Evaluation of green coffee beans quality using near infrared spectroscopy: A quantitative approach

Characterisation of coffee quality based on bean quality assessment is associated with the relative amount of defective beans among non-defective beans. It is therefore important to develop a methodology capable of identifying the presence of defective beans that enables a fast assessment of coffee grade and that can become an analytical tool to standardise coffee quality. In this work, a methodology for quality assessment of green coffee based on near infrared spectroscopy (NIRS) is proposed. NIRS is a green chemistry, low cost, fast response technique without the need of sample processing. The applicability of NIRS was evaluated for Arabica and Robusta varieties from different geographical locations. Partial least squares regression was used to relate the NIR spectrum to the mass fraction of defective and non-defective beans. Relative errors around 5% show that NIRS can be a valuable analytical tool to be used by coffee roasters, enabling a simple and quantitative evaluation of green coffee quality in a fast way.     

Fast Discrimination of Milk Contaminated with <Emphasis Type="Italic">Salmonella</Emphasis> sp. Via Near-Infrared Spectroscopy

Conventional methods to detect Salmonella in milk generally take a long time and are significantly laborious besides requiring the use of different media and reagents. Thus, this research aimed to offer an alternative methodology based on near-infrared spectroscopy (NIRS) to discriminate the presence of Salmonella in milk. For this purpose, different milk samples, contaminated and free of Salmonella, were prepared to be measured by NIRS and evaluated by chemometric method of partial least squares with discriminant analysis. The model to discriminate skimmed milk samples used four latent variables presenting root mean square error of calibration (RMSEC) 0.1639 and prediction (RMSEP) of 0.0971, while the model to the whole milk discrimination six latent variables were needed, presenting RMSEC 0.1351 and RMSEP 0.0928. The validation parameters, sensitivity, and specificity shows that all samples were correctly classified. The results suggest that the methodology could be used to discriminate the presence/absence of Salmonella in milk samples using NIR as a screening method.     

基于Fisher算法和BP-ANN的造纸原料近红外判别

为了实现对造纸原料的快速准确判别,收集了5种共40份原料品种的近红外光谱数据。通过MAF和一阶导数方法进行光谱数据预处理,用主成分方法对光谱数据进行压缩降维,分别利用Fisher算法和BP人工神经网络来建立原料近红外光谱判别模型,并对两种判别模型进行比较。结果表明:两种模型都能较好地进行造纸原料的近红外判别,且BP人工神经网络比Fisher判别函数在容错性上表现得更为优越,建立的模型用于种类判别时表现得更为稳健。     

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.     

A Feasibility Study on the Use of Near Infrared Spectroscopy for the Authentication of Depurated Salmon Fillets

Some species of actinomycetes, fungi, and blue-green algae produce semivolatile off-flavor compounds responsible for earthy-musty odorants in water from aquaculture facilities and tend to bioaccumulate within fish flesh. Although these off-flavor compounds are harmless to human health, high levels within fish tissue lead to an undesirable taste leading to an unmarketable consumption quality. Preharvest processes such as depuration can be used to eliminate most of these off-flavor compounds and ultimately enhance salmon quality. This study examined the potential of near infrared spectroscopy (NIRS) to discriminate depurated from nondepurated salmon fillets (n?=?62). Partial least squares discriminant analysis based on NIR spectra correctly classified 100 % of non-depurated and depurated salmon fillets. The successful classification was likely a consequence of statistically significant differences in the content of off-flavor compounds between both samples, which could have been detected by NIR spectra. Thus, NIRS is a fast, inexpensive, solvent-free, and non-destructive technology that can be used for the authentication of salmon with enhanced quality for marketing purposes.     

Classification of geographical origins and prediction of δ13C and δ15N values of lamb meat by near infrared reflectance spectroscopy

Near infrared spectroscopy (NIRS) combined with chemometric analysis was investigated for its potential to classify the geographical origin and predict δ(13)C and δ(15)N values of lamb meat samples (n=99) from three pastoral regions and two agricultural regions of China. Principal component analysis (PCA), discriminant partial least squares analysis (D-PLS), linear discriminant analysis (LDA) and partial least squares regression (PLSR) were used for data analysis. D-PLS and LDA correctly classified 100% of the both pastoral and agricultural region samples, and gave a total correct classification of 88.9% and 75% to the five individual region samples, respectively. The best PLSR calibration models for predicting δ(13)C and δ(15)N of lamb meat were obtained with the determination coefficient (R(2)) 0.76 and 0.87, respectively. These results show that NIRS combined with chemometrics can be used as a rapid and effective method to discriminate the geographical origin and estimate the δ(13)C and δ(15)N of lamb meat.     

基于指纹图谱和化学计量学的浓香型白酒分类研究

该研究以市售畅销的7个品牌、不同等级的浓香型白酒为研究对象,利用气相色谱-质谱联用（GC-MS）技术建立了浓香型白酒中风味成分的指纹图谱,结合相似度分析、主成分分析（PCA）和判别分析（DA）对不同品牌的浓香型白酒样品进行了有效区分和鉴别。结果表明,不同品牌间样品的相似度存在一定差异;PCA表明前三个主成分累计方差贡献率达到89.71%,能对样品进行聚类和区分;利用判别分析可以将不同产地的酒样区分开,正确率为100%。综上,相同品牌不同等级的白酒具有明显的相关性,不同品牌白酒可以利用指纹图谱结合化学计量学方法进行鉴别和分类,为白酒质量控制及真伪鉴定提供了一种新方法。     

Discrimination and Cluster Analysis of Soy Sauce GLC Profiles

Two hundred chromatograms of volatiles in eight brands of soy sauce were analyzed by discriminant and cluster analysis in order to investigate the difference in gas chromatographic profiles among brands. The sensory tests clearly showed differences in aroma quality among brands. Most of the gas chromatograms were correctly classified by stepwise discriminant analysis into their original brands with a few percent of misclassification. After 48 samples were selected by a random process, cluster analysis was performed. The gas chromatograms in each brand combined to make six or seven clusters on the basis of the selected ten peaks. The connection order of clusters shifted according to the peaks selected.     

The use of the Multiple Comparison Test in checking product variability

The Multiple Comparison Procedure, in which the size of difference of a number of samples from a labelled control is assessed by a panel, was used to demonstrate the flavour variation between jars of two commercial brands of instant coffee. One brand was shown to have moderate to large flavour differences between jars, whereas in the other brand, differences were generally slight. The procedure is suitable as a quality control technique for comparing a number of samples at a panel session.     

Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Influence of torrefacto roast on antioxidant and pro-oxidant activity of coffee

The addition of sugar at the end of the torrefacto roasting process may influence the antioxidant and pro-oxidant properties of coffee because sugar is one of the main precursors the Maillard reaction. The aim of the work was to study and to compare the antioxidant and pro-oxidant properties of some commercial roasted coffees which are selected to represent conventional roasted arabica coffee and arabica/robusta blends, and torrefacto roasted blends. Higher antioxidant activity was observed in Colombian coffees than in conventional roasted coffee blends. On the other hand, when the percentage of torrefacto coffee was increased, an increase of the antioxidant activity and a slight tendency to decrease the pro-oxidant activity were observed. Moreover, principal component analysis allowed separation of: (a) brands by PC1 (46.9%), characterised by colour parameters defined by roasting degree and (b) torrefacto roasted blends by PC2 (33.7%), characterised by antioxidant/pro-oxidant activity.     

Recognition of liquid and flesh food using an `electronic tongue'

An electronic tongue comprising thirty potentiometric chemical sensors and pattern recognition tools for data processing was used for the analysis of mineral waters, coffee, soft drinks and flesh food, namely fish. The electronic tongue appeared to be capable of distinguishing between different sorts of beverages: natural and artificial mineral waters, individual and commercial brands of coffee, and commercial and experimental samples of soft drinks containing different sweeteners. A quantitative correlation between human perception and `electronic tongue' output was obtained. Taste parameter assessments produced by a professional taste panel was used for `electronic tongue' calibration. It was found that the `electronic tongue' is capable of distinguishing sea water and freshwater fish and monitoring their spoilage.     

Trigonelline in coffee

The results of several determinations of trigonelline in green, roasted and instant coffees are reported. The values in normal coffee species and degrees of roast are in agreement with most literature values. InCoffea dewevrei var.excelsa andC. stenophylla lower values were found than reported in the only other publication. The differences from steamed samples were minimal. Fast roasting may result in higher values with the same organic roasting loss, but this was not observed in commercial blends. During industrial extraction of roasted coffee, trigonelline is not completely extracted. The percentage depends on the extraction yield. tween traditionally (slowly) and fast-roasted coffee have not yet been studied, at least not by using a series of samples. In the following, we present such studies. On the importance of trigonelline in coffee analysis see [1].     

基于ICP-MS/MS分析矿物元素的江西茶叶产地溯源

目的 分析江西不同产区茶叶中矿物元素含量的差异,并结合多元统计方法分析矿物元素指纹特征,构建江西茶叶不同产地鉴别的判别模型。方法 采用电感耦合等离子体串联质谱法(ICP-MS/MS)对采集于3个江西代表县的150个茶叶样品中的15种矿物元素进行测定,获得其中的元素成分信息,结合主成分分析技术(PCA)和判别分析技术(DA),分析不同产地茶叶的特征元素变量,研究筛选元素特征指纹。结果 江西不同产地的茶叶中的常量元素、微量元素和重金属含量差异较大,可以成为指纹分析技术的有效产地溯源指标。主成分分析以提取特征值>1.0,可以将15个矿物元素提取2个主成分,总方差贡献率达63.00%,基本保留了原变量的大部分信息。以2个主成分为原始变量,建立判别典型函数,利用判别典型函数可以对三个不同产地的150个茶叶进行99.3%正确判别。结论 矿物元素指纹图谱可以用于江西茶叶的产地溯源判别,为今后江西各种知名茶叶品牌的原产地保护提供技术支撑。     

Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

近红外光谱技术检测液态奶中微量三聚氰胺的可行性研究

为了快速检测液态奶中违法添加的三聚氰胺量,利用近红外光谱技术进行可行性研究分析。对采集的54组添加不同三聚氰胺含量的液态奶样本的近红外漫反射光谱进行小波变换(wavelet transform,WT)结合主成分分析(principal component analysis,PCA)后,提取得到特征信号,利用广义回归神经网络(general regression neural network,GRNN)建模进行三聚氰胺的定量分析,结果显示当三聚氰胺浓度低于0.05%时,模型预测能力较差,预测集模型R2仅为0.654。进一步利用支持向量机(support vector machine,SVM)建模对液态奶中是否添加三聚氰胺进行定性判别,结果显示测试集准确率为94.44%。因此,通过近红外光谱结合适当的化学计量学方法,对液态奶中添加三聚氰胺虽无法精确定量,但可准确对其进行定性判别。     

Determination of Organic Acids and Sucrose in Roasted Coffee by Capiillary Gas Chromatography

Capillary gas chromatography was used to obtain high resolution profiles of 27 organic acids, caffeine, and sucrose in dimethylsulfoxide extracts of roast and ground coffees in a sixty-minute analysis. A shortened procedure was also reported for the quantitative determination of S-caffeoylquinic (SCQA) and quinic (QA) acids and sucrose. Nanogram detection limits were achieved and the reproducibility of the method was under 5% relative. Major, commercial brands of caffeinated and decaffeinated coffee produced under different roasting conditions could be distinguished based on their 5-CQA and sucrose content and on the ration of QA-to-SCQA.     

A gas chromatography-mass spectrometry-based metabolomic approach for the characterization of goat milk compared with cow milk

In this work, the polar metabolite pool of commercial caprine milk was studied by gas chromatography-mass spectrometry and multivariate statistical data analysis. Experimental data were compared with those of cow milk and the discriminant analysis correctly classified milk. By the same means, differences due to heat treatments (UHT or pasteurization) on milk samples were also investigated. Results of the 2 discriminant analyses were combined, with the aim of finding the discriminant metabolites unique for each class and shared by 2 classes. Valine and glycine were specific to goat milk, talose and malic acid to cow milk, and hydroxyglutaric acid to pasteurized samples. Glucose and fructose were shared by cow milk and UHT-treated samples, whereas ribose was shared by pasteurized and goat milk. Other discriminant variables were not attributed to specific metabolites. Furthermore, with the aim to reduce food fraud, the issue of adulteration of caprine milk by addition of cheaper bovine milk has been also addressed. To this goal, mixtures of goat and cow milk were prepared by adding the latter in a range from 0 to 100% (vol/vol) and studied by multivariate regression analysis. The error in the level of cow milk detectable was approximately 5%. These overall results demonstrated that, through the combined approach of gas chromatography-mass spectrometry and multivariate statistical data analysis, we were able to discriminate between milk typologies on the basis of their polar metabolite profiles and to propose a new analytical method to easily discover food fraud and to protect goat milk uniqueness. The use of appropriate visualization tools improved the interpretation of multivariate model results.     

Rapid Discrimination Between Authentic and Adulterated Andiroba Oil Using FTIR-HATR Spectroscopy and Random Forest

Andiroba oil (Carapa guianensis Aubl) from the Amazonian region is one of the most important oils in popular medicine and cosmetic industry, has a high added value and is liable to adulteration with other cheaper or lower quality oils. Based on these facts, this work proposes a simple, fast, and accurate methodology to discriminate authentic andiroba oil from adulterated andiroba oil-containing soybean and corn oils in different proportions. The approach is based on the applicability of FTIR-HATR spectroscopy combined with chemometrics tools for classification such as random forest and partial least squares for discriminant analysis (PLS-DA). The random forest model showed 100% of correct classification and, consequently, an accuracy of 100% in training and test sets, while PLS-DA presented an accuracy of approximately 100 and 94% in training and test sets, respectively. Therefore, the developed methodology can be helpful in routine laboratories, regulatory agencies, and industry for quality analysis of andiroba oil.     

基于近红外光谱法的鱼粉快速判别

基于近红外漫反射光谱分析技术对市场上常见的淡水鱼粉、进口鱼粉和国产鱼粉3 类商品化的鱼粉样品进行自动化判别实验。通过分析鱼粉样品光谱之间的差异,采用主成分分析法建立鱼粉种类的定性判别的分类模型,光谱范围为波长1 100～2 498 nm,交互定标决定系数为0.913 5,交互定标标准误差为0.133 8。通过对验证样品的分析,建立的判别模型预判准确率达到84.6%,外部验证准确率达到100%。结果表明,近红外光谱技术结合化学计量学法可以作为一种快速、无损、可靠的方法用于鱼粉种类的判别。