Coffee varietal differentiation based on near infrared spectroscopy

Near infrared spectroscopy (NIRS) was used to discriminate between arabica and robusta pure coffee varieties and blends of varied varietal composition. Direct orthogonal signal correction (DOSC) pre-processing method was applied on a set of 191 roasted coffee NIR spectra from both pure varieties and blends varying the final robusta content from 0 to 60% (w/w) in order to remove information unrelated to the actual varietal composition of samples. The corrected NIR spectra, as well as raw NIR spectra, were used to develop separate classification models using the potential functions method as class-modelling technique, exploring several options more or less restrictive according to the final number of considered categories. All constructed classification models were compared to evaluate their respective qualities and to show the suitability of applying DOSC method as pre-processing step for developing improved classification models for coffee varietal identification purposes.     

Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis

Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly for the calibration set, and 60 samples (15 for each variety) for the validation set, whereas 24 samples (6 for each variety) for the independent set. Partial least squares discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) were implemented for calibration models. Different input data matrices of LS-SVM were determined by latent variables (LVs) selected by explained variance, and EWs selected by x-loading weights, regression coefficients, modeling power and independent component analysis (ICA). Then the LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS-DA model, and the optimal LS-SVM model was achieved with EWs (4021, 4058, 4264, 4400, 4853, 5070 and 5273 cm⁻¹) selected by regression coefficients. The determination coefficient (R²), RMSEP and total recognition ratio with cutoff value ±0.1 in validation set were 1.000, 0.025 and 100%, respectively. The overall results indicted that the regression coefficients was an effective way for the selection of effective wavelengths. NIR spectroscopy combined with LS-SVM models had the capability to discriminate the varieties of fruit vinegars with high accuracy.     

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R²CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.     

Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection

This paper developed a rapid method using near infrared spectroscopy (NIRS) to differentiate two species of Cortex Phellodendri (CP), Cortex Phellodendri Chinensis (PCS) and Cortex Phellodendri Amurensis (PAR), and to predict quantitatively the content of berberine and total alkaloid content in all Cortex Phellodendri samples. Three alkaloids, berberine, jatrorrhizine and palmatine were analyzed simultaneously with a Thermo ODS Hypersil column by gradient elution with a new mobile phase under high-performance liquid chromatography-diode array detection (HPLC-DAD). Berberine content determined by HPLC-DAD was exploited as a critical parameter for successful discrimination between them. Multiplicative scatter correction (MSC), second derivative and Savitsky-Golay (S.G.) were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra as well as to enhance spectral features in order to give a better correlation with the results obtained by HPLC-DAD. With the use of principal component analysis (PCA), samples datasets were separated successfully into two different clusters corresponding to two species. Furthermore, a partial least squares (PLS) regression method was built on the correlation model. The results showed that the correlation coefficients of the prediction models were R = 0.996 for the berberine and R = 0.994 for total alkaloid content. The influences of water absorption bands present in the NIR spectra on the models were also investigated in order to explore the practicability of NIRS in routine use. The outcome showed that NIRS possibly acts as routine screening in the quality control of Chinese herbal medicine.     

Quantification of the pore size distribution (porosity profiles) in microfiltration membranes by SEM,TEM and computer image analysis

Transport properties of membranes are closely related to morphological properties like surface porosity and variation of their inner pore structure. Scanning electron microscopy (SEM) as well as transmission electron microscopy (TEM) are powerful tools to characterise the microscopical pore structure of membranes in a qualitative manner. In order to provide more quantitative data of surface and cross-sectional pores computer image analysis can be used. Parameters like ‘porous area fraction’ and ‘mean free path length’ have been selected to describe the pore distribution within porosity profiles in order to consider the effect that the pores within the cross-section are connected to each other.     

Components Analysis of Biochar Based on Near Infrared Spectroscopy Technology

This study aims to establish a rapid quantitative analysis method for biochar based on near infrared spectroscopy (NIRS) technology. Near infrared spectra of 163 samples in the 10000–3800 cm^–1 (1000–2632 nm) range were collected, and the contents of fixed carbon (FC), volatile matter (VM) and ash of samples were also analyzed. A partial least square (PLS) model for FC, VM and Ash was established after the model spectral ranges were optimized, the optimal factors were determined, and the raw spectra were pretreated by multiple scatter correction and second derivative (MSC + SD) method. Finally, the prediction performance of predictive model was evaluated. The results showed that the PLS model had a good prediction ability, and the predicted coefficient R²_p of actual values vs prediction values for FC, VM and ash were 0.9423, 0.9517 and 0.9265, respectively. Root mean square error of prediction (RMSEP) was 0.1074, 0.1201 and 0.1243, and ratios of prediction to deviation (RPD) were 3.51, 4.28 and 2.03, respectively. The PLS model had good accuracy and precision for both of FC and VM, and could be used as a quantitative method for FC and VM contents analysis. Nevertheless, PLS model need to improve the precision for Ash analysis according to RPD value. This method provides a fast and effective technical means for the quantitative analysis of biochar components.     

Quality control and discrimination of pericarpium citri reticulatae and pericarpium citri reticulatae viride based on high-performance liquid chromatographic fingerprints and multivariate statistical analysis

High-performance liquid chromatographic (HPLC) fingerprints of Pericarpium Citri Reticulatae (PCR) and Pericarpium Citri Reticulatae Viride (PCRV) were firstly measured for deliberately collected 39 authentic samples and 21 commercial samples. Both correlation coefficients of similarity for chromatograms and absolute peak areas of characteristic compounds were calculated for quantitative expression of the HPLC fingerprints. After principal component analysis (PCA) successfully distinguished the ‘mixed peels’ samples from authentic samples, partial least squares-linear discrimination analysis (PLS-LDA) was then effectively applied to class separation between authentic PCR and PCRV. Furthermore, the unequivocally determined compounds, hesperidin, nobiletin and tangeretin, were screened out by loadings plots of PCA and PLS-LDA. The results indicated that they could be used as chemical markers for discrimination among different groups of samples. The proposed method shows an efficient strategy for quality control of PCR and PCRV, which cannot only distinguish the ‘mixed peels’ but also discriminate authentic PCR and PCRV. This method has potential perspective for quality control of traditional Chinese medicine (TCM).     

Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis

The secondary metabolites of different Ephedra plants are various. Therefore, the discrimination of different Ephedra plants is significant. An objective, easy-to-use, rapid and pollution-free approach is proposed for discriminating Ephedra plants of different species, habitats and picking times on the basis of diffuse reflectance Fourier transform near infrared spectroscopy (FT-NIRS) measurements and multivariate analysis. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from 37 pulverized samples of Ephedra plants put in glass vials in the near infrared (NIR) region between 10 000 and 4000 cm⁻¹, averaging 64 scans per spectrum at a resolution of 4 cm⁻¹. After spectra processing and data pre-processing, spectral data were analyzed respectively with three multivariate analysis techniques: discriminant analysis (DA), self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The proposed method could distinguish not only the Ephedra plants of three species and two habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The performance indexes of the DA model were 84.2-91.9% and the prediction accuracies of both the SOM and the BP-ANN models reached 93.3-100.0%.     

Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apple fruit

Non-destructive, rapid, instrumental tools in fruit production are required for predicting the optimum harvest window and monitoring fruit quality during shelf life. The degree of chlorophyll degradation is a sensitive indicator for fruit maturation and ageing. Adequate indices for chlorophyll prediction by means of non-destructive spectral analysis have been studied in the fields of photosynthesis research and remote sensing developments. However, an evaluation of these indices and multivariate linear regression models does not exist so far for predicting the fruit chlorophyll content.Spectral transmittance recordings in the visible wavelength range were carried out on apple fruit Malus domestica Borkh. ‘Elstar’ (n=99) and ‘Jonagold’ (n=117). The fruit chlorophyll a content of ‘Elstar’ apples was measured wet-chemically and predicted by means of the specific indices: NDVI, Tr₆₉₈/Tr₇₆₀, red-edge, TrII on Tr′(λ), TrII, and RVSI with the correlation coefficients of determination R²=0.84, 0.81, 0.75, 0.81, 0.15, and 0.67, respectively. Partial least-squares (PLS) calibration models were built using calibrated spectra (630-730 nm), first derivative of spectra and second derivative of spectra yielding multivariate correlation coefficients of determination R²=0.81, 0.86, and 0.92, respectively. Similar results were found for ‘Jonagold’ apple fruit. Linear regression of indices and PLS calibration models were empirically tested on the chlorophyll a content of apple fruit measuring the same cultivars grown in a different seasons and growing locations. In the case of ‘Elstar’ apple fruit, correlation coefficients of fruit chlorophyll a content analysed wet-chemically as well as fruit maturation measured as calendar weeks were similar to those in the calibration experiment. Application of linear regression equations of indices and PLS models on spectra of ‘Jonagold’ apple fruit led to less accurate results for those methods, which use wavelengths above 720 nm as indicative range.     

Fingerprinting alterations of secondary metabolites of tangerine peels during growth by HPLC-DAD and chemometric methods

Tangerine peels are herbal materials of two coupled traditional Chinese medicines, Pericarpium Citri Reticulatae (PCR) and Pericarpium Citri Reticulatae Viride (PCRV). In this paper, high-performance liquid chromatographic fingerprints of tangerine peels during growth were firstly measured for deliberately collected 34 samples from three species (Citrus reticulata ‘Chachi’, Citrus reticulata ‘Dahongpao’ and Citrus erythrosa Tanaka). After sixteen characteristic components which have similar change trends in the grown process were screened out with the help of heuristic evolving latent projection (HELP) method, score plots of principal component analysis (PCA) successfully presented the grown footprints of tangerine peels. It implied that July might be the best harvest time for PCRV, November and December were better for PCR. Furthermore, hesperidin, nobiletin and tangeretin were screened as chemical markers by loadings of PCA. The HPLC-HELP-PCA strategy has shown its potential in optimization of harvest time and chemical markers’ screening, which will have wide perspective in the analysis of “coupled TCMs”.     

A visual strip sensor for determination of iron

A visual strip has been developed for sensing iron in different aqueous samples like natural water and fruit juices. The sensor has been synthesized by UV-radiation induced graft polymerization of acrylamide monomer in microporous poly(propylene) base. For physical immobilization of iron selective reagent, the in situ polymerization of acrylamide has been carried out in the presence of 1,10-phenanthroline. The loaded strip on interaction with Fe(II) in aqueous solution turned into orange red color and the intensity of the color was found to be directly proportional to the amount of Fe(II) in the aqueous sample. The minimal sensor response with naked eye was found for 50 ng mL⁻¹ of Fe in 15 min of interaction. However, as low as 20 ng mL⁻¹ Fe could be quantified using a spectrophotometer. The detection limit calculated using the 3s/S criteria, where ‘s’ is the standard deviation of the absorbance of blank reagent loaded strip and ‘S’ is the slope of the linear calibration plot, was 1.0 ng mL⁻¹. The strip was applied to measure Fe in a variety of samples such as ground water and fruit juices.     

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.     

First X-ray structural characterisation of host-guest interactions in tetra-tetrazole macrocycles

The syntheses of tetra-tetrazole macrocycles, containing two 1,3-bis(tetrazole)benzene units linked by a variety of n-alkyl (n=3, 5, 7 or 9 carbon atoms) chain lengths, are described. The crystal structures of two 1,3-bis(tetrazole)benzenes containing pendant bromoalkyl chains (n=3 or 5) are reported. A tetra-tetrazole macrocycle has also been structurally characterised and contains an unexpected ‘host-guest’ interaction through binding of a chloroform solvent molecule. The resulting deviation of the macrocycle from planarity results from a combination of the ‘host-guest’ interaction and strong intermolecular interactions between adjacent tetrazole and phenylene rings.     

Synthesis of (tetrahydrofuran-2-yl)acetates based on a ‘cyclization/hydrogenation/enzymatic kinetic resolution’ strategy

A variety of (tetrahydrofuran-2-yl)acetates and (pyrrolidin-2-yl)acetates have been prepared by hydrogenation of 2-alkylidenetetrahydrofurans and 2-alkylidenepyrrolidines, which are readily available by cyclization reactions of 1,3-dicarbonyl dianions (‘free dianions’) or 1,3-bis-silyl enol ethers (‘masked dianions’) with 1,2-dielectrophiles. The enzymatic kinetic resolution of (tetrahydrofuran-2-yl)acetates with recombinant esterase Est56 proceeded with excellent enantioselectivities (E>100).     

Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy

The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data.     

近红外光谱法结合化学计量学方法用于茶油真伪鉴别分析

采用近红外光谱透反射模式结合化学计量学方法对纯茶油进行真伪鉴别.收集并扫描了163个样品(合格97个,不合格66个),对样本进行光谱数据预处理优化及有效波段筛选.在5750～6000 cm-1波段处,光谱经过平滑,一阶导数以及自归一化后,采用无监督学习算法即主成分分析法(Principal component anal...     

Pre-analytical method for NMR-based grape metabolic fingerprinting and chemometrics

Although metabolomics aims at profiling all the metabolites in organisms, data quality is quite dependent on the pre-analytical methods employed. In order to evaluate current methods, different pre-analytical methods were compared and used for the metabolic profiling of grapevine as a model plant. Five grape cultivars from Portugal in combination with chemometrics were analyzed in this study. A common extraction method with deuterated water and methanol was found effective in the case of amino acids, organic acids, and sugars. For secondary metabolites like phenolics, solid phase extraction with C-18 cartridges showed good results. Principal component analysis, in combination with NMR spectroscopy, was applied and showed clear distinction among the cultivars. Primary metabolites such as choline, sucrose, and leucine were found discriminating for ‘Alvarinho’, while elevated levels of alanine, valine, and acetate were found in ‘Arinto’ (white varieties). Among the red cultivars, higher signals for citrate and GABA in ‘Touriga Nacional’, succinate and fumarate in ‘Aragonês’, and malate, ascorbate, fructose and glucose in ‘Trincadeira’, were observed. Based on the phenolic profile, ‘Arinto’ was found with higher levels of phenolics as compared to ‘Alvarinho’. ‘Trincadeira’ showed lowest phenolics content while higher levels of flavonoids and phenylpropanoids were found in ‘Aragonês’ and ‘Touriga Nacional’, respectively. It is shown that the metabolite composition of the extract is highly affected by the extraction procedure and this consideration has to be taken in account for metabolomics studies.     

Measurement of lactate in whole human blood with near-infrared transmission spectroscopy

We have evaluated the potential of near-infrared spectroscopy (NIRS) as a technique for rapid analysis of lactate in whole blood. To test the NIRS technique, a comparison was made with a standard clinical method using whole blood samples taken from five exercising human subjects at three different stage of exercise. To expand lactate concentration within the physiological range, standard additions method was used to generate 45 unique data points. Spectra were collected over the 2050-2400 nm spectral range with a 1 mm optical path length quartz cell. Reference lactate concentrations in the samples were determined by enzymatic measurements. Estimates and calibration of the lactate concentration with NIRS was made using partial least squares (PLS) regression analysis and leave-N-out cross validation on second derivative spectra. Separate calibrations were determined from each of the subject samples and cumulative PRESS was used to determine the number of PLS factors in the final model. The results from the PLS model presented are generated from the five individual calibration coefficient vectors and provided a correlation coefficient of 0.978 and a standard error of cross validation of 0.65 mmol l⁻¹ between the enzymatic assay and the NIRS technique. To study the parameters that impact the spectra baseline and the correlation between the calculated model and the data, referenced measurements of lactate against baseline spectrum were made for each individual. A correlation coefficient of 0.992 and a standard error of cross validation of 0.21 mmol l⁻¹ were found. The results suggest that NIRS may provide a valuable tool to assess physiological status for both research and clinical needs.     

Microimaging FT-IR of head and neck tumors. V. Odontogenic cystic lesions

This study has been undertaken to investigate spectral features of cystic lesions of the jaw with the aim to understand their tumoral progression and to evidence initial signals of neoplastic changes.Three important groups (according to the World Health Organization classification) representing inflammatory (radicular) and developmental (orthokeratinized odontogenic, OOC) cysts as well as keratocystic odontogenic tumor (KCOT) of the jaw have been studied by microimaging infrared spectroscopy. From the spectral data analysed with the multivariate pattern recognition procedure, representative spectra were isolated and used to build correlation maps to localize the biochemical components in the tissue.The procedure enabled to better understand spectral features of these classes of cysts and to discriminate tumoral from non-tumoral spectra through the analysis of ‘vibrational markers’. In KCOT, the correlation of calcium derivatives (in metaplastic or displastic bones) with the tumor has been highlighted, too. The distribution of various biochemical components in the tissues, achieved through correlation maps of representative spectra, resulted in satisfactory agreement with the histopathological analysis. ‘Spectral isolation’ of micro tumorigenic zones in some normal OOC cysts, demonstrated the potentiality of infrared spectroscopy to evidence the early stage of a hidden lesion.