Quality Evaluation of Tripterygium Glycoside Tablets Based on Quantitative Band-Selective 2D 1H-13C HSQC and 1H NMR Fingerprinting

Tripterygium glycoside tablets (TGTs) are preparations extracted and purified from Tripterygium wilfordii Hook. F and are extensively utilized in the treatment of autoimmune diseases, such as rheumatoid arthritis (RA). However, variations in production processes among manufacturers can lead to challenges in quality control and clinical utilization of TGTs. A band-selective 2D 1H-13C HSQC quantification method was applied for the determination of 13 active ingredients in TGTs. This method was validated following the guidelines of USP-NF 2022. The results demonstrated that the quantitative method exhibited excellent signal resolution, as well as sufficient accuracy, sensitivity, and stability. In addition, the 1H NMR spectra of TGTs from three manufacturers underwent analysis using principal component analysis and orthogonal partial least-squares discriminant analysis. The results revealed significant differences among the TGTs from the three manufacturers, with manufacturer 2 and manufacturer 3 demonstrating superior product consistency compared to manufacturer 1. A quality evaluation system for TGTs was developed based on band-selective 2D 1H-13C HSQC and 1H NMR, encompassing both quality markers and fingerprinting. This system offers reliable approaches and insights for enhancing the quality control of natural products.

Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Combined with Chemometrics for Protein Profiling and Classification of Boiled and Extruded Quinoa from Conventional and Organic Crops

Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

Layer-by-Layer Biopolymer Assembly for the In Situ Fabrication of AuNP Plasmonic Paper-A SERS Substrate for Food Adulteration Detection

Here, we introduce an environmentally friendly approach to fabricate a simple and cost-effective plasmonic paper for detecting food additives using surface-enhanced Raman spectroscopy (SERS). The plasmonic paper is fabricated by in situ growth of gold nanoparticles (AuNPs) on filter paper (FP). To facilitate this green fabrication process, we applied a double-layered coating of biopolymers, chitosan (CS) and alginate (ALG), onto the FP using a layer-by-layer (LbL) assembly through electrostatic interactions. Compared to single-layer biopolymer coatings, double-layered biopolymer-coated paper, ALG/CS/FP, significantly improves the reduction properties. Consequently, effective in situ growth of AuNPs can be achieved as seen in high density of AuNP formation on the substrate. The resulting plasmonic paper provides high SERS performance with an enhancement factor (EF) of 5.7 × 1010 and a low limit of detection (LOD) as low as 1.37 × 10-12 M 4-mercaptobenzoic acid (4-MBA). Furthermore, it exhibits spot-to-spot reproducibility with a relative standard deviation (RSD) of 8.2% for SERS analysis and long-term stability over 50 days. This paper-based SERS substrate is applied for melamine (MEL) detection with a low detection limit of 0.2 ppb, which is sufficient for monitoring MEL contamination in milk based on food regulations. Additionally, we demonstrate a simultaneous detection of β-agonists, including ractopamine (RAC) and salbutamol (SAL), exhibiting the multiplexing capability and versatility of the plasmonic paper in food contaminant analysis. The development of this simple plasmonic paper through the LbL biopolymer assembly not only paves the way for novel SERS substrate fabrication but also broadens the application of SERS technology in food contaminant monitoring.

Benzophenoxazine-based colorimetric and fluorescent probe for highly sensitive detection of amines and food freshness

In this work, we reported a chromogenic and near infrared (NIR) region fluorogenic dual-channel probe NRB, which could visually detect gaseous amines with high sensitivity (eg. 50 and 17 ppt for methylamine (MeNH2) via naked eyes and fluorescence spectrometer respectively). It exhibited a wide fluorescent emission band extending to the NIR region with a peak at 615 nm when stimulated by the MeNH2 solution. The plausible sensing mechanism was proved by mass spectrometry, where the reaction process was based on a nucleophilic substitution between the probe and amines rather than the ester group hydrolysis. Furthermore, NRB was successfully applied to monitor the food freshness (seafood and meat food), because of its low cytotoxicity and excellent photophysical properties. It was worth mentioning that real time monitoring for food quality can be realized visually by using a 365 nm UV lamp. In addition, the probe was stable during the quality guarantee period for perishable packaged food. It was believed that the applied experiments have demonstrated the value of this probe in the practical applications for food safety.

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

Cultivating Food Safety Together: Insights About the Future of Produce Safety in the U.S. Controlled Environment Agriculture Sector

Controlled environment agriculture (CEA) is a rapidly growing sector that presents unique challenges and opportunities in ensuring food safety. This manuscript highlights critical gaps and needs to promote food safety in CEA systems as identified by stakeholders (n=47) at the Strategizing to Advance Future Extension andResearch (S.A.F.E.R.) CEA conference held in April 2023 at The Ohio State University's Ohio CEA Research Center. Feedback collected at the conference was analyzed using an emergent thematic analysis approach to determine key areas of focus. Research-based guidance is specific to the type of commodity, production system type, and size. Themes include the need for improved supply chain control, cleaning, and sanitization practices, pathogen preventive controls and mitigation methods and training and education. Discussions surrounding supply chain control underscored the significance of the need for approaches to mitigate foodborne pathogen contamination. Effective cleaning and sanitization practices are vital to maintaining a safe production environment, with considerations such as establishing standard operating procedures, accounting for hygienic equipment design, and managing the microbial communities within the system. Data analysis further highlights the need for risk assessments, validated pathogen detection methods, and evidence-based guidance in microbial reduction. In addition, training and education were identified as crucial in promoting a culture of food safety within CEA. The development of partnerships between industry, regulatory, and research institutions are needed to advance data-driven guidance and practices across the diverse range of CEA operations and deemed essential for addressing challenges and advancing food safety practices in CEA. Considering these factors, the CEA industry can enhance food safety practices, foster consumer trust, and support its long-term sustainability.

The Use of a Droplet Collar Accessory Attached to a Portable near Infrared Instrument to Identify Methanol Contamination in Whisky

The aim of this study was to evaluate the ability of a droplet collar accessory attached to a portable near-infrared (NIR) instrument to characterize the artificial contamination of methanol in commercial whisky samples. Unadulterated samples (n = 12) were purchased from local bottle shops where adulterated samples were created by adding methanol (99% pure methanol) at six levels (0.5%, 1%, 2%, 3%, 4% and 5% v/v) to the commercial whisky samples (controls). Samples were analyzed using a drop collar accessory attached to a MicroNIR Onsite instrument (900-1650 nm). Partial least squares (PLS) cross-validation statistics obtained for the prediction of all levels of methanol (from 0 to 5%) addition were considered adequate when the whole adulteration range was used, coefficient of determination in cross-validation (R2cv: 0.95) and standard error in cross of validation (SECV: 0.35% v/v). The cross-validation statistics were R2cv: 0.97, SECV: 0.28% v/v after the 0.5% and 1% v/v methanol addition was removed. These results showed the ability of using a new sample presentation attachment to a portable NIR instrument to analyze the adulteration of whisky with methanol. However, the low levels of methanol adulteration (0.5 and 1%) were not well predicted using the NIR method evaluated.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art

There is a necessity to protect the quality and authenticity of herbs and spices because of the increase in the fraud and adulteration incidence during the last 30 years. There are several aspects that make herbs and spices quite vulnerable to fraud and adulteration, including their positive and desirable sensorial and health-related properties, the form in which they are sold, which is mostly powdered, and their economic relevance around the world, even in developing countries. For these reasons, sensitive, rapid, and reliable techniques are needed to verify the authenticity of these agri-food products and implement effective adulteration prevention measures. This review highlights why spices and herbs are highly valued ingredients, their economic importance, and the official quality schemes to protect their quality and authenticity. In addition to this, the type of frauds that can take place with spices and herbs have been disclosed, and the fraud incidence and an overview of scientific articles related to fraud and adulteration based on the Rapid Alert System Feed and Food (RASFF) and the Web of Science databases, respectively, during the last 30 years, is carried out here. Next, the methods used to detect adulterants in spices and herbs are reviewed, with DNA-based techniques and mainly spectroscopy and image analysis methods being the most recommended. Finally, the available adulteration prevention measurements for spices and herbs are presented, and future perspectives are also discussed.

Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging

A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy

The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.

A portable Raspberry Pi-based spectrometer for on-site spectral testing

We designed a portable Raspberry Pi-based spectrometer, which mainly consists of a white LED acting as the wide-spectrum source, a reflection grating for light dispersion, and a CMOS imaging chip aiming at spectral recording. All the optical elements and Raspberry Pi were integrated using 3-D printing structures with a size of 118 mm × 92 mm × 84 mm, and home-built software was also designed for spectral recording, calibration, analysis, and display implemented with a touch LCD. Additionally, the portable Raspberry Pi-based spectrometer was equipped with an internal battery, thus supporting on-site applications. Tested by a series of verifications and applications, the portable Raspberry Pi-based spectrometer could reach a spectral resolution of 0.065 nm per pixel within the visible band and provide spectral detection with high accuracy. Therefore, it can be used for on-site spectral testing in various fields.

Handheld and benchtop vis/NIR spectrometer combined with PLS regression for fast prediction of cocoa shell in cocoa powder

The fermented and dried cocoa beans are peeled, either before or after the roasting process, as peeled nibs are used for chocolate production, and shell content in cocoa powders may result from economically motivated adulteration (EMA), cross-contamination or misfits in equipment in the peeling process. The performance of this process is carefully evaluated, as values above 5% (w/w) of cocoa shell can directly affect the sensory quality of cocoa products. In this study chemometric methods were applied to near-infrared (NIR) spectra from a handheld (900-1700 nm) and a benchtop (400-1700 nm) spectrometers to predict cocoa shell content in cocoa powders. A total of 132 binary mixtures of cocoa powders with cocoa shell were prepared at several proportions (0 to 10% w/w). Partial least squares regression (PLSR) was used to develop the calibration models and different spectral preprocessing were investigated to improve the predictive performance of the models. The ensemble Monte Carlo variable selection (EMCVS) method was used to select the most informative spectral variables. Based on the results obtained with both benchtop (R²_P = 0.939, RMSE_P = 0.687% and RPD_P = 4.14) and handheld (R²_P = 0.876, RMSE_P = 1.04% and RPD_P = 2.82) spectrometers, NIR spectroscopy combined with the EMCVS method proved to be a highly accurate and reliable tool for predicting cocoa shell in cocoa powder. Even with a lower predictive performance than the benchtop spectrometer, the handheld spectrometer has potential to specify whether the amount of cocoa shell present in cocoa powders is in accordance with the Codex Alimentarius specifications.

Rapid Sensing: Hand-Held and Portable FTIR Applications for On-Site Food Quality Control from Farm to Fork

Today, one of the world's biggest problems is the assurance of food integrity from farm to fork. Economically motivated food adulteration and food authenticity problems are increasing daily with considerable health and economic effects. Early detection and prevention of food integrity-related problems could be provided by the application of effective on-site food analysis technologies. FTIR spectroscopy coupled with chemometrics can be used for the rapid quality control of a wide variety of food products with fast, high-throughput, accurate and nondestructive analysis advantages. In particular, hand-held and portable FTIR instruments have the potential to surveil food quality and food safety in various critical segments of the food supply chain. In this review, we explore the abilities of hand-held and portable FTIR spectrometers combined with multivariate statistics to conduct a quality evaluation of various food products in terms of food adulteration and authenticity issues. An examination of the literature showed that comparable results were obtained based on detection limits, correlation coefficient (R²) values, standard error values and discrimination power by using both portable/hand-held FTIR spectrometers and benchtop FTIR spectrometers. In conclusion, this review highlights the potential usefulness of portable and hand-held FTIR spectrometers combined with chemometrics for maintaining the food quality through the presentation of various applications that may shed light for on-site food control at any point of the food supply chain.

Tryptanthrin-malononitrile adduct for highly efficient turn-off fluorescent sensing of aniline

The fluorescence quenching of 2-(8-chloro-12-oxo-12H-indolo[2,1-b]quinazolin-6-ylidene)-malononitrile (5a) by aniline was studied in detail. Time correlated single photon counting measurements (TCSPC) indicated that the quenching involved both static and dynamic mechanisms without ground state complex formation. The selectivity towards aniline sensing in presence of other amines/aromatics and the anti-interference studies together with the low LOD value indicates the potential of 5a as a molecular probe for aniline sensing which is unaffected by pH alteration as well.

Using HPLC with In-Column Derivatization to Authenticate Coffee Samples

Coffee is one of the world's most popular beverages, with the global coffee capsule market worth over USD 4 billion and growing. The incidence of coffee fraud is estimated to be up to one in five coffees being contaminated with cheaper blends of coffee. Given the worsening extent of climate change, coffee crop yields are harder to maintain, while demand is increasing. The 2021 Brazil frost delaying or destroying many coffee crops is an example. Hence, the incidence of coffee fraud is expected to increase, and as the market becomes more complex, there needs to be faster, easier, and more robust means of real-time coffee authentication. In this study, we propose the use of novel approaches to postcolumn derivatization (termed herein as in-column derivatization) to visualize the antioxidant profiles of coffee samples, to be later used as indicators for authentication purposes. We propose three simple mathematical similarity metrics for the real-time identification of unknown coffee samples from a sample library. Using the CUPRAC assay, and these metrics, we demonstrate the capabilities of the technique to identify unknown coffee samples from within our library of thirty.

An overview of nutritional profiling in foods: Bioanalytical techniques and useful protocols

Maintaining a nutritious diet is essential for humans if they want to live a healthier life. Several food businesses and food safety organizations play a significant role and offer useful ways for improving nutritional quality that assists consumers in making informed selections. Making poor food choices and consuming unhealthy meals are the main causes of non-communicable diseases (NCDs). Nutritional profiling (NP) models are developed to evaluate the nutritional value, calorie content, and the amount of micronutrients and macronutrients contained in a given food accompanied by additional details on the nutritional anomaly provided by published standard nutrients and nutritional databases. To construct an ideal nutritional model that can facilitate food consumption, bioanalytical methods such as chromatography, microscopic techniques, molecular assays, and metabolomics can be applied. With the use of these technologies, one can learn more about the health advantages of nutrition and how to prevent disease. A wider element of NP is also provided by the developing technologies in the area of nutrition research, such as nanotechnology, proteomics, and microarray technology. In this review, we are focusing on the different bioanalytical techniques and the various protocols of NP and their application and refinement of the models. We have evaluated various NP techniques currently used in the food industry for the detection of different components present in food items.

Multiplexed Chemical Sensing CMOS Imager

A miniaturized and multiplexed chemical sensing technology is urgently needed to empower mobile devices and robots for various new applications such as mobile health and Internet of Things. Here, we show that a complementary metal-oxide-semiconductor (CMOS) imager can be turned into a multiplexed colorimetric sensing chip by coating micron-scale sensing spots on the CMOS imager surface. Each sensing spot contains nanocomposites of colorimetric sensing probes and silica nanoparticles that enhance sensing signals by several orders of magnitude. The sensitivity is spot-size-invariant, and high-performance gas sensing can be achieved on sensing spots as small as ∼10 μm. This great scalability combined with millions of pixels of a CMOS imager offers a promising platform for highly integrated chemical sensors. To prove its compatibility with mobile electronics, we have built a smartphone accessory based on this chemical CMOS sensor and demonstrated that personal health management can be achieved through the detection of gaseous biomarkers and pollutants. We anticipate that this new platform will pave the way for the widespread application of chemical sensing in mobile electronics and wearable devices.

Simultaneous Multiplexed Quantification of Banned Sudan Dyes Using Surface Enhanced Raman Scattering and Chemometrics

Azo compounds such as the Sudan dyes I-IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I-IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, v/v) as single Sudan dyes (I-IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (Q²) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10^-6, 5.35 × 10^-5, 9.40 × 10^-6 and 1.84 × 10^-6 M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (21⁴ combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the Q² for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples.

Amino Acid Fingerprinting of Authentic Nonfat Dry Milk and Skim Milk Powder and Effects of Spiking with Selected Potential Adulterants

A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV) was used for quantitation of amino acids and to calculate their distribution. The performance of this rapid method of analysis was evaluated and was used to determine the amino acid fingerprint of authentic milk powders. The distribution of different amino acids and their predictable upper and lower tolerance limits in authentic NFDM/SMP samples were established as a reference. Amino acid fingerprints of NFDM/SMP were compared with selected proteins and nitrogen rich compounds (proteins from pea, soy, rice, wheat, whey, and fish gelatin) which can be potential economically motivated adulterants (EMA). The amino acid fingerprints of NFDM/SMP were found to be affected by spiking with pea, soy, rice, whey, fish gelatin and arginine among the investigated adulterants but not by wheat protein and melamine. The study results establish an amino acid fingerprint of authentic NFDM/SMP and demonstrate the utility of this method as a tool in verifying the authenticity of milk powders and detecting their adulteration.

Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis

Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of ⁸⁸Sr/⁸⁶Sr, ⁸⁴Sr/⁸⁶Sr, ⁸⁷Sr/⁸⁶Sr and ⁶³Cu/⁶⁵Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.

Blockchain-Enabled Supply Chain platform for Indian Dairy Industry: Safety and Traceability

Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

Enose Lab Made with Vacuum Sampling: Quantitative Applications

A lab-made electronic nose (Enose) with vacuum sampling and a sensor array, comprising nine metal oxide semiconductor Figaro gas sensors, was tested for the quantitative analysis of vapor–liquid equilibrium, described by Henry’s law, of aqueous solutions of organic compounds: three alcohols (i.e., methanol, ethanol, and propanol) or three chemical compounds with different functional groups (i.e., acetaldehyde, ethanol, and ethyl acetate). These solutions followed a fractional factorial design to guarantee orthogonal concentrations. Acceptable predictive ridge regression models were obtained for training, with RSEs lower than 7.9, R2 values greater than 0.95, slopes varying between 0.84 and 1.00, and intercept values close to the theoretical value of zero. Similar results were obtained for the test data set: RSEs lower than 8.0, R2 values greater than 0.96, slopes varying between 0.72 and 1.10, and some intercepts equal to the theoretical value of zero. In addition, the total mass of the organic compounds of each aqueous solution could be predicted, pointing out that the sensors measured mainly the global contents of the vapor phases. The satisfactory quantitative results allowed to conclude that the Enose could be a useful tool for the analysis of volatiles from aqueous solutions containing organic compounds for which Henry’s law is applicable.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Rapid detection and quantification of the adulteration of orange juice with grapefruit juice using handheld Raman spectroscopy and multivariate analysis

Detecting food adulteration has always been an important task for food safety, especially when grapefruit is the adulterant as components in the juice have undesired interactions with many medicines. In this study we employed a handheld Raman device to detect adulteration of orange juices with grapefruit juices. Fresh fruits of orange and grapefruit were purchased from five different sources and fruit juices were made using a handheld juicer. The extracted juices were then mixed in a way that concentrations of grapefruit juices varied from 0% to 100% in 5% increments. In order to study the impact of the different sources of the fruits, three different sets of mixtures were prepared based on their spectral similarity and dissimilarity. Raman spectra were collected using a handheld instrument with an excitation laser at 785 nm and data analysed using principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least squares regression (PLS-R). PLS-R models were trained and validated on: (i) the full data set from the three different mixture sets, and (ii) each set of the three mixtures separately. The results showed that a good calibration model was obtained using full data which had a coefficient of determination (Q²) of 0.81 and a root mean square error of prediction (RMSEP) of 12.5%. Such results were improved when the PLS-R model was trained and validated on the three separate mixture combinations, where the Q² varied from 0.85 to 0.89 and RMSEP varied from 9.9% to 11.6%. Finally, we adopted a two step approach in which a partial least squares for discriminant analysis (PLS-DA) was trained first to classify the three sample sources and then three different PLS-R models were subsequently trained on samples from the same source. This resulted in a Q² of 0.83 and RMSEP of 12.0%. In conclusion, we have demonstrated that Raman spectroscopy can be used as a portable and rapid analytical tool for detecting adulteration of grapefruit juice added to orange juice.

Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis

The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.

Magnetic-Core/Gold-Shell Nanoparticles for the Detection of Hydrophobic Chemical Contaminants

Magnetic-core/gold-shell nanoparticles (MAuNPs) are of interest for enabling rapid and portable detection of trace adulterants in complex media. Gold coating provides biocompatibility and facile functionalization, and a magnetic core affords analyte concentration and controlled deposition onto substrates for surface-enhanced Raman spectroscopy. Iron oxide cores were synthesized and coated with gold by reduction of HAuCl₄ by NH₂OH. MAuNPs were grafted with polyethylene glycol (PEG) and/or functionalized with 4-mercaptobenzoic acid (4-MBA) and examined using a variety of microscopic, spectroscopic, magnetometric, and scattering techniques. For MAuNPs grafted with both PEG and 4-MBA, the order in which they were grafted impacted not only the graft density of the individual ligands, but also the overall graft density. Significant Raman signal enhancement of the model analyte, 4-MBA, was observed. This enhancement demonstrates the functionality of MAuNPs in direct detection of trace contaminants. The magnetic deposition rate of MAuNPs in chloroform and water was explored. The presence of 4-MBA slowed the mass deposition rate, and it was postulated that the rate disparity originated from differing NP-substrate surface interactions. These findings emphasize the importance of ligand choice in reference to the medium, target analyte, and substrate material, as well as functionalization procedure in the design of similar sensing platforms.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.

Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue

Plums are one of the commercially important stone fruits that are available on the market in both fresh and processed form and the most sought-after products are prunes, cans, jams, and juices. Maturity, harvest, and post-harvest technologies fundamentally determine the relatively short shelf life of plums which is often threatened by Monilinia spp. Causing brown rot worldwide. The aim of the present research was to use advanced analytical techniques, such as hand-held near infrared spectroscopy (NIRS) and electronic tongue (e-tongue) to detect M. fructigena fungal infection on plums and quantify this fungal contamination in raw plum juices. For this purpose, plums were inoculated with fungal mycelia in different ways (control, intact, and through injury) and stored under different conditions (5 °C, and 24 °C) for eight days. The results obtained with the two instruments were analyzed with chemometric methods, such as linear discriminant analysis (LDA) and partial least squares regression (PLSR). The NIRS-based method proved successful when detectability before the appearance of visible signs of the infection was studied. E-tongue was able to detect and quantify the concentration of juice derived from plum developed with M. fructigena with RMSECV lower than 5% w/w. Overall, the two methods proved to be suitable for discriminating between the treatment groups, however, the classification accuracy was higher for samples stored at 24 °C. The research results show both NIRS and e-tongue are beneficial methods to reduce food waste by providing rapid determination of fruit quality.

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.