Implementation of chemometrics in quality evaluation of food and beverages

Study on chemical characterization and sleep-improvement function of Prunella vulgaris L. based on the functional components

Prunella vulgaris L. (P. vulgaris) has great application value and development prospects in improving sleep. In this study, we continued to evaluate the sleep-improvement function and mechanism of P. vulgaris from both chemical characterization and function based on sleep-improvement functional ingredients, rosmarinic acid and salviaflaside, screened out in the previous stage as the index components. The chemical constituents of P. vulgaris and its phenolic acid fraction were characterized by the UPLC-MSn technology. The quality of the sleep-improvement phenolic acid fraction of P. vulgaris was scientifically evaluated by fingerprints combined with quantitative analysis of rosmarinic acid and salviaflaside. The function of phenolic acid parts of P. vulgaris in improving sleep was verified by different insomnia models including the PCPA-induced insomnia model and surface platform sleep deprivation model. HE staining was used to observe the effect of P. vulgaris on the morphology of nerve cells in different brain regions. In vivo experiments and molecular docking explored the sedative-hypnotic effects of functional ingredients of P. vulgaris. All these results investigated the material basis and mechanism of P. vulgaris to improve sleep from multiple perspectives, which contribute to providing a basis for the development of functional food to improve sleep.

Nanotechnology-based optical sensors for Baijiu quality and safety control

Baijiu quality and safety have received considerable attention owing to the gradual increase in its consumption. However, owing to the unique and complex process of Baijiu production, issues leading to quality and safety concerns may occur during the manufacturing process. Therefore, establishing appropriate analytical methods is necessary for Baijiu quality assurance and process control. Nanomaterial (NM)-based optical sensing techniques have garnered widespread interest because of their unique advantages. However, comprehensive studies on nano-optical sensing technology for quality and safety control of Baijiu are lacking. In this review, we systematically summarize NM-based optical sensor applications for the accurate detection and quantification of analytes closely related to Baijiu quality and safety. Furthermore, we evaluate the sensing mechanisms for each application. Finally, we discuss the challenges nanotechnology poses for Baijiu analysis and future trends. Overall, nanotechnological approaches provide a potentially useful alternative for simplifying Baijiu analysis and improving final product quality and safety.

Fast and low-cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near-infrared devices

BACKGROUND

The current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time-consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near-infrared spectroscopy and compare the performance of conventional (1100-2500 nm spectral range) and portable equipment (1100-1700 nm spectral range). Partial least-squares regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest.

RESULTS

The root-mean-square error of prediction values for nitrogen and carbon were low (2.42 g kg-1 and 4.37 g kg-1 respectively) for the benchtop method yielded low but higher for the portable method (3.82 g kg-1 and 10.7 g kg-1 respectively). The benchtop method did not show significant differences when compared with the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork.

CONCLUSION

The methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost-effective, safe analytical data aligning with the principles of green analytical chemistry. © 2023 Society of Chemical Industry.

Discrimination of Dioscorea species (Chinese yam) using FT-IR integrated with chemometric approach

Dioscorea oppositifolia is an important crop and functional food. D. oppositifolia tuber is often adulterated with D. persimilis, D. alata, and D. fordii tuber in the commercial market. This study proposed an integrated Fourier transform infrared spectroscopy (FT-IR) with chemometric approach to differentiate these four Dioscorea species. A total of 107 Dioscorea spp. tuber samples were collected from different locations in China. Principal Component Analysis (PCA), PCA-Class, and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were utilised to classify the FT-IR spectra. In this PCA is unable to differentiate the Dioscorea spp. tuber effectively. However, PCA-Class and OPLS-DA can distinguish spp. these 4 species Dioscorea tuber with high accuracy, sensitivity, and specificity. Additionally, the RMSEE, RMSEP and RMSECV values for OPLS-DA model were low, showing that it is a good model. The combination of FT-IR with the PCA-Class and OPLS-DA is practical in discriminating Dioscorea spp. tubers.

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

Identification of antibiotic residues in aquatic products with surface-enhanced Raman scattering powered by 1-D convolutional neural networks

Universal and fast antibiotic residues detection technology is imperative for the control of food safety in aquatic products. However, accurate surface-enhanced Raman scattering (SERS) quantitative detection of complicated samples is still a challenge. A recognition method powered by deep learning and took advantage of the unique fingerprint information merits of SERS was proposed. Herein, the spectra were collected by Ag nanofilm SERS substrate prepared by self-assembly of Ag nanoparticles on water/oil interface. A SERS-based database of commonly used antibiotics in aquatic products was set up, which is suitable for employed as input data for learning and training. The results show that the five types of antibiotics are successfully distinguished through principal component analysis (PCA) and each antibiotic in every type was successfully distinguished. Furthermore, one-dimensional convolutional neural networks (1-D CNN) was used to distinguish the antibiotics, and the results show that all the test samples were correctly predicted by 1-D CNN model. The results of this research suggest the great potential of the combination of SERS spectra with deep learning as a method for rapid and highly accurate identification of antibiotic residues in aquatic products.

A simple method to quantify azo dyes in spices based on flow injection chromatography combined with chemometric tools

Para Red (PR) and Sudan dyes have been illegally used as colorants to adulterate certain foods by enhancing their red/orange colour. In addition, they are toxic and carcinogenic. This work presents the development of a simple flow injection chromatographic method combined with chemometric tools to perform the determination of PR, Sudan I (SI) and Sudan II (SII) in food samples. The flow chromatographic system consisted of a low-pressure manifold coupled to a reverse phase monolithic column. A Partial Least Square (PLS) model was applied to resolve overlapped absorption spectra registered for each dye at the corresponding retention time. The relative errors of calibration (RMSECV, %) were 0.49, 0.85 and 0.23, and the relative errors of prediction (RMSEP, %) were 1.12, 0.75 and 0.33 for PR, SI and SII, respectively. The residual predictive deviation (RPD) values obtained were higher than 3.00 for all analytes. The method was successfully applied to quantify the dyes in six different commercial spices samples. The results were compared with the HPLC reference method concluding that there were no significant differences at the studied confidence level (α = 0.05). The proposed method can be used to rapidly determine the analytes in a simple, reliable, low-cost and environmentally-friendly manner.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13197-021-05299-8.

NMR technique and methodology in botanical health product analysis and quality control

Botanicals have played an important role in maintaining human health and well-being throughout history. During the past few decades in particular, the use of botanical health products has gained more popularity. Whereas, quality, safety and efficacy concerns have continuously been critical issues due to the intrinsic chemical complexity of botanicals. Chemical analytical technologies play an imperative role in addressing these issues. Nuclear magnetic resonance (NMR) spectroscopy has proven to be a powerful and useful tool for the investigation of botanical health products. In this review, NMR techniques and methodologies that have been successfully applied to the research and development of botanical health products in all stages, from plants to products, are discussed and summarized. Furthermore, applications of NMR together with other analytical techniques in a variety of domains of botanical health products investigation, such as plant species differentiation, adulteration detection, and bio-activity evaluation, are discussed and illustrated with typical examples. This article provides an overview of the potential uses of NMR techniques and methodologies in an attempt to further promote their recognition and utilization in the field of botanical health products analysis and quality control.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.

Fine cocoa beans production: Tracking aroma precursors through a comprehensive analysis of flavor attributes formation

The fine flavor cocoa (FFC) market offers cocoa farmers better monetary and nonmonetary benefits than the bulk market. In this work, during cocoa fermentation, flavor formation was studied at different fermentation times based on sensory profiles, volatile compound contents and untargeted metabolomics. It was observed that chocolate quality is influenced by fermentation time. Thus, at 72 h, the sensory profiles showed no outstanding attributes, while at 96 h, the global quality presented a stronger influence of fine attributes, such as fruitiness, florality, spices and nuttiness. Finally, at 120/144 h, these FFC features diminished. Metabolomic fingerprint of cocoa beans (related to peptides, sugars, amino acids, and phenolic compounds) and the volatile fingerprint of chocolate showed a change according to the fermentation time. This allowed the proposal of 96 h as the optimal fermentation time to produce FFC beans. Additionally, 20 volatiles and 48 discriminating metabolites were defined as potential quality biomarkers.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine

Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Effects of Ephedra alata extract on the quality of minced beef meat during refrigerated storage: A chemometric approach

The biopreservative effect of Ephedra alata aqueous extract (EAE), used at 0.156, 0.312 and 0.624%, on minced beef meat was evaluated by microbiological, physicochemical and sensory analyses during storage at 4 °C for 14 days. The results showed that EAE significantly (P < .05) delayed the formation of thiobarbituric acid-reactive substances and carbonyls and reduced the sulfhydryl loss in a dose-dependent manner, indicating that EAE had a protective effect against lipids and protein oxidation. Concomitantly, an increase of redness and loss of lightness and yellowness was observed. Furthermore, two multivariate exploratory techniques, namely Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were applied to all obtained data describing the main characteristics attributed to refrigerated meat samples. During storage time, the used chemometric approaches were useful in discriminating meat samples, and therefore offers an approach to underlay connections between meat quality features. The obtained findings demonstrated the strong potential of EAE as a natural preservative in meat and meat products.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

An Innovative Metabolomic Approach for Golden Rum Classification Combining Ultrahigh-Performance Liquid Chromatography-Orbitrap Mass Spectrometry and Chemometric Strategies

A comprehensive fingerprinting strategy for golden rum classification considering different categories such as fermentation barrel, raw material, and aging is provided, using a metabolomic fingerprinting approach. A nontarget fingerprinting of 30 different rums using liquid chromatography coupled to high-resolution mass spectrometry (Exactive Orbitrap mass analyzer, LC-HRMS) was applied. Principal component analysis (PCA) was used to assess the overall structure of the data and to identify potential outliers. Different chemometric analyses such as partial least-squares discriminant analysis (PLS-DA) were used. A variable importance in projection (VIP) selection method was applied to identify the most significant markers that allow group separation. Compounds related to aging and fermentation processes such as furfural derivates (e.g., hydroxymethylfurfural) and sugars (e.g., glucose, mannitol) were found as the most discriminant compounds (VIP threshold value >1.5). Suitable separation according to selected categories was achieved, and a classification ability of the models of close to 100% was achieved.

Authenticity and geographic origin of global honeys determined using carbon isotope ratios and trace elements

Honey is the world's third most adulterated food. The addition of cane sugar or corn syrup and the mislabelling of geographic origin are common fraudulent practices in honey markets. This study examined 100 honey samples from Australia (mainland and Tasmania) along with 18 other countries covering Africa, Asia, Europe, North America and Oceania. Carbon isotopic analyses of honey and protein showed that 27% of commercial honey samples tested were of questionable authenticity. The remaining 69 authentic samples were subject to trace element analysis for geographic determination. One-way ANOVA analysis showed a statistical difference (p < 0.05) in trace element concentrations of honey from Australian regions and different continents. Principal component analysis (PCA) and canonical discriminant analysis (CDA) coupled with C5.0 classification modelling of honey carbon isotopes and trace element concentrations showed distinct clusters according to their geographic origin. The C5.0 model revealed trace elements Sr, P, Mn and K can be used to differentiate honey according to its geographic origin. The findings show the common and prevalent issues of honey authenticity and the mislabelling of its geographic origin can be identified using a combination of stable carbon isotopes and trace element concentrations.

Detection and quantification of offal content in ground beef meat using vibrational spectroscopic-based chemometric analysis

As less consumed animal by-product, beef and pork offal have chances to sneak into the authentic ground beef meat products, and thus a rapid and accurate detection and quantification technique is highly required. In this study, Fourier transformed-infrared (FT-IR) spectroscopy was investigated to develop an optimized protocol for analyzing ground beef meat potentially adulterated with six types of beef and pork offal. Various chemometric models for classification and quantification were constructed for the collected FT-IR spectra. Applying optimized chemometric models, FT-IR spectroscopy could differentiate authentic beef meat from adulterated samples with >99% accuracy, to identify the type of offal in the sample with >80% confidence, and to quantify five types of offal in an accurate manner (R² > 0.81). An optimized protocol was developed to authenticate ground beef meat as well as identify and quantify the offal adulterants using FT-IR spectroscopy coupled with chemometric models. This protocol offers a limit of detection <10% w/w of offal in ground beef meat and can be applied by governmental laboratories and food industry to rapidly monitor the integrity of ground beef meat products.

Spectral Mining for Discriminating Blood Origins in the Presence of Substrate Interference via Attenuated Total Reflection Fourier Transform Infrared Spectroscopy: Postmortem or Antemortem Blood?

Often in criminal investigations, discrimination of types of body fluid evidence is crucially important to ascertain how a crime was committed. Compared to current methods using biochemical techniques, vibrational spectroscopic approaches can provide versatile applicability to identify various body fluid types without sample invasion. However, their applicability is limited to pure body fluid samples because important signals from body fluids incorporated in a substrate are affected strongly by interference from substrate signals. Herein, we describe a novel approach to recover body fluid signals that are embedded in strong substrate interferences using attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy and an innovative multivariate spectral processing. This technique supported detection of covert features of body fluid signals, and then identified origins of body fluid stains on substrates. We discriminated between ATR FT-IR spectra of postmortem blood (PB) and those of antemortem blood (AB) by creating a multivariate statistics model. From ATR FT-IR spectra of PB and AB stains on interfering substrates (polyester, cotton, and denim), blood-originated signals were extracted by a weighted linear regression approach we developed originally using principal components of both blood and substrate spectra. The blood-originated signals were finally classified by the discriminant model, demonstrating high discriminant accuracy. The present method can identify body fluid evidence independently of the substrate type, which is expected to promote the application of vibrational spectroscopic techniques in forensic body fluid analysis.

Quantitative Profiling of Ester Compounds Using HS-SPME-GC-MS and Chemometrics for Assessing Volatile Markers of the Second Fermentation in Bottle

A quantitative approach using HS-SPME-GC-MS was performed to investigate the ester changes related to the second fermentation in bottle. The contribution of the type of base wine to the final wine style is detailed. Furthermore, a discriminant model was developed based on ester changes according to the second fermentation (with 100% sensitivity and specificity values). The application of a double-check criteria according to univariate and multivariate analyses allowed the identification of potential volatile markers related to the second fermentation. Some of them presented a synthesis-ratio around 3-fold higher after this period and they are known to play a key role in wine aroma. Up to date, this is the first study reporting the role of esters as markers of the second fermentation. The methodology described in this study confirmed its suitability for the wine aroma field. The results contribute to enhance our understanding of this fermentative step.