Characterisation of Moroccan unifloral honeys using multivariate analysis

Physicochemical properties of 39 samples of Moroccan honeys were analysed; 30 parameters were measured, including water content, pH, acidity (free and lactonic), HMF, diastase activity, electrical conductivity, mineral and sugar content, and colour. In addition, the characterisation of the five unifloral honeys (Eucalyptus sp., Citrus sp., Lythrum sp., Umbelliferae and honeydew) by cluster (CA), principal component analysis (PCA), stepwise discriminant analysis (SDA) and multiLayer perceptrons (MLP) were carried out. The PCA showed that the cumulative variance was approximately 44% for the first 2 PCs. About 97% of the samples were correctly classified by using the SDA and MLP, the best results being obtained for the eucalyptus, citrus, Umbelliferae and honeydew honeys (100% correct).     

Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef

Chemometric MID-FTIR methods were developed to detect and quantify the adulteration of mince meat with horse meat, fat beef trimmings, and textured soy protein. Also, a SIMCA (Soft Independent Modeling Class Analogy) method was developed to discriminate between adulterated and unadulterated samples. Pure mince meat and adulterants (horse meat, fat beef trimmings and textured soy protein) were characterized based upon their protein, fat, water and ash content. In order to build the calibration models for each adulterant, mixtures of mince meat and adulterant were prepared in the range 2–90% (w/w). Chemometric analyses were obtained for each adulterant using multivariate analysis. A Partial Least Square (PLS) algorithm was tested to model each system (mince meat + adulterant) and the chemical composition of the mixture. The results showed that the infrared spectra of the samples were sensitive to their chemical composition. Good correlations between absorbance in the MID-FTIR and the percentage of adulteration were obtained in the region 1800–900 cm^− 1. Values of R² greater than 0.99, standard errors of calibration (SEC) in the range to 0.0001–1.278 and standard errors of prediction (SEP estimated) between 0.001 and 1.391 for the adulterant and chemical parameters were obtained. The SIMCA model showed 100% classification of adulterated meat samples from unadulterated ones. Chemometric MID-FTIR models represent an attractive option for meat quality screening without sample pretreatments which can identify the adulterant and quantify the percentage of adulteration and the chemical composition of the sample.     

Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder

The authentication of food products is critically important in a global economy in public-health and economic terms. The specific aims of this study were to evaluate the application of full-spectrum and NIR spectroscopy and to evaluate the adoption of PLS and LS-SVM models to accomplish a rapid and non-invasive quantification of the two common adulterants, flour and mungbean powder, in Spirulina powder. The results showed that, using all treatment sets, only the LS-SVM models were adequate in predicting either adulterant under both full spectra and NIR spectra. The use of NIR spectra would allow detection of adulterants even when masked by food dyes. Design value analysis indicated that the benefits per unit cost of applying the NIR spectra to quantify adulterants in Spirulina powder significantly exceeded that of using full spectra, and that the value of employing the LS-SVM models under NIR spectra exceeded that of using the PLS models.     

Fourier transform infrared spectroscopy and multivariate analysis for the detection and quantification of different milk species

The authenticity of milk and milk products is important and has extended health, cultural, and financial implications. Current analytical methods for the detection of milk adulteration are slow, laborious, and therefore impractical for use in routine milk screening by the dairy industry. Fourier transform infrared (FT-IR) spectroscopy is a rapid biochemical fingerprinting technique that could be used to reduce this sample analysis period significantly. To test this hypothesis we investigated 3 types of milk: cow, goat, and sheep milk. From these, 4 mixtures were prepared. The first 3 were binary mixtures of sheep and cow milk, goat and cow milk, or sheep and goat milk; in all mixtures the mixtures contained between 0 and 100% of each milk in increments of 5%. The fourth combination was a tertiary mixture containing sheep, cow, and goat milk also in increments of 5%. Analysis by FT-IR spectroscopy in combination with multivariate statistical methods, including partial least squares (PLS) regression and nonlinear kernel partial least squares (KPLS) regression, were used for multivariate calibration to quantify the different levels of adulterated milk. The FT-IR spectra showed a reasonably good predictive value for the binary mixtures, with an error level of 6.5 to 8% when analyzed using PLS. The results improved and excellent predictions were achieved (only 4-6% error) when KPLS was employed. Excellent predictions were achieved by both PLS and KPLS with errors of 3.4 to 4.9% and 3.9 to 6.4%, respectively, when the tertiary mixtures were analyzed. We believe that these results show that FT-IR spectroscopy has excellent potential for use in the dairy industry as a rapid method of detection and quantification in milk adulteration.     

Differentiation of blossom and honeydew honeys using multivariate analysis on the physicochemical parameters and sugar composition

Multivariate analysis was applied on physicochemical parameters (moisture, water activity, electric conductivity, colour, hydroxymethyl furfural, acidity, pH, proline, diastase and invertase), sugar composition (fructose, glucose, sucrose, maltose, isomaltose, trehalose, turanose and melezitose) and palinological parameters determined in blossom and suspected honeydew honeys in order to differentiate them. The majority of the physicochemical, sugar composition and palinological parameters evaluated presented significant differences in the mean values between the suspected honeydew and blossom honeys, with the exception of moisture, water activity, diastase, fructose and maltose. Blossom honey samples tend to differentiate from the suspected honeydew honeys after applying factor analysis on the physicochemical parameters and sugar composition. Stepwise linear discriminant analysis allows the correct classification of all blossom honeys, and only one honeydew honey was erroneously included as blossom honey. So, the use of multivariate analysis on physicochemical parameters and sugar composition can be a useful tool to differentiate these types of honeys.     

Characterization of monofloral honeys with multivariate analysis of their chemical profile and antioxidant activity

Abstract: Various bioactive chemical constituents were quantified for 21 honey samples obtained at Rio de Janeiro and Minas Gerais, Brazil. To evaluate their antioxidant activity, 3 different methods were used: the ferric reducing antioxidant power, the 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) radical‐scavenging activity, and the 2,2′‐azinobis (3‐ethylbenzothiazolin)‐6‐sulfonate (ABTS) assays. Correlations between the parameters were statistically significant (?0.6684 ≤ r ≤?0.8410, P < 0.05). Principal component analysis showed that honey samples from the same floral origins had more similar profiles, which made it possible to group the eucalyptus, morrão de candeia, and cambara honey samples in 3 distinct areas, while cluster analysis could separate the artificial honey from the floral honeys. Practical Application: This research might aid in the discrimination of honey floral origin, by using simple analytical methods in association with multivariate analysis, which could also show a great difference among floral honeys and artificial honey, indicating a possible way to help with the identification of artificial honeys.     

HRMAS-NMR spectroscopy and multivariate analysis meat characterisation

(1)H-high resolution magic angle spinning-nuclear magnetic resonance spectroscopy was employed to gain the metabolic profile of longissimus dorsi and semitendinosus muscles of four different breeds: Chianina, Holstein Friesian, Maremmana and Buffalo. Principal component analysis, partial least squares projection to latent structure - discriminant analysis and orthogonal partial least squares projection to latent structure - discriminant analysis were used to build models capable of discriminating the muscle type according to the breed. Data analysis led to an excellent classification for Buffalo and Chianina, while for Holstein Friesian the separation was lower. In the case of Maremmana the use of intelligent bucketing was necessary due to some resonances shifting allowed improvement of the discrimination ability. Finally, by using the Variable Importance in Projection values the metabolites relevant for the classification were identified.     

HRMAS-NMR spectroscopy and multivariate analysis meat characterisation

¹H-high resolution magic angle spinning-nuclear magnetic resonance spectroscopy was employed to gain the metabolic profile of longissimus dorsi and semitendinosus muscles of four different breeds: Chianina, Holstein Friesian, Maremmana and Buffalo.Principal component analysis, partial least squares projection to latent structure – discriminant analysis and orthogonal partial least squares projection to latent structure – discriminant analysis were used to build models capable of discriminating the muscle type according to the breed. Data analysis led to an excellent classification for Buffalo and Chianina, while for Holstein Friesian the separation was lower. In the case of Maremmana the use of intelligent bucketing was necessary due to some resonances shifting allowed improvement of the discrimination ability. Finally, by using the Variable Importance in Projection values the metabolites relevant for the classification were identified.     

Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef

This study aimed to evaluate the potential of near infrared spectroscopy (NIRS) as a fast and non-destructive tool for detecting and quantifying different adulterants in fresh and frozen-thawed minced beef. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R_P²) of 0.96, 0.94 and 0.95 with standard error of prediction (SEP) of 5.39, 5.12 and 2.08% (w/w) for minced beef adulterated by pork, fat trimming and offal, respectively. The performance of the developed models declined when the samples were in a frozen-thawed condition, yielding R_P² of 0.93, 0.82 and 0.95 with simultaneous augments in the SEP of 7.11, 9.10 and 2.38% (w/w), respectively. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and non-linear regression models (logistic, probit and exponential regression) were developed at the most relevant wavelengths to discriminate between the pure (unadulterated) and adulterated minced beef. The classification accuracy resulting from both types of models was quite high, especially the LDA, PLS-DA and exponential regression models which yielded 100% accuracy. The current study demonstrated that the VIS-NIR spectroscopy can be utilized securely to detect and quantify the amount of adulterants added to the minced beef with acceptable precision and accuracy.     

Quality parameters and antioxidant and antibacterial properties of some Mexican honeys

A total of 14 Mexican honeys were screened for quality parameters including color, moisture, proline, and acidity. Antioxidant properties of complete honey and its methanolic extracts were evaluated by the DPPH, ABTS, and FRAP assays. In addition, the antimicrobial activity of complete honeys against Bacillus cereus ATCC 10876, Listeria monocytogenes Scott A, Salmonella Typhimurium ATCC 14028, and Sthapylococcus aureus ATCC 6538 was determined. Most of honeys analyzed showed values within quality parameters established by the Codex Alimentarius Commission in 2001. Eucalyptus flower honey and orange blossom honey showed the highest phenolic contents and antioxidant capacity. Bell flower, orange blossom, and eucalyptus flower honeys inhibited the growth of the 4 evaluated microorganisms. The remaining honeys affected at least 1 of the estimated growth parameters (increased lag phase, decreased growth rate, and/or maximum population density). Microorganism sensitivity to the antimicrobial activity of honeys followed the order B. cereus > L. monocytogenes > Salmonella Typhimurium > S. aureus. The monofloral honey samples from orange blossoms, and eucalyptus flowers demonstrated to be good sources of antioxidant and antimicrobial compounds. All the Mexican honey samples examined proved to be good sources of antioxidants and antimicrobial agents that might serve to maintain health and protect against several diseases. PRACTICAL APPLICATION: The results of the study showed that Mexican honeys display good quality parameters and antioxidant and antimicrobial activities. Mexican honey can be used as an additive in the food industry to increase the nutraceutical value of products.     

Application of synchronous fluorescence spectroscopy with multivariate data analysis for determination of butter adulteration

The potential of fluorescence spectroscopy for detection of butter adulteration with palm and coconut oils was investigated. Synchronous fluorescence spectra were collected in the range of 240–700 nm with wavelength intervals (?λ) of 10, 30, 60 and 80 nm. The applied technique was used to detect the addition of palm and coconut oils to butter, and the lowest limit of detection for adulteration (LOD – 5.5%) was observed after applying the wavelength interval of 60 nm. The multiple linear regression (MLR) models were used to calculate the level of adulteration with the lowest root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV) of 3.8 and 3.9%, respectively, for the measurements acquired at the wavelength interval of 60 nm.     

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.     

Major flavonoids of Argentinean honeys. Optimisation of the extraction method and analysis of their content in relationship to the geographical source of honeys

In the present research we optimised an extraction procedure for the flavonoid aglycones: myricetin, quercetin and luteolin from honeys (as natural biological matrices), based on Amberlite XAD-4 resin followed by HPLC quantification. In addition, honeys from three geographical regions of Argentina were analysed with regard to the contents of these flavonoids. The extraction procedure was optimised for XAD-4 resin considering: resin/honey ratio, elution volume to desorb flavonoids and colour intensity of honeys. Differences in flavonoid recoveries were observed depending on the colour intensity. The flavonoid aglycones contents, in accordance with differences in geochemical characteristics and typical vegetation, varied with the geographical origin of honeys. The results obtained allowed us to consider these three flavonoids as chemical markers for the phytogeographical origin of honeys. In the case of monofloral honeys, the contribution of each one of the flavonoids was associated with the presence of a dominant pollen kind in these samples.     

Classification of Chinese honeys according to their floral origin by near infrared spectroscopy

The feasibility of near infrared (NIR) spectroscopy and multivariate analysis as tools to classify Chinese honey samples according to their different floral origins was explored. Five kinds of honey, namely, acacia, linden, rape, vitex and jujube, were analysed using a NIR spectrophotometer with a fibre optic probe. Classification models based on the NIR spectra were developed using Mahalanobis-distance discriminant analysis (MD-DA) and a back propagation artificial neural network (BP-ANN). By the MD-DA model, total correct classification rates of 87.4% and 85.3% were observed for the calibration and validation samples, respectively, while the ANN model resulted in total correct classification rates of 90.9% and 89.3% for the calibration and validation sets, respectively. By ANN, the respective correct classification rates of linden, acacia, vitex, rape and jujube were 97.1%, 94.3%, 80.0%, 97.1%, and 85.7% in calibration, and 100%, 93.3%, 80.0%, 100%, and 73.3% in validation. The results indicated that NIR combined with a classification technique could be a suitable technology for the classification of Chinese honeys from different botanical origins.     

Differentiation of saffron from four countries by mid-infrared spectroscopy and multivariate analysis

Mid-infrared spectroscopy combined with multivariate analysis has been applied for the discrimination of 250 saffron samples from Greece (40 samples), Iran (87 samples), Italy (60 samples) and Spain (63 samples). The infrared spectra of saffron filament samples and their organic extracts were recorded. Principal component analysis was applied to different spectral regions. Then the principal components were used as variables in discriminant analysis. The best discriminatory approach was achieved in the spectral region 2000–700 cm⁻¹. The spectral region responsible for the differentiation of Italian samples is in the region of carbonyl group around 1746 cm⁻¹. The spectral region at around 1600 cm⁻¹ and the band at 1670 cm⁻¹ are responsible for the differentiation of samples from the remaining countries. From the original grouped cases, 93.6% were correctly classified. The correct classification rates for saffron samples from Greece, Iran, Italy and Spain were 90.0, 89.5, 96.7 and 98.4%, respectively. The combination of infrared spectroscopic technique with multivariate analysis is a rapid and ambient method to discriminate saffron samples in terms of geographical origin.     

Application of regression analysis as a method to determine total ash content in some selected nectar honeys

We developed a new, indirect method for the determination of mineral substances, expressed as total ash content in bee honey varieities, based on a multiple regression model. This time-saving and effective method could serve as a new procedure in routine quality control plans of bee honey varieties.     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.