Search Results (7)

In order to ensure the price stability of niche agricultural products and enhance farmers’ income, the study delves into the pattern of the ginger price fluctuation rule and its main influencing factors. By combining seasonal decomposition STL, long and short-term memory network LSTM, attention mechanism ATT and Kolmogorov-Arnold network, a combined STL-LSTM-ATT-KAN prediction model is developed, and the model parameters are finely tuned by using multi-population adaptive particle swarm optimisation algorithm (AMP-PSO). Based on an in-depth analysis of actual data on ginger prices over the past decade, the STL-LSTM-ATT-KAN model demonstrated excellent performance in terms of prediction accuracy: its mean absolute error (MAE) was 0.111, mean squared error (MSE) was 0.021, root mean squared error (RMSE) was 0.146, and the coefficient of determination (R²) was 0.998. This study provides the Ginger Industry, agricultural trade, farmers and policymakers with digitalised and intelligent aids, which are important for improving market monitoring, risk control, competitiveness and guaranteeing the stability of supply and price. Full article

When considering agricultural commodity transaction data, long sampling intervals or data sparsity may lead to small samples. Furthermore, training on small samples can lead to overfitting and makes it hard to capture the fine-grained fluctuations in the data. In this study, a multi-scale forecasting approach combined with a Generative Adversarial Network (GAN) and Temporal Convolutional Network (TCN) is proposed to address the problems related to small sample prediction. First, a Time-series Generative Adversarial Network (TimeGAN) is used to expand the multi-dimensional data and t-SNE is utilized to evaluate the similarity between the original and synthetic data. Second, a greedy algorithm is exploited to calculate the information gain, in order to obtain important features, based on XGBoost. Meanwhile, TCN residual blocks and dilated convolutions are used to tackle the issue of gradient disappearance. Finally, an attention mechanism is added to the TCN, which is beneficial in terms of improving the forecasting accuracy. Experiments are conducted on three products, garlic, ginger and chili. Taking garlic as an example, the RMSE of the proposed method was reduced by 1.7% and 1% when compared to the SVR and RF models, respectively. Its $R^2$ accuracy was also improved (by 4.3% and 3.4%, respectively). Furthermore, TCN-attention and TCN were found to require less time compared to GRU and LSTM. The accuracy of the proposed method increased by about 5% when compared to that without TimeGAN in the ablation study. Moreover, compared with TCN, the Gated Recurrent Unit (GRU), and the Long Short-term Memory (LSTM) model in the multi-scale price forecasting task, the proposed method can better utilize small samples and high-dimensional data, leading to improved performance. Additionally, the proposed model is compared to the Transformer and TimesNet models in terms of its accuracy, deployment cost, and other metrics. Full article

Agriculture provides a basis for social and economic development. It is therefore crucial for society and the economy to stabilize agricultural prices. Recent large increases and decreases in Chinese agricultural commodity prices have increased production risks, heightened fluctuations in the domestic agricultural supply, and impacted the stability of the global agricultural market. Meanwhile, big data technology has advanced quickly and now serves as a foundation for the investigation of time series bubbles. Identifying agricultural price bubbles is important for determining agricultural production decisions and policies that control agricultural prices. Using weekly agricultural price data from 2009 to 2021, this paper identifies agricultural price bubbles, pinpoints their time points, and examines their causes. According to our research, prices for corn, hog, green onions, pork, and ginger all have bubbles, but garlic do not. The quantity, length, time distribution, and type of bubbles differ significantly among corn, ginger, green onion, hog, and pork. The main causes for ginger and green onion price bubbles are speculation and natural disasters. Price bubbles for hog and pork are influenced by animal disease and rising costs. Conflicts between supply and demand and changes in price policy cause corn price bubbles to form. This paper advises that the government adopt various regulatory actions to stabilize agricultural prices depending on the characteristics and causes of the various types of agricultural price bubbles, it should also improve the early warning system and response mechanism for agricultural price bubbles and focus on how policies and market processes work together. Full article

Herbal medicines (HMs) are regarded as one of the traditional medicines in health care to prevent and treat some diseases. Some herbal components such as turmeric and ginger are used as HMs, therefore the identification and confirmation of herbal use are very necessary. In addition, the adulteration practice, mainly motivated to gain economical profits, may occur by substituting the high price of HMs with lower-priced ones or by addition of certain chemical constituents known as Bahan Kimia Obat (chemical drug ingredients) in Indonesia. Some analytical methods based on spectroscopic and chromatographic methods are developed for the authenticity and confirmation of the HMs used. Some approaches are explored during HMs authentication including single-component analysis, fingerprinting profiles, and metabolomics studies. The absence of reference standards for certain chemical markers has led to exploring the fingerprinting approach as a tool for the authentication of HMs. During fingerprinting-based spectroscopic and chromatographic methods, the data obtained were big, therefore the use of chemometrics is a must. This review highlights the application of fingerprinting profiles using variables of spectral and chromatogram data for authentication in HMs. Indeed, some chemometrics techniques, mainly pattern recognition either unsupervised or supervised, were applied for this purpose. Full article

After Roux-en-Y gastric bypass surgery (RYGB), rats tend to reduce consumption of high-sugar and/or high-fat foods over time. Here, we sought to investigate the behavioral mechanisms underlying these intake outcomes. Adult female rats were provided a cafeteria diet comprised of five palatable foodstuffs varying in sugar and fat content and intake was monitored continuously. Rats were then assigned to either RYGB, or one of two control (CTL) groups: sham surgery or a nonsurgical control group receiving the same prophylactic iron treatments as RYGB rats. Post-sur-gically, all rats consumed a large first meal of the cafeteria diet. After the first meal, RYGB rats reduced intake primarily by decreasing the meal sizes relative to CTL rats, ate meals more slowly, and displayed altered nycthemeral timing of intake yielding more daytime meals and fewer nighttime meals. Collectively, these meal patterns indicate that despite being motivated to consume a cafeteria diet after RYGB, rats rapidly learn to modify eating behaviors to consume foods more slowly across the entire day. RYGB rats also altered food preferences, but more slowly than the changes in meal patterns, and ate proportionally more energy from complex carbohydrates and protein and proportionally less fat. Overall, the pattern of results suggests that after RYGB rats quickly learn to adjust their size, eating rate, and distribution of meals without altering meal number and to shift their macronutrient intake away from fat; these changes appear to be more related to postingestive events than to a fundamental decline in the palatability of food choices. Full article

Ginger and turmeric, members of the Zingiberaceae family, are widely used for their pungent and aromatic flavour in foods and also for their medicinal properties. Both crops are often grown by smallholders in mountain areas on rich former forest soils with no need for fertilizers and pesticides, fulfilling de facto the conditions of organic agriculture. They are consumed fresh or dried. Drying is often performed without taking into account the content of bioactive compounds in the dried product. Various bioactive compounds have been identified in their rhizomes, and their content affects the price of the dried product. Hence, this study focused on the effects of drying treatments and blanching on the retention of bioactive compounds in the dried products. The bioactive compounds in ginger rhizome (Zingiber officinale Roscoe) are gingerols (particularly 6-gingerol). The drying treatments that were applied to fresh ginger included constant and also changing temperature conditions. Due to the short drying time, 60 °C was the optimal drying temperature to retain 6-gingerol. However, the changing temperature conditions significantly improved the retention of 6-gingerol. As for blanching, it had a significant negative effect on 6-gingerol retention. Turmeric (Curcuma longa) is known for its bright yellow colour and pharmacological properties due to curcumin, a phenolic compound. Drying was performed under constant conditions at 38 °C, 48 °C, 57 °C and 64 °C and a relative humidity of 20% and 40%. Drying at 57 °C with a lower relative humidity was the best drying treatment, yielding the highest amount of curcumin among non-blanched samples. Blanching for 15 min exhibited the highest curcumin yield while blanching for 5 min and 30 min did not have much effect. The findings of this study will benefit the industry in terms of improved quality control and cost reduction. Full article

Thirty eight bottles of red wine (Carbanet Sauvignon) were randomly selected based on vintage, region, price, and age (number of months in a barrel). The total phenolic content of each wine was determined using Folin-Ciocalteau assay. The radical scavenging activity was evaluated using 2,2-diphenyl-1-picryhydrazyl (DPPH) assay. Apart from a few bottles that exhibited above average radical scavenging activity and phenolic content, there was no good correlation of those two quantities with region, price or vintage. The average phenolic amount was 2874 mg/L. The lowest phenolic content was found to be 1648 mg/L for an eight dollar wine. Wine with the highest amount of phenol of 4495 mg/L was a 2007, nine dollar bottle from South America. High amount of phenols did not translate into high radical scavenging activity. Barrel-aging did not increase the amount of phenols or the radical scavenging activity of wine. In order to discover new and potent sources of antioxidants from plants, the following spices were studied: ginger, cilantro, cumin, anise, linden, eucalyptus, marjoram, oregano, sage, thyme and rosemary. Whole spices were crushed and extracted for 96 h at room temperature using a combination of ethyl acetate, ethyl alcohol and water in the ratio of 4.5:4.5:1 (v/v/v). The radical scavenging activity of extracts was evaluated using 2,2-diphenyl-1-picryhydrazyl (DPPH) assay. The total phenolic content of each spice was also determined using the Folin-Ciocalteau assay. Eucalyptus was found to be the most potent antioxidant with an LC₅₀ of 324.1 mg of phenol/L, followed by marjoram with an LC₅₀ of 407.5 mg of phenol/L, and rosemary with an LC₅₀ of 414.0 mg/L. The least potent antioxidants were ginger and cilantro with LC₅₀ of 7604 mg/L of phenol and 7876 mg of phenol/L, respectively. Full article