A Comparative Study of Fourier Transform Raman and NIR Spectroscopic Methods for Assessment of Protein and Apparent Amylose in Rice

Fourier‐transform Raman (FT‐Raman) spectroscopy and near‐infrared (NIR) reflectance spectroscopy were used to compare calibration models for determining rice cooking quality parameters such as apparent amylose and protein. Samples from two seasons were used in each calibration set. The laboratory values ranged from 4.89 to 12.48% for protein and from 0.2 to 25.7% for amylose. The data for both FT‐Raman and NIR were preprocessed with orthogonal signal correction (OSC) for standardization. For both spectroscopic methods, five models were optimized by partial least squares regression (PLSR) and by Martens' uncertainty regression (MUR), including no processing, smoothing, normalization, first derivative (D1), and second derivative (D2). Based solely on standard error of cross‐validation (SECV), the FT‐Raman method was superior to the NIR method for protein. For amylose, the FT‐Raman and NIR methods resulted in similar calibration statistics with a high precision, with the FT‐Raman requiring fewer factors. The best FT‐Raman models were generated from OSC preprocessing with MUR for protein (SECV 0.15%, five factors) and from OSC without MUR for amylose (SECV 0.70%, seven factors). The best NIR models were obtained with D2 transform of OSC spectra for protein (SECV 0.22%, four factors) and with OSC spectra for amylose (SECV 0.57%, 11 factors).     

基于近红外光谱和正交信号-偏最小二乘法对土壤的分类

不同质地的土壤,由于蓄水能力和土壤颗粒大小的不同使得其光谱特性不同,这为采用近红外光谱技术对土壤质地进行判别分析提供了依据。该研究利用正交信号校正(OSC)方法可以获得与浓度有关的谱图信息这一优势,将其与偏最小二乘方法(PLS)结合,采用近红外光谱技术对不同质地的土壤:砂土、壤土、黏土进行判别分析。结果表明:建模样本的相关系数可达0.965,采用该模型对其余45个样本分别进行了预测,三种土壤预测样本的判别正确率分别为:93.3%,86.6%和86.6%。说明OSC方法可以提取谱图中的微弱的质地信息,实现土壤质地的快速鉴别分析。     

基于近红外光谱技术的淡水鱼品种快速鉴别

为探索淡水鱼品种的快速鉴别方法,该文应用近红外光谱分析技术,结合化学计量学方法,对7种淡水鱼品种的判别分类进行了研究。采集了青、草、鲢、鳙、鲤、鲫、鲂等7种淡水鱼,共665个鱼肉样品的近红外光谱数据,经过多元散射校正(multiplicative scatter correction,MSC)、正交信号校正(orthogonal signal correction,OSC)、数据标准化(standardization,S)等20种方法预处理,在1 000~1 799 nm范围内分别采用偏最小二乘法(partial least square,PLS)、主成分分析(principal component analysis,PCA)和BP人工神经网络技术(back propagation artificial neural network,BP-ANN)、偏最小二乘法和BP人工神经网络技术对7种淡水鱼原始光谱数据进行了鉴别分析。结果表明,近红外光谱数据,结合主成分分析和BP人工神经网络技术建立的淡水鱼品种鉴别模型最优,模型的鉴别准确率达96.4%,对未知样本的鉴别准确率达95.5%。模型具有较好的鉴别能力,采用该方法能较为准确、快速地鉴别出淡水鱼的品种。     

Determination of alcohol and extract concentration in beer samples using a combined method of near-infrared (NIR) spectroscopy and refractometry

A new approach of combination of near-infrared (NIR) spectroscopy and refractometry was developed in this work to determine the concentration of alcohol and real extract in various beer samples. A partial least-squares (PLS) regression, as multivariate calibration method, was used to evaluate the correlation between the data of spectroscopy/refractometry and alcohol/extract concentration. This multivariate combination of spectroscopy and refractometry enhanced the precision in the determination of alcohol, compared to single spectroscopy measurements, due to the effect of high extract concentration on the spectral data, especially of nonalcoholic beer samples. For NIR calibration, two mathematical pretreatments (first-order derivation and linear baseline correction) were applied to eliminate light scattering effects. A sample grouping of the refractometry data was also applied to increase the accuracy of the determined concentration. The root mean squared errors of validation (RMSEV) of the validation process concerning alcohol and extract concentration were 0.23 Mas% (method A), 0.12 Mas% (method B), and 0.19 Mas% (method C) and 0.11 Mas% (method A), 0.11 Mas% (method B), and 0.11 Mas% (method C), respectively.     

Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.     

Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins

The use of visible (vis) and near-infrared spectroscopy (NIR) was explored as a tool to discriminate between samples of Australian commercial white wines of different varietal origins (Chardonnay and Riesling). Discriminant models were developed using principal component analysis (PCA), principal component regression (PCR), and discriminant partial least-squares (DPLS) regression. The samples were randomly split into two sets, one used as a calibration set (n = 136) and the remaining samples as a validation set (n = 133). When used to predict the variety of the validation set samples, the DPLS models correctly classified 100% of Riesling and up to 96% of Chardonnay wines. These results showed that vis-NIR might be a suitable and alternative technology that can be easily implemented by the wine industry to discriminate Riesling and Chardonnay commercial wine varieties. However, the relatively limited number of samples and varieties involved in the present work suggests caution in extending the potential of such a technique to other wine varieties.     

Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.     

Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples

Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.     

Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy

The use of least-squares support vector machines (LS-SVM) combined with near-infrared (NIR) spectra for prediction of enological parameters and discrimination of rice wine age is proposed. The scores of the first ten principal components (PCs) derived from PC analysis (PCA) and radial basis function (RBF) were used as input feature subset and kernel function of LS-SVM models, respectively. The optimal parameters, the relative weight of the regression error gamma and the kernel parameter sigma 2, were found from grid search and leave-one-out cross-validation. As compared to partial least-squares (PLS) regression, the performance of LS-SVM was slightly better, with higher determination coefficients for validation ( Rval2) and lower root-mean-square error of validation (RMSEP) for alcohol content, titratable acidity, and pH, respectively. When used to discriminate rice wine age, LS-SVM gave better results than discriminant analysis (DA). On the basis of the results, it was concluded that LS-SVM together with NIR spectroscopy was a reliable and accurate method for rice wine quality estimation.     

Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to identify and authenticate fishmeal batches made with different fish species. Samples from a commercial fishmeal factory (n = 60) were scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), dummy partial least-squares regression (DPLS), and linear discriminant analysis (LDA) based on PCA scores were used to identify the origin of fishmeal produced using different fish species. Cross-validation was used as validation method when classification models were developed. DPLS correctly classified 80 and 82% of the fishmeal samples. LDA calibration models correctly classified >80% of fishmeal samples according to fish species The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of fish species used to manufacture the fishmeal.     

农产品产地加工与储藏工程技术分类

生鲜牛肉的含水率对其牛肉的加工、储藏、贸易与食用质量有重要影响,为了提高牛肉的经济价值和食用品质,需要研究牛肉含水率的无损检测技术。以取自不同超市的内蒙小黄牛和鲁西黄牛背最长肌为研究对象,有效样本86个,其中,75%的样本作为校正集,25%的样本作为验证集。采集牛肉新鲜切口处400～1170 nm波长范围内的漫反射光谱,用国标方法测定牛肉含水率。经过多元散射校正（multiplicative scatter correction, MSC）、变量标准化（standard normalized variate, SNV）和直接正交信号校正（direct orthogonal signal correction, DOSC）等方法预处理,在400～1170 nm范围内分别建立多元线性回归（multiple linear regression, MLR）模型、主成分回归（principal component Regression, PCR）模型和偏最小二乘回归（partial least squares regression, PLSR）模型。结果表明使用MSC预处理方法建立的模型预测效果最佳,其中用PLSR建模结果最好,校正集的相关系数和校正标准差分别是0.92和0.0069,验证集的相关系数和验证标准差分别是0.92和0.0047,外部验证的相关系数和验证标准差分别是0.85和0.0054。结果表明,可见/近红外光谱结合MSC预处理方法建立的PLSR模型,可以对牛肉含水率进行准确的快速无损评价,为生鲜牛肉含水率快速无损检测技术的应用提供理论参考。     

补料发酵法生产玫瑰醋工艺优化及风味分析

为了提高传统玫瑰醋的生产效率,以装料量为500 kg的陶缸作发酵容器,在自然发酵条件下,分别在1.5、2.5、3.5 g/100 mL的初始酸度进行补料,补料体积比(原醋液:黄酒醪)设置为2:1和1:1两种情况,完成玫瑰醋补料发酵工艺的研究。结果表明:在初始酸度2.5 g/100 mL时补加等体积酒醪,发酵过程酸度最高上升到(5.59±0.27)g/100 mL(对照组为(5.19±0.23)g/100 mL),醋酸发酵周期从90 d缩短至78 d;非挥发性有机酸积累量达(27.15±1.11)mg/mL(对照组为(24.57±0.69)mg/mL),样品酸甜适口,酸味柔和;补料成品色率达到1.8×105(对照组色率为1.9×105),色泽接近传统玫瑰醋。该工艺的完成对玫瑰醋生产企业扩大产量、提高效率有积极意义。     

Evolution of phenolic compounds during an experimental aging in wood of Sherry vinegar

Changes in the physicochemical composition of wine vinegars produced by submerged culture system and aged in wood were followed. Five Sherry wine vinegars and a model vinegar solution were aged in six new American oak butts of 16.6 L capacity. A total of 24 phenolic compounds were monitored during the maturation study (24 months), along with other physicochemical parameters (total extract, acidity, residual alcohol and total phenolic index). Multivariate statistical analysis was applied to the data. From the sixth month on, significant changes were produced in most of the phenolic compounds, mainly aromatic aldehydes and 5-(hydroxymethyl)-2-furaldehyde. When all the phenolic compounds were considered as variables, cluster analysis grouped samples according to the wine substrate employed in the elaboration of vinegars under study. Within each subcluster, samples are arranged according to their aging status when phenolic compounds accounting significative changes at 180 days of aging are considered. Discriminant functions were constructed from the phenolic compounds data set. The validity of these functions was tested using 13 samples of aged commercial Sherry wine vinegars and 25 unaged vinegars. A total of 97.4% of the test samples was correctly classified within its respective group.     

利用近红外光谱与PCA-SVM识别热损伤番茄种子

为了研究近红外光谱技术用于热损伤种子快速无损识别的可行性,该文以120粒番茄种子为研究对象,其中60粒番茄种子通过高温加热处理的方式成为热损伤种子组,其他60粒番茄种子为正常种子组,利用实验室自主搭建的近红外光谱检测系统获取单粒番茄种子在980~1 700 nm范围内的光谱,分别采用偏最小二乘判别法(partial least squares discriminant analysis,PLS-DA)和支持向量机(support vector machines,SVM)建立了番茄种子热损伤的定性分析模型。试验结果表明:2种判别模型的验证集总正确率均大于96%,均可用于热损伤种子的判别。其中,基于主成分分析(principal component analysis,PCA)预处理的光谱数据构建的支持向量机模型的判别效果最好,其校正集和验证集的判别正确率均为100%,更适用于种子热损伤识别。因此,应用近红外光谱技术可快速无损识别热损伤番茄种子,为种子检验提供了一种新的方法。     

基于空载数据的鉴别食醋电子鼻信号漂移校正方法

由于传感器老化,环境温度等因素,电子鼻信号的漂移是不可避免的,且严重降低电子鼻的长期稳健检测能力。为了实现电子鼻对6种食醋样品的长期稳健检测,该文提出了一种基于空载数据的小波包分解系数的漂移递归校正方法。通过小波包对电子鼻空载数据的分解,给出空载阈值函数(no-load threshold function,NLTF),然后将NLTF转换为适合样本数据的样本阈值函数(samplethresholdfunction,STF)。在获得的STF基础上,构建样本检测数据小波包分解系数的校正函数。借助于所构建的样本测试数据的校正函数,对6种食醋样品的电子鼻数据进行漂移校正。同时,运用"样本测量时间窗口(sample measurement time window,SMTW)"的概念,实现电子鼻数据的递归校正,进而建立了可实现长期稳健检测的递归鉴别模型。针对6种食醋样品,进行了为期16个月的间歇式测试。当SMTW选为4个月的测试样本及每次递推前移1个月样本数据时,建立的基于递归校正的Fisher判别分析(Fisher discriminant analysis,FDA)模型可完全实现6种食醋样品的长期稳健鉴别,正确鉴别率达到100%,使紧随SMTW后1个月内的测试样本能得到准确鉴别。该校正方法能够有效的去除漂移并且实现了电子鼻的长期稳健检测。     

柑桔黄龙病近红外光谱无损检测

为探讨快速无损检测柑桔黄龙病的可行性,应用近红外光谱技术结合机器学习方法进行研究。在4000~9000cm-1光谱范围内,采集黄龙病、缺素和健康3类叶片样本的近红外光谱。采用一阶导数、平滑和多元散色校正组合的光谱预处理方法,消除光谱的基线漂移和散射效应。分别对偏最小二乘判别模型(PLS-DA)的主成分因子数和最小二乘支持向量机(LS-SVM)的输入变量数量、核函数类型及其参数进行了优化,建立了PLS-DA和LS-SVM模型。采用预测集样本,评价模型的预测能力,经比较,采用11个主成分得分向量为输入、线性核函数和惩罚因子为2.25的LS-SVM模型预测效果最佳,模型误判率为0。结果表明采用近红外光谱技术结合最小二乘支持向量机进行柑桔黄龙病无损检测是可行的。     

Assessment of Heat-Damaged Wheat Kernels Using Near-Infrared Spectroscopy

Heat damage is a serious problem frequently associated with wet harvests because of improper storage of damp grain or artificial drying of moist grain at high temperatures. Heat damage causes protein denaturation and reduces processing quality. The current visual method for assessing heat damage is subjective and based on color change. Denatured protein related to heat damage does not always cause a color change in kernels. The objective of this research was to evaluate the use of nearinfrared (NIR) reflectance spectroscopy to identify heat-damaged wheat kernels. A diode-array NIR spectrometer, which measured reflectance spectra (log (1/R)) from 400 to 1,700 nm, was used to differentiate single kernels of heat-damaged and undamaged wheats. Results showed that light scattering was the major contributor to the spectral characteristics of heat-damaged kernels. For partial least squares (PLS) models, the NIR wavelength region of 750–1,700 nm provided the highest classification accuracy (100%) for both cross-validation of the calibration sample set and prediction of the test sample set. The visible wavelength region (400–750 nm) gave the lowest classification accuracy. For two-wavelength models, the average of correct classification for the classification sample set was >97%. The average of correct classification for the test sample set was generally >96% using two-wavelength models. Although the classification accuracies of two-wavelength models were lower than those of the PLS models, they may meet the requirements for industry and grain inspection applications.     

Analysis of sugars in Chinese rice wine by Fourier transform near-infrared spectroscopy with partial least-squares regression

The feasibility of rapid analysis for oligosaccharides, including isomaltose, isomaltotriose, maltose, and panose, in Chinese rice wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least-squares regression (PLSR) was studied in this work. Forty samples of five brewing years (1996, 1998, 2001, 2003, and 2005) were analyzed by NIR transmission spectroscopy with seven optical path lengths (0.5, 1, 1.5, 2, 2.5, 3, and 5 mm) between 800 and 2500 nm. Calibration models were established by PLSR with full cross-validation and using high-performance anion-exchange chromatography coupled with pulsed amperometric detection as a reference method. The optimal models were obtained through wavelength selection, in which the correlation coefficients of calibration (r(cal)) for the four sugars were 0.911, 0.938, 0.925, and 0.966, and the root-mean-square errors of calibrations were 0.157, 0.147, 0.358, and 0.355 g/L, respectively. The validation accuracy of the four models, with correlation coefficients of cross-validation (r(cv)) being 0.718, 0.793, 0.681, and 0.873, were not very satisfactory. This might be due to the low concentrations of the four sugars in Chinese rice wine and the influence of some components having structures similar to those of the four sugars. The results obtained in this study indicated that the NIR spectroscopy technique offers screening capability for isomaltose, isomaltotriose, maltose, and panose in Chinese rice wine. Further studies with a larger Chinese rice wine sample should be done to improve the specificity, prediction accuracy, and robustness of the models.     

食醋电子鼻检测中一种特征参量评价方法

电子鼻检测中常用的特征鉴别能力评价方法有2种,一是对判别结果的直观分析,二是对判别正确率的统计计算。但是,当判别正确率相同时,对于不同特征间鉴别能力的差异,2种方法都不能进行准确的定量评价。为实现特征鉴别能力的准确度量,以不同种类食醋为检测对象,对检测信号提取面积斜率比、方差、积分、平均微分值、相对稳态平均值、小波能量等6种特征参量,并将特征参量与类别间的相关系数作为特征鉴别能力的度量指标。计算结果可知:面积斜率比特征参量的相关系数绝对值最小,为0.1027,积分特征参量的相关系数绝对值最大,为0.6455。表明面积斜率比特征参量的鉴别能力最低,积分特征参量的鉴别能力最高。Fisher判别结果也证明了特征参量的鉴别能力越高,其分类效果越好。因此,用特征参量与类别间的相关系数作为特征鉴别能力的度量是合适的、也是有效的。     

基于近红外光谱技术的蜂蜜掺假识别

为了实现蜂蜜掺假的快速识别,应用近红外光谱结合模式识别方法对蜂蜜掺假现象进行了识别分析。该研究收集了中国不同品种、不同地域的典型天然蜂蜜样品,根据目前市场上常见的蜂蜜掺假手段,掺假物质及相对含量情况配制了掺假蜂蜜样品,利用傅立叶近红外光谱仪采集其透反射近红外光谱,分别采用偏最小二乘判别分析（PLS-DA）,独立软模式法（SIMCA）,误差反向传播神经网络（BP-ANN）和最小二乘支持向量机（LS-SVM）等模式识别方法,进行蜂蜜掺假识别研究。研究结果表明：利用这4种方法在蜂蜜中掺入果葡糖浆和果葡糖水的情况下均能很好地识别出掺假蜂蜜样品,其中对于掺入果葡糖浆的掺假情况,校正集的正确判别率均达到95%以上,验证集的正确判别率均达到87%以上,对于掺入果葡糖水的掺假蜂蜜校正集的正确判别率均达到93%以上,验证集的正确判别率均达到84%以上。通过比较4种不同的识别算法,发现采用LS-SVM时,对两种掺假情况下校正集和验证集的正确判别率均达到了100%,表明基于近红外光谱的蜂蜜掺假快速准确识别是可行的。