Food Analytical Methods最新文献

Early detection and control of aflatoxin B1 (AFB1) contamination are essential for ensuring food safety. However, conventional analytical methods frequently encounter technical limitations including operational complexity and insufficient sensitivity. To overcome these limitations, an ultrasensitive fluorescent immunoassay detection of AFB1 was developed based on SiO₂ nanospheres. These highly dispersible nanospheres were utilized as carriers to co-load CdSe/ZnS quantum dots (QDs) and AFB1-specific aptamers (Apt), resulting in the construction of SiO₂@QDs@Apt fluorescent probes with enhanced signal amplification (7.51 (times) 10³-fold improvement in sensitivity compared to the QDs@Apt method without SiO₂ nanospheres). The assay achieved an ultra-low detection limit of 5.06 (times) 10^–7 ng/mL and a broad linear range of 10⁻⁷~10 ng/mL. Validation through standard spiking experiments and real sample analysis confirmed the method’s accuracy and reliability. This strategy demonstrated higher sensitivity than the commercial ELISA kit tested, suggesting its potential utility for trace AFB1 monitoring in certain food matrices. Overall, this simple, specific, and cost-effective strategy could serve as a sensitive analytical platform for AFB1 monitoring in food safety laboratories.

Traditional refining is a crucial process used to remove undesired components such as free fatty acids, phospholipids, and oxidation products from crude oil. However, it also results in the loss of some beneficial minor components, including tocopherols and phenolic compounds. Nutritionally important minor components can be retained through alternative refining approaches like minimal refining that minimize processing intensity and reduce the number of refining stages. Calcium hydroxide has been used as an alternative to sodium hydroxide to reduce neutralization loss since it is cheaper and weaker than sodium hydroxide. The research focused on obtaining neutralized corn oil by calcium hydroxide Ca(OH)₂ instead of sodium hydroxide (NaOH), which is typically used in traditional neutralization. Response surface methodology was employed to optimize the process for producing corn oil via reduced FFA and retained α-tocopherol levels. The elucidated optimum values for the neutralization of corn oil by Ca(OH)₂ were at 0.44%, 47.4 °C, and 14.4 min. FFA values for crude, traditional, and minimal neutralized corn oils were 0.21%, 0.20%, and 0.13%, respectively. The α-tocopherol level of the crude, traditional, and minimal neutralized corn oils were 126.43, 100.56, and 125.04 mg/kg oil, respectively.

A reliable analytical method for detecting adulteration in ginger powder (GP) is essential to ensure product quality and maintain consumer trust. In this study, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy was employed to provide a rapid and simple method for detecting GP adulterated with white powder fillers, namely corn starch (CS), potato starch (PS), rice flour (RF), tapioca starch (TS), and wheat flour (WF). Two chemometric approaches were applied for the classification of the mid-infrared spectra (800–650 cm⁻¹): class-modelling and discriminant approaches. Data-driven soft independent modelling of class analogy (DD-SIMCA) was utilized to model only the authentic GP class, while partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were employed as binary classifiers using both authentic and adulterated calibration samples. All models demonstrated good classification performance on the prediction set within their respective frameworks. The discriminant models showed high sensitivity and specificity for the detection of adulterated samples, with efficiency values of 0.980 for PLS-DA and 1.000 for SVM. Although DD-SIMCA yielded a lower efficiency value of 0.919, it successfully classified the authentic GP samples, with a sensitivity value of 0.959, and demonstrated its suitability for one-class authentication tasks. Overall, the results highlight the potential of ATR-FTIR spectroscopy combined with appropriate chemometric strategies for the detection of adulteration in ginger powder.

This study aimed to evaluate the accuracy of food rapid test kits based on colloidal gold immunochromatographic assays (GICA) for detecting chloramphenicol, enrofloxacin, and sulfonamides in livestock, poultry, and aquatic products, and to provide a reference for their practical application. Rapid test kits from different manufacturers were used to detect the above targets in pork, chicken, fish, shrimp, and other samples. The interference resistance, linearity, and sensitivity of the test kits were analyzed by setting up a multi-concentration gradient combined with the interference experiment of flavoring matrix. Test kits D and E showed strong anti-interference ability and specificity for chloramphenicol, though test kit E yielded false positives at 0.05 µg/kg. For enrofloxacin, polynomial fitting correlation coefficients (0.9991, 0.9966, 0.9993) outperformed linear regression. Compared to test kits using sulfadiazine or sulfamethoxazole as the benchmark, those based on sulfamethazine demonstrated superior accuracy. Test kit B achieved an overall accuracy rate of 96.6%. In contrast, test kits using sulfamethoxazole as the benchmark showed no response to sulfadiazine, sulfamerazine, or sulfamethazine, resulting in a false negative rate as high as 100%. The GICA is effective in screening for chloramphenicol in seasoned meat and fish samples; for enrofloxacin, it exhibits a good linear relationship and is suitable for preliminary screening in the circulation chain. The sensitivity for sulfonamides depends heavily on the benchmark substance used in test kit design. We recommend selecting rapid tests developed with a structurally representative benchmark, like sulfamethazine, and validating them across matrices before deployment. Confirm with traditional methods if needed.

Volatile flavor compounds are key determinants of the sensory quality and consumer preference of olive oil, and their compositional profiles are significantly influenced by geographical origin. To elucidate the regional differences in volatile flavors, this study employed headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometrics to analyze oil samples from three major Chinese producing regions—Gansu, Yunnan, and Sichuan—as well as representative imported origins. A total of 56 volatile compounds were identified, including aldehydes, esters, alcohols, ketones, and terpenes. Fingerprint analysis revealed that terpenes were present at specifically high levels in some samples from Gansu, whereas alcohols and esters were more abundant in specific Sichuan samples and among the imported oils. Principal component analysis (PCA) showed limited overall separation among the regions, whereas partial least squares-discriminant analysis (PLS-DA) effectively discriminated samples from different geographical origins and identified 15 potential marker compounds with variable importance in projection (VIP) scores > 1. This study demonstrates that HS-GC-IMS coupled with chemometrics is an effective approach for screening volatile markers indicative of geographical origin, providing a potential research direction and data support for the traceability of olive oil.

Traditional analytical techniques for food analysis, such as HPLC and GC–MS, face limitations including high cost, operational complexity, and a lack of portability. Electrochemical sensors offer a promising alternative, providing rapid, sensitive, and on-site detection of various food contaminants like heavy metals, pesticides, allergens, and pathogens. However, the complexity of food matrices and the resulting sensor data often require advanced interpretation. This is where chemometricals, employing statistical and machine learning algorithms such as PCA, PLSR, and SVM, become crucial for extracting meaningful patterns, classification, and predictive modeling from complex electrochemical signals. This review systematically explores the synergistic combination of electrochemical sensing and chemometrical analysis, highlighting its successful applications in food authenticity, adulteration detection, freshness monitoring, and quality control across diverse products like dairy, coffee, and juices. While challenges such as matrix effects, electrode stability, and model validation persist, the integrated approach demonstrates significant potential to enhance efficiency, safety, and compliance in the food industry. The novelty of this article lies in its comprehensive and critical synthesis of the chemometrical–electrochemical combinatorial approach, specifically addressing advancements, limitations, and future perspectives to advance intelligent food analysis.

As the demand for alternative lipid sources intensifies globally, insect-derived oils offer an eco-efficient solution enriched with essential elements and functional compounds. The current study aims to investigate the eri silkworm (Philosamia ricini) pupal oil through inductively coupled plasma mass spectrometry (ICP-MS) for elemental profiling, Fourier transform infrared spectroscopy (FTIR) for functional group analysis, DPPH, ABTS, and MTT assays for biological evaluation. The results of the ICP-MS revealed the presence of the vital essential elements such as Fe, Zn, Cu, Mn, and Se, playing an important role in antioxidant properties and human metabolism. Further, FTIR confirmed the presence of major functional groups, which include C-H stretching vibrations and C = O stretching characteristic of lipid structures, supporting the oil’s PUFA-rich composition. The oil showed elevated antioxidant activity in free radical scavenging assays and was found to be non-toxic within biologically relevant concentrations in MTT cytotoxicity testing, indicating its safety for nutritional and therapeutic applications. These findings support the nutritional relevance and in vitro biological safety of eri pupal oil and reinforce its potential as a functional lipid source for food, nutraceutical, and cosmetic applications, warranting further in vivo investigation.

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Cadmium (Cd) is a heavy metal that is highly toxic to the human body. Wheat grown in high-Cd soil can accumulate Cd in its kernels at a level exceeding international standards. Currently established methods to measure Cd content in grain are expensive and time-consuming and require skilled sample handling, highlighting the need for a quick and relatively simple method of determining the Cd concentration in wheat grain. The approach reported here predicted the Cd concentration in wheat grain samples by using partial least squares (PLS) regression analysis to associate near-infrared (NIR) spectrometry of grain samples with their corresponding Cd content as determined by chemical analysis. Four spectrum pre-processing methods and three wavelength ranges were compared; detrending of the spectra from 981 to 1095 nm was found to yield the best PLS regression performance in the cross-validation, with a root-mean-square error of 0.082 mg kg⁻¹ and a correlation coefficient of 0.952. Robustness of this method was validated by repeatedly partitioning one-fourth of the samples as the test dataset and then predicting their Cd content with the model developed from the remainder of the samples. NIR spectrometry can measure many wheat grain samples in a short time, making this method feasible for examination of harvested grains at farms and in screening for low-Cd-accumulating parental materials for breeding purposes.

The contamination of plasticizers in food has attracted more and more attention. In this study, microwave detection technology is used to provide a new method for rapid non-destructive detection of phthalate esters (PAEs) plasticizers in edible oil. Microwave data of rapeseed oil samples containing different concentrations of dibutyl phthalate (DBP) were collected. Two feature optimization methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were used to select the features of the pre-processed data. Partial least square regression (PLSR) of linear regression model and support vector regression (SVR) and Extreme Gradient Boosting (XGBoost) of nonlinear regression model were established according to the selected best feature subset. Meanwhile, particle swarm optimization (PSO) was used to optimize the parameters of the two nonlinear models. By comparing the prediction performance of the three models, the CARS-PSO-SVR model established after feature selection and parameter optimization achieved the highest prediction accuracy among the evaluated models under the present experimental conditions, and its coefficient of determination (R²) and root mean square error (RMSE) are 0.994 and 0.467 mg/kg, respectively. The results show that, using the combination of microwave detection technology and machine learning modeling, high precision and rapid non-destructive detection of DBP content in rapeseed oil can be realized.