FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners

Advancements and Challenges in Sucralose Determination: A Comparative Review of Chromatographic, Electrochemical, and Spectrophotometric Methods

This review presents an in-depth analysis of the latest methods used for the determination of sucralose (E955), focusing on research conducted over the past 10 years. As a widely used sugar substitute in the food and pharmaceutical industries, sucralose has raised concerns about its environmental persistence, potential genotoxicity, and health impacts. This study examines several spectrophotometric, chromatographic, and electrochemical techniques, evaluating their sensitivity, selectivity, and limitations in differentiating sucralose from natural carbohydrates and other sweeteners. The review highlights the pressing need for novel detection methods that not only improve accuracy in trace detection but also address growing concerns about its bioaccumulation and conversion into harmful metabolites. Advancing these analytical techniques is essential for enhancing food safety, public health surveillance, and environmental risk assessment. Chromatographic methods are dominant in sucralose determination in foods and environmental objects, as they allow the determination of sucralose at micro- and nanomolar levels. However, spectrophotometric and electrochemical methods are frequently used as complementary to chromatographic methodologies, sensitizing them. On the other hand, purely spectrophotometric methods are less popular, and electrochemical methods remain underdeveloped. Therefore, the advancement of sucralose determination must be due to cheaper chromatographic and classical electrochemical methods.

Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data

Interpreting spectral data to analyze the structure and properties of unknown chemicals requires a lot of time and effort. Herein, we developed a machine-learning model that simultaneously trains on multiple spectroscopic data to identify functional groups of compounds more accurately and quickly. An artificial neural network model trained on Fourier-transform infrared, proton nuclear magnetic resonance, and 13C nuclear magnetic resonance together identified 17 functional groups with a macro-average F1 score of 0.93, outperforming the model using a single type of spectroscopy. The results indicated that training a machine-learning model with multiple spectral data can provide more accurate structural analysis when analyzing the structure of unknown chemicals, as can using multiple spectroscopy methods simultaneously.

Towards healthier low-sugar and low-fat beverages: Design, production, and characterization

Many consumers are adopting low-sugar and low-fat beverages to avoid excessive calories and the negative impact of high trans- and/or saturated fat on health and wellbeing. This article reviews strategies to reduce sugar, fat, and high trans- and/or saturated fat content in beverages while maintaining their desirable physicochemical and sensory attributes. It assesses the impact of various sugar and fat replacers on the aroma, taste, texture, appearance, and nutritional profile of beverages. Combinations of natural sugar replacers and protein or polysaccharide-based fat replacers have shown partial success in mimicking the qualities of sucrose and fat. Future strategies for designing low-sugar and low-fat beverages include developing novel replacers and using odorants to enhance sensory profiles. The article also highlights methods for flavor detection and oral tribology methods, emphasizing their role in development of low-sugar and low-fat beverages. The information presented in this review article is intended to stimulate research into the design of healthier low-sugar and low-fat beverages in the future.

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches

The composition of volatile compounds in beer is crucial to the quality of beer. Herein, we identified 23 volatile compounds, namely, 12 esters, 4 alcohols, 5 acids, and 2 phenols, in nine different beer types using GC-MS. By performing PCA of the data of the flavor compounds, the different beer types were well discriminated. Ethyl caproate, ethyl caprylate, and phenylethyl alcohol were identified as the crucial volatile compounds to discriminate different beers. PLS regression analysis was performed to model and predict the contents of six crucial volatile compounds in the beer samples based on the characteristic wavelength of the FTIR spectrum. The R2 value of each sample in the prediction model was 0.9398-0.9994, and RMSEP was 0.0122-0.7011. The method proposed in this paper has been applied to determine flavor compounds in beer samples with good consistency compared with GC-MS.

Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra

Fourier-transform infrared (FTIR) spectroscopy can detect the presence of functional groups and molecules directly from a mixed solution of organic molecules. Although it is quite useful to monitor chemical reactions, quantitative analysis of FTIR spectra becomes difficult when various peaks of different widths overlap. To overcome this difficulty, we propose a chemometrics approach to accurately predict the concentration of components in chemical reactions, yet interpretable by humans. The proposed method first decomposes a spectrum into peaks with various widths by the wavelet transform. Subsequently, a sparse linear regression model is built using the wavelet coefficients. Models by the method are interpretable using the regression coefficients shown on Gaussian distributions with various widths. The interpretation is expected to reveal the relation of broad regions in spectra to the model prediction. In this study, we conducted the prediction of monomer concentration in copolymerization reactions of five monomers against methyl methacrylate by various chemometric approaches including conventional methods. A rigorous validation scheme revealed that the proposed method overall showed better predictive ability than various linear and non-linear regression methods. The visualization results were consistent with the interpretation obtained by another chemometric approach and qualitative evaluation. The proposed method is found to be useful for calculating the concentrations of monomers in copolymerization reactions and for the interpretation of spectra.

Edible Quality Analysis of Different Areca Nuts: Compositions, Texture Characteristics and Flavor Release Behaviors

The areca nut is one of the most important cash crops in the tropics and has substantial economic value. However, the research information about the edible quality of different areca nuts is still insufficient. This study compared the composition, texture characteristics and flavor release behaviors of four different areca nuts (AN1, AN2, AN3 and AN4) and two commercially dried areca nuts (CAN1 and CAN2). Results showed that AN1 had higher soluble fiber and lower lignin, which was the basis of its lower hardness. Meanwhile, the total soluble solid (TSS) of AN1 was the highest, which indicated that AN1 had a moister and more succulent mouthfeel. After the drying process, the lignification degree of AN1 was the lowest. Through textural analyses, the hardness of AN1 was relatively low compared to the other dried areca nuts. AN1, CAN1 and CAN2 had higher alkaline pectin content and viscosity, and better flavor retention, which indicated better edible quality. The present study revealed the differences of various areca nuts and provided vital information to further advance the study of areca nuts.

Development of a Novel Low-Calorie Lime Juice-Based Prebiotic Beverage Using a Combined Design Optimization Methodology

A novel lime-juice based low-calorie functional beverage was developed by using D-optimal combined design optimization. For the preparation of the beverage, the following functional ingredients were used: lime juice, lime peel essential oil (LEO) as a flavoring agent and bioactive component, sucralose as a low-calorie sweetener, an inulin/polydextrose (I/P) mixture as prebiotic fibers, pectin as a thickening agent and soluble dietary fiber, lutein as a carotenoid colorant and antioxidant, and peppermint extract (ME) as a flavoring agent and bioactive component. A combined design consisting of one mixture factor (LEO/ME ratio), one numeric factor (lutein concentration), and one categoric factor (presence or absence of prebiotics) was used for optimizing the functional beverage based on the sensory quality. Regression models were adequately fitted to the data of sensory acceptance with a determination coefficient >90%. The sample containing a mixture of prebiotics, 2:3 (v/v) ratio of LEO: ME, and 3 mg/100 mL lutein was selected as the best formulation among the six optimal beverages which was suggested by Design-Expert software. This final optimum sample showed the highest total phenolic (44.22 mg gallic acid equivalents/L) and flavonoid (25.49 mg quercetin equivalents/L) contents, and its antioxidant activity (as 2,2-diphenyl-1-picrylhydrazyl radical (DPPH^•) scavenging) was 38.30%. The newly designed beverage has the potential to promote health benefits and in therapeutic applications.

Amperometric microbial biosensor for sugars and sweetener classification using principal component analysis in beverages

Sugar and artificial sweeteners are additives in packaged food and beverage products that are widely used, where excessive sugar consumption can cause an increase in various diseases. Detection and classification of natural sugars sucrose, fructose, glucose, and artificial sweetener aspartame are needed to determine the effects of consuming these sweeteners. This study uses an amperometric biosensor integrated biochip-D, which uses Saccharomyces cerevisiae as a bioreceptor through cellular metabolic respiration activity expressed in dissolved oxygen (DO) levels. The variations of sweetener concentration used were in the range of 50 mM to 250 mM. The measurement results showed that the higher the concentration of sugar and artificial sweeteners, the lower DO levels would be measured. It was due to the yeast cell respiration in consuming oxygen (O2) and producing carbon dioxide (CO2), where the decrease in DO levels of sucrose was 14.24%, fructose was 18.02%, glucose was 16.59%, and aspartame was 20.45% at a concentration of 250 mM. The measurement data was clustered and classified using principal component analysis (PCA), which resulted in data variance percentages of 92.80% and 89.40% for the two main components. In the application studies of the biosensor, sensitive determination of sugar in the beverage samples was investigated.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-022-05625-8.

Identification of hepatocellular carcinoma and paracancerous tissue based on the peak area in FTIR microspectroscopy

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies across the world. The annual incidence and death rates have increased at the highest rate of all cancers in recent years. Surgical resection is a potentially curative option for solitary HCC or unilobar disease without evidence of metastases or vascular invasion. This study focuses on the molecular differences between the HCC foci and paracancerous tissues and provides some valuable biomarkers based on the vibrational spectrum. Fourier transform infrared (FTIR) spectroscopy is a non-invasive and qualitative and semi-quantitative analysis technique that has been widely applied for the identification of macromolecular changes in biological tissues. In this study, the FTIR spectra of the HCC foci and the paracancerous tissues were recorded separately, and ten areas under the absorption peaks of all the specimens were calculated. The result demonstrates that the areas of protein-related absorption peaks at 1398 cm^-1, 1548 cm^-1, 1654 cm^-1 and 3070 cm^-1 may be the key indicators of the two different regions. After coupling with the classification algorithms of k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM), it was found that SVM with an RBF kernel performed best with the AUC (area under the ROC curve) reaching 0.997, and the performance was better than the feature based on the full spectrum. This reveals that the peak area-based FTIR spectra combined with the SVM algorithm may be a promising tool in identifying the HCC foci and the paracancerous tissues.

Structure-guided discovery of antioxidant peptides bounded to the Keap1 receptor as hunter for potential dietary antioxidants

Human health can be damaged by free radicals, and antioxidant peptides are excellent radical scavengers. Antioxidant tripeptides data set based on 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulofnic acid) (ABTS) assay was created, 9 types of descriptors were integrated and 4 quantitative structure-activity relationship (QSAR) models were constructed in this study. Several structural factors influencing the activity of antioxidant tripeptides and the dominant amino acids at each position of tripeptides were revealed by the optimal model. Ten food-derived tripeptides with higher activity were selected for synthesis and activity determination. Molecular docking results demonstrated that these tripeptides were stably bound to the Keap1 receptor, further elucidating the antioxidant mechanism. It was known from the simulation of gastrointestinal digestion experiments that the model results possessed a guiding effect on the selection of proteins with high antioxidant activity. The performance of the model was proved to be robust after validation.

Machine learning concept in de-spiking process for nuclear resonant vibrational spectra - automation using no external parameter

Nuclear resonant vibrational spectroscopy (NRVS) is a relatively new spectroscopic method which measures site-specific vibrational information and is especially useful in the area of biochemical researches. It also has an almost zero background and is suitable for measuring weak signals but needs a lot of scans to complete one real spectrum. Due to various reasons, some NRVS scans have occasional spike(s), which can introduce fake peak(s) when the averaged spectrum is converted into partial vibrational density of state (PVDOS) and can mislead the deduction of the corresponding structural information from it. For better use of the NRVS spectra with occasional spikes, people have to identify and smooth the sporadic spiky points while leaving all other points untouched. In this publication, we used the concept of machine learning and created a fully automated procedure for screening and smoothing the occasional spiky points in NRVS spectra. The procedure uses the statistical information obtained from the particular NRVS scan to be processed itself and needs neither an external parameter nor the information from other NRVS scans. A corresponding R subroutine code is also presented to batch process large numbers of measured NRVS scans. This work is the first attempt toward organizing an automatic de-spiking process for NRVS scans without using an external parameter.

Removal of Zn(II) and Ni(II) heavy metal ions by new alginic acid-ester derivatives materials

The present work concerns the preparation of new materials based on alginic acid (AA) and diols in a facile and efficient process by improving the adsorption properties of Zn(II) and Ni(II) metal ions on the modified AA. The materials were analysed by zeta potential, thermogravimetric analysis (TGA), derivative thermogravimetry (DTG), in addition to the Fourier Transform InfraRed spectroscopy (FTIR), scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) before and after the adsorption behaviour was conducted. The results show that the esterification of AA with diols of different lengths significantly improves its adsorption efficiency of Zn(II) and Ni(II) with Q_max up to 200 mg/g and 185.185 mg/g respectively. Equilibrium and kinetic studies showed that the Langmuir and Freundlich adsorption isotherm models fit the experimental data well, and followed a pseudo-first order kinetic model and the particle diffusion model with correlation coefficients R² ≈ 1.

Analytical Methods for Determination of Non-Nutritive Sweeteners in Foodstuffs

Sweeteners have been used in food for centuries to increase both taste and appearance. However, the consumption of sweeteners, mainly sugars, has an adverse effect on human health when consumed in excessive doses for a certain period, including alteration in gut microbiota, obesity, and diabetes. Therefore, the application of non-nutritive sweeteners in foodstuffs has risen dramatically in the last decade to substitute sugars. These sweeteners are commonly recognized as high-intensity sweeteners because, in a lower amount, they could achieve the same sweetness of sugar. Regulatory authorities and supervisory agencies around the globe have established the maximum amount of these high-intensity sweeteners used in food products. While the regulation is getting tighter on the market to ensure food safety, reliable analytical methods are required to assist the surveillance in monitoring the use of high-intensity sweeteners. Hence, it is also necessary to comprehend the most appropriate method for rapid and effective analyses applied for quality control in food industries, surveillance and monitoring on the market, etc. Apart from various analytical methods discussed here, extraction techniques, as an essential step of sample preparation, are also highlighted. The proper procedure, efficiency, and the use of solvents are discussed in this review to assist in selecting a suitable extraction method for a food matrix. Single- and multianalyte analyses of sweeteners are also described, employing various regular techniques, such as HPLC, and advanced techniques. Furthermore, to support on-site surveillance of sweeteners’ usage in food products on the market, non-destructive analytical methods that provide practical, fast, and relatively low-cost analysis are widely implemented.

Geographical traceability and multielement analysis of edible and medicinal fungi: Taking Wolfiporia cocos (F.A. Wolf) Ryvarden and Gilb. as an example

Different geographical environment has a certain influence on the accumulation of fungi elements and chemical components. However, our knowledge is limited to elucidate the fungi elements in response to heterogeneous environmental and the quality differences among different habitats. Here, multielement analysis, FTIR spectrum, and feature-level fusion technique combined with chemometrics were used to study Wolfiporia cocos from different geographical areas, different sampling sites and different altitude sources. From the results, (1) there is significant difference in element content of samples from different sampling sites and no positive correlation with geographical ranges. (2) There is a correlation between elevation and elements, and relatively low elevation (<1,800 m) is conducive to the enrichment of elements. (3) From the perspective of elements, the W. cocos in Yuxi have relatively better quality. (4) FTIR and feature-level models can well realize origin identification. The SVM models are better than the PLS-DA models, and the feature-level model is better than the single FTIR models. In summary, this study demonstrated that the developed method was reliable and could realize the genuineness evaluation and origin identification of W. cocos. The results have implications for the establishment of the technology system of geographical traceability and the development of high-quality geographical indication products of W. cocos.

Weighted multiscale support vector regression for fast quantification of vegetable oils in edible blend oil by ultraviolet-visible spectroscopy

Weighted multiscale support vector regression combined with ultraviolet-visible (UV-Vis) spectra for quantitative analysis of edible blend oil is proposed. In the approach, UV-Vis spectra of the training set are decomposed into a certain number of intrinsic mode functions (IMFs) and a residue by empirical mode decomposition (EMD) at first. Then support vector regression (SVR) sub-models are built on each IMF and residue. For prediction set, the spectra are decomposed as done on the training set and the final predictions are obtained by integrating SVR sub-model predictions by weighted average. The weight of the sub-model is the reciprocal of the fourth power of the root mean square error of cross-validation (RMSECV). For predicting peanut oil in binary blend oil and sesame oil in ternary blend oil, the proposed method has superiority in root mean square error of prediction (RMSEP) and correlation coefficient (R) compared with SVR and partial least squares (PLS).

Determining High-Intensity Sweeteners in White Spirits Using an Ultrahigh Performance Liquid Chromatograph with a Photo-Diode Array Detector and Charged Aerosol Detector

In China, white spirit is not only an alcoholic drink but also a cultural symbol. A novel and accurate method for simultaneously determining nine sweeteners (most authorized for use in China) in white spirits by ultrahigh performance liquid chromatography (UHPLC) with a photo-diode array detector (PDA) and charged aerosol detector (CAD) was developed. The sweeteners were acesulfame, alitame, aspartame, dulcin, neotame, neohesperidine dihydrochalcone, saccharin, sodium cyclamate, and sucralose. The sweeteners were separated within 16 min using a BEH C18 column and linear gradient-elution program. The optimized method allowed low concentrations (micrograms per gram) of sweeteners to be simultaneously detected. The CAD gave good linearities (correlation coefficients > 0.9936) for all analytes at concentrations of 0.5 to 50.0 μg/g. The limits of detection were 0.16 to 0.77 μg/g. Acesulfame, dulcin, neohesperidine dihydrochalcone, and saccharin were determined using the PDA detector, which gave correlation coefficients > 0.9994 and limits of detection of 0.16 to 0.22 μg/g. The recoveries were 95.1% to 104.9% and the relative standard deviations were 1.6% to 3.8%. The UHPLC-PDA-CAD method is more convenient and cheaper than LC-MS/MS methods. The method was successfully used in a major project called “Special Action against Counterfeit and Shoddy white spirits” and to monitor risks posed by white spirits in China.