Adulteration of the anthocyanin content of red wines: perspectives for authentication by Fourier transform-near infrared and 1H NMR spectroscopies

In the Italian oenological industry, the regular practice used to naturally increase the colour of red wines consists in blending them with a wine very rich in anthocyanins, namely Rossissimo. In the Asian market, on the other hand, anthocyanins extracted by black rice are frequently used as correctors for wine colour. This practice does not produce negative effects on health; however, in many countries, it is considered as a food adulteration. The present study is therefore aimed to discriminate wines containing anthocyanins originated from black rice and grapevine by using reliable spectroscopic techniques requiring minimum sample preparation. Two series of samples have been prepared from five original wines, that were added with different amounts of Rossissimo or of black rice anthocyanins solution, until the desired Colour Index was reached. The samples have been analysed by FT-NIR and (1)H NMR spectroscopies and the resulting spectra matrices were subjected to multivariate classification. Initially, PLS-DA was used as classification method, then also variable selection/classification methods were applied, i.e. iPLS-DA and WILMA-D. The classification with variable selection of NIR spectra permitted to classify the test set samples with an efficiency of about 70%. Probably these not excellent performances are due to the matrix effect, together with the lack of sensitivity of NIR with respect to minor compounds. On the contrary, very satisfactory results were obtained on NMR spectra in the aromatic region between 6.5 and 9.5 ppm. The classification method based on wavelet-based variables selection, permitted to reach an efficiency in validation greater than 95%. Finally, 2D correlation analysis was applied to FT-NIR and (1)H NMR matrices, in order to recognise the spectral zones bringing the same chemical information.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

Minimisation of instrumental noise in the acquisition of FT-NIR spectra of bread wheat using experimental design and signal processing techniques

Spectral resolution (R) and number of repeated scans (S) have a significant effect on the S/N ratio of Fourier transform-near infrared (FT-NIR) spectra, but the optimal values of these two parameters have to be determined empirically for a specific problem, considering separately both the nature of the analysed matrix and the specific instrumental setup. To achieve this aim, the instrumental noise of replicated FT-NIR spectra of wheat samples was modelled as a function of R and S by means of the Doehlert design. The noise amounts in correspondence to different experimental conditions were estimated by analysing the variance signals derived from replicate measurements with two different signal processing tools, Savitzky–Golay (SG) filtering and fast wavelet transform (FWT), in order to separate the “pure” instrumental noise from other variability sources, which are essentially connected to sample inhomogeneity. Results confirmed that R and S values leading to minimum instrumental noise can vary considerably depending on the type of analysed food matrix and on the different instrumental setups, and helped in the selection of the optimal measuring conditions for the subsequent acquisition of a wide spectral dataset.     

Characterization and discrimination of different aged 'Aceto Balsamico Tradizionale di Modena' products by head space mass spectrometry and chemometrics

This work can be seen as an attempt to develop an analytical procedure in the context of quality control and authenticity assessment of typical food.To this aim, head-space mass spectrometry (HS-MS) coupled with multivariate data analysis, is proposed as a fast technique for furnishing a clear visualization and a suitable interpretation of the ageing process of ‘Aceto Balsamico Tradizionale di Modena’ (ABTM) and, for classifying products of different age.Considering the complexity of this food matrix, due to its traditional making procedure, the obtained instrumental data have first been analysed by parallel factor analysis (PARAFAC), an extension of principal component analysis to higher order arrays, in order to visualise the ‘natural’ grouping of vinegar samples and to inspect producers similarity/dissimilarity. On the basis of the PARAFAC results a reasonable class partition with respect to ageing was accomplished and both linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were applied as classification tools. Furthermore, it has been shown that discrimination on age basis can be improved by using feature selection in the wavelet domain through WPTER algorithm.     

Classification of secondary ion mass spectrometry (SIMS) micrographs to characterize chemical phases

This work demonstrates the potential of multivariate image analysis methods in the extraction of useful, problem dependent information from SIMS images. Specific algorithms have been developed to classify SIMS micrographs manually as well as automatically. A feature selection has been achieved by means of principal component analysis with a subsequent image classification.As an application example for these improved digital image processing tools chemical phases within a soldered industrial metal sample have been identified. This is of highly practical value as it was assumed that during the soldering process inhomogeneities occur along the joint site which cause a cracking of the brazed material under mechanical strain conditions.     

煤泥浮选泡沫的数字图像处理

研究安装了煤泥浮选泡沫数字图像获取系统，通过大量的分批浮选实验，获取了许多煤泥精矿泡沫图像；分析了煤泥浮选泡沫数字图像的特点，探讨了用灰度绝对值为刻画泡沫图像特征的可行性；引入了空间灰度相关矩阵和邻城灰度相关矩阵来提取泡沫的纹理特理，并提取基于这两种算法的一系列特征参数来描述泡沫的结构；开发了煤泥浮选泡沫特征参数提取软件，并用其完成了精矿泡沫图像特征参数提取工作；分析了各泡沫特征参数随浮选时间（泡沫纹理）的变化关系，定性地指出了各泡沫特征参数与泡沫纹理的相关性。     

基于独立分量和神经网络的近红外多组分分析方法

采用小波变换对光谱数据进行压缩,用独立分量分析(ICA)方法提取近红外光谱数据矩阵的独立成分和相应的混合矩阵,再用BP神经网络对混合矩阵和实测浓度矩阵进行建模,提出了基于独立分量分析-神经网络回归(ICA-NNR)的近红外分析建模方法。进一步研究了独立分量数和网络中间隐层的神经元数对模型性能的影响,经优化后的ICA-NNR模型在相关系数与均方根误差两个指标上均优于直接用光谱矩阵作为输入所建立的模型。本方法用于玉米中水分、淀粉、蛋白质3种主要成分含量的同时测定,检验样品集的化学检测值与近红外预测值的相关系数分别达到:淀粉r=0.971,蛋白质r=0.976,水分r=0.975。     

Inkjet-printed disposable metal complexing indicator-displacement assay for sulphide determination in water

A sulphide selective colorimetric metal complexing indicator-displacement assay has been developed using an immobilized copper(II) complex of the azo dye 1-(2-pyridylazo)-2-naphthol printed by inkjetting on a nylon support. The change in colour measured from the image of the disposable membrane acquired by a digital camera using the H coordinate of the HSV colour space as the analytical parameter is able to sense sulphide in aqueous solution at pH 7.4 with a dynamic range up to 145 μM, a detection limit of 0.10 μM and a precision between 2 and 11%.     

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures.In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one.The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks’ Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees.A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.     

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.     

Unimodal transform of variables selected by interval segmentation purity for classification tree modeling of high-dimensional microarray data

As a greedy search algorithm, classification and regression tree (CART) is easily relapsing into overfitting while modeling microarray gene expression data. A straightforward solution is to filter irrelevant genes via identifying significant ones. Considering some significant genes with multi-modal expression patterns exhibiting systematic difference in within-class samples are difficult to be identified by existing methods, a strategy that unimodal transform of variables selected by interval segmentation purity (UTISP) for CART modeling is proposed. First, significant genes exhibiting varied expression patterns can be properly identified by a variable selection method based on interval segmentation purity. Then, unimodal transform is implemented to offer unimodal featured variables for CART modeling via feature extraction. Because significant genes with complex expression patterns can be properly identified and unimodal feature extracted in advance, this developed strategy potentially improves the performance of CART in combating overfitting or underfitting while modeling microarray data. The developed strategy is demonstrated using two microarray data sets. The results reveal that UTISP-based CART provides superior performance to k-nearest neighbors or CARTs coupled with other gene identifying strategies, indicating UTISP-based CART holds great promise for microarray data analysis.     

Combining information from headspace mass spectrometry and visible spectroscopy in the classification of the Ligurian olive oils

An electronic nose and an UV-Vis spectrophotometer, in combination with multivariate analysis, have been used to verify the geographical origin of extra virgin olive oils. Forty-six oil samples from three different areas of Liguria were included in this analysis.Initially, the data obtained from the two instruments were analysed separately. Then, the potential of the synergy between these two technologies for testing food authenticity and quality was investigated.Application of Linear Discriminant Analysis, after feature selection, was sufficient to differentiate the three geographical denominations of Liguria (“Riviera dei Fiori”, “Riviera del Ponente Savonese” and “Riviera di Levante”), obtaining 100% success in classification and close to 100% in prediction. The models built using SIMCA as a class-modelling tool, were not so effective, but confirmed that the results improve using the synergy between different analytical techniques.This paper shows that objective instrumental data related to two important organoleptic features such as oil colour and aroma, supply complementary information.     

Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: An application to proteomics data

This paper introduces the ant colony algorithm, a novel swarm intelligence based optimization method, to select appropriate wavelet coefficients from mass spectral data as a new feature selection method for ovarian cancer diagnostics. By determining the proper parameters for the ant colony algorithm (ACA) based searching algorithm, we perform the feature searching process for 100 times with the number of selected features fixed at 5. The results of this study show: (1) the classification accuracy based on the five selected wavelet coefficients can reach up to 100% for all the training, validating and independent testing sets; (2) the eight most popular selected wavelet coefficients of the 100 runs can provide 100% accuracy for the training set, 100% accuracy for the validating set, and 98.8% accuracy for the independent testing set, which suggests the robustness and accuracy of the proposed feature selection method; and (3) the mass spectral data corresponding to the eight popular wavelet coefficients can be located by reverse wavelet transformation and these located mass spectral data still maintain high classification accuracies (100% for the training set, 97.6% for the validating set, and 98.8% for the testing set) and also provide sufficient physical and medical meaning for future ovarian cancer mechanism studies. Furthermore, the corresponding mass spectral data (potential biomarkers) are in good agreement with other studies which have used the same sample set. Together these results suggest this feature extraction strategy will benefit the development of intelligent and real-time spectroscopy instrumentation based diagnosis and monitoring systems.     

Quantification of <Emphasis Type="Italic">Lactobacillus</Emphasis> in fermented milk by multivariate image analysis with least-squares support-vector machines

This paper reports an approach for quantification of Lactobacillus in fermented milk, grown in a selective medium (MRS agar), by use of digital colour images of Petri plates easily obtained by use of a flatbed scanner. A one-dimensional data vector was formed to characterize each digital image on the basis of the frequency-distribution curves of the red (R), green (G), and blue (B) colour values, and quantities derived from them, for example lightness (L), relative red (RR), relative green (RG), and relative blue (RB). The frequency distributions of hue, saturation, and intensity (HSI) were also calculated and included in the data vector used to describe each image. Multivariate non-linear modelling using the least-squares support vector machine (LS-SVM) and a linear model based on PLS regression were developed to relate the microbiological count and the frequency vector. Feasibly models were developed using the LS-SVM and errors were below than 10% for Lactobacillus quantification, indicating the proposed approach can be used for automatic counting of colonies.     

Fusion of 2-D SIMS Images Using the Wavelet Transform

 Secondary ion mass spectroscopy (SIMS) is a powerful method for element distribution examination of conducting and semi-conducting surfaces at high spatial resolution and with a high sensitivity. Routine surface analysis produces about 8 to 15 images in a short time, each of which displays the intensity distribution of one mass, thus generating a multispectral SIMS image. Formation of occlusions, segregations, and the overall location of the elements relative to each other, are difficult to recognise when looking at n separate 2-D images. Image fusion is a process whereby images obtained from various sensors, or at different moments of time, or under different conditions, are combined together to provide a more complete picture of the object under investigation. The process of combining SIMS images may be viewed as an attempt to compensate for the inherent effect of SIMS to channel the information obtained from the sample into different images, corresponding to different element phases. The wavelet transform is a powerful method for fusion of images. This work covers the use of wavelet based fusion algorithms on multispectral SIMS images, evaluating the performance of different wavelet based fusion rules on different type of image systems and comparing the results to conventional fusion techniques. An aim of this study is to increase the information, i.e. the number of masses, which can be merged into one image in order to enhance the perception and interpretation of the SIMS surface images.     

Forward selection radial basis function networks applied to bacterial classification based on MALDI-TOF-MS

Forward selection improved radial basis function (RBF) network was applied to bacterial classification based on the data obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The classification of each bacterium cultured at different time was discussed and the effect of parameters of the RBF network was investigated. The new method involves forward selection to prevent overfitting and generalized cross-validation (GCV) was used as model selection criterion (MSC). The original data was compressed by using wavelet transformation to speed up the network training and reduce the number of variables of the original MS data. The data was normalized prior training and testing a network to define the area the neural network to be trained in, accelerate the training rate, and reduce the range the parameters to be selected in. The one-out-of-n method was used to split the data set of p samples into a training set of size p−1 and a test set of size 1. With the improved method, the classification correctness for the five bacteria discussed in the present paper are 87.5, 69.2, 80, 92.3, and 92.8%, respectively.     

Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: a process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms

Near-infrared calibration models were developed for the determination of content uniformity of pharmaceutical tablets containing 29.4% drug load for two dosage strengths (X and Y). Both dosage strengths have a circular geometry and the only difference is the size and weight. Strength X samples weigh approximately 425 mg with a diameter of 12 mm while strength Y samples, weigh approximately 1700 mg with a diameter of 20 mm. Data used in this study were acquired from five NIR instruments manufactured by two different vendors. One of these spectrometers is a dispersive-based NIR system while the other four were Fourier transform (FT) based. The transferability of the optimized partial least-squares (PLS) calibration models developed on the primary instrument (A) located in a research facility was evaluated using spectral data acquired from secondary instruments B, C, D and E. Instruments B and E were located in the same research facility as spectrometer A while instruments C and D were located in a production facility 35 miles away. The same set of tablet samples were used to acquire spectral data from all instruments. This scenario mimics the conventional pharmaceutical technology transfer from research and development to production. Direct cross-instrument prediction without standardization was performed between the primary and each secondary instrument to evaluate the robustness of the primary instrument calibration model. For the strength Y samples, this approach was successful for data acquired on instruments B, C, and D producing root mean square error of prediction (RMSEP) of 1.05, 1.05, and 1.22%, respectively. However for instrument E data, this approach was not successful producing an RMSEP value of 3.40%. A similar deterioration was observed for the strength X samples, with RMSEP values of 2.78, 5.54, 3.40, and 5.78% corresponding to spectral data acquired on instruments B, C, D, and E, respectively. To minimize the effect of instrument variability, calibration transfer techniques such as piecewise direct standardization (PDS) and wavelet hybrid direct standardization (WHDS) were used. The PDS approach, the RMSEP values for strength X samples were lowered to 1.22, 1.12, 1.19, and 1.08% for instruments B, C, D, and E, respectively. Similar improvements were obtained using the WHDS approach with RMSEP values of 1.36, 1.42, 1.36, and 0.98% corresponding to instruments B, C, D, and E, respectively.     

A comprehensive aerosol spray method for the rapid photocatalytic grid area analysis of semiconductor photocatalyst thin films

Indicator inks, previously shown to be capable of rapidly assessing photocatalytic activity via a novel photo-reductive mechanism, were simply applied via an aerosol spray onto commercially available pieces of Activ™ self-cleaning glass. Ink layers could be applied with high evenness of spread, with as little deviation as 5% upon UV-visible spectroscopic assessment of 25 equally distributed positions over a 10 cm × 10 cm glass cut. The inks were comprised of either a resazurin (Rz) or dichloroindophenol (DCIP) redox dye with a glycerol sacrificial electron donor in an aqueous hydroxyethyl cellulose (HEC) polymer media. The photo-reduction reaction under UVA light of a single spot was monitored by UV-vis spectroscopy and digital images attained from a flat-bed scanner in tandem for both inks. The photo-reduction of Rz ink underwent a two-step kinetic process, whereby the blue redox dye was initially reduced to a pink intermediate resorufin (Rf) and subsequently reduced to a bleached form of the dye. In contrast, a simple one-step kinetic process was observed for the reduction of the light blue redox dye DCIP to its bleached intermediates. Changes in red-green-blue colour extracted from digital images of the inks were inversely proportional to the changes seen at corresponding wavelengths via UV-visible absorption spectroscopy and wholly indicative of the reaction kinetics. The photocatalytic activity areas of cuts of Activ™ glass, 10 cm × 10 cm in size, were assessed using both Rz and DCIP indicator inks evenly sprayed over the films; firstly using UVA lamp light to activate the underlying Activ™ film (1.75 mW cm⁻²) and secondly under solar conditions (2.06 ± 0.14 mW cm⁻²). The photo-reduction reactions were monitored solely by flat-bed digital scanning. Red-green-blue values of a generated 14 × 14 grid (196 positions) that covered the entire area of each film image were extracted using a custom-built program entitled RGB Extractor(C). A homogenous degradation over the 196 positions analysed for both Rz (Red colour deviation = 19% UVA, 8% Solar; Green colour deviation = 17% UVA, 12% Solar) and DCIP (Red colour deviation = 22% UVA, 16% Solar) inks was seen in both UVA and solar experiments, demonstrating the consistency of the self-cleaning titania layer on Activ™. The method presented provides a good solution for the high-throughput photocatalytic screening of a number of homogenous photocatalytically active materials simultaneously or numerous positions on a single film; both useful in assessing the homogeneity of a film or determining the best combination of reaction components to produce the optimum performance photocatalytic film.