Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety

What Can Fluorescence Tell Us About Wine?

Rapid and cost-effective measurements of the autofluorescence of wine can provide valuable information on the brand, origin, age, and composition of wine and may be helpful for the authentication of wine and detection of forgery. The list of fluorescent components of wines includes flavonoids, phenolic acids, stilbenes, some vitamins, aromatic amino acids, NADH, and Maillard reaction products. Distinguishing between various fluorophores is not simple, and chemometrics are usually employed to analyze the fluorescence spectra of wines. Front-face fluorescence is especially useful in the analysis of wine, obviating the need for sample dilution. Front-face measurements are possible using most plate readers, so they are commonly available. Additionally, the use of fluorescent probes allows for the detection and quantification of specific wine components, such as resveratrol, oxygen, total iron, copper, hydrogen sulfite, and haze-forming proteins. Fluorescence measurements can thus be useful for at least a preliminary rapid evaluation of wine properties.

Ultra sensor based on color and UV-excited fluorescence images for predicting quality attributes of Shine-Muscat grape bunches at different maturity stages

Fruit maturity at harvest is a major factor in determining its quality. In this study, external skin color of grape has been utilized to predict their chemical content and, in turn, the maturity of the fruit. Measurements of the chemical content such as Brix and acidity were made on ten bunches of "Shine Muscat" grapes at three different harvest periods (immature, mature, and overmature). Using a machine vision system, color and UV-excited fluorescence images of grape berries and bunches were taken during the respective harvest stages. Acquired images were processed using ImageJ to obtain RGB values. Rratio and a*/b* are strongly related to Brix and sugar-to-acid ratio of grape fruit, with regression coefficients of 0.5626 and 0.5180, repectively. It was found that a* color index was the best predictor of grape bunch maturity. Furthermore, discriminant analysis has shown that color images of grape berries perform better than 365 nm fluorescence images.

Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 h. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodology, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

Metabolomic profiling of Fiore Sardo cheese: Investigation of the influence of thermal treatment and ripening time using univariate and multivariate classification techniques

The effect of different sub-pasteurization heat treatments and different ripening times was investigated in this work. The metabolite profiles of 95 cheese samples were analyzed using GC-MS in order to determine the effects of thermal treatment (raw milk, 57 °C and 68 °C milk thermization) and ripening time (105 and 180 days). ANOVA test on GC-MS peaks complemented with false discovery rate correction was employed to identify the compounds whose levels significantly varied over different ripening times and thermal treatments. The univariate t-test classifier and Partial Least Square Discriminant Analysis (PLS-DA) provided acceptable classification results, with an overall accuracy in cross-validation of 76% for the univariate model and 72% from the PLS-DA. The metabolites that mostly changed with ripening time were amino acids and one endocannabinoid (i.e., arachidonoyl amide), while compounds belonging to the classes of biogenic amines and saccharides resulted in being strongly affected by the thermization process.

Effect of Different Cooking Methods on the Aroma and Taste of Chicken Broth

A single combi oven, known for its versatility, is an excellent choice for a variety of chicken soup preparations. However, the impact of universal steam ovens on the flavor quality of chicken soup remains unclear. This study aimed to explore the impact of different cooking methods on the aroma and taste of chicken soup. Three cooking methods with various stewing times were compared: ceramic pot (CP), electric pressure cooker (EPC), and combi oven (CO). Analyses were conducted using electron-nose, electron-tongue, gas chromatography-ion mobility spectrometry (GC-IMS), automatic amino acid analysis, and chemometric methods. A total of 14 amino acids, including significant umami contributors, were identified. The taste components of CP and CO chicken soups were relatively similar. In total, 39 volatile aroma compounds, predominantly aldehydes, ketones, and alcohols, were identified. Aldehydes were the most abundant compounds, and 23 key aroma compounds were identified. Pearson's correlation analyses revealed distinct correlations between various amino acids (e.g., glutamic acid and serine) and specific volatile compounds. The aroma compounds from the CP and CO samples showed similarities. The results of this study provide a reference for the application of one-touch cooking of chicken soup in versatile steam ovens.

Rapid Fluorescence EEM Spectroscopy Using Super-Cycle Hadamard-Transform Multiplexing

Hadamard-transform (HT) multiplexing has recently been applied to increasingly complex spectroscopic techniques. It had been shown that the data acquisition time for fluorescence excitation emission matrix (EEM) spectroscopy can be reduced by 1 or 2 orders of magnitude using HT multiplexing of the excitation light using a programmable light source. In these previous studies, the data acquisition rate had been limited by the time it took to record an EEM, that is, to complete one cycle of multiplexed excitation spectra. The extraction of chemical information, such as concentration and chemical identity, is then obtained from parallel factor (PARAFAC) analysis of the sequence of EEMs. In this contribution, we increase the data acquisition rate by another order of magnitude, limited ultimately by the time it takes to record a single excitation spectrum. Our algorithm is entirely based on improved data processing, that is, it can be applied to previously recorded HT multiplexed data sets. The algorithm is based on three previously unexplored approaches: (1) we perform a PARAFAC multivariate analysis on the raw (multiplexed) data set, (2) the time-independent PARAFAC loading vectors are obtained prior to obtaining the time-dependent score vectors, and (3) when loading vectors are difficult to obtain from the EEMs, we instead use a rolling-average approach to considerably increase the stability of the fit. Analysis of experimental data shows that the scores of fluorescence EEMs with seven excitation wavelengths and over 1000 emission wavelengths can be obtained in less than 20 ms.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for rapid identification of commercial reconstituted and sweetened grape juices

As a common fruit juice, grape juice is delicious and nutritious, making it very popular among consumers. However, some illegal manufacturers used shoddy products to lower costs and obtain high profits, which seriously threatens the health and interests of consumers. Hence, this paper proposed excitation-emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the rapid identification and classification of commercial grape juices. Spectral characterization of different samples was achieved using the alternating trilinear decomposition (ATLD) algorithm, and chemically meaningful information was obtained and analyzed. Although both reconstituted and sweetened grape juices contain methyl anthranilate (MA) and 2'-aminoacetophenone (o-AAP), the content of MA in sweetened grape juice far exceeds that in reconstituted grape juice, and the MA in sweetened grape juice mainly comes from artificially added grape essence. Then two chemometric methods of hierarchical cluster analysis (HCA) and partial least squares discriminant analysis (PLS-DA) were used for the classification of reconstituted and sweetened grape juices. The results showed that the supervised classification model had a higher correct classification rate (CCR) than the unsupervised classification model, with PLS-DA obtaining 100% CCRs in both training and prediction sets. Therefore, the proposed strategy can be used as a powerful analytical method for the identification and classification of reconstituted and sweetened grape juices and provides a reliable scientific means for ensuring the authenticity and safety of the juice market.

White wine phenolics: current methods of analysis

White wine phenolic analyses are less common in the literature than analyses of red wine phenolics. Analytical techniques for white wine phenolic analyses using spectrophotometric, chromatographic, spectroscopic, and electrochemical methods are reported. The interest of research in this area combined with the advances in technology aimed at the winemaking industry are promoting the establishment of novel approaches for identifying, quantifying, and classifying phenolic compounds in white wine. This review article provides an overview of the current research into white wine phenolics through a critical discussion of the analytical methods employed. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Detection of adulterations in a valuable Brazilian honey by using spectrofluorimetry and multiway classification

Spectrofluorimetry combined with multiway chemometric tools were applied to discriminate pure Aroeira honey samples from samples adulterated with corn syrup, sugar cane molasses and polyfloral honey. Excitation emission spectra were acquired for 232 honey samples by recording excitation from 250 to 500 nm and emission from 270 to 640 nm. Parallel factor analysis (PARAFAC), partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA (UPLS-DA) and multilinear PLS-DA (NPLS-DA) methods were used to decompose the spectral data and build classification models. PLS-DA models presented poor classification rates, demonstrating the limitation of the traditional two-way methods for this dataset, and leading to the development of three-way classification models. Overall, UPLS-DA provided the best classification results with misclassification rates of 4% and 8% for the training and test sets, respectively. These results showed the potential of the proposed method for routine laboratory analysis as a simple, reliable, and affordable tool.

The application of right-angle fluorescence spectroscopy as a tool to distinguish five autochthonous commercial Greek white wines

White wine is among the most widely consumed alcoholic beverages. Varietal discrimination of wines has received increasing attention. Today's consumers require a sense of authenticity and are deterred by falsehood or misrepresentation in product marketing. However, wine can involve various types of frauds, which directly affects the distribution of wine in national and international markets. Right-angle fluorescence spectroscopy is a simple and rapid analytical technique that in combination with chemometric algorithms, constitutes a novel method for wine authentication. In this study, the stepwise-Linear Discriminant Analysis algorithm was applied in three representative spectral regions related to phenolic compounds for the purpose of distinguishing white wines according to the grape variety. The wavelength at 310 nm attributed to the hydroxycinnamic acids and stilbene provided a higher classification rate (95.5%) than the λex 280 and 295 nm regions (79.8%), suggesting that these compounds are highly related to the botanical origin of samples. The chemometric models were validated utilizing cross-validation and an external validation set to enhance the robustness of the proposed methodology. The above-mentioned methodology constitutes a powerful tool for the varietal discrimination of white wines and can be used in industrial setting. The ultimate goal of this study is to contribute to the efforts towards the authentication of Greek white wine which will eventually support producers and suppliers to remain competitive and simultaneously protect the consumers from fraudulent practices.

Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine

Fluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been applied to the analysis and classification of an array of products of agricultural origin. Recognising that fluorescence spectroscopy offered a promising method for wine authentication, this study investigated the unique use of an absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) technique for classification of red wines with respect to variety and geographical origin. Multi-block data analysis of A-TEEM data with extreme gradient boosting discriminant analysis yielded an unrivalled 100% and 99.7% correct class assignment for variety and region of origin, respectively. Prediction of phenolic compound concentrations with A-TEEM based on multivariate calibration models using HPLC reference data was also highly effective, and overall, the A-TEEM technique was shown to be a powerful tool for wine classification and analysis.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.

Excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese lager beers

This paper proposed excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese pale lager beers produced by different manufacturers. The undiluted and diluted beer samples presented different fluorescence fingerprints. Three-way and four-way parallel factor analysis (PARAFAC) were used to decompose the skillfully constructed three-way and four-way data arrays, respectively, to further achieve beer characterization and feature extraction. Based on the features extracted in different ways, four strategies for beer classification were proposed. In each strategy, three supervised classification methods including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (kNN) were used to build discriminant models. By comparison, PARAFAC-data fusion-kNN method in strategy 3 and four-way PARAFAC-kNN method in strategy 4 obtained the best classification results. The classification strategy based on four-way sample-excitation-emission-dilution level data array was proposed to solve the problem of beer classification for the first time.

Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling

With the increased risk of wine fraud, a rapid and simple method for wine authentication has become a necessity for the global wine industry. The use of fluorescence data from an absorbance and transmission excitation-emission matrix (A-TEEM) technique for discrimination of wines according to geographical origin was investigated in comparison to inductively coupled plasma-mass spectrometry (ICP-MS). The two approaches were applied to commercial Cabernet Sauvignon wines from vintage 2015 originating from three wine regions of Australia, along with Bordeaux, France. Extreme gradient boosting discriminant analysis (XGBDA) was examined among other multivariate algorithms for classification of wines. Models were cross-validated and performance was described in terms of sensitivity, specificity, and accuracy. XGBDA classification afforded 100% correct class assignment for all tested regions using the EEM of each sample, and overall 97.7% for ICP-MS. The novel combination of A-TEEM and XGBDA was found to have great potential for accurate authentication of wines.

Varietal classification of white wines by fluorescence spectroscopy

The Slovak Tokaj region is one of the producers of high-quality white wine having protected designations of origin. The main grape varieties of this region are Furmint, Lipovina and Muscat blanc, which have specific sensory characteristics. This research work presents a strategy for the classification of three mentioned varieties of white wines using fluorescence spectroscopy with chemometrics. Emission and synchronous fluorescence spectra were obtained for bulk as well as diluted samples, principal component analysis (PCA) was applied for exploratory analysis and the scores of the selected PCs were used in linear discriminant analysis (LDA). For undiluted samples, the best PCA-LDA models based on either emission spectra excited at 370 nm or synchronous fluorescence spectra obtained at wavelength difference of 40 and 100 nm provided total correct classifications of 100, 100 and 93% for the calibration, validation and prediction steps, respectively. For diluted samples, the best PCA-LDA models (excitation at 280 nm; wavelength difference of 40 nm) again provided total correct classifications as mentioned above.

Sensory and spectroscopic characterization of Argentinean wine and balsamic vinegars: A comparative study with European vinegars

In Argentina, vinegars are cheap agro-food products without exhaustive regulation and the production of high-quality vinegars has not been exploited yet. In fact, Argentinean vinegars have not been studied. In this context, a first study of Argentinean balsamic and wine vinegars was carried out by a sensory and spectroscopic characterization and by a comparison with well-recognized European vinegars. For that, ultraviolet-visible and fluorescence spectroscopies were applied together with principal component analysis (PCA) and parallel factor analysis (PARAFAC) performed on each data set, respectively. Results showed differences between acetification processes, origin countries and a wide variability within Argentinean production. The sensory characterization on Argentinean wine vinegars was performed by triangular and ordering preference tests showing statistically significant preferences toward the traditional and the rapid vinegars. This work highlights the effect of production on quality in order to provide added value to the Argentinean vinegars.

Chemical typicality of South American red wines classified according to their volatile and phenolic compounds using multivariate analysis

In this study, 83 wines representating four commercial categories: "Argentinean Malbec", "Brazilian Merlot", "Uruguayan Tannat" and "Chilean Carménère" were analyzed according to their phenolic and volatile compounds. The objective was to identify the chemical compounds that would typify each category. From approximately about 600 peaks obtained by chromatographic techniques, 169 were identified and 53 of them were selected for multivariate statistical analysis. Chilean Carménère was the best discriminated group by the methods applied in our study, followed by Argentinean Malbec. Brazilian Merlot mixed mainly with some Carménère, whileTannat mixed with all wines categories, especially Malbec. In general, Chilean Carménère wines can be characterized by a bluish color, higher amounts of sulphur dioxide, higher content of octanoic acid, isobutanol, ethyl isoamyl succinate and catechin and a smaller amount of quercetin. These data can contribute for further process of authenticity or typification of South American red wines.

Application of fluorescence spectroscopy using classical right angle technique in white wines classification

The potential of excitation - emission matrices (EEM) measurements using classical right angle technique, in conjunction with chemometrics, was prospected for white wine classification with respect to their cultivar and geographical origin. For this purpose, wines belonging to four cultivars (Chardonnay, Pinot Gris, Riesling and Sauvignon) from two different countries (Romania and France) were investigated. The excitation - emission matrices were statistically processed using parallel factor analysis (PARAFAC). According to Soft Independent Modeling Classification Analogy (SIMCA) model, for cultivar differentiation, only 3 out of 107 wine samples (1 Pinot Gris (Romania); 1 Riesling (Romania) and 1 Sauvignon (France)) were misclassified while for geographical origin assessment, only 2 wines (1 Romania and 1 France) were misclassified. This study  demonstrates the potential of excitation - emission fluorescence matrices spectroscopy using the classical right angle technique in wine authentication, without sample dilution.

Prediction of quality parameters in straw wine by means of FT-IR spectroscopy combined with multivariate data processing

This study represents the first attempt to combine mid infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars content and total acidity in straw wine. 302 Italian samples, representing different vintages, production regions and grape varieties, were analysed using FT-MIR spectroscopy and reference methods. New regression functions based on a combination of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of quality parameters: this approach allows overcoming the issue of matrix complexity, reducing spectral interferences and enhancing the information embodied in fingerprinting data. The models proposed are characterised by an excellent reliability, with low error in prediction (alcohol: 0.28%; sugars: 9.9 g/L; acidity: 0.29 g/L) comparable both to reference methods and table wine models. Results demonstrate that vibrational spectroscopy, combined with a proper multivariate data strategy, represents a suitable strategy for the quick and non-destructive assessment of quality parameters of straw wine.

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.

Quality evaluation and traceability of Bletilla striata by fluorescence fingerprint coupled with multiway chemometrics analysis

BACKGROUND

Traditional methods of evaluating herbs were mainly based on chromatographic techniques. They usually included tedious sample preparation procedures, taking tens of minutes to hours, and consume solvents as well as standards for external calibration. In this paper, the feasibility of employing a fluorescence fingerprint coupled with multi-way chemometrics analysis for quality evaluation and traceability of Bletilla striata were investigated.

RESULTS

Relative concentrations of four markers presented in B. striata were determined by using a four-component self-weighted alternating trilinear decomposition (SWATLD) model. These markers could be applied to accurate classification and quality control of B. striata samples from different regions. Furthermore, multiway principal component analysis, multilinear partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA, and SWATLD-PLS-DA models were applied to classify the B. striata samples according to their geographic origins. Consistent results were obtained showing that B. striata samples could be successfully grouped based on their geographical origins and quality.

CONCLUSION

Our results revealed that the method developed can be used for quality evaluation and traceability of B. striata. Compared with the chromatographic methods, the method employed in this study was more convenient, simpler, and more sensitive. © 2018 Society of Chemical Industry.

Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier-transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (¹H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.

Prediction of Firmness and pH for "Golden Delicious" Apple Based on Elasticity Index from Modal Analysis

An experimental modal test system was established to extract the natural frequencies of "Golden Delicious" apple, after which the elasticity index was calculated to predict the apple quality parameters based on the orthogonal polynomials method. The elasticity index in every vibration mode changed dramatically (P = 0.01) along time revolution. The multivariate regression methods were used to model the predictive relationship between the elasticity index and the apple quality parameters. The models of the apple juice pH based on support vector regression presented adequate determination coefficients of calibration set (Q²  = 0.68) and prediction set (R²  = 0.55), respectively. The models based on partial least squares regression could be used for predicting the apple firmness parameter offset gradient (Q²  = 0.76 and R²  = 0.72). It helped understanding the fruit dynamic properties of the fruit and spontaneously obtaining the fruit chemical parameters. A nondestructive and portable device was viable for fruit quality estimation by the modal test system during storage, transport, and even growth on the tree.

PRACTICAL APPLICATION

A nondestructive and portable device was provided for fruit quality detection during storage, transport and even growth based on experimental modal analysis. A systematic statistical analysis method about outlier detection, data set partitioning, parameter optimization, and multiple regression models were provided.