Food Analytical Methods最新文献

The objective of this work was the multi-element determination of Ca, Zn, Sr, Ba, Cu, Mn, Mg, and Cr in handmade chocolate samples after extraction induced by emulsion breakage (EIEB) using optical emission spectrometry with plasma induced by microwaves (MIP OES). After study of each parameter, the most efficient extraction conditions were obtained using 0.250 g of sample, 5.0 mL of extraction solution consisting of HNO₃ 1.5 mol L⁻¹ and Tween 80 1.5% m/v, submitted to an ultrasonic bath for 5 min, followed by breaking the emulsion by heating at 90 °C in a water bath for 4 min. The detection limits obtained, in mg kg⁻¹, were 0.35 (Cr), 0.013 (Zn), 0.064 (Sr), 0.083 (Ca), 0.46 (Fe), 0.010 (Ba), 0.099 (Cu), 0.016 (Mg), and 0.036 (Mn). Precision, based on the relative standard deviation (RSD%), was less than 9.8% (N = 7). The accuracy was confirmed by analyzing the SRM Baking Chocolate 2384 and comparing the proposed method with a calcination method. The method was applied to samples of chocolate bars made in the southern region of Bahia and containing cocoa contents at 50, 58, 60, 63, 70, 80, and 85%. The average results obtained were 328 to 1424 mg kg⁻¹ (Ca), 561 to 2152 mg kg⁻¹ (Mg), 7.8 to 251 mg kg⁻¹ (Cu), 8.5 to 304 mg kg⁻¹ (Mn), 6.22 to 98.32 mg kg⁻¹ (Fe), 8.38 to 80.2 mg kg⁻¹ (Zn), 3.4 to 175 mg kg⁻¹ (Ba), and 2.15 to 12.79 mg kg⁻¹ (Sr). It was observed that as the percentage increases cocoa, there is a tendency to increase the concentration of the studied elements. The developed method has satisfactory precision and accuracy, and is simple, fast and with low consumption of reagents, has good sensitivity, especially when compared to digestion methods.

A simple, rapid, and robust hydrophilic interaction liquid chromatography–tandem mass spectrometry method has been applied for the determination of acrylamide in bread, potato-based heat-processed products, and cereal-based infant food. The QuEChERS methodology was used for sample extraction and clean-up. Enzyme asparaginase was used to prepare an acrylamide-free bread-like matrix, thereby eliminating the lack of acrylamide-free food matrices as one of the limitations for method development and validation. The obtained linear range was 10–2500 µg/kg, and the limit of detection and the limit of quantification were 2.71 and 10.0 μg/kg (S/N = 3 and 10, respectively). The developed method demonstrated good recoveries (98.9–102.8%), intra-day (2.1–3.8%), and inter-day precision (2.9–4.0%). The acrylamide was found in all samples; the concentrations were lower than benchmark levels set by the European Union. In the baby food sample, the AA concentrations were below the limit of quantification (10 µg/kg), and the benchmark level set by the European Union is 40 µg/kg.

Wolfberry (Lycium barbarum) has a nearly thousand-year history of medicinal and dietary use in China. The separation methodology of its bioactive components has always received much attention. In this study, the free radical scavenging activity of each fraction of wolfberry was assessed via a DPPH assay, after which a high-purity free radical inhibitor (rutin) was prepared directly from wolfberry via a two-step chromatographic process (MCI GEL^® CHP20P and Sephadex^® LH-20 columns). The potential molecular interactions between rutin and target proteins (SREBP-1, HMGCR, AKT1, ACC, AMPK, PPAR, LPL, TNF-α, and FAS) were explored using computer simulation; among these interactions, TNF-α exhibited a strong binding affinity for rutin (-9.49 kcal/mol). In conclusion, these findings provide deeper insights into the bioactivity of wolfberry and advance the study of quality control systems for wolfberry. Moreover, it also provides a potential chromatographic option for future functional food research.

Quality assessment is a cornerstone of fruit production and distribution, particularly regarding storage conditions and duration. Citrus fruits, a staple in global consumption patterns, are the ultimate example. This study employs a nondestructive analytical technique, X-ray computed tomography (CT) scanning, to meticulously analyze a substantial sample of 300 citrus fruits, specifically satsuma, subjected to both ambient (20–22 °C, 50–60% humidity) and refrigeration conditions (6–8 °C, 65–75% humidity). The experiment was conducted through a methodologically rigorous approach, stratified dataset splitting, allocating 60% of the X-ray datasets for training, with 20% dedicated to validation and testing, respectively. The proposed research introduces a pioneering methodology termed features enhanced vision transformer (FEViT), meticulously designed to augment precision in citrus fruit classification and more precise freshness level prediction via X-ray image analysis. Our empirical findings unequivocally demonstrate the superior efficacy of FEViT models vis-a-vis conventional ViT counterparts across new X-ray citrus fruit datasets. Particularly noteworthy are the marked performance gains exhibited by FEViT-large variants, evidenced by notable increases in precision (5.08%), accuracy (5.47%), recall (4.55%), and F1 scores (5.28%) over original variants. This underscores the distinguishable enhanced discriminatory prowess of FEViT models in assessing citrus fruit quality in terms of freshness. Extensive validation through rigorous experimentation ratifies FEViT’s supremacy over traditional deep learning architectures, affirming heightened accuracy (99.25%). The current study heralds the advent of FEViT architecture as a milestone in citrus fruit (satsuma) freshness prediction, promising augmented accuracy and robustness vis-a-vis extant methodologies. This research holds profound implications for the agricultural sector, especially in domains such as citrus and broader fruit classification, where nuanced image analysis is indispensable for quality attribute like freshness evaluation and informed decision-making.

To combat the fraudulent practice of adulterating Jiang-flavor Chinese spirits with edible alcohol, this study proposes the use of UPLC-Q-TOF/MS combined with metabolomics (ultra-performance liquid chromatography with time-of-flight high-resolution mass spectrometry) analysis to investigate the differential components between authentic Jiang-flavor Chinese spirits and those adulterated with edible alcohol. A total of 175 compounds were identified in both authentic and adulterated samples, of which 74 compounds showed significant differences (p < 0.01, fold change > 5), including acids, esters, aldehydes, ketones, alcohols, and heterocyclic compounds. OPLS-DA analysis revealed a clear distinction between authentic Jiang-flavor Chinese spirits and those adulterated with edible alcohol, with Q2 and R2Y values both above 0.9 and greater than 0.5, indicating that the model is stable and reliable for the identification of adulteration in Jiang-flavor Chinese spirits.

Various chemicals present at different stages in the food supply chain can lead to the leaching of heavy metals. These metals can accumulate in the human body through the consumption of contaminated food. Consequently, it is necessary to validate an analytical technique for the quantification chemical that could contaminate food. This study presents a rapid, straightforward, and efficient analytical method for the direct quantification of some potentially toxic elements in aqueous simulants from plastic food contact products using an inductively coupled mass spectrometer (ICP-MS). The method’s validation encompassed the study of the estimated detection limits, practical quantification limits, linearity, accuracy, and measurement uncertainty of aluminium (Al), antimony (Sb), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn) under optimized ICP-MS conditions. The estimated detection limits ranged from 7.5 × 10⁻⁴ to 0.074 mg/kg, while practical quantification limits spanned from 0.02 to 0.8 mg/kg. The average recoveries ± standard deviations at different spiking levels were varied between 85.7 ± 1.51 and 115.6 ± 0.88% with coefficients of variation between 0.42 and 5.85%. The method trueness was verified by using references materials (test material in aqueous acetic acid) purchased from Food Chemistry Proficiency Testing and Analysis (FAPAS) yielding satisfactory results within acceptable recovery and Z-score values. The method precision, in terms of relative standard deviation (RSD), was being below 4.22%. The method uncertainty expressed as expanded uncertainty of all validated elements was found to be ≤ 21.9%. Validated method was employed to determine specific elements in aqueous simulants of thirty commercial plastic food packaging samples, representing three distinct types of plastic polymers. The results showed that the mean concentrations, in mg/kg, were as follows: 2.04 (Al), 0.02 (As), 0.02 (Cd), 0.02 (Co), 0.06 (Cr), 0.41 (Cu), 1.55 (Fe), 0.09 (Mn), 0.15 (Ni), 0.07 (Pb), 0.05 (Sb), and 0.81 (Zn). Furthermore, 30% of analyzed samples exceeding the maximum permissible limits of Al for plastic materials and articles intended to come into contact with food.

Near-infrared spectroscopy has become an important methodology for rapid and non-destructive detection in food and agricultural fields. However, the accuracy of quantitative analysis was seriously restricted by the severe overlap of spectra and the high cost of standard samples. In order to reduce the impact of these problems especially that of small sample size problem, a novel method named weighted clustering ensemble partial least squares (WCE-PLS) is proposed for the protein content analysis of corn. Firstly, the clustering and sampling strategy is introduced in the calibration sets of corn to create different subsets for generating sub-models. Then, root mean square errors of cross-validation (RMSECV) in those sub-models as the crucial criterion are computed for model optimization. Finally, in integrating step, two Gaussian weighted functions used to determine the weights of sub-models are defined. The validation performance of the proposed method is tested with the near infrared spectral data sets of corn and compared with single PLS, bagging PLS, boosting PLS, and data augmentation (DA) PLS. To further demonstrate the effectiveness of the method, another data set of soil was used for supplementary verification. Results of the prediction sets indicated that the RMSEP values of the WCE-PLS are obviously smaller than that of boosting PLS. And the RMSEP of WCE-PLS and bagging PLS is relatively small in most cases. Furthermore, the correlation coefficients between predicted value and chemical value are respectively 0.96587 and 0.90849 for two data sets, which computed by the WCE-PLS is obviously higher than that computed by the other four methods. And the t test also showed the WCE-PLS has smaller t values and larger p values.

Although acids and esters are the trace chemical compounds in Chinese liquor, they affect primarily the flavor and quality of Chinese liquor. The detection of these compounds is important for the control of Chinese liquor. FTIR spectroscopy has gradually been used to detect Chinese liquor in recent years, but this technology has not been directly employed to measure the infrared spectra of acids and esters in Chinese liquor. In this paper, novel FTIR difference spectroscopy is proposed to extract the infrared spectra of acids and esters in Chinese liquor. This difference spectrum is mainly obtained by subtracting the FTIR spectra of aqueous ethanol from that of Chinese liquor. The FTIR spectra of some kinds of Chinese liquor were measured, and it was found that three vibrational peaks at ~ 1260 cm⁻¹, ~ 1377 cm⁻¹, and ~ 1710 cm⁻¹, which were also observed in the infrared spectra of acids and esters in Chinese liquor, were obtained. The flavor of Chinese liquor can be distinguished through the difference spectra. Since the acquisition of FTIR spectra only takes less than 1 min, this FTIR difference spectrum can be developed as a quick control method for Chinese liquor.

Buriti oil is a vegetable oil extracted from the pulp and seeds of buriti (Mauritia flexuosa L.), a palm commonly found in the Amazon region, and is used both in popular medicine and in the cosmetic and food industries. This work aimed to develop a faster and more accessible procedure to quantify the content of carotenoids, polyphenols, and total flavonoids in buriti oils, where predictive models emphasize figures of merit. The study was carried out with 50 buriti oil samples from the state of Pará, Brazil, which were sampled by combining attenuated total reflection (ATR) spectroscopy with mid-infrared Fourier transform (FT-MIR) together with partial least squares regression (PLSR). The confidence and validation matrix were obtained from ultraviolet–visible spectroscopy. The PLSR model regarding the total carotenoid content presented values ​​between 335.33 and 1557.05 μg/g was validated by the concentration demonstration coefficient (R²cal) equal to 0.9556, prediction demonstration coefficient (R²_pred) equal to 0.85642, bias = 5.68.10⁻¹³, performance deviation ratio value (RDP) of 2.0135, and range error rate (RER) equal to 4.3747. Concentrations of phenolic compounds were predicted between 96.2964 and 121.857 GAE/100 g, where the model presented R²_cal = 0.9762, R²_pred = 0.8198, bias = 3.38.10⁻¹⁰, RDP = 5.9028, and RER = 5.7578. The flavonoid prediction model contains concentrations between 86.844 and 133.852 mg EC/100 g that circulate R²_cal = 0.9445, R²_pred = 0.8536, bias = 6.98.10⁻⁸, RDP = 6.7085, and RER = 6.7085. Buriti oil showed high levels of b-carotene. Prediction models are overwhelming and can be used for screening and quality control of natural products.

Caffeic acid contributes to the flavor and aroma of coffee. Monitoring its levels can be important to guarantee the quality of the coffee produced. An innovative electrochemical sensor for the determination of caffeic acid was developed using banana pulp (Musa sapientum) extract as the precursor for the green synthesis of gold nanoparticles. Microscopic images verified the presence of dispersed gold nanoparticles, with an average diameter of 14.4 ± 2.5 nm on graphene sheets. The electrochemical behavior of caffeic acid demonstrated reversibility, with oxidation and reduction peaks. Under optimized conditions, a calibration curve was developed in 0.1 mol L⁻¹ Britton-Robinson buffer (pH 2.0) with linear range from 0.05 to 10.0 µmol L⁻¹, and a detection limit of 16 nmol L⁻¹. The sensor was effective in coffee samples, and the results were comparable to those obtained using UV–vis spectrometry.

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