Food Analytical Methods最新文献

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.

The adulteration of high-value vegetable oils in blended products poses significant challenges for quality control and consumer protection. While Raman spectroscopy offers a rapid and non-destructive analytical tool, conventional chemometric models such as partial least squares (PLS) and support vector machines (SVM) often struggle with complex spectral data due to their reliance on manual feature selection and limited capability in capturing nonlinear relationships. To address these limitations, this study introduces a deep learning-based approach combining Raman spectroscopy with three advanced neural network architectures—1D-CNN, ConvNext-ECA, and CNN-GRU-MHA—for the quantitative determination of camellia oil in ternary blends with rapeseed and corn oils. Compared to traditional machine learning models, all three deep learning models demonstrated superior predictive accuracy. The CNN-GRU-MHA model achieved the best performance, with an R²p of 0.9981 and RMSEP of 0.3714. These results underscore the potential of attention-enhanced deep learning models as a robust and efficient tool for the authentication of blended vegetable oils.

Oxidative deterioration of edible oils leads to rancidity, resulting in the formation of volatile and non-volatile degradation products that adversely affect sensory quality, nutritional value, and consumer acceptance. Conventional methods for rancidity detection, such as peroxide value, p-anisidine value, free fatty acid value, thiobarbituric acid test, chromatography, and spectroscopic techniques, are accurate but time-consuming, costly, and laboratory-based, making them unsuitable for rapid detection. This review comprehensively analyses the potential of electronic nose (e-nose) technologies for rapid detection of rancidity in various edible oils. An e-nose system typically comprises a sample transfer unit, a sensor array (e.g. metal oxide or polymer-based gas sensors) for detecting volatile oxidation products, and a pattern recognition or data analysis system. Recent developments integrating e-nose systems with artificial intelligence, machine learning, computer vision, and hybrid analytical techniques are critically discussed, demonstrating improved accuracy, sensitivity, and practical applicability. Overall, this review bridges the gap between conventional and advanced analytical approaches by highlighting e-nose technologies as cost-effective, user-friendly, and scalable tools for routine quality assessment. The e-nose thus holds great promise for applications in oil processing industries, retail outlets, restaurants, and even household kitchens, contributing to improved food safety, reduced waste, and informed consumer choices.

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Traditional “gold standard” methods for quantifying arsenic (As) in rice are not ideal for routine analysis due to their time-consuming, expensive, and complex nature. Therefore, the aim of this study was to develop a simple, rapid, and minimally destructive method applying X-ray fluorescence (XRF) spectroscopy and chemometric modeling to quantify As in rice. Milled rice was spiked with As (63.47 – 553.43 μg kg⁻¹), pelletized, and analyzed utilizing an energy-dispersive XRF spectrometer. The resultant spectra were used to generate a prediction model via partial least squares regression (PLSR), which showed strong predictive capabilities (R² = 0.99, RMSEC = 14.3 μg kg⁻¹) and strong sensitivity (LOD = 27.76 μg kg⁻¹, LOQ = 92.52 μg kg⁻¹). Validation was conducted using a certified reference material, yielding an error of prediction of only 8.96%. Analysis of rice and rice-based foods showed strong agreement between the current study and traditional methods, demonstrating its robust capabilities for routine analysis.

‘Sous vide’ is a cooking process that uses long time and low temperatures, affecting the quality and composition of the product. This study aimed to develop and validate prediction models for the proximate composition of sous vide beef tenderloin using near-infrared reflectance spectroscopy (NIRS) and to compare the performance of two portable NIR spectrometers. Original spectra were pretreated using the Standard Normal Variate (SNV) method, followed by the 1st derivative. Models using partial least squares regression (PLS) were constructed with all spectral variables, and after the jack-knife algorithm performed wavelength selection. The centesimal composition of the ground samples was accurately determined by the models, except for the carbohydrate content. The prediction errors resulting from external validation (RMSEP) for the InnoSpectra and NeoSpectra evaluated compact instruments were, respectively, 0.92% and 0.65% for moisture, 0.72% and 0.93% for fat, 0.93% and 0.60% for protein, and 0.12% and 0.03% for ash. Conversely, poor results were obtained for samples where readings were taken from whole roasters with or without packaging. A new method for assessing the quality and usefulness of multivariate models was proposed based on RMSEP and comparing the distributions of the reference and validation data. The NIRS-based method is fast, requires simple sample preparation, does not require the use of chemicals, and employs low-cost instruments.

Conventional laboratory testing of moisture content and water activity in honey is often time-consuming and costly. Therefore, alternative analytical methods that are non-destructive, rapid, efficient, and cost-effective are needed, particularly for field applications. This study aims to develop predictive models for determining moisture content and water activity in liquid honey (forest and acacia) and powdered honey using portable near-infrared (NIR) spectroscopy combined with multiple linear regression (MLR) analysis Spectral data were acquired using a NIR SpectraPod (850–1700 nm), followed by laboratory measurements for calibration. Data pre-processing with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) improved model accuracy. A simple K-nearest neighbors (KNN) analysis was included only as a preliminary check of honey-type separability. The developed models demonstrated strong predictive performance across all honey types. For individual honey varieties, the coefficient of determination (R²) ranged from 0.75 to 0.96, while the root mean square error (RMSE) values were between 0.02 and 0.05 for water activity and 2.1–4.6% for moisture content. When acacia and forest honey data were combined, the models maintained good performance, with R² values of 0.58–0.78 and RMSE values of 0.03–0.05 for water activity and 4.0–6.7% for moisture content. These findings demonstrate that portable NIR spectroscopy, integrated with regression modelling, is a reliable and efficient tool for rapid honey quality assessment, offering significant advantages over conventional laboratory methods.