Rapid Determination of Invert Cane Sugar Adulteration in Honey Using FTIR Spectroscopy and Multivariate Analysis

Sugar detection in adulterated honey using hyper-spectral imaging with stacking generalization method

This paper develops a new hybrid, automated, and non-invasive approach by combining hyper-spectral imaging, Savitzky-Golay (SG) Filter, Principal Components Analysis (PCA), Machine Learning (ML) classifiers/regressors, and stacking generalization methods to detect sugar in honey. First, the 32 different sugar concentration levels in honey were predicted using various ML regressors. Second, the six ranges of sugar were classified using various classifiers. Third, the 11 types of honey and 100% sugar were classified using classifiers. The stacking model (STM) obtained R2: 0.999, RMSE: 0.493 ml (v/v), RPD: 40.2, a 10-fold average R2: 0.996 and RMSE: 1.27 ml (v/v) for predicting 32 sugar concentrations. The STM achieved a Matthews Correlation Coefficient (MCC) of 99.7% and a Kappa score of 99.7%, a 10-fold average MCC of 98.9% and a Kappa score of 98.9% for classifying the six sugar ranges and 12 categories of honey types and a sugar.

A new honey adulteration detection approach using hyperspectral imaging and machine learning

This paper develops a new approach to fraud detection in honey. Specifically, we examine adulterating honey with sugar and use hyperspectral imaging and machine learning techniques to detect adulteration. The main contributions of this paper are introducing a new feature smoothing technique to conform to the classification model used to detect the adulterated samples and the perpetration of an adulterated honey data set using hyperspectral imaging, which has been made available online for the first time. Above $$95\%$$ 95 % accuracy was achieved for binary adulteration detection and multi-class classification between different adulterant concentrations. The system developed in this paper can be used to prevent honey fraud as a reliable, low cost, data-driven solution.

Classification of Honey Powder Composition by FTIR Spectroscopy Coupled with Chemometric Analysis

Fourier transform infrared spectroscopy (FTIR) in connection with chemometric analysis were used as a fast and direct approach to classify spray dried honey powder compositions in terms of honey content, the type of diluent (water or skim milk), and carrier (maltodextrin or skim milk powder) used for the preparation of feed solutions before spray drying. Eleven variants of honey powders containing different amounts of honey, the type of carrier, and the diluent were investigated and compared to pure honey and carrier materials. Chemometric discrimination of samples was achieved by principal component analysis (PCA), hierarchical clustering analysis (HCA), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-750 cm^-1) and the extended region (3600-750 cm^-1). As a result, it was noticed that the type of carrier is a significant factor during the classification of different samples of powdered multifloral honey. PCA divided the samples based on the type of carrier, and additionally among maltodextrin-honey powders it was possible to distinguish the type of diluent. The result obtained by PCA-LDA and PLS-DA scores yielded a clear separation between four classes of samples and showed a very good discrimination between the different honey powder with a 100.0% correct overall classification rate of the samples.

Influence of Functional Bio-Based Coatings including Chitin Nanofibrils or Polyphenols on Mechanical Properties of Paper Tissues

The paper tissue industry is a constantly evolving sector that supplies markets that require products with different specific properties. In order to meet the demand of functional properties, ensuring a green approach at the same time, research on bio-coatings has been very active in recent decades. The attention dedicated to research on functional properties has not been given to the study of the morphological and mechanical properties of the final products. This paper studied the effect of two representative bio-based coatings on paper tissue. Coatings based on chitin nanofibrils or polyphenols were sprayed on paper tissues to provide them, respectively, with antibacterial and antioxidant activity. The chemical structure of the obtained samples was preliminarily compared by ATR-FTIR before and after their application. Coatings were applied on paper tissues and, after drying, their homogeneity was investigated by ATR-FTIR on different surface areas. Antimicrobial and antioxidant properties were found for chitin nanofibrils- and polyphenols-treated paper tissues, respectively. The mechanical properties of treated and untreated paper tissues were studied, considering as a reference the same tissue paper sample treated only with water. Different mechanical tests were performed on tissues, including penetration, tensile, and tearing tests in two perpendicular directions, to consider the anisotropy of the produced tissues for industrial applications. The morphology of uncoated and coated paper tissues was analysed by field emission scanning electron microscopy. Results from mechanical properties evidenced a correlation between morphological and mechanical changes. The addition of polyphenols resulted in a reduction in mechanical resistance, while the addition of chitin enhanced this property. This study evidenced the different effects produced by two novel coatings on paper tissues for personal care in terms of properties and structure.

Determination of honey adulteration with beet sugar and corn syrup using infrared spectroscopy and genetic-algorithm-based multivariate calibration

BACKGROUND

Fourier transform infrared spectroscopy (FTIR) equipped with attenuated total reflectance accessory was used to determine honey adulteration. Adulterated honey samples were prepared by adding corn syrup, beet sugar and water as adulterants to the pure honey samples in various amounts. The spectra of adulterated and pure honey samples (n = 209) were recorded between 4000 and 600 cm^-1 wavenumber range.

RESULTS

Genetic-algorithm-based inverse least squares (GILS) and partial least squares (PLS) methods were used to determine honey content and amount of adulterants. Results indicated that the multivariate calibration generated with GILS could produce successful models with standard error of cross-validation in the range 0.97-2.52%, and standard error of prediction between 0.90 and 2.19% (% w/w) for all the components contained in the adulterated samples. Similar results were obtained with PLS, generating slightly larger standard error of cross-validation and standard error of prediction values.

CONCLUSION

The fact that the models were generated with several honey samples coming from various different botanical and geographical origins, quite successful results were obtained for the detection of adulterated honey samples with a simple Fourier transform infrared spectroscopy technique. Having a genetic algorithm for variable selection helped to build somewhat better models with GILS compared with PLS. © 2018 Society of Chemical Industry.

Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration

A rapid method for the detection and quantification of the adulteration of milk powder by the addition of whey was assessed by measuring glycomacropeptide protein using mid-infrared spectroscopy (MIR). Fluid milk samples were dried and then spiked with different concentrations of GMP and whey. Calibration models were developed using multivariate techniques, from spectral data. For the principal component analysis and discriminant analysis, excellent percentages of correct classification were achieved in accordance with the increase in the proportion of whey samples. For partial least squares regression analysis, the correlation coefficient (r) and root mean square error of prediction (RMSEP) in the best model were 0.9885 and 1.17, respectively. The rapid analysis, low cost monitoring and high throughput number of samples tested per unit time indicate that MIR spectroscopy may hold potential as a rapid and reliable method for detecting milk powder frauds using cheese whey.

Direct determination of sugar cane quality parameters by X-ray spectrometry and multivariate analysis

Current methods for quality control of sugar cane are performed in extracted juice using several methodologies, often requiring appreciable time and chemicals (eventually toxic), making the methods not green and expensive. The present study proposes the use of X-ray spectrometry together with chemometric methods as an innovative and alternative technique for determining sugar cane quality parameters, specifically sucrose concentration, POL, and fiber content. Measurements in stem, leaf, and juice were performed, and those applied directly in stem provided the best results. Prediction models for sugar cane stem determinations with a single 60 s irradiation using portable X-ray fluorescence equipment allows estimating the % sucrose, % fiber, and POL simultaneously. Average relative deviations in the prediction step of around 8% are acceptable if considering that field measurements were done. These results may indicate the best period to cut a particular crop as well as for evaluating the quality of sugar cane for the sugar and alcohol industries.

Chemical composition, characterization, and differentiation of honey botanical and geographical origins

Botanical and biographical origins of honey are an important issue in food quality and safety. This chapter focuses on use of chemical components to determine botanical and geographical origins of honey. The botanical and geographical origins of the nectar are related with the chemical composition of honey. Honey can originate from single and multiplant species. In general, the prices of honey from single plant species are much higher than those of common polyfloral honey because of consumer preferences. Single and multiple chemicals and components can well indicate the botanical and geographical origins of the honey. Marker chemicals and components include flavonoids, pollen, aroma compounds, oligosaccharides, trace elements, amino acids, and proteins. If multiple chemicals are used as markers, patterns of the chemicals are often used to detect the botanical and geographical origins of honey. Modern statistical software in combination with advanced analytical instrumentation provides high potential for the differentiation of the botanical and geographical origins of the honey.

Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance

The importance of honey adulteration detection has recently increased owing to the limited production levels in recent years and the relative high price of honey; therefore, this illegal practice has become more and more attractive to producers. Hence, the need has arisen for more effective analytical methods aiming at detecting honey adulteration. The present research presents an effective method to detect adulteration in honey falsified by intentional addition of different concentrations of commercial sugar syrups, using one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) coupled with multivariate statistical analysis. Sixty-three authentic and 63 adulterated honey samples were analyzed. To prepare adulterated honeys, seven different sugar syrups normally used for nutrition of bees were used. The best discriminant model was obtained by 1D spectra, and leave-one-out cross-validation showed a predictive capacity of 95.2%. 2D NMR also furnished acceptable results (cross-validation correct classification 90.5%), although the (1)H NMR sequence is preferable because it is the simplest and fastest NMR technique.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1,100 to 2,500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient (R(2)). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R(2) value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Novel spectropolarimeter employing fixed polarizers for the determination of optically active samples

A novel spectropolarimeter, based on modification of an ordinary, inexpensive, multiwavelength ultraviolet (UV)-visible-near-infrared (NIR) spectrophotometer, is described and applied to the determination of sucrose, sucrose inversion, and enantiomeric composition of solutions of (R)-(+)-limonene and (S)-(-)-limonene. The instrument has no moving parts, and optical rotation measurements are encoded as an apparent absorbance. Apparent absorbance measurements can be combined with multivariate statistical analysis over a wide spectral range, and a background correction technique that employs the sample as its own blank provides an effective means of correcting for the presence of chromophores that also absorb over the wavelengths of interest. The instrument was tested against an ordinary polarimeter and showed good performance with both colorless and colored samples.

Authentication of pomegranate juice concentrate using FTIR spectroscopy and chemometrics

Fourier transform infrared (FTIR) spectroscopy and chemometric techniques were used to detect the adulteration of pomegranate juice concentrate (PJC) with grape juice concentrate (GJC). The main differences between PJC and GJC infrared spectra occurred in the 1780-1685cm(-1) region, which corresponds to CO stretching. Principal component analysis of the spectra was used to: (1) differentiate pure PJC and GJC samples and (2) classify adulterated (containing 2-14% vol/vol GJC) and pure PJC samples. Two principal components explained 99% of the variability in each of these applications. Partial least square analysis of the spectra resulted in prediction of the GJC adulterant concentration in PJC with a correlation coefficient, R(2), of 0.9751. Partial least square analysis of spectra could also predict % titratable acidity and total solids in PJC with correlation coefficients of 0.9114 and 0.9916, respectively. Therefore, FTIR and chemometrics provide a useful approach for authenticating pomegranate juice concentrate.

Midinfrared spectroscopy for juice authentication-rapid differentiation of commercial juices

The determination of food authenticity is a crucial issue for food quality and safety. Midinfrared spectroscopy provides rapid chemical profiling of agricultural products and could become an effective tool for authentication when coupled to chemometrics. This study developed a simple protocol for classifying commercial juices using attenuated total reflectance infrared spectroscopy. Spectra from a total of 52 juices together with their extracted sugar-rich and phenol-rich fractions were obtained to construct multivariate models [hierarchical cluster analysis (HCA) and soft independent modeling of class analogy (SIMCA)] for pattern recognition analysis and prediction. Spectra of the sugar-rich fraction, comprised primarily of sugars and simple acids, almost superimposed the whole juice spectra. Solid-phase extraction enriched phenol compounds and provided signature-like spectral information that substantially improved the SIMCA modeling power over the whole juice or sugar-rich fraction models and allowed for the differentiation of juices with different origins. Zero percent misclassification was achieved by the phenol-rich fraction model. HCA successfully recognized the natural grouping of juices based on ingredients similarity. The infrared technique assisted by a simple fractionation and chemometrics provided a promising analytical method for the assurance of juice quality and authenticity.

Identification and quantification of industrial grade glycerol adulteration in red wine with fourier transform infrared spectroscopy using chemometrics and artificial neural networks

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900-1500 cm(-1). Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis (r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose Sensor

Fourier transform infrared spectroscopy (FTIR) and z-Nose were used as screening tools for the identification and classification of honey from different floral sources. Honey samples were scanned using microattenuated total reflectance spectroscopy in the region of 600-4000 cm(-1). Spectral data were analyzed by principal component analysis, canonical variate analysis, and artificial neural network for classification of the different honey samples from a range of floral sources. Classification accuracy near 100% was achieved for clover (South Dakota), buckwheat (Missouri), basswood (New York), wildflower (Pennsylvania), orange blossom (California), carrot (Louisiana), and alfalfa (California) honey. The same honey samples were also analyzed using a surface acoustic wave based z-Nose technology via a chromatogram and a spectral approach, corrected for time shift and baseline shifts. On the basis of the volatile components of honey, the seven different floral honeys previously mentioned were successfully discriminated using the z-Nose approach. Classification models for FTIR and z-Nose were successfully validated (near 100% correct classification) using 20 samples of unknown honey from various floral sources. The developed FTIR and z-Nose methods were able to detect the floral origin of the seven different honey samples within 2-3 min based on the developed calibrations.