Untargeted Detection of Illegal Adulterations in Chinese Glutinous Rice Flour (GRF) by NIR Spectroscopy and Chemometrics: Specificity of Detection Improved by Reducing Unnecessary Variations

This paper aimed at developing a nondestructive and rapid method to detect adulterations in Chinese glutinous rice flour (GRF) using near-infrared (NIR) spectroscopy and chemometrics. Because various known and unknown ingredients can be potentially used for food adulteration, the commonly used targeted analytical methods focused on detecting one or more known/suspected adulterants usually cannot catch up with the constant “updating” of new adulterants. Therefore, this paper attempted to achieve untargeted detection by modeling the NIR spectra of pure GRF and analyzing those of test samples. Soft independent modeling of class analogy (SIMCA) and a recently suggested one-class partial least squares (OCPLS) was used to develop class models of pure GRF. To highlight the slight variations in NIR spectra caused by low-level doping and enhance the specificity for detecting extraneous adulterants, unwanted variations in pure GRF spectra should be removed. Smoothing, taking second-order derivative (D2), standard normal variate (SNV), and D2-SNV were performed to improve the raw spectra. One hundred thirty pure GRF samples from six main producing areas were prepared and used for training class models. To validate the specificity of class models, 215 adulterated GRF samples were prepared by blending the pure objects with different levels (1, 2, 4, 8, and 10 % (w/w)) of wheat flour, non-GRF, and an illegal food additive, talcum powder, which have been frequently used for GRF adulteration. The best OCPLS model was obtained with D2 spectra with prediction sensitivity of 1.000 and specificity of 0.916; SIMCA with D2-SNV obtained prediction sensitivity of 1.000 and specificity of 0.902. It was demonstrated that adulterations of GRF with 2 % or higher levels of wheat flour, non-GRF, and talcum powder can be safely detected with D2, SNV, or D2-SNV spectra. The analysis results indicate the specificity of untargeted detection of the three adulterants in GRF can be improved by removing the unwanted within-class variations.     

Comparing the Impact of High-Pressure Processing and Thermal Processing on Quality of “Hayward” and “Jintao” Kiwifruit Purée: Untargeted Headspace Fingerprinting and Targeted Approaches

This study evaluated process-induced quality changes in kiwifruit purée of two commercial cultivars (green kiwifruit, “Hayward”, and gold kiwifruit, “Jintao”) treated by equivalent microbial safety-based processing: high-pressure processing (HPP; 600 MPa/3 min) and thermal processing (TP; P ^{85 °C} _{8.3 °C} = 5min). This comparative study was performed using both targeted, analyzing a priori selected quality attributes (color, sugars, organic acids, and vitamin C) and untargeted headspace-solid phase microextraction-gas chromatography-mass spectrometry approaches, combining multivariate data analysis techniques (partial least squares discriminant analysis and variable identification). HPP provided a better retention of color and vitamin C compared to TP. Sugar and organic acid were less affected by HPP and TP. Methyl and butyl esters were detected at higher amounts in both processed purée, compared to untreated purée. For processed samples, furanones, terpenes, and alcohols were detected at higher amounts after TP and aldehydes were detected at higher amount after HPP. Overall, the quality of HP-treated samples is clearly closer to that of fresh samples compared to thermally treated samples and HP treatment avoids the formation of typical temperature-induced compounds.     

Lychee Variety Discrimination by Hyperspectral Imaging Coupled with Multivariate Classification

This current study was carried out to investigate the ability of hyperspectral imaging (HSI) technique and multivariate classification for the differentiation of lychee varieties. A total of 122 lychee samples from three varieties (“Baila,” “Jizhui,” and “Guiwei”) were used. The relationship between reflectance spectra and lychee varieties were established. Principal component analysis (PCA) was implemented on the region of interest (ROI) image to reduce data dimensionality and visualize the cluster trend. The first two principal components (PCs) explained over 97 % of variances of all spectral bands. Linear (soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA)) and nonlinear (back propagation neural network (BPNN) and support vector machine (SVM)) multivariate classification methods were used to develop discrimination models. The results revealed that SVM model achieved the best result, with the identification rate of 100 % in the calibration set and 87.81 % in the prediction set. BPNN had a discrimination rate of 100 % for the training set and 85.37 % for prediction set, while PSL-DA and SIMCA model had a discrimination rate of 78.05 % and 60.98 % for prediction sets, respectively. The nonlinear classification methods used were superior to the linear ones. The overall results showed that HSI system with SVM classification tool could be used in identification of lychee varieties.     

Detection of inverted beet sugar adulteration of honey by FTIR spectroscopy

A combination of Fourier transform infrared (FTIR) spectroscopy and multivariate statistics as a screening tool for the determination of beet medium invert sugar adulteration in three different varieties of honey is discussed. Honey samples with different concentrations of beet invert sugar were scanned using the attenuated total reflectance (ATR) accessory of the Bio‐Rad FTS‐6000 Fourier transform spectrometer. The spectral wavenumber region between 950 and 1500 cm^?1 was selected for partial least squares (PLS) regression to develop calibration models for beet invert sugar determination in honey samples. Results from the PLS (first derivative) models were slightly better than those obtained with other calibration models. Predictive models were also developed to classify beet sugar invert in three different varieties of honey samples using discriminant analysis. Spectral data were compressed using the principal component method, and linear discriminant and canonical variate analyses were used to detect the level of beet invert sugar in honey samples. The best predictive model for adulterated honey samples was achieved with canonical variate analysis, which successfully classified 88–94 per cent of the validation set. The present study demonstrated that Fourier transform infrared spectroscopy could be used for rapid detection of beet invert sugar adulteration in different varieties of honey. © 2001 Society of Chemical Industry     

Chromatographic Fingerprinting with Multivariate Data Analysis for Detection and Quantification of Apricot Kernel in Almond Powder

Adulteration of almond powder samples with apricot kernel was solved by gas chromatographic fatty acid fingerprinting combined with multivariate data analysis methods (principal component analysis (PCA), PCA-linear discriminant analysis (PCA-LDA), partial least squares (PLS), and LS support vector machine (LS-SVM). Different almond and apricot kernel samples were mixed at concentrations ranging from 10 to 90% w/w. PCA and PCA-LDA methods were applied for the classification of almonds, apricot kernels, and mixtures. PLS and LS-SVM were used for the quantification of adulteration ratios of almond. Models were developed using a training data set and evaluated using a validation data set. The root mean square error of prediction (RMSEP) and coefficient of determination (R ²) of validation data set obtained for PLS and LS-SVM were 5.01, 0.964 and 2.29, 0.995, respectively. The results showed that the methods can be applied as an effective and feasible method for testing almond adulteration.     

Characterization and Discrimination of the Floral Origin of Sidr Honey by Physicochemical Data Combined with Multivariate Analysis

Sidr honey represents one of the most expensive monofloral honeys worldwide. The quality control of such honey types usually depends on pollen analysis or comparison of physicochemical characters. In the presented work, 38 different honey samples of which 13 represented genuine Sidr (Ziziphus spina-christy) honey samples were collected from various areas of Yemen. All samples were characterized by physicochemical parameters including moisture content, pH, electrical conductivity, and free acidity. The physicochemical data was subjected to multivariate data analysis including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The development of partial least square discriminant analysis (PLS-DA) model on validation gave 100 % correct classification of the test set samples. All tested honey samples were within the level permitted by the international standards for honey quality. The application of the discriminant technique PLS-DA presented excellent potential for discriminating the botanical origin of Yemeni Sidr honey from other non-Sidr samples and may serve as a discriminant model to be applied to other honey types worldwide.     

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate.     

化学计量学在食品分析中的应用研究进展

综述了化学计量学中的主成分分析(principal components analysis,PCA)、偏最小二乘法(partial least square,PLS)、聚类分析(cluster analysis,CA)、判别分析(discriminant analysis,DA)、基于因子分析的多元分辨方法和小波变换(wavelet transform,WT)等方法在食品营养成分分析、食品分类识别及掺伪分析和食品卫生检测等领域中的应用,并对该领域的研究方向和前景进行了展望。     

Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy

Adulteration of honey with sugars is the most crucial quality assurance concern to the honey industry. The application of Fourier transform infrared spectroscopy as a screening tool for the determination of the type of sugar adulterant in honey was investigated. Spectra of honey adulterated with simple and complex sugars were recorded in the mid-infrared range using the attenuated total reflectance accessory of a Fourier transform infrared spectrometer. Adulterants considered were sugars (glucose, fructose and sucrose) and invert sugars (cane invert and beet invert). Predictive models were developed to classify the adulterated honey samples using discriminant analysis. Spectral data were compressed using principal component analysis and partial least-square methods. Linear discriminant analysis was used to discriminate the type of adulterant in three different honey varieties. An optimum classification of 100% was achieved for honey samples adulterated with glucose, fructose, sucrose and beet and cane invert sugars. Results demonstrated that discriminant analysis of the spectra of adulterated honey samples could be used for rapid detection of adulteration in honey.     

The use of near infrared reflectance spectroscopy to identify the origin of oak shavings used in wine aging

Near infrared (NIR) reflectance spectroscopy combined with chemometrics was used to classify toasted and untoasted oak wood shavings sourced from two countries (France and USA). Oak wood shaving samples (n = 96) were scanned in the NIR region (680–2,500 nm) using a monochromator instrument operating in reflectance mode. Principal component analysis, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples according to their country of origin and level of toasting. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. Correct classification rates of 83, 87 and 100 % for samples sourced from USA, France and toasted treatment were obtained using PLS-DA. For LDA, correct classification rates of 80.4, 85 and 100 % were achieved for samples sourced from USA, France and toasted treatment, respectively. These results demonstrated the ability of NIR spectroscopy to discriminate between oak wood shavings sourced from two different countries and two levels of toasting.     

Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been used to detect adulteration of honey samples. The sample set contained 135 spectra of authentic (n = 68) and adulterated (n = 67) honey samples. Spectral data were compressed using wavelet transformation (WT) and principal component analysis (PCA), respectively. In this paper, five classification modeling methods including least square support vector machine (LS-SVM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were adopted to correctly classify pure and adulterated honey samples. WT proved more effective than PCA, as a means for variables selection. Best classification models were achieved with LS-SVM. A total accuracy of 95.1% and the area under the receiver operating characteristic curves (AUC) of 0.952 for test set were obtained by LS-SVM. The results showed that WT-LS-SVM can be as a rapid screening technique for detection of this type of honey adulteration with good accuracy and better generalization.     

A Fast and Inexpensive Chemometric-Assisted Method to Identify Adulteration in Acai (<Emphasis Type="Italic">Euterpe oleracea</Emphasis>) Using Digital Images

This work is concerned with an analytical method for detecting acai adulteration based on digital image (DI) assisted by mean one-class classification (OCC) chemometric approaches, namely data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OC-PLS). In this study, two adulterants were considered, wheat flour and cassava. Digital images were acquired, in triplicate, using a webcam (WC040 MULTILASER of 5 Mp, 2G glass lenses with USB connection) in a closed wooden box with appropriate lighting and stored in JPEG format (24 bits) with a dimension of 2880?×?1620 pixels. For all the images, a central circular area was defined, used the working region to construct the frequency histograms in the color levels considering the standard RGB (red-green-blue), HSI (hue-saturation-intensity), and grayscale color models. Preliminary results obtained by principal component analysis (PCA) indicated the formation of two sample clusters (adulterated and unadulterated). On the other hand, the formation of sample clusters with respect to the type of adulterant (wheat and cassava) was not observed. OCC (DD-SIMCA and OC-PLS) models were built using eight and four factors, respectively, showing satisfactory fit. In the prediction of an external set of samples, the following results were obtained: error rate (ER) 2 and 31%, SEN 100% for both models, and specificity (SPE) 98.14 and 78.69 for DD-SIMCA and OC-PLS, respectively.     

Discrimination of yerba mate (<Emphasis Type="Italic">Ilex paraguayensis</Emphasis> St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis

Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens.     

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.     

Authentication of yerba mate according to the country of origin by using Fourier transform infrared (FTIR) associated with chemometrics

This study deals with the development of a method for classification of yerba mate (Ilex paraguariensis) using attenuated total-reflectance Fourier transform infrared (ATR-FTIR) and multivariate analysis. Fifty-four brands of yerba mate from southern South America were analysed in order to classify the commercialised yerba mate according to the respective country of yerba mate processing. The yerba mate was ground in a cryogenic mill, and the reflectance was directly measured in the region ranging from 4000 to 650 cm^?1. Different pre-processing algorithms and three methods of multivariate analysis were investigated, including principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA). The yerba mate classification was 100% correct when the reflectance spectra were pre-treated (derived at first order, normalised by standard normal variation, smoothed and mean centred) and analysed using the SVM-DA method.     

Ripeness Classification of Astringent Persimmon Using Hyperspectral Imaging Technique

Nondestructive detection of fruit ripeness is crucial for improving fruits’ shelf life and industry production. This work illustrates the use of hyperspectral images at the wavelengths between 400 and 1,000 nm to classify the ripeness of persimmon fruit. Spectra and images of 192 samples were investigated, which were selected from four ripeness stages (unripe, mid-ripe, ripe, and over-ripe). Three classification models—linear discriminant analysis (LDA), soft independence modeling of class analogy, and least squares support vector machines were compared. The best model was LDA, of which the correct classification rate was 95.3 % with the input consisted of the spectra and texture feature of images at three feature wavelengths (518, 711, and 980 nm). Feature wavelengths selection and texture feature extraction were based on successive projection algorithm and gray level co-occurrence matrix, respectively. In addition, using the same input of ripeness detection to make an investigation on firmness prediction by partial least square analysis showed a potential for further study, with correlate coefficient of prediction set r _pre of 0.913 and root mean square error of prediction of 4.349. The results in this work indicated that there is potential in the use of hyperspectral imaging technique on non-destructive ripeness classification of persimmon. The experimental results could provide the theory support for studying online quality control of persimmon.     

Classification and Authentication of Barley (<Emphasis Type="Italic">Hordeum vulgare</Emphasis>) Malt Varieties: Combining Attenuated Total Reflectance Mid-infrared Spectroscopy with Chemometrics

Attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy combined with chemometrics was explored as a tool to classify and authenticate Australian barley varieties. Grain samples (n = 162) were sourced from eight commercial barley varieties and analysed in the MIR range. Principal component analysis (PCA), discriminant partial least squares regression (PLS-DA), linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were used to classify the barley grain samples according to variety. PLS-DA correctly classified barley varieties between 91 and 100 %. The results have demonstrated the usefulness of ATR-MIR spectroscopy combined with chemometrics as a rapid method to classify barley grain samples according to their variety. Although MIR is not routinely available at the receival point in most of the cereal trade companies, it has the potential to be used in breeding programmes.     

Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA

Due to the human health benefits already scientifically proven, tea (Camellia sinensis) has been widely studied in the literature. Several studies report the classification of the variety or geographical origin of teas, separately. Thus, this paper has proposed a methodology for simultaneous classification of tea samples according to their varieties (green or black) and geographical origins (Brazil, Argentina, or Sri Lanka). For this purpose, near-infrared (NIR) spectroscopy and three differing supervised pattern recognition techniques, namely SIMCA (soft independent modeling of class analogy), PLS-DA (partial least squares-discriminant analysis), and SPA-LDA (successive projections algorithm associated with linear discriminant analysis) have been used. Despite having good results, both full-spectrum PLS-DA and SIMCA were not able to achieve 100 % classification accuracy, regardless of the significance level for the F test in the case of the SIMCA model. On the other hand, the resulting SPA-LDA model successfully classified all studied samples into five differing tea classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea) using 12 wave numbers alone.     

Discrimination of citrus fruits using FT-IR fingerprinting by quantitative prediction of bioactive compounds

High throughput screening of citrus samples containing elevated concentrations of total carotenoids, flavonoids, and phenolic compounds was accomplished using ultraviolet–visible spectroscopy and Fourier transform infrared (FT-IR) spectroscopy, combined with multivariate analysis. Principal component analysis and partial least squares discriminant analysis using FT-IR spectra were able to differentiate seven citrus fruit groups into three distinct clusters corresponding to their taxonomic relationship. Quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds in citrus fruit was established using a partial least squares regression algorithm from the FT-IR spectra. The regression coefficients (R ²) of predicted and estimated values of total carotenoids, flavonoids, and phenolic compounds were all 0.99. The results indicated that accurate quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from citrus fruit FT-IR spectra, and that the resulting quantitative prediction model might be useful as a rapid selection tool for citrus fruits containing elevated carotenoids, flavonoids, and phenolic compounds.     

核磁共振氢谱结合化学计量学快速检测掺假茶油

摘 要：以纯茶油和掺假茶油（掺入大豆油、玉米油）作为核磁共振氢谱检测对象,结合化学计量学方法分析处理核磁数据,建立一种能快速预测茶油掺假的方法。结果表明：纯茶油和掺假茶油在主成分分析得分图上有较好地区分,且掺假样品随掺假比例在图中呈规律性分布,但少部分低体积分数的掺假油与纯茶油重叠。而采用偏最小二乘判别（partial least squares discriminant analysis,PLS-DA）法可以得到更好的分离效果,在该模型中,纯茶油的判别准确率为100%。进一步采用PLS可实现对茶油掺假水平的准确定量测定。该方法可简单、快速地用于茶油的掺假鉴别,在茶油品质控制及评价方面具有很大的应用潜力。