Detecting methoxypyrazines such as 3-isobutyl-2-methoxypyrazine (IBMP) in wine grapes constitutes a significant challenge due to the extremely low concentrations at which they impart orthonasal sensory effects. Associated with vegetal or 'green' aromas in wine, IBMP accumulates in immature grapes and diminishes with maturity. Such aromas can be viewed as detrimental to the quality of red wines, with Cabernet-Sauvignon being a particularly vulnerable variety. The sensory threshold for red wine matrices is recognised to range from 2 to 16 ng/L. Hence, detection conventionally requires expensive, time-consuming analysis by gas chromatography with mass spectrometry. Seeking a fast (i.e., < 10 min per sample) and inexpensive assay, this study investigated the ability to empirically predict IBMP in red winegrape varieties using HORIBA's patented absorbance-transmittance and fluorescence excitation-emission matrix (A-TEEM) spectroscopy. Cabernet-Sauvignon, Carménère, Merlot, and Cabernet franc grapes were sourced and extracted during harvest season over a period of 4 years from > 70 vineyards throughout Chile. A-TEEM spectral data represented the fusion of the UV-Vis absorbance and unfolded fluorescence EEM to yield more than 10,000 variables per sample. Model optimisation included a recursive partial least squares variable selection for the calibration set, and IBMP test set predictions were evaluated by multivariate regression using a randomly selected calibration/validation set of 1816/584 samples (including replication). Importantly, both cross-validation (CV) and test set prediction were based on exclusive sample repetition classes to eliminate replicate trapping. Support vector machine (SVM) regression yielded root mean square errors for calibration, CV, and test set prediction of < 0.4 ng/kg, with an R2 = 0.879. The regression results were supplemented by SVM discriminant analysis based on a pass/fail threshold of 2 ng/kg, yielding a Matthews correlation coefficient of 0.970 with no misclassified samples in the < 2 ng/kg class. This work highlights the potential of A-TEEM with machine learning data analysis to provide an inexpensive, rapid, and accurate screening method for IBMP in extracts of susceptible red winegrape varieties at or below the typical wine sensory threshold.

BACKGROUND:Cannabis plants (Cannabis sativa) contain a diverse group of terpenophenolic compounds known as phytocannabinoids, with 131 cannabinoids identified to date. Rapid and low-cost analytical approaches capable of quantifying both major and minor cannabinoids are increasingly important for research, quality control, and regulatory applications. This study evaluates the patented Absorbance-Transmittance Excitation-Emission Matrix (A-TEEM™) spectroscopic technique as a fast and reliable alternative to conventional chromatographic methods. A-TEEM integrates ultraviolet-visible absorbance and fluorescence measurements while correcting for absorbance-dependent inner-filter effects, enabling linear relationships between fluorescence intensity and analyte concentration. The primary objective was to calibrate and validate machine learning models using A-TEEM data for cannabinoid quantification, benchmarked against a validated high-performance liquid chromatography-photodiode array (HPLC-PDA) reference method. MATERIALS AND METHODS:A total of 49 dry cannabis flower extracts were analyzed using the A-TEEM technique to quantify 14 cannabinoids. Spectral data generated by A-TEEM were directly compared with concentration data obtained from an established and validated HPLC-PDA method. Extreme gradient boosting regression models were developed using HPLC-PDA results as reference values to predict cannabinoid concentrations from A-TEEM spectral data and to evaluate quantitative performance. RESULTS:The A-TEEM method demonstrated rapid, robust, and sensitive quantification of all 14 target cannabinoids. Model performance metrics, including coefficients of determination (R2) and limits of detection (LOD) and limits of quantification (LOQ), are scaled proportionally with the maximum cannabinoid concentrations present in the samples. For major cannabinoids exceeding 0.35% concentration, the mean combined cross-validation and validation R2 reached 0.994 ± 0.005, with mean LOD and LOQ values of 0.0146% and 0.0442%, respectively. Cannabinoids present between 0.35% and 0.1% showed mean LOD/LOQ values of 0.00278% and 0.00842%, while minor cannabinoids below 0.1% exhibited even lower LOD/LOQ values of 0.0004% and 0.00128%, respectively. In addition, A-TEEM concentration profiles enabled clear qualitative and quantitative differentiation of three cannabis chemovars: tetrahydrocannabinol (THC)-dominant, cannabidiol (CBD)-dominant, and THC-CBD-intermediate hybrids. CONCLUSIONS:The A-TEEM technique provides a sensitive, rapid, and cost-effective approach for the qualitative and quantitative determination of both major and minor cannabinoids in solution. Its analytical performance is comparable to that of the reference HPLC-PDA method while offering substantial advantages in speed, simplicity, and suitability for high-throughput analysis.

Wine is an important global alcoholic beverage produced in many regions, in a wide array of styles, and from different grape varieties. Quality is an important concept for wine production, but that depends on the viewpoint (e.g., winemaker, novice consumer, expert), making it challenging to simply define the quality of wine and the grapes from which it originates. Based on extensive research, however, some consensus on the definition of quality is available, which helps underpin how to measure, evaluate, and control grape and wine quality using chemical and sensory methods. Recently, with the growing demand for simple, rapid, and cost-effective techniques to objectively evaluate the quality of grape, wine, and wine-derived spirits in the wine industry, novel spectroscopic technologies, used in conjunction with chemometrics, have been developed and implemented. Among these, fluorescence spectroscopy has shown advantages as an analytical tool in a number of aspects of grape and wine research and winemaking practice. This chapter provides an overview of the definition of grape and wine quality from different perspectives and summarizes commonly used methods for measurement, evaluation, and control of grape and wine quality after providing an understanding of the chemical components of importance. Finally, the chapter provides an update on the current progress of research and application of fluorescence spectroscopy combined with chemometrics and machine learning devoted to grape and wine analysis, including phenolic detection and prediction, grape maturity monitoring, wine classification, and authentication, among others.

Fuel oil is widely used within Eskom, a power generation company in South Africa. Eskom's coal-fired power stations use up to 30,000 L of fuel oil per hour during a cold start-up, a consequence of which results in oil leaks to the dams. Oil contamination in water treatment plants causes irreversible membrane fouling, requiring costly replacement. This research work focused on the development of a rapid method for the identification of low concentrations of the water-soluble oil component fraction of crude fuel oil. For the developed method, known volumes of the water-soluble fraction of crude oil were spiked into various matrices of process water. FEEMs were collected using the patented HORIBA Aqualog spectrometer and data were modelled with PARAFAC. The results were well described with a four-component model, which included an oil component and three natural organic matter components, with a split-half validation match of 90%. The oil component was verified using linear regression of the PARAFAC component scores yielding an R2 value of 0.98. From the scores, a qualitative pass/fail test was developed such that process water can be analysed and subjected to the model to indicate the presence of oil contamination beyond a damaging threshold.

The molecular fingerprint obtained from fluorescence spectroscopy has been used in combination with chemometrics for the authentication of red wine according to region, variety, and vintage. The approach relies on dilution of a small amount of wine prior to absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) analysis, which provides UV/Vis spectra and fluorescence landscapes for determining wine color parameters and for authentication. This protocol describes pre-operation checks, sample preparation and spectral analysis, data pre-treatment, and examination, along with parallel factor analysis for fluorophore assignment and extreme gradient boosting discriminant analysis for classification, while highlighting key points in the overall methodology.

A 5-day test duration makes BOD5 measurement unsatisfactory and hinders the development of a quick technique. Protein-like fluorescence peaks show a strong correlation between the BOD characteristics and the fluorescence intensities. For identifying and measuring BOD in surface water, a simultaneous absorbance–transmittance and fluorescence excitation–emission matrices (A-TEEM) method combined with PARAFAC (parallel factor) and PLS (partial least squares) analyses was developed using a tyrosine and tryptophan (tyr–trpt) mix as a surrogate analyte for BOD. The use of a surrogate analyte was decided upon due to lack of fluorescent BOD standards. Tyr–trpt mix standard solutions were added to surface water samples to prepare calibration and validation samples. PARAFAC analysis of excitation–emission matrices detected the tyr–trpt mix in surface water. PLS modelling demonstrated significant linearity (R2 = 0.991) between the predicted and measured tyr–trypt mix concentrations, and accuracy and robustness were all acceptable per the ICH Q2 (R2) and ASTM multivariate calibration/validation procedures guidelines. Based on a suitable and workable surrogate analyte method, these results imply that BOD can be detected and quantified using the A-TEEM–PARAFAC–PLS method. Very positive comparability between tyr–trypt mix concentrations was found, suggesting that tyr–trypt mix might eventually take the place of a BOD-based sampling protocol. Overall, this approach offers a novel tool that can be quickly applied in water treatment plant settings and is a step in supporting the trend toward rapid BOD determination in waters. Further studies should demonstrate the wide application of the method using real wastewater samples from various water treatment facilities.

Background: Phytocannabinoids naturally occur in the cannabis plant (Cannabis sativa), and Delta(9)-tetrahydrocannabinol (THC) and cannabidiol (CBD) predominate. There is a need for rapid inexpensive methods to quantify total THC (for statutory definition) and THC-CBD ratio (for classification into three chemotypes). This study explores the capabilities of a spectroscopic technique that combines ultraviolet-visible and fluorescence, absorbance-transmittance excitation emission matrix (A-TEEM).Methods: The A-TEEM technique classifies 49 dry flower extracts into three C. sativa chemotypes, and quantifies the total THC-CBD ratio, using validated gas chromatography (GC)-flame ionization (FID) and High-Performance Liquid Chromatography (HPLC) methods for reference. Multivariate methods used are principal components analysis for a chemotype classification, extreme gradient boost (XGB) discriminant analysis (DA) to classify unknown samples by chemotype, and XGB regression to quantify total THC and CBD content using GC-FID and HPLC data on the same samples.Results: The A-TEEM technique provides robust classification of C. sativa samples, predicting chemotype classification, defined by THC-CBD content, of unknown samples with 100% accuracy. In addition, A-TEEM can quantify total THC and CBD levels relevant to statutory determination, with limit of quantifications (LOQs) of 0.061% (THC) and 0.059% (CBD), and high cross-validation (>0.99) and prediction (>0.99), using a GC-FID method for reference data; and LOQs of 0.026% (THC) and 0.080% (CBD) with high cross-validation (>0.98) and prediction (>0.98), using an HPLC method for reference data. A-TEEM is highly predictive in separately quantifying acid and neutral forms of THC and CBD with HPLC reference data.Conclusions: The A-TEEM technique provides a sensitive method for the qualitative and quantitative characterization of the major cannabinoids in solution, with LOQs comparable with GC-FID and HPLC, and high values of cross-validation and prediction. As a spectroscopic technique, it is rapid, with data acquisition <45 sec per measurement; sample preparation is simple, requiring only solvent extraction. A-TEEM has the sensitivity to resolve and quantify cannabinoids in solution based on their unique spectral characteristics. Discrimination of legal and illegal chemotypes can be rapidly verified using XGB DA, and quantitation of statutory levels of total THC and total CBD comparable with GC-FID and HPLC can be obtained using XBD regression.

Authentication of wine can be considered at different scales, with classification according to country, province/state, or appellation/wine producing region. An absorbance-transmission and excitation-emission matrix (A-TEEM) technique was applied for the first time to examine intraregional differences, using Shiraz wines (n = 186) produced during three vintages from five subregions of Barossa Valley and from Eden Valley. Absorption spectra and EEM fingerprints were modelled as a multi-block data set for initial exploration with k-means cluster analysis and principal component analysis, and then with machine learning modelling using extreme gradient boosting discriminant analysis (XGBDA). Whereas some clustering was evident with the initial unsupervised approaches, classification with XGBDA afforded an impressive 100% correct class assignment for subregion and vintage year. Extending the utility and novelty of the A-TEEM approach, predictive models for chemical parameters (alcohol, glucose + fructose, pH, titratable acidity, and volatile acidity) were also validated using A-TEEM data with XGB regression.

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes were obtained for BPA concentration (R2 = 0.996; standard deviation to prediction error’s root mean square ratio (RPD) = 3.41; and a Pearson’s r value of 0.998). With these values of R2 and Pearson’s r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the method were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

Wine is a luxury product and a global beverage steeped in history and mystery. Over time, various regions have become renowned for the quality of wines they produce, which adds considerable value to the regions and the brands. On the whole, the international wine market is worth many hundreds of billions of dollars, which attracts unscrupulous operators intent on defrauding wine consumers. Countering such fraudulent activities requires the means to test and classify wine, but the task is considerable due to the complexity of wine. However, just as wine origin influences chemical and sensory profiles, indicators of wine provenance are naturally embedded in the chemical composition of wine. A range of methods of varying intricacy are available to analyse wine for authentication of variety or geographical origin. Instruments and techniques within the domain of research laboratories are not so practical or deployable in winery or supply chain settings, however. This is where spectroscopic methods are attractive, as they can be rapid, cost-effective and simple. In the search for such a method, we identified fluorescence spectroscopy, and more specifically, the collection of an excitation-emission matrix (EEM) that acts like a molecular fingerprint. Multivariate statistical modelling is then used in conjunction with the EEM data to develop classification models for wines from various regions. We have developed such a technique, using a relatively new type of machine learning algorithm known as extreme gradient boosting discriminant analysis. This unique approach, which can routinely achieve a level of accuracy of 100% in comparison to ICP-MS at an average of 85%, is being applied to a range of studies on Shiraz and Cabernet Sauvignon wines from different regions of Australia.

A-TEEM spectroscopy is presented as a novel rapid quantitative analysis method for 44 individual phenolic and basic wine chemistry compounds. To date no practical and combined analysis method for these recognized quality parameters important to the wine industry exists. The method was implemented in a Lambert-Beer linear concentration range to facilitate traceable absorbance and fluorescence spectral signatures. Both components were comparatively analyzed as single-and combined multi-block variable sets, and regressed against HPLC-DAD, UV-vis spectroscopy and other analytical reference data, using the Extreme Gradient Boost Regression (XGBR) and Partial Least Squares Regression (PLSR) algorithms. The approach was applied on 126 wines, and subsequently validated by a random split of 13% of the set and an additional independent set of 16 wines. XGBR with multi-block data organization systematically yielded the highest prediction accuracy and precision with respective overall valid fits indicated by mean R(2 )and relative bias of 0.94 +/- 0.04 and 4.1 +/- 1.8%.

Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy was investigated as a rapid method for predicting maturity indices using Cabernet Sauvignon grapes produced under four viticulture treatments during two growing seasons. Machine learning models were developed with fused spectral data to predict 3-isobutyl-2-methoxypyrazine (IBMP), pH, total tannins (Tannin), total soluble solids (TSS), and malic and tartaric acids based on the results from traditional analysis methods. Extreme gradient boosting (XGB) regression yielded R-2 values of 0.92-0.96 for IBMP, malic acid, pH, and TSS for externally validated (Test) models, with partial least squares regression being superior for TSS prediction (R-2 = 0.97). R-2 values of 0.64-0.81 were achieved with either approach for tartaric acid and Tannin predictions. Classification of grape maturity, defined by quantile ranges for red colour, IBMP, malic acid, and TSS, was investigated using XGB discriminant analysis, providing an average of 78 % correctly classified samples for the Test model.

Rapid and accurate quantification of grape berry phenolics, anthocyanins and tannins and identification of grape varieties are both important for effective quality control of harvesting and initial processing for winemaking. Current reference technologies, including High-Performance Liquid Chromatography (HPLC), can be rate-limiting and too complex and expensive for effective field operations. In this paper, we analyse robotically prepared grape extracts from several key varieties (n = Calibration/n = Prediction samples), including Cabernet-Sauvignon (64/10), Grenache (16/4), Malbec (14/4), Merlot (56/10), Petite Sirah (52/10), Pinot noir (54/8), Syrah (20/2), Teroldego (14/2) and Zinfandel (62/12). Key phenolic and anthocyanin parameters measured by HPLC included Catechin, Epicatechin, Quercetin Glycosides, Malvidin 3-glucoside, Total Anthocyanins and Polymeric Tannins. Split samples diluted 50-fold in 50 % EtOH pH 2 were analysed in parallel using the A-TEEM method following Multi-block Data Fusion of the absorbance and unfolded EEM data. A-TEEM chemical data were calibrated (n = 390) using Extreme Gradient Boosting (XGB) Regression and evaluated based on the Root Mean Square Error of the Prediction (RMSEP), the Relative Error of Prediction (REP) and Coefficient of Variation (R2P) of the Prediction data (n = 62). The regression results yielded an average Relative Error of Prediction (REP) of 5.89 ± 2.47 % and an R2P of 0.941 ± 0.025. While we consider the REP values to be in the acceptable range at significantly < 10 %, we acknowledge that both the grape extraction method repeatability and HPLC reference method sample repeatability (5-8 % RSD) likely constituted the major sources of variation compared to the A-TEEM instrumental sample repeatability (< 2 % RSD). The varietal classification was analysed using Agglomerative Hierarchical Cluster Analysis (HCA) and XGB discrimination analysis of the multi-block data. The classification results yielded 100 % True Positive and True Negative responses for the Calibration and Prediction Data for all tested varieties. We conclude that the A-TEEM method requires a minimum of sample preparation and rapid acquisition times (< 1 min) and can serve as an accurate secondary method for both grape varietal identification and phenolic quantification. Importantly, the software application of the regression and classification models can be effectively automated for operators.

As a robust analytical method, spectrofluorometric analysis with machine learning modelling has recently been used to authenticate wine from different regions, vintages and varieties. This preliminary study investigated whether the molecular fingerprint obtained with this approach is maintained throughout the winemaking process, along with assessing different percentages of wine in a blend. Monovarietal wine samples were collected at different stages of the winemaking process and analysed with the absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique. Wines were clustered tightly according to origin for the different winemaking stages, with some clear separation of different regions and varieties based on principal component analysis. In addition, wines were classified with 100 % accuracy according to varietal origin using extreme gradient boosting (XGB) discriminant analysis. The sensitivity of the A-TEEM technique was such that it allowed for accurate modelling of wine blends containing as little as 1 % of Cabernet-Sauvignon or Grenache in Shiraz wine when employing XGB regression, which performed better than partial least squares regression. The overall results indicated the potential for applying A-TEEM and machine learning modelling to wine chemical traceability through production to guarantee the provenance of wine or identify the composition of a blend.

Conventional oil-in-water analyzers used by waterworks have hydrocarbon detection limits at mg/L levels and do not identify the type of oil compounds. The objectives of this study were to evaluate a more sensitive optical instrument and the analysis method to (1) determine the signature excitation and emission matrixs of each type of oil (such as diesel, heavy oil, gasoline and kerosene) or their indicator organic compounds and enter them into the instrument's software library and (2) test out the effectiveness of the instrument in detecting the above-mentioned oil in local waterworks’ source and treated water. The patented simultaneous absorbance-transmittance excitation-emission matrix (A-TEEM) instrument method was used to identify and quantify low levels of organic contaminants present in a much higher background of other dissolved organic matter components in raw and treated water. Multivariate regression and machine learning techniques were applied and shown to have potential for alerting plant operators to organic contamination events.

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.

The food, beverage and natural supplements industries face many challenges relating to quality control as influenced by natural product variation, formulation errors and product adulteration. Conventional chromatographic and spectrophotometric assays can be time-consuming, costly and insensitive to key quality parameters and adulterants. Thus, there is a recognized need for a rapid, sensitive, non-destructive optical technique. This study investigates optimization of multivariate calibrations of chemical product compositions analyzed using A-TEEM spectroscopy. The A-TEEM method provides rapid (s-min) analyses of all chromophoric and fluorescent compounds in the UV-VIS range with micro- to sub-microgram/L sensitivity to many aromatic compounds. Conventional fluorescence EEM analyses primarily focus only on the inner-filter-effect corrected fluorescence data. In this paper, we systematically investigated the statistical significance of evaluating the separate absorbance, EEM and combined multi-block absorbance and EEM data variable sets. We report that there is a consistent improvement for the majority of A-TEEM models using ‘multi-block’ data organization compared to the separate absorbance or EEM variable data sets. We attribute the increased statistical advantage to the fact that most chemicals, even with similar structures, in a given solvent exhibit unique molar extinction coefficients, fluorescence quantum efficiencies as well as absorbance- and fluorescence excitation and emission spectral shapes. The A-TEEM data analyses further compared the Extreme Gradient Boosting (XGB) algorithm for both discrimination and regression to other comparable methods including Partial-Least Squares and Support Vector Machine. The conclusion, based on a multi-instrument comparison, was that the XGB analyses of the multi-block A-TEEM data lead to the most effective validation for both discrimination and regression models of food, beverage and natural supplements chemical composition and adulteration.

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.