Identification of animal meat muscles by visible and near infrared reflectance spectroscopy

Visible (VIS) and near infrared reflectance spectroscopy (NIRS) was used to identify and authenticate different meat muscle species. Samples from beef (n: 100), lamb (n: 140), pork (n: 44) and chicken (n: 48) muscles were homogenised and scanned in the visible (VIS) and near infrared (NIR) region (400-2500 nm) in a monochromator instrument in reflectance. Both Principal Component Analysis (PCA) and dummy partial least-squares regression (PLS) models were developed to identify different meat species. The models correctly classified more than 80% of the meat sample muscles according with the muscle specie. The results showed the potential of VIS and NIR spectra as an objective and rapid method for authentication and identification of meat muscle species.     

Fast determination of boiling time of yardlong bean using visible and near infrared spectroscopy and chemometrics

The performance of visible and near infrared (Vis-NIR) spectroscopy as a rapid and non-destructive technique to determine the boiling time of yardlong beans was investigated. Vis-NIR spectra of beans boiled for 0, 30 to 300 s were measured. Robust least-squares support vector machines (R-LS-SVM) with RBF kernel obtained the best result. Partial least square based variable elimination (VE_PLS) and robust least square-support vector machines based variable elimination (VE_R-LS-SVM) were used for variable selections. Four most important variables at 409, 614, 880, and 984 nm were selected. After the variable selection, 90% of the variables were eliminated and the model’s residual predictive deviation (RPD) only decreased 10% compared to that of the model without the variable elimination. The results showed that the Vis-NIR spectra can be used to determine the boiling time of yardlong beans during the boiling process.     

Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy

Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety “Momotaro”. Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix.     

基于可见/近红外光谱技术的橘小实蝇侵染柑橘检测研究

目的 使用可见/近红外光谱技术实施橘小实蝇侵染柑橘不同时期的无损检测。方法 研究选取人工制备的不同侵染时期的柑橘样本作为研究对象,利用搭建的可见/近红外光谱系统测量的光谱信息结合人工标定的侵染时期,对原始光谱进行了5种预处理,采用竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)、连续投影算法(successive projections algorithm, SPA)两类方法提取反映侵染柑橘时期变化的光谱特征波长,应用偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)建立基于特征波长光谱的柑橘侵染时期分类模型,对比分析不同光谱预处理方法的模型分类效果。结果 原始光谱经多元散射校正(multiplicative scatter correction, MSC)预处理的模型分类效果最佳,分别经CARS方法和SPA方法优选出了34和16个光谱特征波长。采用MSC-CARS-PLS-DA方法构建的模型分类效果最好,总准确率、假阳率分别为96.8%和0.0...     

Non-destructive classification of durian maturity of ‘Monthong’ cultivar by means of visible spectroscopy of the spine

Characteristics of the spine of ‘Monthong’ durian such as color of the tip have been known to be an indicator of fruit maturity. Visible spectroscopy of the spine of durian was investigated for classification of maturity. Partial least squares discriminant analysis was performed to model the classification. The model using absorbance spectra transformed by the standard normal variate achieved the best accuracy of classification (94.7%) into four maturity classes ranging from 113 to 134 days after anthesis. The classification was attributable to the absorbance of chlorophyll a, carotenoids and anthocyanins in the spine.     

Direct determination of polymerised triacylglycerides in deep-frying vegetable oil by near infrared spectroscopy using Partial Least Squares regression

A green method for the determination of polymerised triacylglyceride (PTG) in deep-frying vegetable oils of different botanic origin has been developed employing near infrared (NIR) spectroscopy and Partial Least Squares (PLS) regression. Four different types of oil were heated during several hours, with and without the addition of foodstuff. NIR transmission spectra were obtained directly from sample aliquots stored in glass vials, thus avoiding the consumption of solvents and minimising waste generation. Variables employed for building the PLS models were selected applying interval PLS (iPLS) as well as Uninformative Variable Elimination-PLS (UVE-PLS). A global PLS model using spectra of all four types of oils was compared to PLS models established for each oil type. Due to the small differences observed in the NIR spectra that can be related to the different botanic origin and results obtained from the PLS model comparison, the use of a global PLS model is recommended leading to prediction errors of 2.28% (w/w) for the determination of PTG in oils employed for frying different kinds of foods.     

Non-destructive internal quality assessment of “Hayward” kiwifruit by waveguide spectroscopy

Soluble solids content (SSC) and Magness-Taylor flesh firmness (MTf) of “Hayward” kiwifruits were non-destructively assessed by means of a waveguide, that houses the fruit, connected to a sweeper oscillator and a spectrum analyzer. A preliminary test was conducted with a plastic fruit filled with solutions with different SSC values in the frequency range from 2 to 20 GHz (with a step of 1 GHz). The best linear correlations (R² up to 0.987) between electric signals and SSC solutions in the above described test were found in the 2-3 GHz and 15-16 GHz steps. These steps were used for the dielectric measurements on kiwifruit samples during storage of 28 days at 14 °C. Partial least squares (PLS) regression were then used to predict SSC and MTf from these acquired spectra. In “test set” validation, PLS models showed R² values up to 0.804 (RMSE = 0.98 °Brix) and 0.806 (RMSE = 8.9 N) for the prediction of SSC and MTf, respectively.     

Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy

The potential of visible/near infrared reflectance (Vis/NIR) spectroscopy for non-destructive discrimination of paddy seeds of different storage age was examined based on Vis/NIR spectroscopy coupled with chemometrics. Data from 210 samples of paddy seed were collected from 325 to 1075 nm using a field spectroradiometer. The spectral data were processed and analyzed by chemometrics, which integrated the methods of wavelet transform (WT), principal component analysis (PCA) and artificial neural networks (ANN) modelling. The noise of spectral data was filtered and diagnostic information was extracted by the WT method. Then, diagnostic information from WT was visualized in principal components space, in which the structures with the storage period were discovered. Finally, the first eight principal components, which accounted for 99.94% of the raw spectral variables, were used as the input for the ANN model. A promising model was achieved with a high discrimination accuracy rate of 97.5%. Thus, an effective and non-destructive way to discriminate paddy seeds of different storage periods was put forward.     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.     

枇杷蜂蜜中饴糖的近红外光谱检测

实验通过对纯枇杷蜂蜜及主动掺入1%、2%……30%饴糖的假枇杷蜂蜜进行近红外光谱扫描,采用TQAnalysisv6对数据进行预处理,建立饴糖含量的定性及偏最小二乘法和主成分回归法定量分析模型,并将模型应用于蜂蜜样品的分析预测。结果显示,采用原始光谱或一阶微分处理建立的判别分析模型均能够较好地区分掺饴糖蜂蜜与纯蜂蜜。根据PLS算法、PCR算法建立的定量模型相关系数分别为0.99771、0.98654,用于预测的蜂蜜样品实际值与预测值之间的决定系数分别为0.992、0.974。由此可见,用近红外光谱技术鉴别蜂蜜中是否添加饴糖是可行的,在实际操作中可以采用近红外光谱法快速定性判别蜂蜜中是否含有饴糖,也可根据化学计量法确定饴糖的含量,为蜂蜜打假提供依据。     

Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy

The objective of the present study was to evaluate the ability of near-infrared (NIR) spectroscopy to predict chemical compositions of Thai steamed pork sausages in relation to different types of sample presentation forms of NIR measurements (with and without plastic casing). NIR spectra of sausages were scanned to predict the chemical compositions, protein, fat, ash and carbohydrate non-destructively. NIR spectrum features of the sausage samples were strongly influenced by physical properties of the samples, such as the presence of plastic casing and inhomogeneous physical structure inside the samples, yielding significant baseline fluctuations. Thus, regression models were developed using partial least squares (PLS) regressions with two pretreatment methods, namely multiplicative scatter correction (MSC) and second derivative, which overcame the baseline problems. The prediction results suggest that the contents for the protein, fat and moisture can be estimated well with the proper selection of the pretreatment method.     

基于近红外漫反射光谱的面包老化过程中非冻结水含量无损检测

目的 建立一种基于近红外光谱技术快速无损测定面包老化过程中的非冻结水含量的方法。方法 应用近红外漫反射光谱技术采集新鲜面包在放置2h、2d、3d、4d、5d、6d、7d时的光谱，对比导数、S-G平滑(Savitzky Golay smooth)、标准正态变量变换(Standard normal variable transformation，SNV)及多元散射校正(Multiplicative scatter correction，MSC)预处理方法，利用偏最小二乘回归法(Partial least square regression，PLSR)和多元线性回归法（Multiple Linear Regression，MLR）建立面包老化过程中的非冻结水含量的预测模型，并对比两种模型预测结果。结果 利用PLSR建模相较MLR建模结果较好，建立的模型预测结果较好，模型的校正集相关系数（Rc）和均方根误差（RMSEC）分别为0.9386和0.0236 , 验证集相关系数（Rv）和均方根误差（RMSEP）分别为0.9271和0.0245。结论 通过近红外光谱技术结合偏最小二乘法建立面包老化过程中的非冻结水含量模型可作为面包老化过程中的非冻结水含量无损快速测定的可行性方法，其含量变化可以有效预测面包老化，为面包老化的无损检测提供了新的可行方案。     

Quality control of beer using high-resolution nuclear magnetic resonance spectroscopy and multivariate analysis

High-resolution nuclear magnetic resonance (NMR) spectroscopy is introduced for the quality control and authenticity assessment of beer in official food control. Measurements were performed using a 400-MHz NMR spectrometer using flow injection technology for automatic sample changing. Only degassing and addition of buffer (pH 5.6 in D₂O for locking and 0.1% TSP for referencing) is required to prepare the beer samples. Differences in the spectral profiles of beers varying in type and origin were studied by principal component analysis (PCA), considering the spectrum to be a characteristic fingerprint. For the first time, the high throughput of a Flow-Injection NMR system allowed a comprehensive database of beer spectra for PCA classification to be established efficiently. Beers made with barley malt could be distinguished from those made with wheat malt. Clustering of beers from the same brewing sites was observed, as well as significant discrimination of beers with deteriorated quality. Using the partial least squares (PLS) method to correlate NMR spectra with results from reference methods, models for calculating the original gravity, ethanol and lactic acid were established. The results obtained suggest that NMR is a useful tool in the quality control of beer samples, since quantitative determination of essential compounds as well as chemometric classification are simultaneously possible. Compared to conventional methods, ¹H NMR spectroscopy is faster and requires simpler sample preparation.     

A new approach to discriminate varieties of tobacco using vis/near infrared spectra

Vis/near infrared reflectance spectroscopy appears to be a rapid and convenient non-destructive technique that can measure the quality and compositional attributes of many substances. Principal component analysis (PCA), which offered a qualitative analysis of tobacco samples, was used to analyze the clustering of tobacco samples. A new method combined wavelet transform (WT) with Artificial Neural Network (ANN) was presented to establish a discrimination model. The model regarded the compressed spectra data as the input of ANN, and 80 samples were selected randomly as calibration collection whereas the remaining 20 were being prediction collection. High correlation coefficient (r=0.999) was achieved, which was better than PCA-SRA-ANN and PLS-ANN. It indicated that WT combined with ANN is an available method for variety discrimination based on the Vis/NIR spectroscopy technology. Some sensitive wave bands were also analyzed to develop tobacco varieties discrimination apparatus through PLS models.     

近红外光谱分析技术在动物源性食品检测中的应用进展

近年来动物源性食品的消费量日益增大,关于动物源性食品的质量安全也受到人们的高度重视。近红外光谱(NIR)分析技术具有分析速度快、非破坏性、所需样品量小、多组分同时测定,且可对生产过程进行实时监控等优点。本文对NIR分析技术进行了简介,并列举了常用的光谱预处理方法;综述了该技术在动物源性食品检测中的研究进展,包括品质评价和安全评价;并对该技术在动物源性食品检测中的发展趋势进行了阐述。表明NIR分析技术适合评价动物源性食品的品质和安全。      

基于近红外光谱技术的大米品质分析与种类鉴别

应用近红外光谱(NIRS)技术建立了大米食味品质分析与种类快速鉴别的方法。提取了102份粉碎后大米样品的近红外光谱,采用偏最小二乘法(PLS)建立了大米水分、蛋白质和直链淀粉定量分析模型,对模型预测结果的准确性进行了评价。预测模型的内部交叉验证决定系数(R2)分别为:0.992、0.9792和0.9736;内部交叉验证标准差(RMSECV)分别为:0.141、0.201和0.209;模型外部验证决定系数(R2)分别为0.9861、0.912和0.9373;外部验证标准差(RMSEP)分别为0.179、0.206和0.243。通过计算样品的近红外光谱图之间的欧氏距离来反映不同样品间的差异,对不同属性和不同产地的大米进行了定性聚类分析,种类识别准确率达到100%。结果表明,NIRS分析技术可以用于对大米品质和种类的快速无损检测。     

近红外光谱分析法快速测定稻谷常规化学指标

采集150份有代表性的我国南方地区稻谷样品的近红外光谱,用偏最小二回归分析法(PLS),建立了稻谷的水分、直链淀粉、蛋白以及胶稠度的近红外定量分析模型,并对30份预测集样品进行了验证。水分、直链淀粉、蛋白以及胶稠度的校正集模型的决定系数所(R2)分别为0.990 3、0.560 3、0.913 2以及0.678 0,交互验证均方根误差(RMSECV)分别为0.372 8%、1.456 9%、0.305 4%以及5.031 5%;验证集标准预测偏差(RMSEP)分别为0.382 5%、1.465 0%、0.510 0%以及5.052 1%。结果表明,近红外光谱分析法可以满足快速分析的要求。     

基于近红外光谱技术的老陈醋可溶性固形物定量分析

可溶性固形物含量(SSC)是食品行业的重要技术参数之一。利用近红外光谱技术对不同醋龄的老陈醋SSC进行分析。在不同光谱预处理下,分别采用主成分回归(PCR)和偏最小二乘法(PLS)建立SSC的定量分析模型。结果表明,采用5点平滑预处理后,利用PLS建立的老陈醋SSC的定量分析模型最优,其校正集的相关系数R为0.999 9,校正标准偏差(RMSEC)为0.038 3,预测标准偏差(RMSEP)和交叉验证标准偏差(RMSECV)分别为0.082 1,0.096 4。表明采用近红外光谱技术对不同醋龄的老陈醋SSC进行定量分析建模是可行的。