Mid-infrared spectroscopy and authenticity problems in selected meats: a feasibility study

This paper describes the results of a feasibility study into the use of mid-infrared spectroscopy for addressing certain authenticity problems with selected fresh meats. Preliminary analyses for meat speciation, the detection of ‘frozen-thawed’ meat, and semi-quantitative analysis of meat mixtures are reported. Fourier transform mid-infrared spectroscopy, attenuated total reflectance sample presentation, principal component analysis and partial least squares regression were used. It was possible to distinguish minced chicken, pork and turkey meats from their infrared spectra, and for each meat species it was possible to differentiate between fresh and frozen-thawed samples. Mid-infrared spectroscopy was also able to semi-quantitatively measure the levels of turkey and pork mixed with chicken meat. The method, which is rapid and easy to use, could with further development have the potential for authentication and quality control of meat products.     

Discrimination of Apples Using Near Infrared Spectroscopy and Sorting Discriminant Analysis

Near infrared spectra of apples contain the most useful information of the soluble solids content and firmness of apples. A new feature extraction method, called sorting discriminant analysis, was proposed to use a sorting method based on principal component analysis and linear discriminant analysis to extract the features of near infrared spectra. The objective of this research was to make use of feature extraction methods, such as principal component analysis, linear discriminant analysis, discriminant partial least squares, and sorting discriminant analysis to extract information from near infrared spectra of the “Huaniu” apples and the “Fuji” apples. After feature extraction, the nearest neighbor classifier was used to classify the apples, and the classification results were compared to study that which feature extraction method performed best. The experimental results showed principal component analysis + linear discriminant analysis and sorting discriminant analysis could extract discriminant information from near infrared spectra of apples better than principal component analysis and discriminant partial least squares, and sorting discriminant analysis was the best one. Sorting discriminant analysis can not only compress the high-dimensional near infrared spectra to the low-dimensional data but also project near infrared spectra to a new feature space where the data can be classified easily and effectively, and sorting discriminant analysis is superior to principal component analysis + linear discriminant analysis in most cases.     

利用可见/近红外反射光谱无损检测小米的粘度

目的建立适用于小米粘度无损检测的可见/近红外反射光谱法。方法使用光谱仪获取小米在367～1020 nm波段范围内的漫反射光谱,采用多元散射校正法(multiple scattering correction,MSC)和一阶导数法(first derivation,1~(st)-D)对原始反射光谱进行处理,并且使用主成分分析确定最佳主成分数,建立小米粘度判别模型,使用全交叉验证法进行模型验证。结果使用原始反射光谱、MSC处理光谱和1~(st)-D处理光谱,分别提取了6、12和12个主成分,建立的峰值粘度模型RCV在0.86以上,对验证集的预测结果 Rp在0.82～0.86之间;而使用1~(st)-D处理光谱提取12个最优主成分,建立的模型可较好地预测小米粘度的破损值,RCV为0.8573,对验证集的预测结果 Rp为0.8309。结论该方法适用于小米粘度的无损检测,为小米加工品质的快速检测提供一定的理论支持。     

Potential use of Fourier transform infrared spectroscopy for differentiation of bovine and porcine gelatins

In order to classify unknown gelatin into their species of origin, a simple and rapid method for the qualitative determination was developed using Fourier transform infrared (FTIR) in combination with attenuated total reflectance (ATR) and discriminant analysis. The spectra were analysed using a chemometric method, principal component analysis (PCA), to classify and characterise gelatin compounds using regions of the FTIR spectra in the range of 3290–3280 cm^?1 and 1660–1200 cm^?1 as calibration models. Results from PCA, which were subsequently represented by the Cooman’s plot showed a clear distinction between gelatin samples of bovine and porcine origins. This qualitative approach, besides providing a rapid determination of the source of gelatin, may also be established based on a second derivative study of the FTIR spectrum to alleviate any doubt of the gelatin source for applications in the food and pharmaceutical industries.     

Can near‐infrared reflectance of green coffee be used to detect introgression in Coffea arabica cultivars?

Most new coffee cultivars disseminated over the last 15 years are derived from the Timor Hybrid (Coffea arabica × C canephora). Introgression of genes from the C canephora genome has been estimated at between 9 and 29% of the genome. It has been shown that introgression can have a negative impact on the cup quality of cultivars derived from the Timor Hybrid. Consequently, coffee buyers or roasters may wish to assess whether the coffee they are purchasing comes from introgressed varieties. The possibility of distinguishing between non‐introgressed Arabicas and genotypes carrying chromosome fragments introgressed from C canephora was investigated (i) using some classical chemical compounds (caffeine, chlorogenic acids, trigonelline, fat and sucrose) and (ii) using a new approach based on spectra acquired by near‐infrared reflectance of green coffee. Near‐infrared spectra were obtained for 129 samples from two collections (Nicaragua and Costa Rica) of introgressed and non‐introgressed coffee trees. The spectral collections were treated by principal component and factorial discrimination. When the introgressed coffee trees were compared with the non‐introgressed trees using the chemical compounds, small but significant differences were found in caffeine, trigonelline and chlorogenic acid contents. However, the small variations in those compounds are not enough to detect introgression. The spectral collections treated by principal component and factorial discrimination made it possible to class from 92.30 to 94.87% of the analysed samples correctly, while the percentages of correctly classified samples in the verification file varied from 88.23 to 94.11%. The NIRS method appears to be an efficient method for determining whether a green coffee comes from an introgressed variety. Copyright © 2005 Society of Chemical Industry     

DETERMINATION OF FIRMNESS AND SUGAR CONTENT OF APPLES USING NEAR‐INFRARED DIFFUSE REFLECTANCE1

The objective of this research was to study the potential of near‐infrared (NIR) diffuse reflectance between 800 nm and 1700 nm for determining the firmness and sugar content of apples and to ascertain the effects of apple peel and variety on the NIR prediction of these two quality attributes. The spectral reflectance data were acquired from both peeled and unpeeled ‘Empire', ‘Golden Delicious', and ‘Red Delicious’ apples. Statistical models were developed using principal component analysis/regression. Lower correlations of prediction were obtained (r=0.38 to 0.58) between NIR measurement and Magness‐Taylor firmness for both unpeeled and peeled fruit, with the standard errors of prediction (SEP) between 6.6 N and 10.1 N. Improved predictions were obtained when NIR reflectance was correlated with the slope of the Magness‐Taylor force‐deformation curves. Excellent predictions of the sugar content in peeled apples were obtained (r=0.93 to 0.97; SEP=0.37 to 0.42 °Brix). The SEP, on average, increased by about 0.17 °Brix for the unpeeled apples. Variety did not have a large effect on the model performance on sugar content predictions.     

基于层次-关联度和主成分分析的无核鲜食葡萄品质评价

通过对银川引种的10个无核鲜食葡萄果实品质的综合评价,筛选出品质优良品种,为宁夏地区鲜食葡萄发展提供可开发资源的科学依据.对供试品种的14个基本品质指标进行观察测定,应用层次一关联度和主成分分析综合评价了无核鲜食葡萄品质,并根据综合得分进行排序.结果表明,在层次一关联度分析中,建立了3个层次,分别是目标层、准则层、指标...     

基于可见近红外光谱玉米种子活力的无损 检测方法

目的基于近红外漫反射技术,初步探讨玉米种子活力的快速、无损检测方法。方法本研究利用实验室自行搭建的近红外光谱检测系统获取60粒表面平整无明显损伤的M017玉米种子450~900 nm光谱曲线,其中校正集和验证集比例为3:1。利用红墨水染色法判定玉米种子样品是否具有活力。通过进行SG-5点平滑(Savitzky-Golay smoothing,SG)预处理方法减小曲线噪声,基于主成分分析(principal component analysis,PCA)方法提取主要判别成分,并依据测定的种子活力情况和其光谱曲线应用支持向量机(support vector machine,SVM)建立判别模型进行分析。结果当累计贡献率达到96%时,选取6个主成分,建立的模型判别正确率最高,近红外漫反射光谱数据能够较好的判别种子活力的有无,其中校正集和预测集判别正确率分别为95.56%和86.67%。结论证明该方法可行,基本能够满足快速无损检测判别玉米种子活力的要求,为今后快速无损检测玉米种子活力奠定了基础。     

Discrimination between immature and mature green coffees by attenuated total reflectance and diffuse reflectance Fourier transform infrared spectroscopy

The objective of this work was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) in the characterization and discrimination between immature and mature or ripe coffee beans. Arabica coffee beans were submitted to FTIR analysis by reflectance readings employing attenuated total reflectance (ATR) and diffuse reflectance (DR) accessories. The obtained spectra were similar, but in general higher absorbance values were observed for nondefective beans in comparison to immature ones. Multivariate statistical analysis (principal component analysis, PCA, and agglomerative hierarchical clustering, AHC) was performed in order to verify the possibility of discrimination between immature and mature coffee samples. A clear separation between immature and mature coffees was observed based on AHC and PCA analyses of the normalized spectra obtained by employing both ATR and DR accessories. Linear discriminant analysis was employed for developing classification models, with recognition and prediction abilities of 100%. Such results showed that FTIR analysis presents potential for the development of a simple routine methodology for separation of immature and mature coffee beans. Practical Application: The ultimate goal of this research is to be able to propose improvements in the way immature coffee beans are separated from graded mature beans in coffee facilities (cooperatives and other coffee producer's associations). The results obtained herein point toward FTIR as a potential tool for the aimed improvements.     

基于主成分和聚类分析的曲拉品质的综合评价

为提高牧区牦牛曲拉品质的一致性,对我国曲拉主产区的曲拉样品进行综合评价。采集8个地区95份牦牛曲拉样品,对其营养成分、抗氧化指标、色度值及5-羟甲基糠醛(5-hydroxymethylfurfural,5-HMF)进行测定与分析,应用主成分分析筛选曲拉品质评价指标,同时通过聚类分析对曲拉样品进行分类,运用方差分析对曲拉进行综合评价。结果表明,根据相关性分析得到大多数指标间均存在极显著(P0.01)或显著(P0.05)相关性;主成分分析筛选出2个主成分因子,PC1(72.846%)为外观色泽因子,PC2(13.763%)为营养品质因子,L*值、a*值、b*值以及5-HMF含量在PC1上的载荷因子均在0.9以上,说明外观色泽是评价曲拉品质的主要指标;聚类分析可将95份曲拉样品分为4类,且此分类结果与主成分分析结果基本一致。该4类曲拉样品品质存在极显著差异(P0.01),其中总体14.74%左右的曲拉样品品质不佳,69.47%左右的曲拉样品品质良好,15.79%左右的曲拉样品品质优良。     

基于近红外化学成分特征吸收光谱的野生台蘑鉴别模型研究

本文建出一种应用近红外光谱技术鉴别野生台蘑的新方法。使用FieldSpec3便携式近红外光谱仪对包括野生台蘑在内的13种蘑菇进行漫反射光谱采集。将采集的数据经小波去噪后,对可见与近红外光谱(350～2500nm)进行峰谷筛选,所得峰谷集经主成分分析降维,取方差贡献率大于99.9%的5个主成分作为BP神经网络的输入值,建立数学模型。该模型在偏差±0.05内,对未知样本正确识别率为100%。本结果表明利用近红外漫反射光谱可以很好地鉴别野生台蘑。     

基于多元统计分析的冻干草莓果粉品质评价因子筛选

本文通过测定21个草莓品种冻干果粉的品质性状,基于描述性统计、相关性分析、主成分分析和聚类分析对不同品种冻干草莓果粉品质进行评价。结果表明,测定的21项品质指标中,仅DPPH自由基清除率和b*值的变异系数小于6%,其余指标变异系数均较大;各指标间存在不同程度的相关性。对各品质指标进行主成分分析,前6个主成分的累积方差贡献率达81.895%,基本反映了原指标的信息,主成分综合得分较高的品种为凤凰、梦、假章姬和紫金香玉,再进一步根据聚类结果,最终将草莓果粉21项品质指标简化为吸湿性、果糖含量、a*值、可溶性蛋白含量、V_C含量、果胶含量、产出率和L*值8项具有代表性的核心评价指标。本研究结果为草莓冻干果粉品质指标的确定提供了理论依据。     

Direct determination of lactulose in heat‐treated milk using diffuse reflectance infrared Fourier transform spectroscopy and partial least squares regression

The aim of this work was the direct determination of lactulose (LCT) concentration in freeze‐dried heat‐treated milks using diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) without any chemical pretreatment of the milk. The ‘actual’ lactulose concentrations estimated by means of high‐performance liquid chromatography (HPLC) were correlated with the spectral region 1286–754 cm^?1 of DRIFTS spectra in the second‐derivative form of the milk samples using partial least squares (PLS) regression. A linear relationship was established between the ‘actual’ and the concentrations recalculated using the built model. The proposed DRIFTS method is simple, rapid and low cost.     

Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study

Color, instrumental texture, and sensory attributes of steaks from 24 beef carcasses at 2, 4, 8, 14, and 21 days post mortem were predicted by visible/near infrared (visible/NIR) reflectance spectroscopy in 400-1080 nm region. Predicting the Hunter a, b, and E* yielded the coefficient of determination (R(2)) in calibration to be 0.78-0.90, and R(2) was between 0.49 and 0.55 for tenderness, Hunter L, sensory chewiness and juiciness. The prediction R(2) for tenderness was in the range of 0.22-0.72 when the samples were segregated according to the aging days. Based on partial least square (PLS) model predicted tenderness, beef samples were classified into tender and tough classes with a correct classification of 83%. Soft independent modeling of class analogy of principal component analysis (SIMCA/PCA) model of measured tenderness showed great promise in the classification of tender and tough meats with over 96% success.     

Determination of protein concentration in raw milk by mid-infrared fourier transform infrared/attenuated total reflectance spectroscopy

This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% (weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.     

Approaches to Adulteration Detection in Instant Coffees using Infrared Spectroscopy and Chemometrics

Fourier transform infrared (FTIR) spectroscopy is examined as a rapid alternative to wet chemistry methods for the detection of adulteration of freeze-dried instant coffees. Spectra have been collected of pure coffees, and of samples adulterated with glucose, starch or chicory in the range 20–100 g kg⁻¹. Two different FTIR sampling methods have been employed: diffuse reflectance, and attenuated total reflectance. Three different statistical treatments of the spectra were carried out. Firstly, the spectra were compressed by principal component analysis and a linear discriminant analysis performed. With this approach, a 98% successful classification rate was achieved. Secondly, a simultaneous partial least square regression was carried out for the content of three added carbohydrates (xylose, glucose and fructose) in order to assess the potential of FTIR spectroscopy for determining the carbohydrate profile of instant coffee. Lastly, the discrimination of pure from adulterated coffee was performed using an artificial neural network (ANN). A perfect rate of assignment was obtained. The generalization ability of the ANN was tested on an independent validation data set; again, 100% correct classifications were achieved.     

不同品种莲藕游离氨基酸多样性分析

以28 个不同品种莲藕为研究对象,通过测定其游离氨基酸组成与含量,并采用相关性分析、主成分分析及聚类分析进行综合评价,明确莲藕游离氨基酸的含量、组成与呈味的品种多样性。结果表明：莲藕中含有17 种游离氨基酸,必需氨基酸、非必需氨基酸和总氨基酸平均含量分别为2.05、12.27 mg/g和14.33 mg/g。3 个主成分累计方差贡献率为80.16%,较好地反映出莲藕中氨基酸的综合信息。综合得分排列前5 位的品种为‘金华无籽藕’‘细卜’‘鄂莲5号’‘武义白姆食用藕’和‘金华红莲’。聚类分析将28 个莲藕品种分为3 类,与主成分分析结果一致,较好地反映出莲藕游离氨基酸组成与呈味的品种差异性。本研究结果为今后莲藕品种选育、品质提升、加工利用与贮藏保鲜提供了良好理论基础。     

基于近红外及中红外光谱融合技术快速检测黄酒中的总酚含量及其抗氧化能力

为了实现对黄酒中总酚含量(TPC)及其抗氧化能力(TAC)的快速检测,探索了将傅立叶红外光谱技术应用于快速检测这两项重要指标的可行性。协同区间偏最小二乘算法(Si PLS)用于选出有效波长区间以提高模型的预测能力。支持向量机(SVM)和主成分分析(PCA)用来融合由Si PLS选出的中红外(ATR-IR)和近红外(FT-NIR)光谱的有效波段。实验结果表明基于Si PLS筛选的有效光谱变量而建立的偏最小二乘回归模型(PLS)的精度优于基于全光谱建立的经典PLS模型。基于ATR-IR建立的模型的效果略优于基于FT-NIR光谱建立的模型。此外,基于提取自ATR-IR合FT-NIR的有效区间而建立的SVM模型的预测能力要好于建立的PLS或Si PLS模型。因此,ATR-IR及FT-IR结合特征谱区筛选方法可以作为理化检测的替代手段实现对黄酒中的TAC和TPC的快速检测,同时基于两种光谱的融合技术可显著提高模型的预测精度。     

利用可见/近红外光谱判别干枣品种

目的利用可见/近红外反射光谱技术快速判别干枣的品种。方法使用光谱仪获取山西永和枣、山西板枣和新疆和田枣3种干枣在345~1100 nm波段范围内的漫反射光谱;分别使用多元散射校正(MSC)法和一阶导数法(1~(st)-D)和二阶导数法(2~(st)-D)对反射光谱进行预处理;对预处理光谱进行主成分分析,全交差验证法确定最佳主成分数量,提取主成分,结合马氏距离法和线性判别法建立品种判别模型,建立模型过程中使用全交叉验证法确定最佳主成分数,将模型应用于干枣的品种判别。结果可见/近红外反射光谱经过MSC处理后提取主成分建立品种预测模型对枣的品种判别结果最好,利用前4个主成分结合马氏距离法建立的判别模型和利用前5个主成分结合线性判别法建立判别模型,对于3个品种的枣的校正和验证判别准确率都达到了100%。结论可见/近红外反射光谱技术可以较好地判别干枣品种,本研究可为可见/近红外光谱技术在于枣品种和产地的快速鉴别和溯源中的应用提供一定的技术基础。