Bioaccessibility of iron in developed pectin iron complex using Citrus limon Burm F. peels subjected to in-vitro gastro-pancreatic digestion.

Pectin from the citrus peel waste has novel applications in food and biomedical industries. The present work focused on addressing iron deficiency, which is a global health concern, by developing a functional ingredient using pectin extracted from Assam lemon (Citrus limon Burm. F) and supplementing iron via the pectin­iron complex (PIC). Extracted pectin was incubated with iron chloride hexahydrate (0.90-1.80 mM) for 180 h to optimize the complexation conditions, with the optimal concentration being 1.36 mM. The iron bioavailability and its absorption in the PIC was assessed using in-vitro simulation digestion and Caco-2 cell monolayers. The bioaccessible form of iron in the developed PIC during the intestinal phase was 5.34 ± 0.16%, which was negligible in pectin. The absorption of bioaccessible iron in the PIC was found to be 2.93 ± 0.03%. The results demonstrated that PIC could reduce iron deficiency and increase fibre intake, leading to several health benefits.

Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI).

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Isolation, structural, biological activity and application of Gleditsia species seeds galactomannans.

Gleditsia fruits have been known as a valuable traditional Chinese herb for tens of centuries. Previous studies showed that the galactomannans are considered as one of the major bioactive components in Gleditsia fruits seeds (GSGs). Here, we systematically review the major studies of GSGs in recent years to promote their better understanding. The extraction methods of GSGs mainly include hot water extraction, microwave-assisted extraction, ultrasonic extraction, acid extraction, and alkali extraction. The analysis revealed that GGSs exhibited in the form of semi-flexible coils, and its molecular weight ranged from 0.018 × 103 to 2.778 × 103 KDa. GSGs are composed of various monosaccharide constituents such as mannose, galactose, glucose, and arabinose. In terms of pharmacological effects, GSGs exhibit excellent activity in antioxidation, hypoglycemic, hypolipidemic, anti-inflammation. Moreover, GSGs have excellent bioavailability, biocompatibility, and biodegradability, which make them used in food additives, food packaging, pharmaceutical field, industry and agriculture. Of cause, the shortcomings of the current research and the potential development and future research are also highlighted. We believe our work provides comprehensive knowledge and underpinnings for further research and development of GSGs.

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis.

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.

Authenticating Emergent Adulterant 6-Benzylaminopurine in Bean Sprouts: Virtual Hapten Similarity Enhanced Immunoassay.

6-Benzylaminopurine (6-BA), a plant growth regulator with cytokinin-like properties, was recently reported to be illegally used in bean sprouts to increase their commercial appearance. It is still nevertheless challenging to quickly detect this adulteration. In this work, four novel haptens (haptens 1-4) of 6-BA were rationally designed with computer-assisted modeling analysis and then synthesized for use as immunizing haptens to produce antibodies. One of two obtained antibodies showed high sensitivity and specificity toward 6-BA. Based on the most sensitive anti-6-BA antibody, an indirect competitive enzyme-linked immunosorbent assay (icELISA) was performed, which demonstrated a 50% inhibition concentration (IC50) of 1.18 µg/L and a limit of detection of 0.075 µg/L. The average recoveries of this icELISA for 6-BA of spiked samples ranged from 87.2 to 95.0% with a coefficient of variation of less than 8.7%. Furthermore, the blind samples were detected simultaneously by the method and HPLC-MS/MS, and the results showed good agreement with each other. Therefore, the proposed icELISA can facilitate the rapid surveillance screening of adulterated 6-BA in sprout vegetables.

Developing an ultrasensitive immunochromatographic assay for authentication of an emergent fraud aminopyrine in herbal tea.

Aminopyrine is a nonsteroidal anti-inflammatory drug only for medical purposes, however, it has been illegally added in traditional Chinese herbal teas for fraud activity recently. In this study, a specific antibody against aminopyrine with IC50 of 3.00 ng/mL was obtained for the first time by a rational hapten design. Furthermore, an ultrasensitive gold nanoparticles immunochromatographic assay (AuNPs-ICA) for determination of aminopyrine based on a portable reader was firstly developed, with cut-off value of 100.00 ng/mL, limit of detection (LOD) of 4.80 ng/mL and limit of quantification (LOQ) of 5.71 ng/mL for herbal tea, respectively. The recovery rates ranged from 93.21 % to 105.61 %, with inter-assay coefficient of variation (CV) from 1.08 % to 3.82 %. Additionally, 24 blind samples were examined simultaneously by AuNPs-ICA and LC-MS/MS, demonstrating a good consistency for each other. The proposed AuNPs-ICA is an ultrasensitive and reliable tool for on-site surveillance screening of fraud additives in herbal tea.

Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum.

In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases.

Effect of novel combination processing technologies on extraction and quality of rice bran oil.

Rice bran, a primary by-product from the rice processing industries, containing 10-15% oil, attracts significant attention from consumers due to its many health-promoting effects. The extraction methodology used is one of the most critical factors affecting the quality and yield of oil from rice bran. Using solvents is the current commercial process for rice bran oil extraction, which has its setbacks. It is challenging and expensive, and there is a risk of traces of solvent residue in the oil. Emerging combination extraction technologies offer zero to minimal solvent residues or chemical deformation while considering increasing environmental and energy footprint. Emerging combination processing technologies include new-age methods like supercritical fluid extraction, sub-critical fluid extraction, ultrasound-assisted enzymatic extraction, ohmic heating, and microwave-assisted extraction. These techniques have been reported to extract oil from rice bran, improving extraction efficiency and quality. These techniques demonstrate solid prospects for future applications. The present review discusses and compares these emerging technologies for oil extraction from rice bran commercially.

Recent Advances in Analytical Methods for the Detection of Olive Oil Oxidation Status during Storage along with Chemometrics, Authenticity and Fraud Studies.

Olive oil is considered to be a food of utmost importance, especially in the Mediterranean countries. The quality of olive oil must remain stable regarding authenticity and storage. This review paper emphasizes the detection of olive oil oxidation status or rancidity, the analytical techniques that are usually used, as well as the application and significance of chemometrics in the research of olive oil. The first part presents the effect of the oxidation of olive oil during storage. Then, lipid stability measurements are described in parallel with instrumentation and different analytical techniques that are used for this particular purpose. The next part presents some research publications that combine chemometrics and the study of lipid changes due to storage published in 2005-2021. Parameters such as exposure to light, air and various temperatures as well as different packaging materials were investigated to test olive oil stability during storage. The benefits of each chemometric method are provided as well as the overall significance of combining analytical techniques and chemometrics. Furthermore, the last part reflects on fraud in olive oil, and the most popular analytical techniques in the authenticity field are stated to highlight the importance of the authenticity of olive oil.

Facile Fabrication of Highly Quantum Dot/AuNP-Loaded Tags for a Dual-Modal Colorimetric/Reversed Ratiometric Fluorescence Immunochromatographic Assay.

Developing an easily-prepared, sensitive, and accurate point-of-need immunochromatographic assay (ICA) is significant in food safety screening, clinical diagnosis, and environmental monitoring. However, the current single-modal ICAs are limited in certain instinct drawbacks that restrict analytical performances. Herein, we introduce an ultrasensitive dual-modal colorimetric/reversed ratiometric fluorescence ICA based on facilely prepared immunoprobes with a high loading capacity of red quantum dots and AuNPs. By smartly integrating these red-colored/fluorescent signal probes with an immobilized green quantum dot antigen on the test lines, discrete "turn-on" visual inspection and reversed ratiometric quantification via a portable smartphone-based analyzer were accomplished. As an application, this method was employed to detect 11 phosphodiesterase-5 inhibitors in health foods with ultralow detection limits (0.0028-0.045 ng/mL), high repeatability (coefficient of variations of 0.3-1.91%), and reasonable accuracy (recoveries of 86.6-107%). The proposed method was further validated by the authorized liquid chromatography with tandem mass spectrometry method in actual sample detection. This new assay format can be extended to ultrasensitive flexible detection of other food contaminants, environmental pollutants, or tumor biomarkers within minutes, and it just requires simply prepared signal reporters, easy-to-operate procedures, and a low-cost miniaturized analyzer.

Complementary Strategy Enhancing Broad-Specificity for Multiplexed Immunoassay of Adulterant Sulfonylureas in Functional Food.

Sulfonylureas, a family of anti-diabetic drugs widely used in the clinical treatment of type 2 diabetes, have recently emerged as an illegal adulterant in functional foods, to enhance the claimed anti-diabetic activity. To establish a screening assay method against their adulteration, with the aid of molecular simulation of hapten, two antibodies were raised and complementarily used to enhance the broad-specificity of an enzyme-linked immunosorbent assay (ELISA), which demonstrated simultaneous detection capability to 6 sulfonylureas; the detection limits ranged from 0.02 to 1.0 ng/mL, and recoveries were between 78.3% to 104.5%. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) confirmed the reliability of the proposed ELISA, based on real samples. These results suggest that the proposed ELISA could be an ideal method for screening to monitor for illicit adulteration of sulfonylureas in functional pill products.

One-step ultra-sensitive immunochromatographic strip authenticating an emergent fraud acetophenetidin in herbal tea.

Herbal tea is a highly popular and widely consumed beverage. However, a pain-relieving and fever-reducing drug, acetophenetidin, was recently found to illegally occur in herbal tea for a fraud purpose. Due to the potential health risk and urgent requirement for on-site screening method, a one-step and high specificity strip for identifying acetophenetidin was developed for the first time. Assisted by computational chemistry, four haptens were designed to prepare immunogens and coating antigens for antibody generation, and a specific antibody with ultra-sensitivity and high specificity was generated, showing half maximal inhibitory (IC50) of 16.46 ng/mL for acetophenetidin, less than 3.5% of cross-reactivity to analogs by ELISA. A gold nanoparticles immunochromatographic strip was developed for detection of acetophenetidin in herbal tea, demonstrating a cut-off value of 160 ng/mL and a limit of detection of 1.63 ng/mL. The recovery rates were ranged from 102.2% to 106.1% with coefficient of variation between 2.21% and 7.20%. The analysis of real samples (n = 20) by the strip was well correlated with that of the confirmatory method, liquid chromatography-tandem mass spectrometry. The proposed strip has the potential to be used for rapid screening of acetophenetidin in herbal tea.

Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends.

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

Optimization of the composition of a solid culture medium for Mycobacterium avium subsp. paratuberculosis using factorial design and response surface methodology.

AIM: To develop an optimized solid culture medium for improved growth of Mycobacterium avium subsp. paratuberculosis (MAP). METHODS AND RESULTS: Seven medium constituents (factors) were assessed at various concentrations for their ability to positively affect MAP growth. The factors tested were Tween 80, egg yolk, casitone, taurocholic acid, Mycobactin J, agar and either OADC or ADC supplement. After an initial screening of individual factors, a fractional factorial design and a response surface methodology (RSM) central composite design were used to assess the effects of multiple factors simultaneously and design a new solid culture medium. MAP growth became visible on streak plates of the optimized solid medium 2 weeks earlier than on Herrold's egg yolk medium (HEYM). CONCLUSIONS: MAP grew faster on the optimized solid medium than on HEYM. It consisted of Middlebrook 7H9 broth with 1.0% Tween 80, 0.019% casitone, 1.4% bacteriological agar, 10% egg yolk, 10% ADC and 1.65 µg ml-1 Mycobactin J. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study to use an RSM approach to optimize the composition of a solid medium for MAP culture. The new medium could improve MAP culture in future by reducing incubation times and increasing MAP colony numbers.

Untargeted metabolomics by liquid chromatography-mass spectrometry for food authentication: A review.

Food fraud is currently a growing global concern with far-reaching consequences. Food authenticity attributes, including biological identity, geographical origin, agricultural production, and processing technology, are susceptible to food fraud. Metabolic markers and their corresponding authentication methods are considered as a promising choice for food authentication. However, few metabolic markers were available to develop robust analytical methods for food authentication in routine control. Untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS) is increasingly used to discover metabolic markers. This review summarizes the general workflow, recent applications, advantages, advances, limitations, and future needs of untargeted metabolomics by LC-MS for identifying metabolic markers in food authentication. In conclusion, untargeted metabolomics by LC-MS shows great efficiency to discover the metabolic markers for the authenticity assessment of biological identity, geographical origin, agricultural production, processing technology, freshness, cause of animals' death, and so on, through three main steps, namely, data acquisition, biomarker discovery, and biomarker validation. The application prospects of the selected markers by untargeted metabolomics require to be valued, and the selected markers need to be eventually applicable at targeted analysis assessing the authenticity of unknown food samples.

Broad-specific immunochromatography for simultaneous detection of various sulfonylureas in adulterated multi-herbal tea.

Sulfonylureas (SUs) are a series of anti-diabetic drugs widely used for type 2 diabetes mellitus for clinic treat. However, it is often illegally adulterated in multi-herbal tea to improve the claimed anti-diabetic activity in recent years. In this study, a novel hapten was rationally designed, and a broad-specific monoclonal antibody (anti-SUs mAb) recognizing nine SUs was developed. This mAb was used to develop a colloidal gold lateral flow immunochromatographic assay (CG-LFIA). The anti-SUs mAb demonstrated half inhibitory concentration (IC50) ranged from 0.15 ng/mL to 3.25 ng/ mL for nine SUs by ELISA. The cut-off value of developed CG-LFIA for nine SUs was from 3 to 100 ng/ mL for the spiked samples. LC-MS/MS confirmed the reliability of the new CG-LFIA. The results indicated that the proposed CG-LFIA could be an ideal method in on-site screening surveillance assay for SUs illegally adulterated in multi-herbal tea products.

Integration of Untargeted and Pseudotargeted Metabolomics for Authentication of Three Shrimp Species Using UHPLC-Q-Orbitrap.

In this work, an untargeted and pseudotargeted metabolomics combination approach was used for authentication of three shrimp species (Litopenaeus vanmamei, Penaeus japonicus, and Penaeus monodon). The monophasic extraction-based untargeted metabolomics approach enabled comprehensive-coverage and high-throughput analysis of shrimp tissue and revealed 26 potential markers. The pseudotargeted metabolomics approach confirmed 21 markers (including 9 key markers), which realized at least putative identification. The 21 confirmed markers, as well as 9 key markers, were used to develop PLS-DA models, correctly classifying 60/60 testing samples. Furthermore, DD-SIMCA and PLS-DA models were integrated based on the 9 key markers, with 59/60 and 20/20 samples of the species that were involved and uninvolved in model training correctly classified. The results demonstrated the potential of this untargeted and pseudotargeted metabolomics combination approach for shrimp species authentication.

Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy.

The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screening technique for mineral and elemental analysis with applications in the traceability of foodstuffs, including tea. This study aims to develop a reliable multivariate classification model using XRF spectroscopy to obtain the mineral content. A total of 75 tea samples from tea producing countries throughout the world were analysed. After variable shortlisting, 18 elements were used to construct the multivariate models. Tea origin was determined by classifying the tea into 5 major geographical regions producing most of the global tea. PCA showed initial clustering in some regions, although the types of teas included in the study (black, green, white, herbal) showed no discrete cluster membership. The prediction power of each classification model developed was determined by using two multivariate classifiers, SIMCA and PLS-DA, against an independent validation set. The average overall correct classification rates of PLS-DA models were between 54-85% while the results of SIMCA models were between 70-84% resolving the poor clustering initially shown by PCA. This study demonstrated the potential of geographical origin of tea prediction using elemental contents of tea. Naturally, the classification can be linked not only to origin but to the type of tea as well. PRACTICAL APPLICATION: Wholesalers and retailers need a rapid and robust screening tool to confirm the origin and type of tea they sell to consumers. X-Ray fluorescence spectroscopy proved a good technique for achieving this in commercial teas sourced worldwide. Building on multivariate models, broad classification was accomplished both in terms of origin (Asian vs non-Asian) and in tea type with zero sample preparation and low cost of analysis.

Multivariate approach for the authentication of vanilla using infrared and Raman spectroscopy.

Many different versions of vanilla extracts exist in the market in a variety of origins, purity levels and composition with little effective regulation. In this study, vanilla is authenticated both in terms of purity and geographical origin applying a multivariate approach using near infrared (NIR), mid infrared (MIR) and Raman spectroscopy following a complex experimental design. Partial least squares-discriminant analysis (PLS-DA) was applied to the spectral data to produce qualitative models. The prediction accuracy of the models was externally validated from the specific success/error contingencies. The results showed that MIR and Raman are reliable for authenticating vanilla in terms of purity, obtaining sensitivity, specificity, precision, and efficiency values equal to 1.00, and Raman is especially suitable for indicating the geographical origin of vanilla extracts, achieving performance metrics around 0.9.

Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression.

BACKGROUND: The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT's full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000-550 cm-1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization-mDP, procyanidins:prodelphidins ratio-PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. RESULTS: The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). CONCLUSIONS: MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.