Potato Research最新文献

In this study, the effect of pretreatments ((hot water blanching (HWB), ultrasound (US) and ohmic heating (OH)) on reducing the oil absorption of potato during frying and the changes in the quality parameters of the product ((total polyphenol (TP), in vitro digestion, total flavonoid (TF), antioxidant capacity (AC), chlorogenic acid (CA), textural and sensorial properties)) were investigated. The pretreatments applied significantly affected oil absorption and quality parameters of fried potato. The oil content of fried potatoes ranged from 26.06 to 32.01% depending on the pretreatment. OH-pretreated potato had the highest content of TP (41.27 mg GAE/100 g dry matter-DM), TF (32.89 mg RE/100 g DM) and CA (1.72 mg/100 g DM). However, there was no significant difference between the pretreatments in terms of bioaccessibility of polyphenols at the end of digestion. Also, AC value of fried potato pretreated by OH (124.13 mmol AAE/100 g DM) was higher compared to that of fresh potato (83.91 mmol AAE/100 g DM), but other two pretreatments caused a decrease in AC. The highest hardness was observed in HWB-treated potato strips. OH-treated potato had the best color parameters. Sensory data indicated that US-pretreated potato had the highest sensory scores followed by OH- and HWB-pretreated ones, respectively. Consequently, based on the above comprehensive quality evaluation, it can be suggested that OH pretreatment is a better choice for preparing deep fried potato.

The dynamics of the potato market in Agra, Uttar Pradesh, India, represent significant price volatility that affects stakeholders across the supply chain. This study addresses the critical need for accurate forecasting of potato price, which is utmost for optimising production, marketing strategies and inventory management. However, existing forecasting models often fail to provide the accuracy required for effective planning and resource allocation. This research aims to bridge this gap by investigating the potential of advanced predictive models to offer closer approximations of potato prices. Covering the period from January 1, 2006, to July 31, 2023, the methodology employed the H2O AutoML framework to identify and evaluate predictive models based on two distinct train-test split ratios, 80:20 and 70:30. The selection of the top 20 models for each configuration, assessed using the root mean square error, revealed the 70:30 split’s superior performance. Further analysis identified the top three models: stacked ensemble, gradient boosting machine and extreme gradient boosting, with the stacked ensemble model emerging as the optimal choice with forecasting errors ranging from 0.08 to 2.09% for daily prices of potato. This result illustrates the effectiveness of the stacked ensemble model in advancing strategic decision-making and resource distribution within the potato industry, with a notable improvement in the accuracy of price predictions contributing to more efficient and informed operational strategies.

Cold-induced sweetening (CIS) is a common phenomenon in many plants including potatoes that help in osmoregulation and cryoprotection. However, CIS is associated with quality deterioration in potato tubers due to accumulation of reducing sugars at low temperatures. We investigated two different strategies to modulate CIS in potato, overexpression of RING finger (SbRFP1) as anti-sweetening gene and by double-stranded RNA-mediated gene silencing of the vacuolar invertase gene. In silico analysis predicted that the ubiquitination activity of the RING finger protein was responsible for regulating the expression of invertase during cold-induced stress. Moreover, the in silico predicted binding stability of siRNA-mRNA duplex suggested efficient gene silencing of the invertase gene. We successfully generated four single and three dual transgenic potato lines that were positive for transgene insertion and integration as revealed in PCR and Southern blot. The amount of reducing sugars found in tubers obtained from single transgenics showed maximum decrease of 1.67 folds while tubers obtained from dual transgenic line depicted 4.86 folds reduced accumulation of reducing sugars compared to non-transgenic control when analyzed through HPLC analysis post 60-day storage at low temperature (4°C). Further, the invertase activity was 1.46 folds reduced in single transgenics while this reduction was 2.13 folds in dual transgenics. The downregulation of the invertase gene was up to 3.36 folds in dual transgenic potato lines, 2.26 folds in single transgenic compared to control, non-transgenic post 60-day cold storage at low temperature. Conclusively, the utilization of multiple strategies to regulate CIS in low-temperature stored potato tubers positively regulate CIS in transgenic potatoes and can be employed to generate CIS resistant potato varieties.

The present study investigated the effects of different drying methods (drum and tray drying) on overall quality of potato flakes prepared from Lady Rosetta (LR) (white fleshed potato) and PP-1901 (purple potato) varieties. It was found that drum drying had more significant (p<0.05) effect in reducing the colour values of flakes. Drum dried (DD) samples showed higher values of water and oil absorption capacity than tray dried (TD) samples for both varieties. Purple variety flakes had significantly very high values of phytocompounds in comparison to white fleshed flakes. Regardless of the varieties, TD flakes had higher values of total phenolics, flavonoids and anthocyanin contents than DD which was attributed to more heat severity in case of drum drying. Also, ascorbic acid content and antioxidant activity were higher in TD samples. Higher retention of phytocompounds in TD samples was also confirmed using Fourier transform infrared spectroscopy. Due to more pregelatinisation, DD samples had the highest changes in its crystallinity and had the lowest value of pasting temperature indicating the lowest cooking temperature confirmed using X-ray diffraction and rapid viscoanalyser, respectively. Practical application: Potato flakes are utilised as an ingredient in extruded snacks, potato chips, soups, snack pellets, rolls or bakery products. Utilising coloured variety potatoes for developing flakes can give a healthier option to consumers. The present study is the first one to develop coloured potato flakes by using drum and tray dryer along with investigating their effect on the potato matrix. These dryers are widely adopted commercially from past many decades.

The dry matter content (DMC) is a critical factor for assessing the quality of the potato. Using a fish effluent in the sprinkler irrigation has unknown impacts on the DMC of the tuber. Hence, different irrigation treatments were carried out for the irrigation of potato, including T1: fresh water treatment, T2: fish effluent treatment and T3: combined fresh water and fish effluent treatment in which leaf washing was used. A multiple regression model (MLR) was developed in which nutrient concentrations were considered as the input while DMC was the output of the model. The model was evaluated by the root mean squared error (RMSE) as well as the mean absolute percentage error (MAPE). Then, sensitivity analysis of DMC due to changing the nutrient concentration was carried out through regression models by the sensemaker package. The results illustrate that the developed regression model is highly accurate due to low RMSE and MAPE. The results of the sensitivity analysis indicate that the impact of nitrate on the DMC due to adding a confounder is weak. In other words, nitrate can be removed from the list of independent variables for developing regression models to simulate DMC. The results show that the averages of dry matter content in T1, T2 and T3 were 20.26%, 21.53 and 25.72%, respectively. The results indicate that DMC is increased in the irrigation treatment in which leaf washing is used to mitigate the impact of fish effluent. It is recommendable to utilize the leaf washing with fresh water when using fish effluent is planned for irrigating potato.

Field studies were conducted to determine the contribution of seed tuber size on late blight management, weed abundance, crop performance and net farm income. Seed tuber sizes were as follows: small size (15 to 27 mm), size 1 (28–35 mm), size 2 (36–45 mm) and size 3 (46–60 mm) of Shangi, Kenya Mpya, Unica and Dutch Robijn potato varieties. Fungicide spray regimes were weekly, biweekly and triweekly. Data on late blight severity, weed frequency and density, growth parameters, costs and revenues were collected. Results revealed that seed tuber size, variety and fungicide application regime had significant (P ≤ 0.05) effects on late blight severity. As such, weekly spray intervals reduced blight severity by 50%, and the blight-tolerant variety (K. Mpya) suppressed the disease to below 1%. Whereas the lowest disease severity was observed on seed sizes 2 (44%) and 3 (43%), the highest blight score was recorded in small seed size (59%) after 70 days of emergence. Notwithstanding the variety used, a combination of seed size 2 with weekly spray interval showed the lowest disease progression as compared to any other combination. Crop growth parameters differences were phenomenal among seed sizes: stem count increased six-fold, while height and canopy were double the observation made in the small seed size for seed sizes 2 and 3. Weed frequency, relative frequency and density decreased with increasing seed size planted. As a result, seed tuber sizes 1, 2 and 3 augmented marketable yield by 49%, 62% and 65% as compared to the small tuber size, respectively. However, seed size 2 had the highest cost–benefit ratio (1.50), followed by size 3 (1.05).

The study of production behaviour, growth, trend and export of potatoes from India, the second largest producer, is of utmost importance for food and nutritional security. Using time series data on area, production, productivity and export, the analysis reveals that along with the traditional potato producing states the non-traditional states viz. Gujarat, Madhya Pradesh, etc. have come up significantly to enrich the Indian potato basket. Gujarat is showing maximum growth in area and production with a compound annual growth rate of 6.47 and 8.86, respectively, and likely to be the best yielder during 2025–2026. The study reveals that Nepal, Oman, Sri Lanka, Saudi Arabia, Malaysia have changed their import scenario with declining Sri Lankan import. Under pressure on natural resources the study advocates for attaining maximum productivity per unit of resource use and export.

The cultivation of potatoes is one of the most important parts of the world’s agricultural system, so forecasting methods that can precisely predict the direction of production are needed. We focus on the area of optimization techniques herein in this study and develop a particular use of metaheuristic algorithms applied to improve predictive models. Among such algorithms, the Waterwheel Plant Algorithm (WWPA) is notable for its efficiency in enhancing the autoregressive integrated moving average (ARIMA) model. Feature selection, an essential preprocessing step in machine learning, is of the highest significance in our approach. We apply the bWWPA method to select the most central features from the dataset, which, in turn, improves the whole predictive model’s performance. Through the identification of the main patterns and links in the data, feature selection allows for the model to focus on the most influential factors, giving way to more precise predictions. The WWPA-ARIMA model obtained by our method captures the essential features after optimization and thus involves a very low root mean square error (RMSE) of 0.0001. Such a high level of precision emphasizes the efficiency of our optimization procedure in adjusting the ARIMA model parameters carefully to reveal the hard-to-catch patterns in potato production data. To evaluate the robustness of our method, we employ strong statistical analyses, such as ANOVA and the Wilcoxon signed-rank test. This test also gives additional evidence that our optimization method works better than alternative approaches.

The main objective of the present work was to study the optimization of microwave drying of potatoes that have different flesh colors. The effects of independent variables of microwave power (300, 450, 600 W), slice thickness (2–4, 6 mm), and steam blanching time (2, 5, 8 min) on the color, total phenolic content (TPC), antioxidant activity, starch ratio, and total monomeric anthocyanin content (TMA) were investigated by using the Response Surface Methodology (RSM). Before drying, potato slices that had different thicknesses were blanched in steam at 90 °C for indicated times. Optimization was applied to improve bioactive compounds, starch ratio, and color. The optimum drying parameters were determined as 300 W, 6 mm, and 8 min for purple-fleshed potatoes, and 450 W, 6 mm, and 2 min for yellow-fleshed potatoes. This study is beneficial to the development of the processing of potatoes in the food industry and provides more insights into the application of microwave drying technology.

Potatoes stand as one of the most vital staple crops globally, providing essential nourishment and sustenance to millions of people worldwide. Their significance lies in their versatility, nutritional richness, and ability to thrive in diverse climates, making them crucial for global food security. However, accurately forecasting potato production is paramount for effective agricultural planning and ensuring an adequate food supply. In this research endeavour, we introduce a novel approach to enhance the precision of potato production forecasts using advanced machine learning techniques. Our methodology revolves around employing long short-term memory (LSTM) models, which are optimised through the innovative Balance Dynamic Biruni Earth Radius Optimization Algorithm (BDBER). This algorithm dynamically adjusts exploration and exploitation strategies, effectively navigating the solution space to optimise the parameters of the LSTM model. By harnessing the power of machine learning and algorithmic optimization, we aim to improve the accuracy of annual potato production forecasts. To evaluate the efficacy of our approach, we compare the performance of the optimised LSTM models with traditional machine learning algorithms. Various performance metrics are scrutinised, and statistical tests, including ANOVA and Wilcoxon signed rank tests, are conducted to bolster the credibility of our findings. Our analysis reveals that the LSTM models optimised by BDBER surpass alternative methods, exhibiting superior accuracy and stability in potato production forecasting. Notably, the root mean square error (RMSE) of 0.00899 and fitted time of 0.00449 underscore the robustness of our approach. This study represents a pivotal contribution to the advancement of agricultural forecasting techniques. By providing more accurate and reliable predictions, our methodology equips policymakers and stakeholders with invaluable insights for informed decision-making. Ultimately, our research endeavours to bolster global food security and promote sustainable agricultural practices.