Food Analytical Methods最新文献

β-Carotene is a natural food dye that is important for human health. An ecofriendly vortex-assisted dispersive liquid–liquid microextraction strategy depending on supramolecular solvents (VA-DLLME-SMS) combined with spectrophotometric analysis was developed for determination of β-carotene. The extraction was carried out at pH 7.0 (phosphate buffer), without the addition of salt. Three distinct supramolecular solvents (1-octanol/tetrahydrofuran, 1-decanol/tetrahydrofuran, and 1-dodecanol/tetrahydrofuran) were evaluated for the separation of β-carotene from aqueous medium using vortex and centrifugation. The impact of analytical variables and the matrix ion tolerance limit were presented. The findings illustrated that the preconcentration factor, detection limit, quantification limit, and relative standard deviation were 40.0, 8.0 µg L⁻¹, 20.0 µg L⁻¹, and 1.8–2.2%, respectively, under optimal conditions. The accuracy of the process was estimated by processing spiked fruit juice samples. The findings showed that the approach was applicable for determining, preconcentrating, and extracting β-carotene from food samples. Because it employs green solvents, lowers reagent quantities, and generates less waste, the approach also conforms with the principles of green chemistry as estimated by Analytical GREEnness (AGREE) and blue applicability grade index (BAGI) scales.

White button mushrooms (Agaricus bisporus) are highly perishable due to their high moisture content, rapid respiration rate, and lack of protective cuticle, which leads to rapid deterioration in texture, color, and microbial quality. Conventional quality assessment methods such as physicochemical and microbiological testing are time-consuming, destructive, and not applicable for real-time monitoring across the supply chain. This creates a pressing need for intelligent, non-destructive, and cost-effective tools that can provide real-time freshness information. This study introduces a novel pH-sensitive freshness indicator for real-time quality monitoring of white button mushrooms (Agaricus bisporus). The indicator was developed by immobilizing anthocyanin extracted from black carrot, a natural food-grade additive, onto Whatman-42 filter paper. Its performance was assessed for colorimetric response, structural integrity, and mechanical stability. Black carrot anthocyanins exhibited a distinct color shift from light pink (acidic pH) to spruce blue (alkaline pH) across the range of pH 2–10, demonstrating inherent pH sensitivity. Field emission scanning electron microscopy confirmed a porous microstructure, verifying successful physical immobilization without chemical modification. Fourier transform infrared spectroscopy highlighted hydrogen bonding as the immobilization mechanism, and X-ray diffraction revealed minor crystallinity reduction. Mechanical testing demonstrated unaffected tensile strength. The indicator was tested on fresh mushrooms stored in biodegradable trays under ambient (25 ± 1 °C) and refrigerated (5 ± 1 °C) conditions. The indicator’s performance was validated through colorimetric monitoring, physicochemical analysis, volatile organic compound profiling, and principal component analysis (PCA). The indicator effectively distinguished freshness stages as “fresh,” “still fresh,” and “spoiled” based on visually discernible color changes. Statistically significant correlations (p < 0.01) were observed between the indicator’s response and mushroom quality parameters, with weight loss (r = 0.93) showing the strongest correlation. PCA further confirmed three distinct freshness phases: fresh (days 1–4), still fresh (days 6–8), and spoiled (days 10) with distinct classification among the clusters correlated with change in color of the indicator. This freshness indicator offers a promising, sustainable solution for intelligent packaging applications, with significant implications for improving food quality control, reducing postharvest losses, and enhancing transparency in the food industry.

Food allergies have become a significant public health issue, particularly lipid transfer protein (LTP) allergies, which are highly stable allergens and can cause severe allergic reactions. This research aims to develop and validate an AI-driven solution for detecting LTPs in food using near-infrared spectroscopy (NIRS), exploring the feasibility of non-invasive allergen identification using AI-assisted spectroscopy. The methodology involves collecting spectral data from various food samples, building a machine learning model, and optimizing it iteratively to improve detection accuracy. The results show that the AI model achieved an accuracy of 87% and an F1-score of 89.91%, indicating its potential for enhancing food safety. In conclusion, this solution demonstrates the viability of using NIRS and AI for allergen detection, with promising future applications in healthcare.

Accurate and uniform temperature and humidity within the Qu-room ensure the formation of the required flavor compounds and aroma substances in Daqu, which will ultimately determine the flavor of Chinese liquor. Thus, precisely controlling these parameters is the key to ensuring the quality of the Daqu. Aiming to reduce the temporal, nonlinear, and spatial variability of the fermentation environment and the data feedback lag, a CNN-LSTM-Self-attention model for the prediction of temperature and humidity in the fermentation environment of Daqu fermentation was developed. First, the random forest (RF) algorithm was utilized to select sensor point data within the Qu-room. Then, the CNN and LSTM components of the model learned the local features and long-term dependencies of the temperature and humidity time series, and the interactions between temperature and humidity were captured using the self-attention mechanism (SAM). When predicting the temperature of the Qu-room, the average MAE, RMSE, and R² values of the CNN-LSTM-Self-attention model were 0.016, 0.012, and 0.991, respectively, and when predicting the humidity of the Qu-room, the average MAE, RMSE, and R² values were 0.01, 0.014, and 0.989, respectively. Furthermore, the temperature and humidity values R² values are 0.4–3% higher than the R² values of LSTM, BiLSTM, and CNN-LSTM models. Moreover, the CNN-LSTM-Self-attention model was able to accuracy and efficiently predict variations in temperature and humidity in different fermentation stages and seasons. This method can effectively solve the hysteresis problem of traditional parameter acquisition and provide a reference for the feasible optimization quality control of Daqu fermentation.

To achieve unscrupulous economic gain, some chicken meat retailers use dead broiler chickens to replace the meat from properly slaughtered birds, which may lead to severe health consequences for the chicken meat consumers. This study was undertaken to differentiate the quality attributes of chicken from dead and slaughtered broiler birds to judge the substitution. Therefore, slaughtered, dead, and a mix of the both samples were comparatively evaluated for different quality parameters. Results showed a significant difference (p < 0.05) in water holding capacity, extract release volume, drip loss, total pigments, myoglobin content, thiobarbituric acid reactive substance, total volatile basic nitrogen, myoglobin content, and L-lactate among the chicken meat samples. The malachite green test could efficiently differentiate the slaughtered, dead, and mix samples based on the available residual blood in them. The color parameters (redness, chroma) and histopathological parameters could also differentiate slaughtered, dead, and admixture samples. The sensory scores were higher for the dead than for both slaughtered and mix samples. Higher microbial counts were (p < 0.05) observed in dead samples as compared to others. On the basis of the findings, we could conclude that the physicochemical, histopathological, microbiological evaluation, and malachite green test could efficiently differentiate the slaughtered, dead, and mix chicken samples.

Vibrio parahaemolyticus is a major pathogen responsible for bacterial gastroenteritis associated with seafood in temperate and tropical marine and coastal waters worldwide. Detection of viable bacterial cells is crucial for food safety control. In this study, a PMA-LAMP method was developed and evaluated for detecting viable V. parahaemolyticus in aquatic products. Both live and dead cells were treated with PMA in dark for 10 min and subsequently exposed to a 650 W halogen lamp for 10 min. The DNA was prepared and amplified by PMA-LAMP. The primers which targeted six distinct regions in the bla_CARB-17 gene of V. parahaemolyticus were designed for the PMA-LAMP assay. The results showed that the treatment with 15.7 µM of PMA in dark for 10 min and a further exposure to light for 15 min was the optimum condition for PMA-LAMP to detect viable cells from V. parahaemolyticus. A total of 206 control strains were used to evaluate the specificity. The PMA-LAMP assay exhibited 100% specificity, without cross reaction with the tested non-V. parahaemolyticus cells. The limit of detection (LOD) for the PMA-LAMP assay was approximately 1.6 CFU/mL, with high sensitivity. This PMA-LAMP could contribute to the rapid, reliable, and simultaneous detection of viable V. parahaemolyticus in aquatic foods.

This investigation evaluated the chemical composition and the biological activities of the ethanol and petroleum ether extracts of Salvia lanigera Poiret from the M’sila region, Algeria. Phytochemical analysis identified 17 compounds in the ethanol extract (HPLC–DAD), with cynarin, ellagic acid, and rutin as major components. Petroleum ether extract (GC–MS) revealed 16 compounds, predominantly palmitic acid and stearic acid. Antioxidant activity was assessed using four assays: the ethanol extract showed significant activity in the phenanthroline assay (1.94 ± 0.18 μg/mL), and SNP assay (124.78 ± 0.59 μg/mL), compared to the BHA standard. Both extracts demonstrated antibacterial and antifungal effects, with inhibition zones of 10–13 mm and MIC values ranging from 0.78 to 3.125 mg/mL against tested strains. Enzymatic assays revealed α-glucosidase inhibition by the ethanol extract (IC₅₀ = 27.07 ± 0.78 μg/mL), while α-amylase inhibition was lower (ethanol: 429.85 ± 1.43 μg/mL; petroleum ether: 520.31 ± 1.63 μg/mL). Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibition were minimal (IC₅₀ > 200 μg/mL for AChE; ethanol: 365.84 ± 5.48 μg/mL, petroleum ether: 636.13 ± 4.49 μg/mL for BChE). Urease inhibition was notable for the ethanol extract (54.88%) and comparable for the petroleum ether extract (52.00%). These findings highlight the potential of S. lanigera extracts as sources of bioactive compounds with antioxidant, antimicrobial, and enzymatic inhibitory properties, warranting further exploration for therapeutic applications.

Early weed detection is crucial for optimizing agricultural productivity and minimizing crop loss. Traditional manual methods of weed identification are labor-intensive and inefficient, particularly in expansive fields. To address this challenge, this study proposes an innovative approach utilizing advanced image processing and deep learning techniques to create an automated weed detection system. We introduce 3SW-Net, a novel deep convolutional neural network specifically designed for weed detection. The method leverages the Simple Linear Iterative Clustering (SLIC) algorithm for efficient segmentation of weed regions and the Histogram of Oriented Gradients (HOG) technique to extract edge and texture features from weed images. By combining the outputs from SLIC, HOG, and grayscale images, a comprehensive feature set is created, significantly enhancing the model’s accuracy. The integrated feature fusion approach demonstrates outstanding performance, achieving a recall of 98.99%, specificity of 99.68%, and an overall accuracy of 99.56% on weed dataset. These results indicate that the combination of SLIC segmentation and HOG feature extraction significantly boosts the effectiveness of the convolutional neural network. The promising outcomes from this model pave the way for developing a robust real-time weed detection system, which can play a crucial role in promoting sustainable agricultural practices and ensuring efficient resource management.

Biogenic amines are crucial indicators of food freshness, and their detection is vital for ensuring food quality. This paper proposed a novel method for detecting three biogenic amines, namely spermine, spermidine, and histamine, based on dopant-assisted positive photoionization ion mobility spectrometry (DAPP-IMS) equipped with a thermal desorption sampler. Acetone was employed as the optimum dopant following a screening process based on the response of three biogenic amines and the peak-to-peak separation of the product ions and reactant ions. The different thermal desorption times of spermine, spermidine, and histamine resulted in varying entry times of the analytes into the reactive region, which partly reduced the competitive ionization between the analytes. The drift tube temperature significantly influenced the response signals of spermine, spermidine, and histamine in DAPP-IMS and the formation of histamine product ions. Quantitative analysis revealed that DAPP-IMS could realize the quantitative detection of spermine, spermidine, and histamine across mass ranges of 2.5 to 50 ng, 5 to 125 ng, and 25 to 175 ng, respectively, with the limits of detections of 0.5 ng, 0.5 ng, and 20 ng. These results highlighted that DAPP-IMS has great potential for the detection of biogenic amines in food to ensure quality and safety.