Food identification by high performance liquid chromatography fingerprinting and mathematical processing

A Review of Gastrodia Elata Bl.: Extraction, Analysis and Application of Functional Food

Gastrodia elata Bl. still widely known as a medicinal plant due to its anti-inflammatory, neuroprotection, cardiovascular protection etc. Additionally, these medical applications cannot be separated from its antioxidant, anti-aging, regulating cell apoptosis ability, which make it have potential as a functional food as well as it has been eaten for more than 2,000 years in China. At present, although Gastrodia elata Bl. has appeared in a large number of studies, much of the research is based on drugs rather than foods. The review of Gastrodia elata Bl. from the perspective of food is one of the necessary steps to promote related development, by reviewing the literature on analytical methods of Gastrodia elata Bl. in recent years, critical components change in the extraction, analytical methods and improvement of food applications, all of aspects of it was summarized. Based on the report about physical and chemical changes in Gastrodia elata Bl. to discover the pathway of Gastrodia elata Bl. functional food development from current to the future.

Mass spectrometry imaging in plants, microbes, and food: a review

Plant health, which affects the nutritional quality and safety of derivative food products, is influenced by symbiotic interactions with microorganisms. These interactions influence the local molecular profile at the tissue level. Therefore, studying the distribution of molecules within plants, microbes, and plant-based food is crucial to assess plant health, ensure the safety and quality of the agricultural products that become part of our food supply, and plan agricultural management practices. Within this framework, the molecular distribution within plant-based samples can be visualized with mass spectrometry imaging (MSI). This review describes key MSI methodologies, highlighting the role they play in unraveling the localization of metabolites, lipids, proteins, pigments, and elemental components across plants, microbes, and food products. Furthermore, investigations that involve multimodal molecular imaging approaches combining MSI with other imaging techniques are described. The advantages and limitations of the different MSI techniques that influence their applicability in diverse agro-food studies are described to enable informed choices for tailored analyses. For example, some MSI technologies involve meticulous sample preparation while others compromise spatial resolution to gain throughput. Key parameters such as sensitivity, ionization bias and fragmentation, reference database and compound class specificity are described and discussed in this review. With the ongoing refinements in instrumentation, data analysis, and integration of complementary techniques, MSI deepens our insight into the molecular biology of the agricultural ecosystem. This in turn empowers the quest for sustainable and productive agricultural practices.

Targeted and untargeted screening of a plant extract library established from raw materials originating from Upper Austria

Plant extracts rich in phytochemicals are known for their health benefits. Plant extract library from edible plants obtained from the region of Upper Austria was prepared. Food grade extraction procedures were applied, and relevant physico-chemical parameters measured. A focus on polyphenolic compounds revealed a significant correlation between the total phenolic content (measured by a colorimetric assay) and the cumulated concentration of main individual polyphenols (measured by HPLC-DAD), demonstrating the comparability of these parameters. Targeted screening was performed by HPLC-FLD and -MS for the presence of phytomelatonin. 20 extracts were identified with concentrations of up to 1.4 µg/mL of this phytochemical, which attracts much attention from the food industry. Finally, chemometric methods were employed to cluster extracts based on their phenolic compound profile. This approach allows for an informed preselection of extracts without the need for comprehensive chemical analysis.

Comprehensive quality control of silkworm chrysalis using chemical fingerprints combined with antioxidant activity and acetylcholine content

In recent years, some insects have become foods due to their high nutritional value. In order to solve the problem of the lack of quality control methods for insect foods, this study proposes a comprehensive control model using silkworm chrysalis (SC) as an example. Firstly, five-wavelength mean fusion fingerprints (FWMFF) and UV quantum fingerprints of 21 batches of SC were established. And the 21 batches of SC were classified into different grades from different perspectives by using the comprehensive linear quantified fingerprint method (CLQFM) as a quality evaluation method for qualitative and quantitative analysis. Secondly, this paper fully considered the issue of the reliability of fingerprint evaluation, which guaranteed the accuracy of the evaluation results. On this basis, the antioxidant capacity of the samples was used in vitro 1,1-Diphenyl-2-picrylhydrazylradical (DPPH) scavenging assay using IC50. The relationship between fingerprints and antioxidant activity was also discussed. Finally, the content of endogenous neurotransmitter (ACh) in SC determined by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) in the range of 0.25-2.11μg/g. Overall, the present study proposes a comprehensive quality control strategy for functional foods based on the quality assessment of SC.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Several Feature Extraction Methods Combined with Near-Infrared Spectroscopy for Identifying the Geographical Origins of Milk

Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed to extract the near-infrared spectral information of milk by combining fuzzy set theory with direct linear discriminant analysis (DLDA). First, spectral data of the milk samples were collected by a portable NIR spectrometer. Then, the data were preprocessed by Savitzky-Golay (SG) and standard normal variables (SNV) to reduce noise, and the dimensionality of the spectral data was decreased by principal component analysis (PCA). Furthermore, linear discriminant analysis (LDA), DLDA, and FDLDA were employed to transform the spectral data into feature space. Finally, the k-nearest neighbor (KNN) classifier, extreme learning machine (ELM) and naïve Bayes classifier were used for classification. The results of the study showed that the classification accuracy of FDLDA was higher than DLDA when the KNN classifier was used. The highest recognition accuracy of FDLDA, DLDA, and LDA could reach 97.33%, 94.67%, and 94.67%. The classification accuracy of FDLDA was also higher than DLDA when using ELM and naïve Bayes classifiers, but the highest recognition accuracy was 88.24% and 92.00%, respectively. Therefore, the KNN classifier outperformed the ELM and naïve Bayes classifiers. This study demonstrated that combining FDLDA, DLDA, and LDA with NIR spectroscopy as an effective method for determining the origin of milk.

Advances, challenges, and opportunities for food safety analysis in the isothermal nucleic acid amplification/CRISPR-Cas12a era

Global food safety stands out as a prominent public concern, affecting populations worldwide. The recurrent challenge of food safety incidents reveals the need for a robust inspection framework. In recent years, the integration of isothermal nucleic acid amplification with CRISPR-Cas12a techniques has emerged as a promising tool for molecular detection of food hazards, presenting next generation of biosensing for food safety detection. This paper provides a comprehensive review of the current state of research on the synergistic application of isothermal nucleic acid amplification and CRISPR-Cas12a technology in the field of food safety. This innovative combination not only enriches the analytical tools, but also improving assay performance such as sensitivity and specificity, addressing the limitations of traditional methods. The review summarized various detection methodologies by the integration of isothermal nucleic acid amplification and CRISPR-Cas12a technology for diverse food safety concerns, including pathogenic bacterium, viruses, mycotoxins, food adulteration, and genetically modified foods. Each section elucidates the specific strategies employed and highlights the advantages conferred. Furthermore, the paper discussed the challenges faced by this technology in the context of food safety, offering insightful discussions on potential solutions and future prospects.

Identification of Adulteration of Flaxseed Oil From QINGHAI Area Using GC-MS Profiling of Phytosterol Composition and Chemometrics

Flaxseed oil is an important source of vegetable oil with a polyunsaturated fatty acid. It is significant to establish a method to quickly identify adulterated flaxseed oil. In the present study, the qualitative and quantitative analysis of phytosterol of flaxseed oil from different varieties and different production areas in the Qinghai area was first performed by gas chromatography-mass spectrometry (GC-MS) and the phytosterol standard profile of flaxseed oil was established. Then, a combination of similarity evaluation and cluster analysis was used to distinguish pure flaxseed oil from flaxseed oil adulterated with concentrations of 10-50% rapeseed oil, peanut oil, sunflower oil, and sesame oil, and discriminant analysis was used to identify the types of adulterated flaxseed oil. The results showed that similarity evaluation combined with cluster analysis can distinguish pure and adulterated flaxseed oil when the concentration of the adulterant was greater than 10%. Discriminant analysis models accurately identified the types of adulterating oil in flaxseed oil when the concentration of rapeseed, peanut, or sunflower oil was greater than 20%, and that of sesame oil was greater than 30%. This study shows that the determination of the phytosterol composition and chemometrics is a valuable tool to evaluate the purity of flaxseed oil.

Application of DNA-fueled molecular machines in food safety testing

Food is consumed by humans, which is indispensable to human life. Therefore, considerable attention of the whole society has been paid to food safety. Over the last few years, dramatic social development has brought new challenges to food safety, making developing new and quick methods for on-site food safety testing an important necessity. As a result, DNA-fueled molecular machines, characterized by high efficiency, accuracy, and sensitivity in testing, have come into the spotlight, based on which sensors can be constructed to detect toxic and harmful substances in food products. This study reviewed recent research on several DNA-fueled molecular machines, including DNA tweezers, DNA walkers, and DNA origami, for rapidly detecting toxic and harmful substances. Based on the above studies, the sensitivity and timeliness of several DNA molecular machines were summarized and compared, and the development prospect of DNA fuel molecular machines in the field of food safety detection was prospected.

Quality evaluation of Keteling capsules based on fingerprinting, multicomponent quantification, and quantitative prediction

The Keteling capsule (KC) is a traditional Chinese medicine (TCM) made from the dried extract of Ficus microphylla and an appropriate amount of chlorpheniramine maleate. It is widely used to treat cough and relieve asthma. Despite its extensive usage, a rapid and comprehensive quality evaluation strategy for KC remains a challenge. This study introduces an electrochemical fingerprint analysis technique, in addition to the commonly employed HPLC fingerprints, for efficient and convenient quality evaluation. Moreover, a cost-effective, rapid, and accurate multi-component quantification technique known as the "Multi-markers assay by the monolinear method (MAML)" and the "FT-IR quantitative model" were explored. The HPLC fingerprints were evaluated using a systematically quantified fingerprint method, while the electrochemical fingerprints, based on the Belousov-Zhabotinsky oscillation reaction principle, were effectively analyzed and characterized using oxidation induction times and oscillation lifetimes. Multi-component quantitative analysis was carried out through the MAML and FT-IR quantitative models. The HPLC fingerprint successfully classified the 22 samples into eight grades with excellent discrimination. Active ingredient content analysis was achieved using reliable parameters obtained from electrochemical fingerprinting. The no significant difference in the quantitative results proves the accuracy of the MAML method. Additionally, successful FT-IR quantitative prediction models were developed for chlorogenic acid, isovitexin, and chlorpheniramine maleate. This study offers a dependable and effective approach for enhancing the quality control of KC, and it can provide new insights for improving the quality analysis methods in the field of TCM.

Analysis of the fluorescent properties of vaginal fluid upon ageing

Detection and identification of body fluids are crucial aspects of forensic investigations, aiding in crime scene reconstructions and providing important leads. Although many methods have been developed for these purposes, no method is currently in use in the forensic field that allows rapid, non-contact detection and identification of vaginal fluids directly at the crime scene. The development of such technique is mainly challenged by the complex chemistry of the constituents, which can differ between donors and exhibits changes based on woman's menstrual cycle. The use of fluorescence spectroscopy has shown promise in this area for other biological fluids. Therefore, the aim of this study was to identify specific fluorescent signatures of vaginal fluid with fluorescence spectroscopy to allow on-site identification. Additionally, the fluorescent properties were monitored over time to gain insight in the temporal changes of the fluorescent spectra of vaginal fluid. The samples were excited at wavelengths ranging from 200 to 600 nm and the induced fluorescence emission was measured from 220 to 700 nm. Excitation and emission maps (EEMs) were constructed for eight donors at seven time points after donation. Four distinctive fluorescence peaks could be identified in the EEMs, indicating the presence of proteins, fluorescent oxidation products (FOX), and an unidentified component as the dominant contributors to the fluorescence. To further asses the fluorescence characteristics of vaginal fluid, the fluorescent signatures of protein and FOX were used to monitor protein and lipid oxidation reactions over time. The results of this study provide insights into the intrinsic fluorescent properties of vaginal fluid over time which could be used for the development of a detection and identification method for vaginal fluids. Furthermore, the observed changes in fluorescence signatures over time could be utilized to establish an accurate ageing model.

Comprehensive HPLC fingerprint analysis based on a two-step extraction method for quality evaluation of Perilla frutescens (L.) Britt

Abundant chemical components are key to ensure the evaluation accuracy of fingerprint analysis of traditional Chinese medicines (TCMs). A two-step extraction method combining supercritical fluid extraction (SFE) and water ultrasonic extraction was established for the quality evaluation of Perilla frutescens (L.) Britt. Weakly polar components were extracted under optimal SFE conditions (15% co-solvent (EtOH : n-hexane = 1 : 14, (v/v)), 40 °C, 250 bar, and 30 min), and polar components were subsequently extracted by an ultrasonic step (100% water as solvent, 40 °C, and 45 min). Then, HPLC methods were established, which were validated to be accurate, stable, and reliable. In this work, 25 batches of samples were evaluated and the data were analysed by similarity analysis (SA) and hierarchical cluster analysis (HCA). The similarity values of SFE extracts and aqueous extracts were respectively 0.616-0.999, and 0.252-0.997, proving the importance of the extraction method for the accuracy of the subsequent fingerprint analysis results. For the HCA, 25 samples were divided into two categories (leaves and stems), among which four batches of leaves with less similarity were considered as stems, indicating that quality differences of P. frutescens depending on medicinal parts and origin exist. The two-step extraction method developed in this work has been proved to be suitable for the quality evaluation of TCMs with complex compositions.

Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R²p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R²p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.

Geographical traceability of gelatin in China using stable isotope ratio analysis

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ13C, δ15N, and δ2H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ13C, δ15N, and δ2H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ13C, δ15N, and δ2H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

Study on the Correlation Between the Appearance Traits and Intrinsic Chemical Quality of Bitter Almonds Based on Fingerprint-Chemometrics

Bitter almond is a well-known and commonly used traditional Chinese medicine (TCM) for relieving coughs and asthma. However, the bioactive chemical composition of bitter almonds, especially their amygdalin content, which determines their quality for TCM use, is variable and this can cause problems with formulating and prescribing TCMs based on bitter almonds. Therefore, a simple method was developed to evaluate the compositional quality of bitter almonds from their appearance traits, based on a combination of chromatographic fingerprinting and chemometrics. Bitter almonds were analyzed by high-performance liquid chromatography (HPLC). Hierarchical cluster analysis (HCA) and principal components analysis (PCA) were applied to classify bitter almonds, which split the samples into two independent clusters. Three chemical markers (amygdalin, prunasin, and one unidentified component) were found by partial least squares-discriminant analysis (PLS-DA). What's more, a new PLS-DA model was reconstructed to confirm the obtained chemical markers from PLS-DA. Additionally, the appearance trait indices and amygdalin content of bitter almond were determined and the classification was confirmed by one-way analysis of variance. This method can easily determine the quality of bitter almonds from their appearance alone, high quality correlated closely with kernels that were larger, oblong in shape and heavier.

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Advances in the Application of Liquid Chromatography in the Detection of Pollutants

Food is easy to be contaminated because of its complex composition. Therefore, in order to protect people from potential food contaminants, it is very necessary to test for various contaminants in food. Liquid chromatography is widely used in the field of food safety detection. In addition, with the development of liquid chromatography technology, more and more new instruments are combined with liquid chromatography. Compared with traditional liquid chromatography, combined liquid chromatography has great advantages in efficiency and operation. Therefore, it is rapidly promoted in the field of food safety testing. In this paper, the results of the determination of three kinds of food pollutants by different liquid chromatography methods are reviewed, and the indexes are compared and analyzed.

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.

Identification and Characterization of the Bioactive Polyphenols and Volatile Compounds in Sea Buckthorn Leaves Tea Together With Antioxidant and α-Glucosidase Inhibitory Activities

Sea buckthorn leaves have been used for tea making in food field gradually. This study was carried out to characterize the bioactive polyphenols and volatile compounds in sea buckthorn leaves (SL), sea buckthorn leaves green tea (SGT), and sea buckthorn leaves black tea (SBT) by using high-performance liquid chromatography with an UV detector (HPLC-UV), the liquid chromatography-mass spectrometry (LC-MS), and headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), in combination with multivariate analysis. A total of 48 non-volatile metabolites and 295 volatile metabolites were identified. Then, the total polyphenol and total flavonoid contents in SL, SGT, and SBT were also analyzed. Moreover, SL and SGT showed greater antioxidant activities based on 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), and oxygen radical absorbance capacity (ORAC) results. At the concentration of 0.1 mg/ml, their DPPH and ABTS radical scavenging ratios were 66 to 95%, while SBT exhibited lower antioxidant activity of 26 to 44%. SL, SGT, and SBT displayed moderate α-glucosidase inhibitory activity.

Unsaturated fatty-acid based HPLC fingerprints in combination with quantitative analysis of multi-components by single-marker for the classification of Rana chensinensis ovum

Comprehensive quality evaluation strategy was established for Rana chensinensis ovum based on analytical chemistry and chemometrics.

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.

Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review

In the last decades, the demand for molecular tools for authenticating and tracing agri-food products has significantly increased. Food safety and quality have gained an increased interest for consumers, producers, and retailers, therefore, the availability of analytical methods for the determination of food authenticity and the detection of major adulterations takes on a fundamental role. Among the different molecular approaches, some techniques such as the molecular markers-based methods are well established, while some innovative approaches such as isothermal amplification-based methods and DNA metabarcoding have only recently found application in the agri-food sector. In this review, we provide an overview of the most widely used molecular techniques for fresh and processed agri-food authentication and traceability, showing their recent advances and applications and discussing their main advantages and limitations. The application of these techniques to agri-food traceability and authentication can contribute a great deal to the reassurance of consumers in terms of transparency and food safety and may allow producers and retailers to adequately promote their products.

Meat species identification accuracy improvement using sample set portioning based on joint x-y distance and laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was suitable for the identification of meat species due to fast and less sample preparation. However, the problem of low accuracy rate of the recognition model caused by improper selection of training set samples by random split has severely restricted the development of LIBS in meat detection. Sample set portioning based on the joint x-y distance (SPXY) method was applied for dividing the meat spectra into a training set and a test set. Then, the five kinds of meat samples (shrimp, chicken, beef, scallop, and pig liver) were classified by the support vector machine (SVM). With the random split method, Kennard-Stone method, and SPXY method, the recognition accuracies of the SVM model were 90.44%, 91.95%, and 94.35%, respectively. The multidimensional scaling method was used to visualize the results of the sample split for the interpretation of the classification. The results showed that the identification performance of the SPXY method combined with the SVM model was best, and the accuracy rates of shrimp, chicken, beef, scallop, and pig liver were 100.00%, 100.00%, 100.00%, 78.57%, and 92.00%, respectively. Moreover, to verify the broad adaptability of the SPXY method, the linear discriminant analysis model, the K-nearest neighbor model, and the ensemble learning model were applied as the meat species identification model. The results demonstrated that the accuracy rate of the classification model can be improved with the SPXY method. In light of the findings, the proposed sample portioning method can improve the accuracy rate of the recognition model using LIBS.

Attenuated total reflectance Fourier transform (ATR-FTIR) spectroscopy and chemometrics for organic cinnamon evaluation

Organic food consumption has increased significantly over time. This contributes to the increased demand and price of this kind of food. Among the organic products, cinnamon stands out for its characteristic flavor and bioactive compounds. Thus, the work aimed to verify the potentials of attenuated total reflectance Fourier transform mid-infrared spectroscopy (ATR-FT-MIR) coupled with Parallel Factor Analysis (PARAFAC) for evaluation of cinnamon organic samples. As result, the proposal is feasible in the differentiation of organic cinnamon powder, in which ATR-FT-MIR coupled with PARAFAC showed the differentiation of organic from non-organic ones on the scores mode, the precision at repeatability level on one loading mode, and the spectral region, on the other loading mode, above 2600 cm^-1 was related to the differentiation of the organic and non-organic samples.

Mass spectrometry-based protein and peptide profiling for food frauds, traceability and authenticity assessment

The ever-growing use of mass spectrometry (MS) methodologies in food authentication and traceability originates from their unrivalled specificity, accuracy and sensitivity. Such features are crucial for setting up analytical strategies for detecting food frauds and adulterations by monitoring selected components within food matrices. Among MS approaches, protein and peptide profiling has become increasingly consolidated. This review explores the current knowledge on recent MS techniques using protein and peptide biomarkers for assessing food traceability and authenticity, with a specific focus on their use for unmasking potential frauds and adulterations. We provide a survey of the current state-of-the-art instrumentation including the most reliable and sensitive acquisition modes highlighting advantages and limitations. Finally, we summarize the recent applications of MS to protein/peptide analyses in food matrices and examine their potential in ensuring the quality of agro-food products.

Study on multiple fingerprint profiles control and quantitative analysis of multi-components by single marker method combined with chemometrics based on Yankening tablets

In this study, we explored the quality consistent evaluation method of Yankening Tablets (YKNT) from different manufacturers by using multiple fingerprint profiles, including dual-wavelength ultra-high performance liquid chromatography (UPLC) serial fingerprint and Fourier Transform Infrared Spectroscopy (FT-IR) fingerprint, combined with quantitative analysis of multi-components by single marker (QAMS) method. In the Average method of systematic quantified fingerprint method (AMSQFM), three fingerprint parameters of macro qualitative similarity (S_m-UPLC-FTIR), macro quantitative similarity (P_m-UPLC-FTIR), and the variation coefficient of fingerprint homogeneity (α_UPLC-FTIR) were calculated based on the ratio method. The S_m-UPLC-FTIR values of all the samples were greater than 0.80, the α_UPLC-FTIR values were less than 0.20, and the P_m-UPLC-FTIR values range from 72.8% to 119.8%. Method validation results showed the established fingerprint method had good precision, solution stability, and method repeatability, all samples could be roughly divided into different levels. The contents of berberine (BBR) and baicalin (BCL) measured by the calibration curve method (CCM) and QAMS method were compared, and t-test results (Pvalue > 0.05) indicated there was no significant difference between the two methods, which showed that QAMS could accurately quantify the markers of the YKNT. The explanatory ability (R²Y) values of BBR and BCL in the PLS model were both greater than 0.94, and the root mean square error of estimation (RMSEE) and root mean square error of prediction (RMSEP) values were both less than 2.5, indicating that the established model was reliable. Hierarchical cluster analysis divided all samples into four categories. This research made a major contribution to the quality consistent evaluation of Traditional Chinese Medicine (TCM) and food.

The analysis of active compounds in Flos Chrysanthemi Indici by UHPLC Q exactive HF hybrid Quadrupole-Orbitrap MS and comprehensive quality assessment of its preparation

Flos Chrysanthemi Indici (FCI) is a common food and its preparation (FCIP) is generally made to realize the medicinal value of FCI in a more accurate and controllable way. In this paper, an efficient UHPLC Q Exactive HF Hybrid Quadrupole-Orbitrap MS method was exploited to elucidate the difference of chemical components between FCI and FCIP. Multi-fingerprints (HPLC-FP, UV-FP, and DSC-FP) were established with the evaluation results integrated by a weighted mean algorithm using variation coefficient (CVWM), which is more objective and reasonable. Through this method, the quality grades of 25 FCIP samples from four manufacturers were successfully discriminated. Meanwhile, the PLS model for the profile-efficacy relationship between the HPLC-FP and the antioxidant activities was established, as well as the correlation between the antioxidant ability (IC50) and the DSC curve (enthalpy values) were preliminary explored. In conclusion, this study provides a novel and holistic analytical strategy for food herbal remedies and its preparation.

Standardization of chromatographic signals - Part II: Expanding instrument-agnostic fingerprints to reverse phase liquid chromatography

There is a large amount of literature relating to multivariate analytical methods using liquid chromatography together with multivariate chemometric/data mining methods in the food science field. Nevertheless, dating the obtained results cannot be compared as they are based on data acquired by a particular analytical instrument, thus they are instrument-dependant. Therefore, this creates difficulties in generating a database large enough to gather together all the variability of the samples. The solution to this problem is to obtain an instrument-agnostic chromatographic signal that is independent of the chromatographic state, i.e., measuring instrument or particular condition of the same instrument from which it was acquired. This paper describes the methodology to be followed to obtain standardized instrumental fingerprints when liquid chromatography is used for prior separation. For this purpose both internal and external chemical standards series are used as references. As an application example, we have applied this methodology for the determination of biophenols in olive oil by liquid chromatography coupled to ultraviolet-visible detector (LC-UV), using three different LC-UV instruments. The instrument-agnostic fingerprints obtained show a high grade of similarity, regardless of the state of the chromatographic system or the time of acquisition.

Quality and antioxidant activity evaluation of dandelion by HPLC with five-wavelength fusion fingerprint

A novel strategy was carried out to evaluate the quality of dandelion, with the combination of fusion fingerprint and antioxidant activity.

Development of Electrochemical Oscillation Method for Identification of Prunus persica, Prunus davidiana, and Prunus armeniaca Nuts

In this work, an electrochemical oscillation system has been developed using the Belousov-Zhabotinsky reaction. The effect of the combination of each reagent, reaction temperature, and stirring speed on the induction period, oscillating period, and oscillating life were optimized. The nuts of Prunus persica, Prunus davidiana, and Prunus armeniaca have been widely used for medical purposes. The proposed electrochemical oscillation system was then used for the identification of P. persica, P. davidiana, and P. armeniaca. Three nuts exhibited very different electrochemical oscillation profiles. The dendrogram was divided into three main principal infrageneric clades. Each cluster only contains one species, suggesting that no outlier was observed in this study. Based on the discussed results, we proposed a simple method for herbal medicine identification.

Quality control method of sterols in fermented Cordyceps sinensis based on combined fingerprint and quantitative analysis of multicomponents by single marker

In this study, we established a new pattern for differentiating and comprehensively evaluating the quality of fermented Cordyceps sinensis based on high-performance liquid chromatography (HPLC) fingerprint analysis combined with similar analysis (SA), principal component analysis (PCA), hierarchical cluster analysis (HCA), and the quantitative analysis of multicomponents by single marker (QAMS). These methods indicated that fermented Cordyceps sinensis samples could be categorized into one class by PCA and HCA. The fingerprints of fermented Cordyceps sinensis were established, and four HPLC peaks were identified as ergosterol, daucosterol, stigmasterol, and β-sitosterol in Jinshuibao capsules and tablets (two products of fermented Cordyceps sinensis). Ergosterol was chosen as the internal reference substance, and the relative correction factors (RCFs) between ergosterol and the other three sterols were calculated using the QAMS method. Moreover, the accuracy of the QAMS method was confirmed by comparing the relative error between the results of the method used with those of an external standard method (ESM). No significant difference between the two methods was observed. The total sterols content in Jinshuibao products were calculated by the QAMS method, and the total sterols content of the two products were similar. This study showed that the method established herein was efficient and successful in the identification fermented Cordyceps sinensis and may further act to facilitate systematic quality control of fermented Cordyceps sinensis products.

Variation in secondary metabolites in a unique set of tomato accessions collected in Turkey

In this study, 50 tomato landraces grown in Turkey were investigated in terms of their secondary metabolite profiles. Each accession was planted in 2016 and 2017 in 3 replicates in an open field. In this study, color, pH and brix of the fruit samples were measured and an unbiased LCMS-based metabolomics approach was applied. Based on Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of the relative abundance levels of >250 metabolites, it could be concluded that fruit size was the most influential to the biochemical composition, rather than the geographical origin of accessions. Results indicated substantial biodiversity in various metabolites generally regarded as key to fruit quality aspects, including sugars; phenolic compounds like phenylpropanoids and flavonoids; alkaloids and glycosides of flavour-related volatile compounds. The phytochemical data provides insight into which Turkish accessions might be most promising as starting materials for the tomato processing and breeding industries.

Dispersive liquid-liquid microextraction based on the solidification of floating organic droplets for HPLC determination of three strobilurin fungicides in cereals

In this paper, a dispersive liquid-liquid microextraction method based on the solidification of floating organic droplets, combined with high-performance liquid chromatography (DLLME-SFOD-HPLC), was developed for the detection of strobilurin fungicides (azoxystrobin, pyraclostrobin, and trifloxystrobin) in cereals. Natural fatty acids were used as an extractant and have low toxicity, density, and freezing point. The extractant nonanoic acid was evenly dispersed as droplets in sample solution and was then solidified in the upper layer of sample solution after centrifugation and ice bath, which improved the extraction and collection efficiency. The dispersive liquid-liquid microextraction procedure was optimised by univariate analysis and the Box-Behnken response surface methodology. Optimum conditions were as follows: the volume of nonanoic acid was 82 μL, the volume of acetonitrile was 620 μL, and the amount of salt was 256 mg. Under optimised conditions, the method had good linearity with a correlation coefficient higher than 0.997, and the limit of detection was 2.57-4.87 μg kg^-1. The recoveries of azoxystrobin, pyraclostrobin, and trifloxystrobin in rice, corn, and wheat were 82.0%-93.2%, and the relative standard deviations were 1.6%-7.4%. Therefore, the method was successfully applied to detect target fungicides in cereals.