A comparative study on physicochemical characteristics and antioxidant activity of sumac (Rhus coriaria L.), cumin (Cuminum cyminum), and caraway (Carum carvil) oils

Comparison of the push-out bond strength of AH Plus sealer to dentin after using different herbal irrigation solutions as the final rinse

The aim of the present study was to evaluate the push-out bond strength of AH Plus sealer to dentin treated with the essential oils of Cumimum cyminum and Cinnamomum zeylanicum as herbal final irrigants. Forty human mandibular first premolars were selected. After smear layer removal, the samples were divided into 4 groups and irrigated with experimental solutions for 1 min and later with distilled water. G1: Cinnamomum zeylanicum (CZ) in minimum inhibitory concentration (MIC); G2: Sodium hypochlorite 2.5%; G3: Sodium hypochlorite in MIC; G4: Cuminum cyminum (CC) in MIC. After obturation, the roots were sectioned in order to obtain 1-mm discs for push-out assessment. The push-out test was performed using a universal testing machine. The slices were examined using a stereomicroscope at 30× to determine the mode of failure. The data were analyzed using one-way analysis of variance and Tukey's post-hoc test. The teeth irrigated with CZ showed significantly lower push-out resistance than those irrigated with NaOCl 2.5% and NaOCl at MIC. The other groups had no significant difference. The modes of failure were predominantly mixed. Under the limitations of the present study, CC does not have adverse effects on the bond strength of AH Plus and can be used as a good alternative for currently used final irrigants.

Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine

Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits.

Natural diversity in fatty acids profiles and antioxidant properties of sumac fruits (Rhus coriaria L.): Selection of preferable populations for food industries

The current study screened the oil content, fatty acids profile, and antioxidant properties of twelve Iranian sumac fruit accessions. The oil contents were variable among the investigated populations (ranging from 5.15 to 16.70%). Oleic acid (32.3-47.41%), palmitic acid (18.90-36.29%), and linoleic acid (10.31-35.39%) were the predominant fatty acids in the oil samples. According to principal component and cluster analysis, sumac germplasms were categorized into three groups: i.e., group I (five populations rich in linoleic acid), group II (four populations rich in oleic acid), group III (three populations rich in palmitic acid). The highest fruits weight, oil percentage, and linoleic acid content was obtained from Arasbaran population. Arasbaran population possessed the highest ∑PUSFA (i.e, 34.53%) and ∑UNSFA: ∑SFA ratio. Meanwhile, Paveh population possessed the highest antioxidant attributes. Such variabilities provide the possibility of using elite populations containing a high ratio of unsaturated fatty acids and antioxidant compounds in the food industry.

Effect of Elevated CO2 on Biomolecules' Accumulation in Caraway (Carum carvi L.) Plants at Different Developmental Stages

Caraway plants have been known as a rich source of phytochemicals, such as flavonoids, monoterpenoid glucosides and alkaloids. In this regard, the application of elevated CO₂ (eCO₂) as a bio-enhancer for increasing plant growth and phytochemical content has been the focus of many studies; however, the interaction between eCO₂ and plants at different developmental stages has not been extensively explored. Thus, the present study aimed at investigating the changes in growth, photosynthesis and phytochemicals of caraway plants at two developmental stages (sprouts and mature tissues) under control and increased CO₂ conditions (ambient CO₂ (a CO₂, 400 ± 27 μmol CO₂ mol^-1 air) and eCO₂, 620 ± 42 μmol CO₂ mol^-1 air ppm). Moreover, we evaluated the impact of eCO₂-induced changes in plant metabolites on the antioxidant and antibacterial activities of caraway sprouts and mature plants. CO₂ enrichment increased photosynthesis and biomass accumulation of both caraway stages. Regarding their phytochemical contents, caraway plants interacted differently with eCO₂, depending on their developmental stages. High levels of CO₂ enhanced the production of total nutrients, i.e., carbohydrates, proteins, fats and crude fibers, as well as organic and amino acids, in an equal pattern in both caraway sprouts and mature plants. Interestingly, the eCO₂-induced effect on minerals, vitamins and phenolics was more pronounced in caraway sprouts than the mature tissues. Furthermore, the antioxidant and antibacterial activities of caraway plants were enhanced under eCO₂ treatment, particularly at the mature stage. Overall, eCO₂ provoked changes in the phytochemical contents of caraway plants, particularly at the sprouting stage and, hence, improved their nutritive and health-promoting properties.

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy

Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.

Phytosterol Profiling of Apiaceae Family Seeds Spices Using GC-MS

Phytosterols are nutritionally vital phytoconstituent owing to their cholesterol (low-density plasma lipoprotein-cholesterol, LDL-C)-lowering, anti-inflammatory, and antioxidant properties. Among the widely used spices and herbs, the seeds spices of the Apiaceae family represented the healthiest fatty acid profile. Thus, to explore the other health-beneficial lipids, the present study was aimed to analyze the phytosterol profile of eight seed spices of the Apiaceae family, utilizing gas chromatography (GC)-mass spectrometry (MS). The sterols contents calculated on an oil (mg/100 g of oil) and spice weight (mg/100 g spices; dry weight) basis varied significantly among the seed spices (p < 0.05; Turkey HSD). The β-sitosterol and stigmasterol were the most dominating sterols among the studied spices, together accounted for 40.3 (Ajwain) to 69.8% (celery) of total sterols in the seed oil. Among the studied spices, the oil extracted from caraway seeds showed the highest total sterols (602.2 mg/100 g of oil). Interestingly, based on spice weight, fennel seeds also showed the similar highest number of total sterols (134.2 mg/100 g in fennel and 133.3 mg/100 g in caraway), owing to the high contents of oil (25.9%) in fennel seeds. Overall, celery, caraway, fennel, and anise seeds oil are rich sources of health-beneficial phytosterols.