量测定模型

采集不同施胶量纸张的近红外光谱,利用偏最小二乘法建立测定纸张表面施胶量基于近红外光谱的校正模型。得到校正模型的交叉验证均方差（RMSECV）和外部验证均方差（RMSEP）分别为0.0928和0.1460,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别为0.9609和0.9294,表明所建立的校正模型具有较高的预测精度和较好的推广性,为纸张无损伤、无预处理的快速、简便、准确的检测提供了新的途径,并且可望实现纸机上的在线检测。     

基于近红外光谱技术的腐竹脂肪定量分析

采用近红外光谱技术结合化学计量学方法,建立腐竹脂肪含量的快速分析方法。收集不同生产线、不同时间的腐竹样本180 份,利用积分球附件采集漫反射光谱（4 000～10 000 cm－1）。为消除颗粒散射影响和光谱基线漂移,二阶导数和卷积平滑用于光谱预处理。采用反向区间偏最小二乘法、组合区间偏最小二乘法、搜索组合移动窗口偏最小二乘法和遗传偏最小二乘法优化建模变量,最终构建了定量预测模型。结果显示,4 种方法均可有效地提取信息变量、降低模型维度、提高预测性能;遗传偏最小二乘法一次优选获得143 个变量,构建的模型性能最佳,其校正相关系数、校正均方根误差、预测相关系数、预测均方根误差分别为0.96、0.95、0.92和1.17。研究表明,经过信息变量提取后所构建的近红外模型简单、预测精度高,可用于腐竹脂肪含量的日常监测。     

基于近红外光谱的大鲵肉粉掺伪鉴别及纯度检测

利用近红外光谱技术进行大鲵肉粉的掺伪鉴别及纯度检测。分别采集大鲵纯肉粉、掺入江团鱼肉粉、草鱼肉粉和土豆淀粉的掺伪大鲵肉粉（各40 个样本,4 类共160 个样本）的近红外光谱图。原始光谱经光谱预处理后,利用偏最小二乘-判别分析（partial least square-discriminant analysis,PLS-DA）法分别建立2分类（纯样和掺伪样）和4分类（纯样、掺江团鱼样、掺草鱼样和掺淀粉样）的定性判别模型,利用偏最小二乘回归（partial least squares regression,PLSR）分析法分别建立3 类掺伪大鲵肉粉的纯度定量校正模型。结果表明,PLS-DA定性模型中,经一阶导数＋多元散射校正光谱预处理后,所建2分类和4分类模型性能均为最佳,校正集和预测集的预测准确率均为100%;PLSR定量模型中,大鲵肉粉掺江团鱼肉粉、大鲵肉粉掺草鱼肉粉和大鲵肉粉掺土豆淀粉模型的校正集相关系数（Rc2）分别为0.990 6、0.986 4和0.993 3,校正集的均方根误差分别为1.14%、1.39%和0.88%;测试集的相关系数（Rp2）分别为0.994 4、0.992 4和0.990 8,测试集的均方根误差分别为0.83%、0.89%和1.22%。运用近红外光谱技术结合化学计量学方法能够对大鲵肉粉进行掺伪鉴别及纯度检测。     

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R ²) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.     

Rapid determination of sugar level in snack products using infrared spectroscopy

Real-time spectroscopic methods can provide a valuable window into food manufacturing to permit optimization of production rate, quality and safety. There is a need for cutting edge sensor technology directed at improving efficiency, throughput and reliability of critical processes. The aim of the research was to evaluate the feasibility of infrared systems combined with chemometric analysis to develop rapid methods for determination of sugars in cereal products. Samples were ground and spectra were collected using a mid-infrared (MIR) spectrometer equipped with a triple-bounce ZnSe MIRacle attenuated total reflectance accessory or Fourier transform near infrared (NIR) system equipped with a diffuse reflection-integrating sphere. Sugar contents were determined using a reference HPLC method. Partial least squares regression (PLSR) was used to create cross-validated calibration models. The predictability of the models was evaluated on an independent set of samples and compared with reference techniques. MIR and NIR spectra showed characteristic absorption bands for sugars, and generated excellent PLSR models (sucrose: SEP < 1.7% and r > 0.96). Multivariate models accurately and precisely predicted sugar level in snacks allowing for rapid analysis. This simple technique allows for reliable prediction of quality parameters, and automation enabling food manufacturers for early corrective actions that will ultimately save time and money while establishing a uniform quality. Practical Application: The U.S. snack food industry generates billions of dollars in revenue each year and vibrational spectroscopic methods combined with pattern recognition analysis could permit optimization of production rate, quality, and safety of many food products. This research showed that infrared spectroscopy is a powerful technique for near real-time (approximately 1 min) assessment of sugar content in various cereal products.     

基于近红外光谱的酸枣仁不同伪品掺假检测

本文采用近红外光谱技术对酸枣仁及其三种常见伪品理枣仁、枳椇子和兵豆进行定性定量检测研究。分别制备不同伪品掺杂质量分数为1%~90%的单种掺杂物实验样品,以及多种伪品同时掺杂的样品,采集800~2500 nm范围的近红外光谱数据。首先利用主成分分析（principal component analysis,PCA）对酸枣仁及三种伪品进行初步定性鉴别。对于单一掺假物样品,采用五种不同预处理方法对光谱数据进行去噪。利用偏最小二乘回归（partial least squares regression,PLS）方法,建立PLS1模型定量预测掺假物含量,并采用连续投影算法（successive projection algorithm,SPA）挑选最优波长,优化定量模型。结果表明,理枣仁掺假建立的3波长检测模型的预测集决定系数R2_p为0.9659,均方根误差（root mean square error,RMSEP）为6.1910%。枳椇子掺假建立的8波长检测模型的预测集决定系数R2_p为0.9491,均方根误差（RMSEP）为7.6232%。兵豆掺假建立的5波长检测模型的预测集决定系数R2_p为0.9666,均方根误差（RMSEP）为6.1437%。对于多掺杂物样品,建立了PLS2模型同时对不同成分进行定量预测,酸枣仁效果最好,R2_p≥0.7115,枳椇子预测效果最差,R2_p≥0.2007。研究表明,利用近红外光谱技术可以实现酸枣仁不同伪品掺假的快速无损检测。所建方法为后续酸枣仁及其他种子类中药材便携式无损检测仪器的开发提供了理论基础与参考依据,对保证中药材质量安全具有重要社会意义。     

磨盘柿褐变指标的可见/近红外漫反射无损预测研究

为了建立可见/近红外漫反射光谱与磨盘柿果皮和果肉褐变之间的关系,作者在全光谱区域(570～1 848 nm)对比分析了不同处理方法对磨盘柿果皮颜色b*和果肉浊度定标模型的影响。结果表明,应用MPLS、原始光谱和无散射处理建立果皮颜色b*的定标模型预测性能较好,Rp2为0.968,RMSEP为1.417 7,RPD为7.92。应用PLS、一阶导处理和无散射处理建立磨盘柿果肉浊度的定标模型预测性能较好,Rp2为0.757,RMSEP为0.107 9,RPD为2.22。因此,可见/近红外漫反射技术对磨盘柿果皮颜色b*和果肉浊度的快速无损检测具有可行性。     

Comparison of FTIR, FT-Raman, and NIR Spectroscopy in a Maple Syrup Adulteration Study

ABSTRACT: Maple syrup is prone to adulteration with cheaper sugars, such as corn syrup, due to its simplicity in chemical composition. The adulterated samples were characterized by Fourier Transform infrared (FTIR) spectroscopy in the region of 400 to 4000 cm_-1. Other techniques used for detection and in characterization of samples were the near infrared (NIR; 600 to 1700nm) and Fourier Transform-Raman (FT-Raman; 400 to 4000cm_-1) spectroscopy. Quantifying and classifying adulterants using chemometrics shows that all spectroscopic methods adopted were efficient, but FTIR and FT-Raman were superior to NIR in quantitative characterization of adulterants in maple syrup.     

近红外光谱法用于掺假羊奶的快速无损鉴别

利用近红外光谱技术结合多种化学计量学方法,研究了快速鉴别掺假羊奶的方法。将淀粉溶液,含尿素的淀粉溶液,含尿素和奶油的淀粉溶液按不同比例掺入纯羊奶中,进行近红外光谱采集。分别采用偏最小二乘差别分析(PLS-DA),fisher线性判别和多层感知器(MLP)神经网络法建立校正模型并进行检验验证。结果表明,MLP神经网络的鉴别效果最好,其校正模型的正判率达到99.4%,验证集的正判率达到100%。说明采用近红外光谱技术结合适当的化学计量学方法可以实现羊奶掺假检测的快速无损鉴别。     

傅立叶近红外光谱法测定桃果中果胶含量

目的 基于傅里叶近红外光谱(Fourier transform near infrared)检测桃果中果胶含量的研究。方法 近红外光谱采集样品利用两个品种的桃,探究光谱预处理对建模的影响,建模采用偏最小二乘法(PLS)以及主成分回归(PCR)方法,模型的评价标准采用建模相关系数(R_C)、建模均方偏差(RMSE_C)、预测相关系数(R_P)、预测均方偏差(RMSE_P)。结果 两个品种的近红外光谱图和果胶含量无明显差异(P＞0.05),采用标准正态变量变换(SNV)和多元散射校正(MSC)对原始光谱的光程进行选择,所得建模结果影响基本一致,合适光谱数据格式以及平滑处理,能提高PLS和PCR模型的预测精度和稳定性。综合得出模型最佳是利用PLS方法建模并采用MSC/SNV结合一阶导数和 Savitzky-Golay (S-G)平滑对近红外光谱图进行预处理,评价参数分别为R_C=0.7795、R_P=0.7545、RMSE_C=0.0933、RMSE_P=0.0534和R_C=0.7800、R_P=0.7530、RMSE_C=0.0932、RMSE_P=0.0534。结论 该方法为利用近红外建模快速检测桃果中果胶含量提供重要依据。     

Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

The use of fibre optic diffuse reflectance near infrared spectroscopy (NIR) in combination with chemometric techniques has been investigated to discriminate authenticity of honey. NIR spectra of unadulterated honey and adulterated honey samples with high fructose corn syrup were registered within 10,000–4000 cm⁻¹ spectral region. Discriminant partial least squares (DPLS) models were constructed to distinguish between unadulterated honey and adulterated honey samples and main bands responsible for the discrimination of samples are in the range of 6000–10,000 cm⁻¹. For these models, the correct classification rate for calibration samples were above 90%. Hundred percentage of unadulterated honey and 95% of adulterated honey samples from test set were correctly classified after appropriate preprocessing of first derivative, 13 smoothing points, followed by mean centering pre-treatment and eight model factors, respectively. Our results showed that NIR spectroscopy data with chemometrics techniques can be applied to rapid detecting honey adulteration with high fructose corn syrup.     

基于近红外光谱技术检测红曲米中的红曲色素

采用近红外光谱技术结合化学计量学方法构建红曲米中红曲橙色素、红曲红色素、红曲黄色素的预测模型。分别采用多元线性回归(SMLR)、偏最小二乘回归(PLS)、主成分回归(PCR)构建所有色素组分的数学模型,以相关系数(R)、校正均方根误差(RMSEC)、预测均方根误差(RMSEP)、预测相对分析偏差(RPD)值来评价模型的综合性能。结果显示,MSC、SNV方法能够消除红曲米粉颗粒不均对光谱的散射影响;导数处理消除了基线漂移;对于红曲橙色素、红曲黄色素、红曲红色素三种模型均具有良好的稳定性;利用三种模型对未知红曲样品预测时,预测结果具有较高的线性,预测性能较好(RPD=2.86~5.39),可用于准确定量预测。结果表明近红外光谱技术可用于红曲色素的快速无损测定,为红曲米质量的智能化控制提供了新的途径。     

Carotenoids and Their Geometry Isomers in Selected Tropical Fruits

This study determined the carotenoids content in cherry tomato, pink guava, and red grapefruit pulps and juices. Cherry tomato pulp exhibited the highest β-carotene content whereas pink guava pulp had the highest lycopene content. However, β-carotene and lycopene contents in the studied fruit juices were lower than their pulps in the same sample portion. Interestingly, six to twelve cis-isomers of carotenoids were identified in the fruit pulps and juices studied. A higher number of trace amounts of cis-carotenoids was found in fruit pulps as compared to juices. Therefore, consumption of whole fruit is recommended as the studied fruit juices have lower carotenoids content.     

基于近红外光谱对牛奶中掺杂尿素的判别分析

采集40个合格的纯牛奶样品,并配制含有尿素为1～20g/L的40个牛奶样品,研究掺杂尿素牛奶的二维相关近红外特性,在此基础上选择波数4200～4800cm-1为建模区间,采用偏最小二乘法建立定性、定量模型。结果指出通过判别偏最小二乘法可以实现纯牛奶及掺杂尿素牛奶的定性鉴别,判别正确率为100%;掺杂牛奶校正集相关系数R为0.999,交叉验证均方差为0.242,对未知样品集预测相关系数R达到0.999,预测标准偏差为0.57,这表明所建模型具有较好的预测效果。     

柑橘浮皮果与可溶性固形物光谱同时在线检测

柑橘浮皮果和可溶性固形物是评价柑橘品质的重要指标,在运动速度5个/s、积分时间100ms条件下,采集350~1 150nm范围内的柑橘光谱,探讨在同一条生产线上同时在线检测浮皮果与可溶性固形物的可行性,同时探索柑橘正常果,轻度、重度浮皮果的光谱响应特征,建立柑橘浮皮果与正常果的定性判别模型,并对比分析两种判别模型,同时还建立柑橘可溶性固形物的定量检测模型,最终实现了柑橘浮皮果与可溶性固形物同时在线检测。采用未参与建模的35个样品对模型在线分选的准确性进行评价,其中柑橘浮皮果都被正确的推入预设的浮皮果出口,正确判别率为100%,而可溶性固形物正确分级率为97%。研究可为柑橘在线分选提供分选策略与理论依据。     

蚁群和遗传算法优化花茶花青素近红外光谱预测模型的比较

以建立花茶花青素含量的最优近红外光谱模型为目标,对比研究了蚁群算法(Ant ColonyOptimization,ACO)和遗传算法(Genetic Algorithm,GA)优化近红外光谱谱区的效果。ACO-i PLS将全光谱划分为12个子区间时,优选出第1、9、10共3个子区间,所建的校正集和预测集相关系数分别为0.901 3和0.864 2;交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.160 0 mg/g和0.202 0 mg/g;GA-i PLS将全光谱划分为15个子区间时,优选出第1、5共2个子区间,所建模型的校正集和预测集相关系数分别为0.906 3和0.879 3,交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.156 0 mg/g和0.206 0 mg/g。研究结果表明:ACO-i PLS和GA-i PLS均可以有效选择近红外光谱特征波长,其中GA-i PLS模型的精度更高。     

近红外光谱法快速测定烟草中的总挥发酸与总挥发碱

应用傅立叶变换近红外光谱法测定730个具有代表性的烟草近红外光谱数据,采用偏最小二乘回归建立了近红外光谱信息与其含量之间的定量校正模型,并对50个验证样品进行预测验证。总挥发碱和总挥发酸的预测标准差(RMSEP)分别为0.020和0.009,验证样品的相对标准偏差各为1.120%和0.919%。     

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。