Journal of Food Measurement and Characterization最新文献

This study aims to characterize and discriminate Rhus chinensis Mill. honeys from different geographical origins based on their physicochemical parameters, mineral elements, volatile compounds, and antioxidant activities. Rhus chinensis Mill. honey samples were collected from four different geographical origins for analysis. The results demonstrated statistical differences in nine physicochemical parameters, nine mineral elements, 12 volatile compounds, total phenolic content (TPC), and antioxidant activities of Rhus chinensis Mill. honey from different origins. The principal component analysis (PCA) biplot and partial least squares-discriminate analysis (PLS-DA) plot constructed with 35 statistically different variables demonstrated that different origins of Rhus chinensis Mill. honey could be effectively discriminated, among which 14 variables had variable importance in projection (VIP) > 1. These variables could serve as potential markers to discriminate Rhus chinensis Mill. honey from different origins. Our findings improve the understanding of Rhus chinensis Mill. honey and can facilitate the tracing of its origin.

This study presents a novel investigation into the impact of baking on the physiochemical, antioxidant, techno-functional, textural, thermal, and rheological properties of honey cookie fillings, which were prepared using the combination of xanthan gum (XG) and watermelon rind paste (WMRP). At a constant concentration of XG (1.0% w/w), increasing WMRP from 15% (w/w) to 30% (w/w) resulted in an increase in the moisture content by 39.23% and 39.93%, total phenolic content (TPC) by 27.04% and 73.05%, radical scavenging activity (RSA) by 6.21% and 12.38%, and degree of syneresis (DOS) by 290.49% and 162.60%, while decreasing the total soluble solids (TSS) by 9.58% and 10.13%, 5-hydroxymethylfurfural (HMF) content by 42.80% and 14.61%, and diastase activity by 33.82% and 39.41% in fresh and baked fillings, respectively. In comparison, at the higher concentration of XG at 2.0% (w/w), increasing WMRP from 15% (w/w) to 30% (w/w) led to more pronounced increases in moisture content by 41.83% and 39.05%, TPC by 14.11% and 20.48%, RSA by 2.63% and 4.48%, DOS by 450.98% and 181.63%, while a similar reduction was observed in TSS by 9.45% and 10.29%, HMF content by 41.88% and 7.96%, and diastase activity by 45.73% and 36.18% in fresh and baked honey fillings, respectively. Increasing WMRP concentration from 15% (w/w) to 30% (w/w) decreased the viscoelastic nature of fresh honey-filling samples; however, no significant change was observed in baked samples. Furthermore, increasing XG concentration from 1.0% (w/w) to 1.5% (w/w) significantly enhanced the strength of gel network and viscoelastic behaviour of both fresh and baked honey filling samples. On the thermal properties, an increase in WMRP (15% w/w and 30% w/w) showed a significant impact in comparison to the XG.

Graphical Abstract

The assessment of the surface quality of pre-processed soybean kernels is crucial in determining their market acceptance, storage stability, processing quality, and overall consumer approval. Conventional techniques of surface quality evaluation are time-consuming, reliant on personal judgement, and lack consistency. Conversely, the existing techniques are restricted to either selecting healthy soybean kernels from damaged ones without categorizing the damaged ones, or separating different varieties. The lack of a labelled, high-magnification image dataset and the use of advanced CNN models have hindered the exploration of a detailed classification of damage in soybean kernels. These models excel at end-to-end tasks, minimize pre-processing, and eliminate the need for manual feature extraction, enabling quick, accurate, and precise classification. This study demonstrates the use of a machine vision system to create an image dataset consisting of 9866 high-magnification (2.85 µm/pixel) images of soybean kernels with damages. The dataset encompasses eight distinct damage classes: healthy, heat damage (HD), immature damage (IMD), mold damage (MD), purple mottled and stained (PMS), stinkbug damage (SBD), shriveled/wrinkle damage (SWD), and tear damage (TAD). Due to on-field collection a high degree of imbalance was encountered among the damage classes with healthy being the top-class accounting for the 41% of the total dataset while SBD and PMS being the classes with least number of images; accounting for just 5% of total dataset. Secondly, three advanced memory-efficient Deep-CNN models, namely, EfficientNet-B0, ResNet- 50, and VGG- 16, are utilized and fine-tunned to classify damaged soybean kernels. Results from experiments demonstrate that the EfficientNet-B0 model outperforms others in terms of accuracy, average recall, and F1-score and second best in terms of precision. The individual class accuracy achieved is as follows: 77% for HD class, 92% for healthy class, 78% for IMD class, 77% for MD class, 84% for PMS class, 72% for SBD class, 75% for SWD class and 92% for TAD class. In addition, the performance of model in handling of class imbalance among the eight damage classes is also analyzed by comparing the F1-score. Five out of eight classes achieved a F1-score of above 80% including the PMS. The class having the least F1-score was SBD with a score of 68%. The EfficientNet-B0 model attains an overall classification accuracy of 85% with a nominal size of 47 MB. It also has a minimum prediction time of under 9 s while predicting 1480 data points simultaneously. In summary, this study shows that using Deep CNN architectures on a high-magnified and highly unbalanced complex image dataset can accurately classify damaged soybean kernels. The model also performs well in handling data imbalance, making it a useful tool for objective quality assessment of damaged soybean grains in market and trading locations.

Ber (Ziziphus mauritiana Lamk.) is an important arid fruit tree cultivated in arid regions, valued for its unique taste, appealing texture, and high nutritional content. To minimize quality degradation during storage, the study aimed to investigate the effect of pre-harvest foliar application of different chemicals viz., 0.5%,1.0%, 1.5% CaCl₂, 0.5%, 1.0%, 1.5% Ca(NO₃)₂; 1 mM, 2 mM salicylic acid; 0.25%, 0.5% boric acid and a control treatment (tap water). These treatments were applied 15 days prior to harvest in the experimental orchard CCS Haryana Agricultural University, Regional Research Station, Bawal, which is located at latitude 28.09^oN, longitude 76.59^oE, and 266 m above mean sea level to analyze their effects on the physico-chemical attributes of the fruit. The data was analyzed using a randomized block design. Among different treatments, the pre-harvest spray of 1.5 percent Ca(NO₃)₂ (T₆) performed better in terms of average fruit weight (13.24 ± 0.19 and 14.26 ± 0.20 g), TSS (20.70 ± 0.18 and 21.50 ± 0.22°B), total sugars (12.47 ± 0.24 and 12.51 ± 0.27%) while minimum titratable acidity 0.21 ± 0.01% (T₆), 0.28 ± 0.01 (T₇), TSS/acid ratio 98.6 ± 1.0 (T₆) and 72.1 ± 1.0 (T₃). However, 1 mM salicylic acid was found to be better in terms of ascorbic acid (59.5 ± 1.5 and 73.2 ± 1.4 mg/100 g pulp) and phenol content (239.4 ± 6.3 and 275.4 ± 7.3 mg GAE/ 100 g pulp) observed in ambient and low-temperature storage conditions, respectively. Pre-harvest application of different chemicals reduced physicochemical properties of ber during storage at a very slow rate compared to control. The performance of quality parameters under low-temperature storage was found to be better than that of ambient temperature storage conditions. The fruit weight, titratable acidity, TSS: acid, ascorbic acids, and phenol content decreased with storage up to 10 days of storage; however, TSS and total sugars increased initially and thereafter decreased.

This study focused on the effect of thermal processing including atmospheric boiling (AB), high-pressure boiling (HPB), and baking (B) on structural properties, physicochemical properties, and in vitro digestibility of starch and protein of whole grain highland barley (HB). AB and HPB treatments led to aggregation of starch granules due to starch gelatinization. AB treatment decreased the values of peak viscosity, trough viscosity and peak time of HB, and increased the setback value of HB compared to native HB. Thermal treatments increased short-disorder of HB starch granule and reduced the relative crystallinity of HB from 14.12% (control) to 11.24% (B-HB), 5.65% (AB-HB), and 4.93% (HPB-HB), respectively. AB and HPB treatments increased the content of rapidly digestible starch and decreased the contents of slowly digestible starch and resistant starch. Protein digestibility in vitro of HPB-HB and AP-HB was increased and resultant free amino acid content was decreased compared to that of control HB. Thermal treatment also affected the molecular weight of protein subunits of HB at different degree. Overall, this research provided theoretical basis for the effect of thermal processing on whole grain HB regarding to physicochemical properties, and in vitro digestibility of starch and protein.

To address the drawbacks of traditional pesticide detection methods such as sample disruption and procedural complexity, a rapid, non-destructive spectral detection system was developed in this paper. This system consists of a handheld spectrometer, detection algorithm, cloud computing, and an app that enables real-time detection of thiophanate-methyl content in cherry tomatoes. As the key of the system and the focus of this study, a novel deep learning algorithm called SpecTransformer was proposed to drive the spectrometer for spectral feature extraction and model detection. The algorithm was designed as a module architecture including input layer, spectral preprocessing layer, Block1, Block2 and output layer, which could achieve better detection performance than other current spectral algorithms. The results showed that the determination coefficient (R²) of the spectrometer for thiophanate-methyl detection was 0.91, with a root mean square error (RMSE) of 1.05. The effective detection range was between 1:100 and 1:5000 dilutions, with a limit of detection (LOD) of 1:5000 dilution (0.2 g/L). The spectrometer is compact, user-friendly, and has strong scalability. It can be expanded to detect multiple pesticide residues in the future, which provides new insights into rapid and accurate measurement of pesticide residues on agricultural produce.

Acrylamide (AA) is known as a food contaminant, with the possibility of causing cancer in humans, which is formed after heating some foods like breaded-fried foods. In this study, the effect of cooking methods including oven-cooking, deep-frying, and pan-frying on amount of AA in schnitzels was measured by HPLC-UV. Additionally, health risk assessment for university staff and students at Aja University of Medical Sciences in Iran was estimated. The results showed that, all cooked samples were significantly different in terms of fat, protein, peroxide value, pH, and color (p < 0.05). The free fatty acid (FFA) content was 0.42, 0.96, 0.88, and 0.48% in control raw (CN), deep-fried (DF), pan-fried (PF), and oven-cooked (OC) schnitzels, respectively. Acrylamide was detected only in DF (83.33 µg/kg) and PF (32.37 µg/kg) samples. Moreover, the estimated daily intake (EDI) of AA was 0.022 and 0.008 µg/kg.bw.day for the DF and PF samples. The margin of exposure (MOE) results also showed that the consumption of deep-fried schnitzels can make us concerned about the carcinogenic properties of AA. Furthermore, the incremental lifetime cancer risk (ILCR) values ​​for DF schnitzels after 7 years of exposure were equal to 1.98 ⋅ 10^− 7 indicating low risk and concern. To conclude, due to the various daily intake sources of AA, it is suggested to use mild treatments like oven cooking instead of deep frying in all university meals.

The objective of the research was to enhance the quality and stability of guava nectar by adding tomato seed protein isolate (TSPI) and hydrolysate (TSPH) at different levels (1, 1.5 and 2%). The physico-chemical, sugar, protein profile, rheological, Fourier Transform Infrared Spectroscopy (FTIR) and sensory parameters of the protein enriched guava nectar were assessed. The findings showed that with the enrichment of TSPI and TSPH, the crude protein content (0.10 to 1.72%), total polyphenols (146.62 to 156.08 mg GAE/100mL), flavonoids (27.42 to 30.19 mg RE/100mL), and antioxidant activity (34.34 to 37.15%), of the guava nectar enhanced in comparison to control guava nectar. The rheological properties such as viscosity curve and frequency sweep were altered by the enrichment of TSPI and TSPH in guava nectar. According to the sensory study; colour, flavour, mouth feel, and overall acceptability decreased with increasing enrichment levels of protein isolate and hydrolysate. The sensory characteristics of guava nectar enriched with 1% TSPI and TSPH were comparable to that of control guava nectar. Thus, this work offers insightful information about how TSPI and TSPH may be used to enrich the protein content, bioactive properties and technological quality of guava nectar.

Graphical abstract

Walnuts are widely consumed around the world, and are often used as a filling for pastries, especially in East Asia. In this study, a popular pastry called “şöbiyet” was examined to evaluate the interactions between semolina cream and walnuts. The phenolic profile, antioxidant activity, and in vitro bioaccessibility were analyzed. Samples containing 35% (W35%) and 20% (W20%) walnuts (w/w) were prepared with and without the addition of semolina cream. Total phenolic content (TPC) of undigested samples ranged from 417.6 ± 73.11 mg GAE 100 g⁻¹ sample to 726.2 ± 21.66 mg GAE 100 g⁻¹ sample. Although samples with added semolina cream showed higher TPC levels, the differences were not statistically significant (p > 0.05). In contrast, total antioxidant activity (TAC) levels, as measured by the DPPH and CUPRAC assays, indicated that the addition of semolina cream significantly decreased antioxidant activity (p < 0.05). A similar trend was observed in the in vitro bioaccessibility of individual phenolic compounds. In most cases, no significant differences were observed between samples with higher walnut content (W35%) regardless of whether they contained semolina cream (p > 0.05). However, samples with lower walnut content (W20%) lost some of the phenolic compounds, TPC and TAC during intestinal digestion phase when semolina cream was added (p < 0.05). Overall, the results highlighted that adjusting the walnut content and semolina cream composition in pastries can improve the bioaccessibility of polyphenols, suggesting potential applications in the development of functional foods with enhanced health benefits.