Classification of plum spirit drinks by synchronous fluorescence spectroscopy

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

A fast and low-cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

BACKGROUND

Cachaça is the distilled beverage typical of Brazil and can be subjected to the aging process in wooden barrels. In addition to oak barrels, cachaça is also aged in barrels of different Brazilian native woods, resulting in a wide variety of its sensory characteristics. In this work, 172 cachaças aged in bálsamo, jequitibá, oak, and umburana barrels were analyzed by synchronous fluorescence spectroscopy and by the classification methods of principal component analysis and partial least squares discriminant analysis. Spectra were preprocessed by the first derivative by Savitzky-Golay smoothing, using a filter width and polynomial order determined through face-centered central composite designs. Multivariate analysis was realized using the spectra recorded at different wavelength differences, and models were compared by the classification errors in the test sets.

RESULTS

The principal component analysis applied to the synchronous fluorescence spectra presented a tendency of separation by the wood used in the aging process, and the partial least squares discriminant analysis model constructed using the fluorescence spectra recorded at a wavelength difference of 30 nm provided better performance parameters (efficiency 91-97%, sensitivity 81-100%, and specificity 91-100%).

CONCLUSION

Synchronous fluorescence spectroscopy offers a promising approach for the classification of cachaças aged in bálsamo, oak, jequitibá, and umburana barrels, and the discriminant model can be used for routine analysis as a screening method. © 2022 Society of Chemical Industry.

Synchronous fluorescence spectroscopy and multivariate classification for the discrimination of cachaças and rums

Although cachaça and rum are distilled beverages obtained from the same raw material, they present differences in their chemical compositions. In this study, synchronous fluorescence spectroscopy was used combined with supervised classification models based on the partial least squares discriminant analysis to develop a rapid and low-cost model for discriminating between 50 cachaça and 40 rum samples. Partial least squares discriminant analysis models were constructed using synchronous fluorescence spectra recorded at wavelength differences of 10-100 nm. Initially, spectra were preprocessed by the first derivative with the Savitzky-Golay smoothing, and filter width and polynomial order were selected through face-centered central composite designs. For the construction and validation models, the spectra data were split into two datasets: the training and the test sets containing 60 (C, n = 33; R, n = 27) and 30 (C, n = 17; R, n = 13) samples, respectively. The best discrimination was achieved using fluorescence spectra recorded at wavelength difference 10 nm, allowing the discrimination of cachaça and rum with a classification efficiency of 98%. These results indicate that synchronous fluorescence spectroscopy offers a promising approach for the authentication of cachaças and rums.

NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning

Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general framework for origin identification is proposed. It includes steps such as data preprocessing, feature selection, model building and cross validation. Compare multiple preprocessing algorithms and multiple machine learning algorithms under the framework. Based on NIR spectroscopy to identify the origin of orange, a good identification result was obtained. Improve the accuracy of orange origin identification and obtained the best origin identification accuracy of 92.8%.

Absorbance Spectroscopy of Heads, Hearts and Tails Fractions in Fruit Spirits

There is a large economic interest to characterize heads, hearts and tails fractions during fruit spirit distillation by simple, fast, low-volume and low-cost analytical methods. This study evaluated the potential of ultraviolet (UV)-visible-infrared spectroscopy (230–1000 nm) to characterize and differentiate these distillate fractions. Heads, hearts and tails fractions of 10 different fruit spirits were separated by sensory evaluation and investigated by absorbance spectroscopy. Principal component analysis indicated that UV spectroscopy at a wavelength range from 230 to 310 nm had the highest potential to differentiate all three distillate fractions. While all tails fractions showed significantly different UV spectra, a clear differentiation between heads and hearts fractions was limited. However, an additional UV spectroscopy of 100 mL subfractions sampled during the shift from heads to hearts in three additional distillations did reveal significant differences. The calculated integrals of the according best-fit trendline functions of the spectra indicated a trend towards reduced area-under-the-curve and zero-point values during the shift. This could be a new lead to implement an analytical method for in-line process control during fruit spirit production.

A case study comparing distillation technologies for plum palinka production

Palinka production has a long tradition in Hungary and the neighboring countries. Previously, the fruit distillate was produced exclusively using the traditional Pot-Still Double Distillation (PSDD) technology. This distillation method means, in practice, a simple fractional distillation repeated twice. However, in other industries, such as the petroleum industry or the pharmaceutical industry, a continuous, so-called repeated distillation procedure is used (RCDS – Rectification Column Distillation Systems). In the production of palinka, the latter procedure has gained more and more ground in recent years, thus displacing the traditional technology. In the territory of today’s Hungary, there are more than 16,000 registered private palinka distillers. However, based on public databases, it is not possible to know the proportion of the two different palinka making processes used in palinka production. The two processes differ to a large degree. The amount of hearts obtained using the continuous operation plate rectification column (RCDS) is lower, while its alcohol content is very high: 75 – 90 vol%, depending on the fruit. On the other hand, when using the traditional pot-still double distillation (PSDD) method, the amount of hearts is higher, but its alcohol content is lower (60 – 70%). The continuous procedure, also called single-stage, is faster. This is one of the reasons for its popularity because it makes production more economical. The objective of our research was to find out whether a significant difference could be detected between the two plum palinkas produced using the two different distillation technologies, based on current legal requirements. Our research also included sensory testing to determine whether consumers could distinguish between the products manufactured in different ways. Our analyses were carried out in 2019 in the accredited laboratory of the National Food Chain Safety Office and among the students and staff of the Gödöllő campus of Szent István University.

Influence of Alcohol Content and Storage Conditions on the Physicochemical Stability of Spirit Drinks

The purpose of this study was to investigate the effects of alcohol by volume (ABV) and storage temperature on changes in the clarity of rye and plum distillates, and their content of volatile compounds. Distillates with initial ABVs of 93.26% v/v (rye distillate) and 82.03% v/v (plum distillate) were diluted with deionized water to 40, 50, and 70% v/v. The samples were stored in darkness at different temperatures (−18 °C, 0 °C, 8 °C, 20 °C) for 8 weeks. The results showed that reducing the alcohol content and storage temperature caused turbidity to increase. The samples prepared from rye distillate were characterized by significantly lower turbidity than those produced from plum distillate. The highest increase in turbidity in comparison to the controls was observed in the samples with 40% v/v alcohol content stored at a temperature of −18 °C. Storage of the rye and plum distillates samples at different temperatures resulted in changes to the concentrations of volatile compounds, i.e., lower levels of acetaldehyde and higher alcohols, and increased content of esters. However, the alcohol content and storage temperature had no statistically significant effect on methanol concentration.

Comparison of different fluorescence techniques in brandy classification by region of production

Fluorescence spectrometry coupled with chemometrics was used to discriminate between 44 brandies originating from different countries. The kind of spectrum (emission, total luminescence and synchronous fluorescence), the geometry of sample illumination (front-face and right angle), and the sample type (bulk and diluted) were considered to compare the brandy classification. Firstly, the emission and synchronous fluorescence spectra (SFS) were processed by the principal component analysis (PCA) and the excitation-emission matrix (EEM) fluorescence spectra were modeled by unfolded PCA and parallel factor analysis (PARAFAC). Secondly, the scores of PCA/PARAFAC components were used in the linear discriminant analysis (LDA). Finally, the quality of the PCA-LDA and PARAFAC-LDA models was compared. Total correct classification using emission spectra was poor, regardless of the experimental conditions. The highest total correct classification (95.5%) was achieved by processing the SFS recorded at wavelength difference of 20 and 60nm on the diluted samples. However, 90.9% observed for bulk samples and their SFS at wavelength difference of 20nm in the right angle geometry as well as EEM fluorescence spectra in both geometries is still an acceptable result.

Use of zero order diffraction of a grating monochromator towards convenient and sensitive detection of fluorescent analytes in multi fluorophoric systems

White light excitation fluorescence (WLEF) is known to possess analytical advantage in terms of enhanced sensitivity and facile capture of the entire fluorescence spectral signature of multi component fluorescence systems. Using the zero order diffraction of the grating monochromator on the excitation side of a commercial spectrofluorimeter, it has been shown that WLEF spectral measurements can be conveniently carried out. Taking analyte multi-fluorophoric systems like (i) drugs and vitamins spiked in urine sample, (ii) adulteration of extra virgin olive oil with olive pomace oil and (iii) mixture of fabric dyes, it was observed that there is a significant enhancement of measurement sensitivity. The total fluorescence spectral response could be conveniently analysed using PLS2 regression. This work brings out the ease of the use of a conventional fluorimeter for WLEF measurements.