Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

The combination of UV, visible (Vis), near-infrared (NIR) and mid-infrared (MIR) spectroscopy with multivariate data analysis was explored as a tool to classify commercial Sauvignon Blanc (Vitis vinifera L., var. Sauvignon Blanc) wines from Australia and New Zealand. Wines (n = 64) were analysed in transmission using UV, Vis, NIR and MIR regions of the electromagnetic spectrum. Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) were used to classify Sauvignon Blanc wines according to their geographical origin using full cross validation (leave-one-out) as a validation method. Overall PLS-DA models correctly classified 86% of the wines from New Zealand and 73%, 86% and 93% of the Australian wines using NIR, MIR and the concatenation of NIR and MIR, respectively. Misclassified Australian wines were those sourced from the Adelaide Hills of South Australia. These results demonstrate the potential of combining spectroscopy with chemometrics data analysis techniques as a rapid method to classify Sauvignon Blanc wines according to their geographical origin.     

Utilisation of mid-infrared spectroscopy for determination of the geographic origin of Gruyère PDO and L’Etivaz PDO Swiss cheeses

The potential of mid-infrared spectroscopy (MIR), using an attenuated total reflectance (ATR) cell, was evaluated for the authentication of 25 Gruyère PDO and L’Etivaz PDO cheeses produced at different altitudes in Switzerland. In order to test the ability of MIR to authenticate the investigated cheeses, chemometric tools, such as principal component analysis (PCA) and factorial discriminant analysis (FDA), were applied to the three spectral regions of the MIR (e.g. 3000–2800 cm⁻¹, 1700–1500 cm⁻¹, and 1500–900 cm⁻¹). By applying the FDA to the first 10 principal components (PCs) of the PCA applied to each spectral regions, the best rate of correct classification was obtained in the 3000–2800 cm⁻¹ and 1500–900 cm⁻¹ spectral regions, since 90.5% and 90.9% were achieved, respectively. It can be concluded that these two spectral regions could be considered as valuable tools for the determination of the geographical origin of the investigated cheeses.     

Determination of Calcium Content in Powdered Milk Using Near and Mid-Infrared Spectroscopy with Variable Selection and Chemometrics

Near infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were evaluated to determine calcium content in powdered milk. A hybrid spectral variable selection algorithm combined with uninformation variable elimination (UVE) and successive projections algorithm (SPA) selected 11 NIR and 15 MIR variables from full 2,756 NIR and 3,727 MIR variables, respectively. Predicted results of least-squares support vector machine models for the samples in the prediction set show that the 15 MIR variables obtained much better results (0.930 for coefficient of determination (r ²), 3.703 for residual predictive deviation (RPD), 30.162 for root mean square error of prediction set (RMSEP) and 5.22% for relative errors of prediction (RSEP)) than 11 NIR variables did (0.636 for r ², 1.587 for RPD, 78.815 for RMSEP, and 13.40% for RSEP). The overall results indicate that MIR spectroscopy could be applied as a precision and rapid method to determine calcium content in powdered milk. The good performance shows a potential application using UVE-SPA to select NIR and MIR effective variables.     

Application of the MIR for the determination of some chemical parameters in European Emmental cheeses produced during summer

This study examines the feasibility of using the mid infrared (MIR) spectroscopy for the determination of some parameters in European Emmental cheeses produced in summer from different geographic origins. A total of 72 Emmental cheeses (4 samples from Finland, 6 samples from Germany, 8 samples from Austria, 27 samples from France and 27 samples from Switzerland) were investigated. Total nitrogen (TN), water soluble nitrogen (WSN), non protein nitrogen (NPN), sodium chloride (NaCl) and pH were analysed by the reference methods. The MIR-transmission of the investigated cheeses was measured by a Nicolet Magna 750 IR spectrophotometer in a measurement range between 4000 and 400 cm⁻¹. The best results for WSN (R ²=0.80; ratio of standard deviation to root mean square error of prediction (RPD) =2.22), NPN (R ²=0.71, RPD=1.85), pH (R ²=0.56, RPD=1.50), NaCl (R ²=0.47; RPD=1.37) and TN (R ²=0.33; RPD=1.11) were obtained when the spectra were subjected to the first derivation and smoothing after being subjected to maximum normalisation. It can be concluded that the MIR-transmission spectroscopy could be considered as an alternative technique for the determination of NPN and WSN of Emmental cheeses originating from different European countries. The NaCl, pH and TN can also be estimated, but with much lower precision.     

Investigating the impact of botanical origin and harvesting period on carbon stable isotope ratio values (13C/12C) and different parameter analysis of Greek unifloral honeys: A chemometric approach for correct botanical discrimination

The aim of this study was to investigate the impact of botanical origin and harvesting period on carbon stable isotope ratio (¹³C/¹²C), colour intensity (CI), radical scavenging activity (%RSA), P and Sn content of Greek unifloral honeys. Thus, twenty‐four honey samples were collected during harvesting periods 2011–2012 and 2012–2013, from four different regions in Greece. ¹³C/¹²C ratios and minerals were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma optical–emission spectroscopy (ICP‐OES), respectively. CI and %RSA were measured using spectrophotometric assays. Results showed that only ¹³C/¹²C values and %RSA were affected by both botanical origin and harvesting period (P < 0.05). Applying then chemometric analyses to the collected data set, honeys were correctly classified according to honey type (correct classification rate 87.5% and 79.2% using the original and cross‐validation method, respectively). The use of different origin parameters has the potential to aid in honey authentication.     

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n = 121) and Riesling (n = 91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classification of juice samples of different varieties.     

A comparison and joint use of NIR and MIR spectroscopic methods for the determination of some parameters in European Emmental cheese

The determination of winter cheese chemical properties, namely, fat, sodium chloride (NaCl), pH, non protein nitrogen (NPN), total nitrogen (TN) and water soluble nitrogen (WSN) was done using spectroscopic technologies with different wavelength zones. The Emmental cheeses provided from different European countries were studied. A total of 91 cheeses produced during the winter time in Austria (n=4), Finland (n=6), Germany (n=13), France (n=30) and Switzerland (n=38) were analysed by near infrared (NIR) and mid infrared (MIR) spectroscopies. The combination of these two spectral regions (sum of their spectra) was also studied. The partial least square (PLS) regression with the leave one-out cross validation technique was used to build up calibration models using data set designated as calibration set. These models were validated with another data set designated as validation set. The obtained results suggest the use of the NIR for the determination of fat and TN contents, and the MIR for NaCl and NPN contents as well as for the pH. Similar results were obtained for WSN using the two techniques together. The combined spectra of both NIR and MIR did improve the results, while providing comparable results to those obtained from either the NIR or MIR spectroscopy.     

Mid infrared spectroscopy and multivariate analysis: A tool to discriminate between organic and non-organic wines grown in Australia

The combination of mid infrared (MIR) spectroscopy and multivariate analysis was explored as a tool to classify commercial wines sourced from organic (ORG) and non-organic (NORG) production systems. Commercial ORG (n = 57) and NORG (n = 115) red and white wine samples from 13 growing regions in Australia were analysed using a MIR spectrophotometer. Discriminant models based on MIR spectra were developed using principal component analysis (PCA), discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Overall, the LDA models based on the PCA scores correctly classified on average, more than 75% of the wine samples while the DPLS models correctly classified more than 85% of the wines belonging to ORG and NORG production systems, respectively. These results showed that MIR combined with discriminant techniques might be a suitable method that can be easily implemented by the wine industry to classify wines produced under organic systems.     

Toxicity profile of commercially produced indigenous banana beer

Mycotoxins, together with endotoxins, represent important classes of naturally occurring contaminants in food products, posing significant health risks to consumers. The aim of this study is to investigate the occurrence of both Fusarium mycotoxins and endotoxins in commercially produced traditional banana beer. Two brands of commercially produced traditional banana beer were collected from a local retail market in Kigali, Rwanda. Beer samples were analysed for the presence of deoxynivalenol (DON), fumonisin B₁ and zearalenone (ZEA), using an enzyme-linked immuno-sorbent assay (ELISA) method. The quantification of bacterial endotoxin using Limulus amoeboecyte lysate (LAL) assay was also conducted. The contamination levels were 20 and 6.7?µg?kg^?1 for DON; 34 and 31.3?µg?kg^?1 for FB₁; 0.66 and 2.2?µg?kg^?1 for ZEA in brands A and B of the beers, respectively. Results indicate that the levels of Fusarium toxins and bacterial endotoxin reported in this study did not pose adverse human health effects as a result of drinking/consuming banana beer. However, exposure to low/sub-threshold doses or non-toxic levels of endotoxins magnifies the toxic effect of xenobiotic agents (e.g. fungal toxins) on liver and other target organs. Considering Fusarium toxins and/or endotoxin contamination levels in other agricultural commodities intended for human consumption, health risks might be high and the condition is aggravated when beer is contaminated by mixtures of the mycotoxins, as indicated in this study.     

Evaluation of elemental profile coupled to chemometrics to assess the geographical origin of Argentinean wines

Traceability of wines requires knowledge of their characteristics, which are associated with the geographical origin of grape, soil, water, climate as well as the winery techniques. The aim of this work was to classify wines and soil from three production areas of Argentina according to multielement data. The influence of the provenance soil on the wine element composition was also investigated.Eleven elements were determined in 31 wine samples and 137 soil samples from regions under study. Stepwise discriminant analysis allows us to correctly classify 100% of the wines analysed from the three regions using only seven parameters (K, Fe, Ca, Cr, Mg, Zn and Mn) and 92% correct classification for soils using seven variables (Ca, Cr, K, Fe, Cu, Zn and, Mg). Canonical analysis between soils and wines datasets affords a correlation coefficient of 0.85 (P-value < 0.001). Thus, almost 85% of variability observed amongst wines could be attributed to the soil in which the vines were cultivated.The analysis of elemental concentrations in the wines and soils, in combination with chemometrics, provides a powerful tool to verify the geographical origin of wines.     

Determining the geographic origin of Emmental cheeses produced during winter and summer using a technique based on the concatenation of MIR and fluorescence spectroscopic data

A technique that used multivariate data analysis to combine mid-infrared (MIR) spectroscopy with front-face fluorescence spectroscopy was used to discriminate between Emmental cheeses originating from different European countries: Austria (n=12), Finland (n=10), Germany (n=19), France (n=57), and Switzerland (n=65). In total, 163 Emmental cheeses produced in winter (n=91) and summer (n=72) periods were investigated. When Factorial Discriminant Analysis was applied to either the infrared or fluorescence spectral data the classifications were not satisfactory. Therefore, the first twenty principal components (PCs) of the PCA extracted from each data set (MIR and tryptophan fluorescence spectra) were pooled (concatenated) into a single matrix and analysed by Factorial Discriminant Analysis. Correct classifications were obtained for the samples for 89% of the calibration spectra and 76.7% of the validation spectra. The discrimination for cheeses from Finland was excellent, while Austrian, German, French and Swiss cheeses were also discriminated well although a few samples were misclassified. It was concluded that concatenation of the data from the two spectroscopic techniques is an efficient technique for authenticating Emmental cheeses independently of their manufacturing period.     

Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000–2800, 1700–1500 and 1500–900 cm⁻¹), the classification rate was not satisfactory. Therefore, the first principal component (PCs) scores (corresponding to 3, 10 and 10 for, respectively, the 3000–2800, 1700–1500 and 1500–900 cm⁻¹) of the principal component analysis (PCA) extracted from each of the data sets were pooled (concatenated) into a single matrix and analysed by FDA. Correct classification amounting to 71.7% was obtained. Finally, the same procedure was applied to the MIR and fluorescence data sets and 98% of milk samples were found to be correctly classified. Milk samples belonging to control and soybean groups were 100% correctly classified. Regarding milk samples originating from the scotch bean group, only 2 out of 33 samples were misclassified. It was concluded that concatenation of the data sets collected from the two spectroscopic techniques is an efficient tool for authenticating milk samples according to their feeding systems, regardless of the lactation stage.     

Characterisation of the phenolic and flavonoid fractions and antioxidant power of Italian honeys of different botanical origin

BACKGROUND: Twenty‐seven Italian honey samples of different floral origin were analysed for total phenolic and flavonoid contents by a spectrophotometric method and for antioxidant power and radical‐scavenging activity by the ferric‐reducing/antioxidant power (FRAP) and 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) assays respectively. In addition, the phenolic and flavonoid profiles were analysed using high‐performance liquid chromatography with UV detection (HPLC‐UV). RESULTS: The results of this study showed that honey contains copious amounts of phenolics and flavonoids. HPLC‐UV analysis showed a similar qualitative polyphenolic profile for all honey samples analysed. The main difference among samples was in the contribution of individual analytes, which was affected by floral origin. Total phenolic and flavonoid contents varied from 60.50 to 276.04 mg gallic acid equivalent kg^?1 and from 41.88 to 211.68 mg quercetin equivalent kg^?1 respectively. The antioxidant capacity was high and differed widely among samples. The FRAP value varied from 1.265 to 4.396 mmol Fe²⁺ kg^?1, while the radical‐scavenging activity expressed as DPPH‐IC₅₀ varied from 7.08 to 64.09 mg mL^?1. Correlations between the parameters analysed were found to be statistically significant (P < 0.05). CONCLUSION: The present study shows that honey contains high levels of phenolics and flavonoids and that the distribution of these compounds is influenced by the honey's floral origin. Copyright © 2009 Society of Chemical Industry     

Geographical discrimination of honeys through antioxidant capacity,mineral content and colour

The assessment of geographical origin of honey is economically important for producers and consumers as every region may present particular quality characteristics. In this study, honeys from the seven different regions of Buenos Aires province (Argentina) were characterised by their antioxidant capacity (DPPH, FRAP), total phenolic content (TPC), mineral composition, colour and ash. Honeys showed significant differences among their antioxidant capacity (DPPH), ash, colour and mineral content (P ≤ 0.05). Besides, a good antioxidant activity and low amounts of Cu and Zn (<1.0–1.5 and 0.7–1.8 mg kg^?1, respectively) were found in the samples. Significant Pearson's correlations (P ≤ 0.05) among the different parameters were found. Moreover, the linear discriminant analysis allowed the classification of honeys in their original groups with a prediction success of 98%. The present results suggest that honeys could be correctly classified by their geographical origin through their TPC, colour, ash and mineral concentrations.     

Physicochemical properties and pollen spectrum of imported and local samples of blossom honey from the Pakistani market

Fifteen samples of different geographical origin including Pakistan were analysed for density, ash, water content, electrical conductivity, total acidity, pH, total solid, hydroxylmethylfurfural (HMF), minerals and trace metals. The physicochemical parameters were found to be within acceptable ranges (specific gravity 1.40–1.46, ash 0.03–0.21%, moisture 15.6–19.2, total solid 78.7–81.4%, free acidity 23.55–58.52 meq kg^?1, conductivity 0.27–0.37 mS cm^?1, pH 3.29–4.05, viscosity 33.4–136.4 poise). However, the analysis of HMF showed that imported samples were either exposed to a high temperature during processing or were overage. Greater attention is required, therefore, in the analysis of HMF and in deciding the shelf life, particularly for the imported samples before marketing. Pollen analysis revealed that all the analysed samples were of a multifloral type. All the data were statistically tested using principal component analysis (PCA) with the aim of characterizing the honeys and identifying the most significant parameters in the analysed samples.     

Preliminary study on the use of near‐infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants

Near‐infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole‐wheat plant (Triticum aestivum L). Whole‐wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross‐validation (leave‐one‐out method). Coefficient of determination in calibration (R²_CAL) and the standard error of cross‐validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R²_CAL and SECV for N compared with fresh presentation (R²_CAL > 0.90, SECV < 0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried). Copyright © 2006 Society of Chemical Industry     

Geographical origin discrimination of wheat kernel and white flour using near-infrared reflectance spectroscopy fingerprinting coupled with chemometrics

Wheat kernel and flour with three genotypes across 4 years procured from three different geographical areas of China were analysed using near-infrared reflectance (NIR) spectroscopy coupled with chemometrics to better classify wheat according to the origin, production year and genotypes, respectively. For this purpose, principle component analysis-linear discriminant analysis and multi-way anova were applied to the NIR data. The best classification percentages were obtained for flour matrix both for geographical origin and production years with the correct percentages of 100% and 73%, respectively. For genotypes, wheat whole kernel showed better classification percentage (98.2%). All the samples were validated using external validation procedure and the obtained percentages were found satisfactory with the average prediction abilities of >85% in all regions indicating the suitability of the developed model. Multivariate anova showed that NIR fingerprints of wheat kernels and flours were significantly influenced by regions, years, genotypes and their interactions. In conclusion, white flour showed better performance in discriminating the geographical origin as compared to wheat whole kernel.     

Use of Fourier transform-infrared spectroscopy to predict spoilage bacteria on aerobically stored chicken breast fillets

In this research work we explored the potential of mid infrared (MIR) spectroscopy to determine spoilage microorganism on the surface of chicken breast fillets that were kept aerobically at 5 °C for 0, 1, 2, 3, 5 and 8 days, and at 15 °C for 0, 0.5, 1, 2, 3 and 5 days. It is shown that MIR spectroscopy (4000 – 1000 cm⁻¹ range) coupled with attenuated total reflectance (ATR) accessory can be used directly on the surface of meat samples to produce fingerprints. Culture dependent methods were used to determine total viable count (TVC), Pseudomonas, Enterobacteriaceae and Brochothrix thermosphacta on chicken breast fillets at each step of the 2 kinetics. In parallel, MIR spectra were recorded. For each kinetic, partial least square discriminant analysis (PLSDA) results showed 100% of good classifications for the six investigated storage times using 4 PLS factors. PLS regression was carried out to predict the microbial counts from the MIR spectral data. Using PLS model with four factors, good correlation (R² = 0.99) and very small root mean squares error of validation (between 0.01 and 0.97 log cfu/cm²) showed a strong correlation between MIR spectral data sets and the results obtained using traditional methods.     

The combination of stable isotope abundance ratios of H,C, N and S with Sr/Sr for geographical origin assignment of orange juices

Within the EU-project “Pure Juice” established stable isotope methods (δ²H, δ¹³C, δ¹⁵N) have been applied and improved in order to determine and verify the geographical origin of orange juices. In addition, new approaches employing analyses of δ³⁴S and ⁸⁷Sr/⁸⁶Sr have been developed and tested. Approximately 150 authentic orange juice samples from several regions in North- and South-America, Africa and Europe have been analysed. A discrimination of orange producing regions, based on the results which ultimately depend on geographical, climatic and lithological differences was successfully performed. Furthermore, we demonstrate that blending of single strength juice by adding concentrate can be revealed by comparing ⁸⁷Sr/⁸⁶Sr of soluble and insoluble components of the juices. We conclude that regional assignment of orange juice samples is most successful when single parameters are combined in a “multi-element approach”.     

Multi-element analysis for determining the geographical origin of mutton from different regions of China

Elemental fingerprints were investigated for their potential to classify mutton samples according to their geographical origin. The concentration of 25 element contents in 99 mutton samples from three pastoral regions and two agricultural regions of China were analysed by ICP-MS. Multivariate statistical analysis including principal component analysis (PCA) and linear discriminate analysis (LDA) were used for this purpose. Twenty-one elements (Be, Na, Al, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Ag, Sb, Ba, Tl, Pb, Th and U) in de-fatted mutton showed significant differences (p < 0.05). LDA gave an overall correct classification rate of 93.9% and cross-validation rate of 88.9%. Furthermore, mutton samples from agricultural regions and pastoral regions were differentiated with 100% accuracy. These results demonstrate the usefulness of multi-element fingerprints as indicators for authenticating the geographical origin of mutton in China.