The aim of the study was to investigate the content of macro- and microelements in fresh multi-flower honey from different regions of Kosovo. The samples of fresh honey were collected in July and September 2019 from 99 sampling locations. The samples were mineralised by microwave method and analysed by inductively coupled plasma - atomic emission spectroscopy (ICP-AES) and inductively coupled plasma - mass spectrometry (ICP-MS). Three groups of elements of mixed origin were identified by Factor analysis; the first group is mainly of anthropogenic origin (Mo, Pb, Sb, Se, Ti, Tl) and two other groups are of mixed anthropogenic and geogenic origin (As, K, Mg, Mn, P and Ag, Al, Li). The contents of some elements in honey from Kosovo were determined within the following mean values: Ca: 110, Mn: 2.4, Mg: 65, P: 75, Zn: 2.8, Cu: 2.0, Fe: 4.6, Na: 56 and K: 970 mg/kg. The contents of the elements in the samples can be given in the following descending order: K > Ca > P > Mg > Na > Fe > Zn > Mn > Cu. In addition, it was found that the average contents of some heavy metals are as follows: Cd: 23 µg/kg, Cr: 0.38 mg/kg, Pb: 0.20 mg/kg, As: 6.8 µg/kg, which are below the limit allowed by the Codex Alimentarius CXS 193-1995 of the Food and Agriculture Organisation and the World Health Organisation (FAO/WHO).

Geographical traceability of marine bivalves has been explored continually, however the traceability results have not been recognized owing to the absence of a reasonable explanation for a certain model prediction. To address this problem, we employed an explainable machine learning to identify the origin of mussels based on stable isotope ratio and compositions of carbon (C), nitrogen (N), oxygen (O), and hydrogen (H). Our findings proved that the Extreme gradient boosting (XGBoost) model may be the best model based upon its high accuracy (93.75 %), precision (93.75 %), recall (94.51 %), F1 score (93.72 %) and AUC (0.990). The feature impact for model out of XGBoost was interpreted by SHapley Additive exPlanations (SHAP) on global and local perspective. This study demonstrated the potential of explainable machine learning to elucidate the complex relationships between desirable geographical information of mussels and the selected features.

Glycerol core aldehydes (GCAs) are non-volatile aldehydes residing in frying oil. In this study, near-infrared spectrum (NIR) was used in conjunction with partial least squares (PLS) to create a model for quantifying the total and monomeric content of GCAs. The total and monomer GCAs content in high oleic sunflower oil determined by the GC-MS method was used to assess the model's prediction accuracy. After comparison, the optimal spectrum ranges for both total GCAs and GCAs (9-oxo, 10-oxo-8, and 11-oxo-9) were 8800-6600 cm-1, and the optimal spectrum range for GCAs (8-oxo) was 7420-6600 cm-1. The optimal preprocessing for total GCAs and GCAs (8-oxo, 9-oxo, 10-oxo-8, and 11-oxo-9) were first derivative spectrum, first derivative, original spectrum, first derivative spectrum + NDF smoothing, and first- derivative spectrum + S-G smoothing, respectively. The results demonstrated that the NIR-PLS model had good predictive ability, and the coefficients of determination (R2) for total GCAs and four monomers in the proposed quantitative analysis models were 0.9772, 0.9383, 0.9688, 0.9674, and 0.9745, respectively, and the relative root means square errors of prediction (RMSEP) were 0.0362, 0.00708, 0.03, 0.00337, 0.00433, and can be predicted quickly and nondestructively, non-toxic, and easy to operate and use.

A simple, precise, and rapid method for preconcentration and determination of copper in food and water samples, based on sonication/vortex-free solid phase extraction with high resolution-continuum source flame atomic absorption spectrometry has been developed. In this study, BaTiO3 was synthesized using a single-step hydrothermal method and used as an adsorbent. BaTiO3 was characterized by FT-IR, SEM-EDX, STEM, XRD, and BET methods. The significant feature of the adsorbent is its high surface-to-volume ratio (177.4 m²/g). Important parameters (pH, adsorbent amount, eluent type and volume, adsorption and desorption time, model solution volume, and matrix effects) were optimized. The linear working range (5 – 250 µg L-1) and the detection limit (1.6 µg L-1) were calculated. The presented method was validated with certified reference material and the addition-recovery tests. The application of the method was evaluated for the extraction and determination of Cu(II) in selected food and water samples and quantitative data were obtained.

To clarify the effect of caffeic acid-grafted-chitosan/polylactic acid (CA-g-CS/PLA) packaging on the bacterial infestation and volatile flavor of postharvest Agaricus bisporus, electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GCMS) analysis was utilized to detect the volatile flavor. 16 s rDNA sequencing technology was used to determine the species and changes of bacterial flora on the surface of Agaricus bisporus. The correlation between volatile flavor substances and bacterial communities was analyzed. The results showed that compared with polyethylene (PE) packaging, CA-g-CS/PLA packaging significantly delayed the loss of characteristic volatiles such as 1-octene-3-ol, 3-octanol, 3-octone and the ability to inhibit the growth of total bacteria count (TBC) was elevated by 12.6% at 15 d of storage. Moreover, CA-g-CS/PLA packaging significantly reduced the abundance of spoilage bacteria such as Flavobacterium, Pedobacter and Pseudomonas compared to PE packaging and the sum of the three fell from 38% to around 8%. The correlation analysis showed that spoilage bacteria were significantly negatively correlated with 1-octene-3-ol and 3-octone and significantly positively correlated with spoil flavors such as benzaldehyde, indole and naphthalene. These results indicated that CA-g-CS/PLA packaging could delay the deterioration of the postharvest volatile flavor of Agaricus bisporus by inhibiting the abundance of specific spoilage bacteria.

To date, the majority of studies have assessed the health risks of toxic metals (TMs) in raw rice and/or in rice that is cooked in the laboratory under controlled conditions. However, people usually consume rice that is cooked in metallic cookware using ordinary cooking water and various additives. Therefore, this study focused on the contamination levels of TMs in raw rice and rice cooked under real conditions by the rural households of northern Iran. The levels of As and Cd in the cooking water used in rural households were negligible. The As levels in raw and cooked rice were 257 ± 134 and 243 ± 28 μg/kg, respectively, and the corresponding values for Cd were 8.54 ± 6.94 and 8.64 ± 3.18 μg/kg, respectively. Probabilistic health risk assessment showed unacceptable health risks for the As contamination of both raw and home-cooked rice but with different probability distributions. Overall, this study showed that the cooking water with low TMs levels was not sufficient for the significant reduction of these contaminants in rice during home cooking. Therefore, it is essential to monitor the TMs levels in home-cooked rice, instead of raw and/or artificially cooked rice, for a more realistic assessment of attributed health risks to the general public.

A novel fluorescent membrane based on carbon dot embedded photonic crystal molecularly imprinted polymer (PCMIP@CDs) was developed for fluorometric and colorimetric dual-mode determination of sulfadimethoxine with excellent selectivity, quick response, and reusability. In the presence of sulfadimethoxine, fluorescence quenching and red shift of diffraction peak from PCMIP@CDs were observed because of the photo-induced electron transfer and structural change of PCMIP@CDs, accompanied by a visual color change from blue to green. The fluorescence quenching efficiency and the red-shift of the diffraction peaks were positively correlated with the logarithms of the sulfadimethoxine concentration at 10−12–10−5 and 10−10–10−5 mol L−1, respectively. The sensor achieved a limit of detection of 0.08 pmol L−1 (fluorescence method) and 2.75 pmol L−1 (colorimetric method, 3σ, n = 9). The developed PCMIP@CD membrane was applied for the detection of sulfadimethoxine in fish samples with favorable recovery of 84.8–102.9%.

In this work, we designed a simple, disposable, highly sensitive electrochemical aptasensor to detect di(2-ethylhexyl) phthalate (DEHP) in edible vegetable oils. The aptasensor was created by decorating polyaniline multi-walled carbon nanotubes (PANI-MWCNTs), Cu metal-organic framework (CuMOF), and gold nanoparticles (AuNPs) on a screen printing carbon electrode (SPCE). With its large surface area, CuMOF modified the electrode by improving the adhesion rate of gold nanoparticles, thereby improving the detection limit of the sensor. The distinct peak in the square wave voltammetry (SWV) was used to measure DEHP. The sensor had high stability, excellent repeatability, reproducibility, and specificity, with a wide linear range (0.1–100 ng/mL) and a low detection limit (0.03 ng/mL). When the aptasensor was used to analyze the DEHP content in edible vegetable oils, the recovery rate was between 95.11 % and 98.29 %, and the results were consistent with the GC-MS measurements.

In this study, a colorimetric assay was designed for ultrasensitive detection of chloramphenicol (CAP). In the presence of CAP, Ni-Fe layered double oxide/DNA networks (Ni-Fe LDO/DNA NWs) can be formed based on the combination of G-quadruplex (G4), DNA and magnetic Ni-Fe LDO nanosheets (NSs). Then, the as-formed Ni-Fe LDO/DNA NWs was applied to catalyze the oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) based on the enhanced peroxidase-like catalytic activity for the colorimetric analysis of CAP. To effectively enhanced the detection sensitivity, enzyme-free amplification methods including catalytic hairpin assembly (CHA) and hybridization chain reaction (HCR) were employed during this process. In addition, the background signal can be effectively reduced by means of the magnetic separation. Under optimal conditions, this strategy exhibited a wide linear range from 0.1 fM to 0.01 nM (r2 = 0.998), and the limit of detection (LOD) was obtained as 11.6 aM. Furthermore, the high specificity and stability of this strategy allowed its successful application to the quantitative analysis of residual CAP in genuine milk samples.

This study aimed to explore the mid-infrared technique for the characterization of a non-dairy fermented beverage. Fruit juice was used as a substrate for fermentation made by microorganisms from water kefir. The fermented beverage was evaluated by standardized methods regarding pH, total titratable acidity, soluble solids, and total reducing sugars. Then, infrared spectra were obtained and calibration models were built to predict these parameters in the fermented beverage. All obtained models showed high coefficients of determination values (R2) in the calibration and prediction stages, in addition to high residual predictive deviation (RPD) values. For the prediction data, the values of R² were equal to 0.91, 0.93, 0.95, and 0.91 for pH, total titratable acidity, soluble solids, and total reducing sugars, respectively. It was evidenced that the soluble solids content was the most suitable physicochemical property for estimation by the elaborated calibration models (higher R2 and RPD values). The results showed that such parameters could be estimated in kefir quickly through the mid-infrared analysis.

This study compares the performance of Hanseniaspora vineae (Hv) under mixed culture fermentation with Saccharomyces cerevisiae, with a conventional control (Sc) for the production of base wine. Hv exhibited fermentation kinetics comparable to controls when its nutritional requirements were satisfied. Volatile acidity in Hv from one batch was significantly lower (–0.3 g/L) compared to the controls, while in the other two batches the levels were equal. Interestingly, Hv trials showed a significant degradation of malic acid (–0.9 g/L on average) without variations of pH. Even though the matrix influenced the volatile composition, the characteristic features of H. vineae remained intact, with 24-fold higher levels of 2-phenylethyl acetate. There was no higher production of 2-aminoacetophenone, albeit tryptophan (known as a 2-aminoacetophenone precursor) is a preferred amino acid for H. vineae. Results suggest the potential of H. vineae to produce base wine with a broad spectrum of aroma compounds that increase flavor complexity, if obtained from nutrient-balanced grape musts.

Chloride contaminants are the culprit for the formation of harmful compounds such as the 3-monochloropropane-1,2-diol esters (3-MCPDE) and 2-monochlororpropane-1,3-diol esters (2-MCPDE) during edible oil processing. Understanding the source of these chloride’s donor is important for steps to be taken to mitigate the formation of undesirable chloropropanols in refined edible oils. Nuclear magnetic resonance (NMR) was used to differentiate between the inorganic chloride and organochlorine present in crude palm oil, palm superolein, refined olive oil and refined sunflower oil. A quantitative NMR (qNMR) method was developed to analyse the amount of organochlorine present in the aforementioned edible oils. Excellent regression coefficient (R ≥ 0.9995) and LOQ (0.45 ppm) showed that the method is linear and feasible. Recoveries of the method spanned between 90 % and 94 % for the four types of oils analysed. This showed the potential of qNMR for the determination of organochlorine in edible oils.

4-Mercaptobutyl glucosinolate (4-MBGSL) is a unique glucosinolate of rocket species (Diplotaxis, Eruca) in terms of having a free thiol group, rendering it capable of forming homo- and heteromeric disulfides. This oxidation process has been attributed to either incorrect sample preparation approaches or natural, stress-related events; accordingly, the conjugate of glutathione and 4-MBGSL could be regarded as an artefact. In our study, a set of experiments with UV-B irradiation was conducted on Eruca sativa plants to evaluate the formation of this conjugate. For validation purposes, the commercially unavailable conjugate of glutathione and 4-MBGSL was prepared through iodide assisted mild oxidation and characterized with high resolution mass spectrometry. The experiments revealed that UV-B-irradiation initiated the formation of not only this conjugate but a series of eight novel disulfide and polysulfide (up to four sulfur atoms linked) derivatives of 4-MBGSL, 1-thio-β-D-glucose, and glutathione metabolites, the structures of which were tentatively identified with MS/MS analysis. The natural formation of di- and polysulfides was also proven by ion mobility analysis to differentiate them from mass spectrometry (ion source) related artefact entities.

In this work, a novel bioluminescence immunoassay inspired by sparkling fireflies at night was established to detect ochratoxin A (OTA). This platform relied on the mechanism by which firefly luciferase hydrolyze ATP to catalyze the transformation of luciferin to oxidized oxyluciferin, which emits bioluminescence. Since the concentration of ATP can be controlled by alkaline phosphatase (ALP), the bioluminescence intensity was related to the concentration of ALP. Thus, the correlation between target OTA and bioluminescence intensity was realized by ALP-mediated enzyme-linked immunosorbent assay (ELISA). This bioluminescence immunoassay exhibited a relatively wide linear range (1.0-316 ng/mL), which was 30 times wider than that of classical HRP-mediated ELISA (0.8-12.8 ng/mL). The limit of detection (LOD) of the proposed bioluminescence immunoassay (0.316 ng/mL) was lower than the maximum OTA residue limits (2 ng/mL to 10 ng/mL) specified by the European Union (EU) and China Government. In addition, the bioluminescence immunoassay showed good selectivity for OTA and satisfactory recoveries in corn samples ranging from 83.39% to 111.60%.

Original liquor quality control and grade identification are important for the quality improvement of finished liquor. For the purpose of this study, a meticulous collection of 209 samples of original Chinese mild-flavored liquors was conducted. The grading of these samples was determined through sensory tasting, which is the prevailing method used in major liquor factories. These samples were scanned by three-dimensional fluorescence spectroscopy to obtain the respective excitation-emission matrices (EEM). The suitability of EEM was evaluated for the classification of these samples using principal component analysis (PCA), Discrete Cosine Transform (DCT), Fast Independent Component Analysis (Fast-ICA) combined with linear discriminant analysis (LDA) and support vector machine (SVM). In general, it was found that a fast and labor-saving model for quality identification of original liquor was obtained, PCA was a more suitable algorithm for reducing the number of variables. The classification accuracy of the original liquor obtained by the SVM method after PCA dimensionality reduction was the highest, the training set accuracy was 89.81%. Therefore, the PCA-SVM model was selected, and after testing, its prediction accuracy was 84.62%. The model has undergone five-fold cross-validation (CV), and the CV accuracy is 81.53%, which proves that the model is sufficiently stable.

Additives used for food adulteration are difficult to detect because they have properties similar to the samples they are added to. Organic foods have more positive effects on food safety compared to their conventional counterparts. Therefore, it is important to assess the detectability of additives that resemble the main ingredient and are used for adulteration purposes in organic powdered products. In this study, the feasibility of using a chromameter and near infrared spectroscopy (NIRS) to discriminate adulterated organic black carrot powders, which were dried by hot air (HA) and intermittent microwave (IMW), was investigated. Principal component analysis (PCA) and partial least squares regression (PLSR) were used for data analysis. The best PLSR models for estimating the adulteration level (%) were obtained using NIRS data (R2val=0.98, RMSEP=4.9%) for samples dried by HA. Furthermore, results showed that temperature or power levels applied during drying affected the efficiency of adulteration determination in samples.

Silver ion (Ag+), as a heavy metal ion easily absorbed by organisms, can be enriched through the food chain. This study present a novel multimodal detection system for Ag+ using Prussian blue nanoparticles (PB NPs) with excellent photothermal effect and peroxidase-like activity. The PB NPs undergo a dissolution-precipitation equilibrium, converting blue PB NPs into colourless silver ferrocyanide ions in the presence of Ag+. The color, photothermal effect, and peroxidase-like activity of the PB NPs change with the concentration of Ag+, making them an effective probe for detecting Ag+. The key parameters that affect the performance of multimode detection assay were optimized. Under optimal conditions, the detection limit of visual detection was estimated to be 2.94 μM. Based on catalytic color development and the photothermal effect of PB NPs, the detection limit of color development detection and photothermal analysis were reduced to 0.57 μM and 0.23 μM, respectively. And the detection range was also broadened than visual direct detection. Besides, the multimode detection of Ag+ improves the accuracy of detection by mutual verification. The promising multimode system exhibited good repeatability and specificity of Ag+ in different sample implementations, which has great potential for applications in food safety regulation and environmental monitoring.

The USDA Food and Nutrient Database for Dietary Studies (FNDDS) is an application database created for analyzing dietary intakes from What We Eat in America (WWEIA), National Health and Nutrition Examination Survey (NHANES). FNDDS 2019–2020, the 10th version released contains 5624 food codes and their descriptions. Values for 65 nutrients are included for each item, as well as weights for common portions. Most nutrient values are derived from USDA’s FoodData Central and newer analytical data from Foundation Foods are incorporated. Changes to the recipe calculation process generate nutrient profiles that represent a variety of items. Several categories were reviewed and updated including beef, pork, seafood, candy and cakes/pies. Both the baby-toddler foods and ready-to-eat cereals categories were updated to have generic food codes and recipe calculations, which represent a variety of commercial products versus prior FNDDS versions which contained food codes for specific products matched to the same Standard Reference codes. The FNDDS is available in both SAS® and Access® formats for download. FNDDS At A Glance provides quick viewing in Microsoft Excel® file format. All are publicly available at www.ars.usda.gov/nea/bhnrc/fsrg.

The present study shows the use of an Artificial Neural Network (ANN) as a novel strategy to study the relationship between the physicochemical profile and the color acquired by Tequila 100% agave during its maturation process. As a first step, the physicochemical characterization of different classes of Tequila (n = 12) was carried out using gas chromatography, Ultraviolet-Visible (UV-Vis) spectroscopy, and RGB analysis. The results show a linear increase between the aging time and the concentration of higher alcohols, esters, and aldehydes in the beverage. Likewise, UV-Vis spectroscopy show a linear increase in the intensity at 365 nm, attributable to the total flavonoid index, with aging time. Digital image processing based on the RGB color model was used to determine the Color Intensity and Tonality. The above information was fed to an ANN as inputs with the aim of classifying Tequila 100% agave samples into their respective classes based on their maturation time. With a confusion matrix, a correct classification was done in all the samples having an excellent correlation with the measurements performed in the analytical techniques with Tequila 100% agave maturation process. The results show the use of tools based on artificial intelligence as an auxiliary strategy for evaluating quality control in aged beverages.

Biogenic amines (BAs) are nitrogen compounds formed during decarboxylation of amino acids with the participation of bacteria. A method based on high-performance liquid chromatography with diode array detector (HPLC–DAD) for the determination of eight biogenic amines in ripened meat products was developed and validated. Biogenic amines were extracted with 0.1 M hydrochloric acid and pre-column derivatized with dansyl chloride at 60 °C for 15 min, extracted with toluene, and dried under a stream of nitrogen. After reconstitution in acetonitrile, the extract was subjected to clean-up by dispersive solid-phase extraction (dSPE) using Z-Sep+ sorbent. The method was validated with the BAs at three concentration levels: 50, 100 and 200 mg/kg. The limit of detection (LOD) ranged from 1.51 to 2.20 mg/kg, and limit of quantification (LOQ) was between 5.08 and 7.34 mg/kg. The obtained correlation coefficients (R2) ranged from 0.9997 to 0.9999 for all BAs. The precision (RSDr) values were < 20% and the recovery values for all amines were in the satisfactory range of 70–120% The developed method was applied to the analysis of 39 ripened meat products samples as retailed in Poland.