Journal of Food Quality

Watermelon seeds are a significant source of nutrition in the diet. To assess the potential of near-infrared hyperspectral imaging technology for swift and nondestructive identification of watermelon seed varieties, near-infrared hyperspectral imaging (NIR-HSI) technology was used. The Savitzky–Golay (SG) smoothing algorithm and standard normal variable (SNV) algorithm were combined to preprocess the extracted spectral data. The successive projections algorithm (SPA) was used to reduce the dimensionality of the spectral data. Subsequently, three deep learning models (LeNet, GoogLeNet, and ResNet) were used to classify 10 common watermelon seeds. SPA was used to reduce the dimensionality of hyperspectral data. In terms of full band, the ResNet model achieved a classification accuracy of 86.77% on the test set. By using characteristic bands, the GoogLeNet model achieved a classification accuracy of 83.85% on the test set. The ensemble fusion model based on a scoring mechanism achieved accuracy rates of 99.56%, 90.88%, and 87.97% on the training, validation, and test sets, respectively. The results indicated that the ensemble fusion model based on a scoring mechanism can enhance accuracy. Combining deep learning with NIR-HSI can effectively distinguish different varieties of watermelon seeds.

Mancozeb is an extensively consumed fungicide, which often leaves high residue levels on agricultural products. The conventional method for detecting mancozeb involves a time-consuming process using gas chromatography (GC) after a 2-hour water-bath acidolysis, resulting in low efficiency and recovery rates. This study developed a rapid method for detecting mancozeb in fruits and vegetables using microwave-assisted acidolysis and extraction coupled with GC analysis. Mancozeb underwent “one-pot” acidolysis to generate CS₂ gas and was subsequently extracted from samples using microwave treatment, requiring only 50 seconds of pretreatment time. The average recoveries of mancozeb ranged from 81% to 112%. The limit of detection and limit of quantification were 0.003 and 0.01 mg kg⁻¹, respectively. The scanning electron microscope imaging showed that strong cell crumpling after microwave treatment improved the acidolysis rate significantly, where the acidolysis rate was 91.8% for mancozeb. In addition, this method is rapid, simple, and precise for detecting residues of mancozeb and other dithiocarbamate fungicides.

Fontitrygon margarita liver oil, rich in unsaturated fatty acids, is susceptible to oxidation during storage, which can diminish its antibacterial qualities. This study examines the effects of storage temperature and the addition of spices on the stability and antibacterial properties of F. margarita liver oil. Oils with added spices were stored in opaque bottles at room temperature (28 ± 2°C) and in a refrigerator (4°C) and were periodically analyzed over a six-month period. Standard methods were used to determine oil quality indices; the Fourier transform infrared (FTIR) profile was assessed by spectroscopy; and antibacterial activities were measured using the broth microdilution method. The quality indices, FTIR profile, and antibacterial activities of the oil were evaluated and compared based on the incorporation of spices. The quality indices of oil extracted without a stabilizer and stored at room temperature significantly increased over time. The antibacterial activity of these oils gradually decreased during storage, with the minimal inhibitory concentrations (MICs) on bacterial strains of Escherichia coli (EC 137), Enterobacter cloacae (ENT 119 and ENT 51), and Yersinia enterocolitica (YERB 1) increasing from 16 to 128 mg/ml. Regardless of oil quality indices, oils stored in a refrigerator had lower values and better antibacterial activities than those stored at room temperature ((16 ≤ MIC ≤ 64 mg/ml on the strains of EC 137, YERB 1, ENT 51, and Klebsiella pneumoniae (KL 11)). The inclusion of spices significantly reduced the oxidative reaction in the oils and maintained the antibacterial activities of the tested oils. Given its antibacterial properties, F. margarita liver oil holds significant potential for the nutraceutical industry and could be used as a dietary supplement. This research underscores the importance of proper storage conditions and the use of natural stabilizers in maintaining the quality of such valuable natural resources.

Tarangabin manna (TM) is a resinous substance having a yellowish sticky character with a reasonably sweet taste. It is largely collected in Iran and Afghanistan. This study for the first time presents a comprehensive investigation of the techno-functional, rheological, and interfacial characteristics of water-soluble components for TM. The composition analysis revealed protein, moisture, fat, ash, and carbohydrate contents of 1.58, 2.98, 0.51, 2.04, and 92.90%, respectively. The effects of TM concentration on the physicochemical, structural, rheological, interfacial, emulsion, and foaming ability and stability were evaluated. X-ray diffraction analysis showed an amorphous structure for the purified sample and a crystalline structure for the raw sample. TM solutions exhibited Newtonian behavior, with the apparent viscosity decreasing as temperature increased, fitting well with the Arrhenius model. The TM solutions exhibited weak viscoelastic properties, primarily demonstrating a dominant viscous character. The surface tension and interfacial tension of the TM solution prepared at a concentration of 50% were measured at 45.23 mN/m and 7.74 mN/m, respectively. The contact angle of the dry thin layer of TM was determined to be 31.74°. Remarkably, the TM solution at a concentration of 50% exhibited the highest foaming ability (76.80%), foaming stability (91.92%), and emulsifying activity index (24.53%). The findings, coupled with TM appropriate foaming ability and stability, sweetness, and characteristic flavor, suggest that TM holds potential as a special food ingredient.

Steamed chicken breast meat attracts people for its unique flavor and nutritional benefits. In this study, the sensory evaluation of Lueyang black chicken breast meat during steaming was first performed, and their volatile organic compounds (VOCs) were further analyzed by gas chromatography-ion mobility spectroscopy (GC-IMS) combined with stoichiometry. The sensory results demonstrated that the Lueyang black chicken breast meat steamed for 15–30 min was more acceptable in taste, flavor, and chewiness. A total of 60 volatile flavor signal peaks were obtained, and 46 VOCs were recognized from qualitative analysis, containing 24 aldehydes (51.19–72.57%), 8 ketones (10.15–16.97%), 9 alcohols (7.98-13.16%), 2 furans (2.24–10.85%), 2 esters (0.54–1.56%), and 1 ether (0.05–2.47%). A stable and reliable prediction model was set up by orthogonal partial least squares-discriminant analysis (OPLS-DA), and 18 characteristic VOCs (including 10 aldehydes, 3 alcohols, 3 ketones, 1 furan, and 1 ether) were picked out through variable importance in the projection (VIP>1.0 and ). Principal component analysis (PCA) results indicated that the cumulative contribution ratio was 92% with PC1 68.7% and PC2 23.3%, respectively, indicating that these characteristic VOCs could well discriminate the steaming time of Lueyang black chicken breast meat. Heatmap clustering analysis also demonstrated a similar distinguishing effect. These results could provide references for the research, development, and quality control of ready-to-eat steamed products for Lueyang black chicken breast meat in the future.

Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry.