Evaluation of the Spoilage of Raw Chicken Breast Fillets Using Fourier Transform Infrared Spectroscopy in Tandem with Chemometrics

The aim of this work was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy as a rapid and accurate technique to detect and predict the onset of spoilage in fresh chicken breast fillets stored at 3, 8, and 30 °C. Chicken breasts were excised from carcasses at 6 h post-mortem; cut in fillets; packed in air; stored at 3, 8, and 30?ºC; and periodically examined for FTIR, pH, microbiological analysis, and sensory assessment of freshness. Partial least squares regression allowed estimations of total viable counts (TVC), lactic acid bacteria (LAB), Pseudomonas spp., Brochothrix thermosphacta, Enterobacteriaceae counts and pH, based on FTIR spectral data. Analysis of an external set of samples allowed the evaluation of the predictability of the method. The correlation coefficients (R²) for prediction were 0.798, 0.832, 0.789, 0.810, 0.857, and 0.880, and the room mean square error of prediction were 0.789, 0.658, 0.715, 0.701, 0.756 log cfu g^?1 and 0.479 for TVC, LAB, Pseudomonas spp., B. thermosphacta, Enterobacteriaceae, and pH, respectively. The spectroscopic variables that can be linked and used by the models to predict the spoilage/freshness of the samples, pH, and microbial counts were the absorbency values of 375 wave numbers from 1,700 to 950 cm^?1. A principal component analysis led to the conclusion that the wave numbers that ranges from 1,408 to 1,370 cm^?1 and from 1,320 to 1,305 cm^?1 are strongly connected to changes during spoilage. These wave numbers are linked to amides and amines and may be considered potential wave numbers associated with the biochemical changes during spoilage. Discriminant analysis of spectral data was successfully applied to support sensory data and to accurately bound samples freshness. According to the results presented, it is possible to conclude that FTIR spectroscopy can be used as a reliable, accurate, and fast method for real time freshness evaluation of chicken breast fillets during storage.     

柑橘近红外光谱无损检测技术研究进展

柑橘是世界第一大水果,中国是柑橘生产和销售大国.现阶段我国柑橘产业存在各环节分离、加工技术粗糙、采后品质分级落后等问题,导致我国柑橘在国际市场上缺乏竞争力.对柑橘产品进行检测与分级是提高竞争力的有效手段,然而传统的柑橘品质检测手段如肉眼识别法、图像识别法、化学滴定法等存在费时费力、精度不高等缺陷,且化学滴定法等对样品具...     

在线近红外光谱系统快速检测整块鸡胸肉菌落总数含量

以整块鸡胸肉为研究对象,利用在线近红外光谱系统采集其900~1650 nm波长范围内的光谱信息,探究光谱信息与细菌菌落总数(Total Viable Count,TVC)之间的定量关系。对采集的原始光谱信息进行高斯滤波平滑(Gaussian Filter Smoothing,GFS)等五种预处理后,建立全波段偏最小二乘(Partial Least Squares,PLS)回归模型。采用回归系数法(Regression Coefficient,RC)和连续投影算法(Successive Projections Algorithm,SPA)筛选最优波长,构建优化的PLS模型和多元线性回归(Multiple Linear Regression,MLR)模型。结果表明,基于全波段GFS光谱构建的GFS-PLS模型预测鸡胸肉TVC效果最佳(r_P=0.964,RMSE_P=0.806 lg CFU/g)。基于SPA法从GFS光谱中筛选出的25个最优波长(907.0、913.7、923.8、927.2、937.2、947.3、974.0、987.3、997.3、1007.3、1040.4、1080.1、1099.9、1132.9、1155.9、1185.5、1215.0、1241.2、1270.6、1358.2、1380.8、1403.3、1419.3、1578.9和1615.2 nm),建立的SPA-GFS-MLR模型预测性能(r_P=0.944,RMSE_P=1.022 lg CFU/g)最接近GFS-PLS模型。基于在线近红外光谱系统可实现对大批量整块鸡胸肉细菌总数含量的快速无接触检测。     

蜂蜜中还原糖组分的近红外光谱应用研究

为探索快速测定还原糖含量的方法,提出了用傅立叶变换近红外光谱技术结合偏最小二乘法(PLS)建立近红外光谱与蜂蜜还原糖含量的数学模型并进行预测。通过光谱扫描还原糖含量在61.3%～75.22%范围的蜂蜜样本,选择11992.1～7494.6cm-1波数范围、二阶导数、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,模型的校正决定系数(Rcal)、校正均方差(RMSEE)、交叉验证决定系数(RCV)、交叉验证均方差(RMSECV)分别为99.71%、0.27%、98.44%、0.45%。用该模型对验证集样本进行预测并统计分析,表明预测值与测定值无显著差异。因此,用该方法快速准确定量分析大批蜂蜜中的还原糖含量具有重要意义。     

Quality Assessment of Intact Chicken Breast Fillets Using Factor Analysis with Vis/NIR Spectroscopy

Factor analysis (FA) method was tested to assess quality of chicken breast fillets with the visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm. According to inherent correlation, three factors were extracted from the measured eight quality traits (L*, a*, b*, pH, moisture, drip loss, expressible fluid, and salt-induced water gain). The extracted “grade factor” (F ₁), “color factor” (F ₂), and “moisture factor” (F ₃) could respectively represent the characteristics and the variation tendency of the corresponding quality traits and were defined as three new quality assessment indexes. Furthermore, partial least squares regression (PLSR) models were established to quantitatively relate spectral information to eight individual quality traits and three factors. The results indicated that the models for predicting each factor performed better than those for individual quality traits. Key wavelengths of each quality trait were then selected, and the corresponding spectra were taken to build new PLSR prediction models. The selected key wavelengths showed obvious practical significance, and the new models had comparable predictive performance to those models developed based on the full spectra, among which the new models of F ₁ and F ₂ had acceptable and robust predictive abilities (R²p?=?0.73, RPD?=?1.91; R²p?=?0.74, RPD?=?1.97). Our results in the present study demonstrate the potential for FA and Vis/NIR spectroscopy as a useful method to assess the quality of chicken breast fillets.     

基于傅里叶变换红外光谱技术对木耳中镰刀菌的识别检测

该研究建立了一种基于傅里叶变换红外光谱的木耳中镰刀菌的定性定量检测方法：对经高压灭菌的木耳样品分别接种木耳中常见的五种镰刀菌（层出镰刀菌189975、串珠镰刀菌340687、尖孢镰刀菌120618、木贼镰刀菌124121、茄病镰刀菌121547）,并于28 ℃,相对湿度80%的条件下进行储存培养,同时采集不同储存阶段的木耳样品在1 800~900 cm-1的红外光谱信息。分别运用主成分分析（PCA）、线性判别分析（LDA）以及偏最小二乘回归分析（PLSR）建立木耳中镰刀菌的快速识别检测模型。结果表明：LDA模型对受不同镰刀菌侵染的木耳样品的平均判别正确率达到87.50%,对受单一镰刀菌侵染的木耳样品霉变状态的平均判别正确率达到82.50%;PLSR模型对木耳样品中菌落总数的预测实现了较好的定量结果（R2 p=0.842 8,RMSEP=0.292 log CFU/g,RPD=2.81）;通过实际样品验证分析表明傅里叶变换红外光谱方法可以实现木耳中镰刀菌的快速识别检测。     

基于FT-NIR光谱技术快速定量分析米糠中结合态与游离态酚类含量

针对新鲜米糠酚类含量检测的时效性不足的问题,本文建立了一种基于傅里叶转化近红外光谱技术(FTNIR)的米糠酚类组分快速无损检测方法。以多批次的新鲜米糠作为实验原料,定量分析了其游离态酚类、结合态酚类以及总酚含量,构建了基于全波段和特征波段的偏最小二乘回归(PLSR)、支持向量机(SVM)、BP人工神经网络(BPNN)的预测模型。结果表明：在全波段数据建模中,基于PLSR模型的预测结果(结合态、游离态以及总酚)相对最佳,对应的R_p²为0.944、0.943和0.937,RPD为3.031、2.779和2.863;采用竞争适应性重加权采样法(CARS)和连续投影算法(SPA)分别提取了4～8个特征波段,其中基于CARS-PLSR(结合态、游离态以及总酚)预测效果相对最佳,对应的R_p²为0.953、0.932和0.944,RPD为3.301、2.759和3.031,建模的运行时间缩短2倍,仅需2 s,符合米糠中酚类物质检测的时效性需求。本研究结果证实了基于FT-NIR技术可以实现米糠中酚类含量组分的快速定...     

Fourier Transform Infrared and Raman Spectroscopy Studies of the Time-Dependent Changes in Chicken Meat as a Tool for Recording Spoilage Processes

Time-dependent changes of chicken meat were studied using Fourier transform IR and Raman techniques. Small pieces of intact chicken breast muscle (pectoralis major) were used in the investigations. They were stored in air at 22 °C up to 10 days and their IR and Raman spectra were measured successfully. Analysis of the obtained spectra was performed using a deconvolution of the experimental bands into Lorentz components. All integral intensities of the observed bands were standardized using the statistical R2 coefficient of determination. The R² values were automatically created as the output of the Origin software. The time-dependent changes of the spectra were used for meat spoilage detection. The analytical relationships between the integral intensities of selected bands have been derived indicating an increase of free amino acids content as the main effect of the chicken breast muscle spoilage.     

近红外光谱快速测定巧克力中掺假淀粉

巧克力作为一种休闲食品,以其细腻的口感和独特的口味而广泛受到消费者的青睐。然而,近几年来关于巧克力掺假的报道不断涌入人们的视野。其中,以廉价淀粉掺假巧克力的手段最为常见。本文研究利用近红外光谱快速检测巧克力中掺假红薯淀粉和马铃薯淀粉的方法,采用主成分回归(principal component regression,PCR)和偏最小二乘法(partial least squares regression,PLS)建立校正模型,并对比了光谱区间、光谱预处理方式以及主因子数对模型的影响。结果显示,采用PLS建模,光谱采用一阶导数处理(7pts),光谱区间选择在7000~4200 cm-1,主因子数为8时,模型预测效果最佳。结果表明,模型的预测误差均方根RMSEP=1.7%,实际值与预测值相关系数RP2=0.9426。该模型对不同掺假比例样品的加样回收率为94.2%~105.6%,日内RSD为4.7%~8.9%,日间RSD为5.1%~11.3%。结果表明,近红外光谱技术可用于快速检测巧克力中掺假淀粉。     

白酒基酒中己酸、乙酸的近红外快速检测

建立白酒基酒中的己酸、乙酸的快速检测方法,通过分析白酒基酒样品的近红外光谱图,对光谱数据进行不同处理。结果表明：白酒基酒中己酸、乙酸对近红外有特异吸收,最佳预处理方法与最优波段分别为：一阶导数＋减去一条直线、一阶导数＋矢量归一化预处理光谱;谱区选择6 101.7～5 446 cm－1和11 998.9～7 501.7 cm－1,6 101.7～5 449.8 cm－1和11 998.9～7 497.9 cm－1。利用偏最小二乘法与傅里叶变换近红外光谱相结合,采用内部交叉验证法建立模型,通过对模型进行优化,己酸、乙酸校正集样品的化学值与近红外的预测值的决定系数分别为99.73%、97.00%;内部交叉验证均方根差分别为0.90、0.63 mg/100 mL;进一步对己酸、乙酸模型进行验证和评价,己酸和乙酸模型验证集的决定系数分别为99.47%、95.63%,预测标准偏差分别为1.00、1.73 mg/100 mL。结果表明建立的模型效果很好,具有较高的精密度和良好的稳定性,能满足白酒生产中己酸和乙酸的快速检测要求。     

Rapid Differentiation of Listeria monocytogenes Epidemic Clones III and IV and Their Intact Compared with Heat‐Killed Populations Using Fourier Transform Infrared Spectroscopy and Chemometrics

The objectives of this study were to determine if Fourier transform infrared (FT‐IR) spectroscopy and multivariate statistical analysis (chemometrics) could be used to rapidly differentiate epidemic clones (ECs) of Listeria monocytogenes, as well as their intact compared with heat‐killed populations. FT‐IR spectra were collected from dried thin smears on infrared slides prepared from aliquots of 10 μL of each L. monocytogenes ECs (ECIII: J1‐101 and R2‐499; ECIV: J1‐129 and J1‐220), and also from intact and heat‐killed cell populations of each EC strain using 250 scans at a resolution of 4 cm^?1 in the mid‐infrared region in a reflectance mode. Chemometric analysis of spectra involved the application of the multivariate discriminant method for canonical variate analysis (CVA) and linear discriminant analysis (LDA). CVA of the spectra in the wavelength region 4000 to 600 cm^?1 separated the EC strains while LDA resulted in a 100% accurate classification of all spectra in the data set. Further, CVA separated intact and heat‐killed cells of each EC strain and there was 100% accuracy in the classification of all spectra when LDA was applied. FT‐IR spectral wavenumbers 1650 to 1390 cm^?1 were used to separate heat‐killed and intact populations of L. monocytogenes. The FT‐IR spectroscopy method allowed discrimination between strains that belong to the same EC. FT‐IR is a highly discriminatory and reproducible method that can be used for the rapid subtyping of L. monocytogenes, as well as for the detection of live compared with dead populations of the organism.     

Classification of Barley according to Harvest Year and Species by Using Mid-infrared Spectroscopy and Multivariate Analysis

In order to monitor malt quality in the malting industry, despite yearly variations in the barley quality, 394 barley samples were analysed using conventional （moisture, protein and B-glucan content） and mid-infrared Fourier transform spectroscopy FT-IR. The experimental dataset included barley from three harvest years, two barley species, 77 barley varieties, and two-row and six-row barley, from 16 cultivation sites. For each sample, the malt quality indices were also assessed according to European Brewing Convention （EBC） standards. Principal component analysis （PCA） was carried out on mean-centred, normalized and derivative spectra using 200/cm width spectral bands. The most informative spectral bands were observed in the 800-1,000/cm and 1,000-1,200/cm ranges. PCA revealed that barley harvested in 2010 and in 2011 had bands that were very close together, while 2009 harvest clearly displayed a difference in its quality. PCA made it possible to distinguish two species and confirmed that two-row winter barley quality was closer to two-row spring barley quality than to six-row winter barley. Results indicate that mid-infrared spectrometry （MIR） could be a very useful and rapid analytical tool to assess barley qualitative quality.     

傅立叶变换近红外光谱法快速检测锅盔中水分含量

应用傅立叶变换近红外光谱技术,建立锅盔水分含量分析模型.测定61份锅盔的近红外光谱,经一阶导数+MSC预处理以滤去噪声,在7 501.9～4 597.6 cm-1谱段范围内,选择维数10,利用偏最小二乘法建立近红外光谱与国标参考方法测得的水分含量之间的相关模型.最终得到水分定量校正模型决定系数(R2)为99.03％,内部交叉验证均方差(RMSECV)为0.409％.用该模型对19个未知锅盔样品进行外部验证,其水分外部验证决定系数(R2)为97.99％,预测标准偏差(RMSEP)为0.341％.结果表明,近红外定量分析技术有较高的准确度,能满足锅盔水分的快速检测精度要求.     

Unfrazzled by Fizziness: Identification of Beers Using Attenuated Total Reflectance Mid-infrared Spectroscopy and Multivariate Analysis

Mid-infrared (MIR) spectroscopy coupled with attenuated total reflectance (ATR) was used to analyse a series of different beer types in order to confirm their identity (e.g. ale vs lager, commercial vs craft beer). Multivariate data analyses such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse and to discriminate the beer samples analysed based on their infrared spectra. Correct classification rates of 100% were achieved in order to differentiate between ale and lager and also between commercial and craft beer sample types, respectively. Overall, the results of this study demonstrated the capability of MIR spectroscopy combined with PLS-DA to classify beer samples according to style (ale vs lager) and production (commercial vs craft). Furthermore, dissolved gases in the beer products were proven not to interfere as overlapping artefacts in the analysis. The benefits of using MIR-ATR for rapid and detailed analysis coupled with multivariate analysis can be considered a valuable tool for researchers and brewers interested in quality control, traceability and food adulteration. The novelty of this study is potentially far reaching, whereby customs and agencies can utilise these methods to mitigate beverage fraud.     

Fourier Transform Infrared Spectroscopy Combined with Multivariate Calibrations for the Authentication of Avocado Oil

Avocado oil is one of the functional oils having high quality and high price in the market. This oil shows many benefits for the human health and is applied in many cosmetic products. The authentication of avocado oil becomes very important due to the possible adulteration of avocado oil with other lower priced oils, such as palm oil and canola oil. In this study, Fourier transform infrared spectroscopy using attenuated total reflectance in combination with chemometrics techniques of partial least squares and principal component regression is implemented to construct the quantification and classification models of palm oil and canola oil in avocado oil. Partial least squares at the wavenumbers region of 1260–900 cm^–1 revealed the best calibration models, having the highest coefficient of determination (R² = 0.999) and the lowest root mean square error of calibration, 0.80%, and comparatively low root mean square error of prediction, 0.79%, for analysis of avocado oil in the mixture with palm oil. Meanwhile, the highest R², root mean square error of calibration, and root mean square error of prediction values obtained for avocado oil in the mixture with canola oil at frequency region of 3025–2850 and 1260–900 cm^–1 were 0.9995, 0.83, and 0.64%, respectively.     

基于可见/近红外光谱技术快速检测花椰菜中硫代葡萄糖苷的含量

为了快速无损检测不同品种("松花"、"雪白")花椰菜中硫代葡萄糖苷的含量。本实验采集新鲜收获的"松花"、"雪白"花椰菜样本进行可见/近红外光谱的采集、提取和分析。首先,采用基线校正(Baseline)、标准正态变量变换(Standard Nomal Variate transform,SNV)、中值滤波(Median Filter,MF)、高斯滤波(Gaussion Filter,GF)、S-G平滑(Savitzky-Golay)五种方法进行原始光谱的预处理分析。然后,分别采用连续投影算法(Successive Projections Algorithm,SPA)、回归系数法(Regression Coefficient,RC)进行特征波段的提取,并采用主成分分析法(PrincipalComponentAnalysis,PCA)进行主成分的提取,在此基础上,结合最佳预处理方法建立偏最小二乘回归(Partial Least Squares Regression,PLSR)模型。结果表明:"松花"花椰菜的光谱数据所建立的MF-PCA-PLS模型最佳,校正集模型参数Rc=0.89,RMSEC=1.23,预测集模型参数Rp=0.89,RMSEP=0.63。"雪白"花椰菜光谱数据所建的MF-RC-PLS模型最优,校正集模型参数Rc=0.87,RMSEC=1.31,预测集模型参数Rp=0.73,RMSEP=0.46。由此可见,近红外光谱结合PLSR算法能够快速、无损、准确地检测花椰菜中硫代葡萄糖苷的含量。     

Rapid Determination of α-Tocopherol in Vegetable Oils by Fourier Transform Infrared Spectroscopy

The aim of this study was to develop a rapid methodology for the analysis of α-tocopherol in vegetable oils as an alternative to the high-performance liquid chromatography (HPLC) methods: Fourier transform infrared (FT-IR) methodology. Thirteen vegetable oils (corn, peanut, soybean, sunflower and mixtures) commercially obtained were analysed by reverse-phase HPLC with fluorescence detection (FD) in order to obtain standard values for α-tocopherol. Validation tests were performed concerning the HPLC method. The HPLC method is valid for α-tocopherol analysis in the 1–90 mg/L linear range. Method repeatability was 3.6%, and accuracy results were within 70–95%. FT-IR spectra of the vegetable oils were acquired in the attenuated total reflection mode (45° and 60° crystals of ZnSe). To predict the α-tocopherol content in samples, calibration models were designed, and the partial least squares method was used to analyse data from FT-IR spectral region at 1,472–1,078 cm⁻¹. Results obtained showed that the calibration model implemented with a 45° crystal is more suitable for the proposed analysis. Five extra samples of vegetable oils were analysed by HPLC/FD and by FT-IR. Using the calibration model implemented for FT-IR (45° crystal), the α-tocopherol content in samples was determined. The results obtained by HPLC/FD and FT-IR were compared, and there were no significant differences among them. Results showed that FT-IR can be used as an alternative method for rapid screening of α-tocopherol in vegetable oils without sample pre-treatment. Presented in part at the “AOAC Europe section international workshop: Enforcement of European Legislation on Food and Water: Analytical and Toxicological Aspects,” in Lisbon, April 2008, and published in abstract form.     

基于拉曼光谱技术的掺鸡肉牛肉馅快速判别方法

利用拉曼光谱技术结合化学计量学方法对掺入鸡肉的掺假牛肉馅进行快速判别。选取89 个样本,采集样 本的拉曼光谱,对原始光谱进行卷积平滑预处理,采用主成分分析法进行聚类分析,并利用支持向量回归建立模 型。结果表明：掺假牛肉馅样本校正模型的决定系数R2 c为0.999 4,均方根误差（root mean sruare error,RMSE）为 0.230 0;交互验证决定系数R2 cv为0.999 3,RMSE为0.298 0;预测模型的决定系数R2 p为0.971 6,RMSE为0.236 0。因 此,利用拉曼光谱技术结合化学计量学方法对掺鸡肉的牛肉馅进行快速判别是可行的。     

基于支持向量机对云南常见野生食用牛肝菌中红外光谱的种类鉴别

利用傅里叶变换中红外光谱鉴别云南野生牛肝菌种类,明确不同数据挖掘方法对模型分类性能的影响,为云南省食用菌的鉴别和质量控制提供参考依据.扫描云南8种827个常见野生牛肝菌样本的中红外光谱,分析光谱特征,结合支持向量机建立判别模型,并利用预处理、提取特征变量及两者组合等方法挖掘光谱信息,比较各模型分类性能,找出野生牛肝菌种...     

近红外光谱技术快速检测腊肉酸价和过氧化值

探讨应用傅里叶近红外光谱技术快速定量检测腊肉酸价和过氧化值的方法。腊肉样品经粉碎、混匀后在AntarisⅡ傅里叶近红外光谱分析仪上扫描,获得其近红外光谱与国标法测定的酸价和过氧化值含量数据进行关联,用傅里叶变换近红外光谱技术结合偏最小二乘法建立近红外光谱与腊肉酸价和过氧化值含量的数学模型并进行预测。结果表明：酸价模型中,校正决定系数和交叉验证决定系数分别是0.99582和0.98687,校正均方差和交叉验证均方差分别是0.1370和0.1900;过氧化值模型中,校正决定系数和交叉验证决定系数分别是0.99999和0.99926,校正均方差和交叉验证均方差分别是0.756×10-4和0.684×10-3。用该模型对验证集样本进行预测并统计分析,表明预测值与测定值无显著差异,傅里叶近红外光谱技术快速定量检测腊肉酸价和过氧化值是可行的。