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利用多种综合多营养级情景进行罗勒生产的分析框架

An Analytical Framework on Utilizing Various Integrated Multi-Trophic Scenarios for Basil Production.

作者信息

Petrea Ștefan-Mihai, Simionov Ira Adeline, Antache Alina, Nica Aurelia, Oprica Lăcrămioara, Miron Anca, Zamfir Cristina Gabriela, Neculiță Mihaela, Dima Maricel Floricel, Cristea Dragoș Sebastian

机构信息

Food Science, Food Engineering, Biotechnology and Aquaculture Department, Faculty of Food Science and Engineering, "Dunarea de Jos" University of Galati, Domnească Street, No. 111, 800008 Galaţi, Romania.

Faculty of Economics and Business Administration, "Dunarea de Jos" University of Galati, Nicolae Bălcescu Street, 59-61, 800001 Galati, Romania.

出版信息

Plants (Basel). 2023 Jan 25;12(3):540. doi: 10.3390/plants12030540.

Abstract

Here, we aim to improve the overall sustainability of aquaponic basil ( L.)-sturgeon () integrated recirculating systems. We implement new AI methods for operational management together with innovative solutions for plant growth bed, consisting of shells (R), considered wastes in the food processing industry. To this end, the ARIMA-supervised learning method was used to develop solutions for forecasting the growth of both fish and plant biomass, while multi-linear regression (MLR), generalized additive models (GAM), and XGBoost were used for developing black-box virtual sensors for water quality. The efficiency of the new R substrate was evaluated and compared to the consecrated light expended clay aggregate-LECA aquaponics substrate (H). Considering two different technological scenarios (A-high feed input, B-low feed input, respectively), nutrient reduction rates, plant biomass growth performance and additionally plant quality are analysed. The resulting prediction models reveal a good accuracy, with the best metrics for predicting N-NO concentration in technological water. Furthermore, PCA analysis reveals a high correlation between water dissolved oxygen and pH. The use of innovative R growth substrate assured better basil growth performance. Indeed, this was in terms of both average fresh weight per basil plant, with 22.59% more at AR compared to AH, 16.45% more at BR compared to BH, respectively, as well as for average leaf area (LA) with 8.36% more at AR compared to AH, 9.49% more at BR compared to BH. However, the use of R substrate revealed a lower N-NH and N-NO reduction rate in technological water, compared to H-based variants (19.58% at AR and 18.95% at BR, compared to 20.75% at AH and 26.53% at BH for N-NH; 2.02% at AR and 4.1% at BR, compared to 3.16% at AH and 5.24% at BH for N-NO). The concentration of Ca, K, Mg and NO in the basil leaf area registered the following relationship between the experimental variants: AR > AH > BR > BH. In the root area however, the NO were higher in H variants with low feed input. The total phenolic and flavonoid contents in basil roots and aerial parts and the antioxidant activity of the methanolic extracts of experimental variants revealed that the highest total phenolic and flavonoid contents were found in the BH variant (0.348% and 0.169%, respectively in the roots, 0.512% and 0.019%, respectively in the aerial parts), while the methanolic extract obtained from the roots of the same variant showed the most potent antioxidant activity (89.15%). The results revealed that an analytical framework based on supervised learning can be successfully employed in various technological scenarios to optimize operational management in an aquaponic basil ( L.)-sturgeon () integrated recirculating systems. Also, the R substrate represents a suitable alternative for replacing conventional aquaponic grow beds. This is because it offers better plant growth performance and plant quality, together with a comparable nitrogen compound reduction rate. Future studies should investigate the long-term efficiency of innovative R aquaponic growth bed. Thus, focusing on the application of the developed prediction and forecasting models developed here, on a wider range of technological scenarios.

摘要

在此,我们旨在提高罗勒(L.)-鲟鱼()综合循环水养殖系统的整体可持续性。我们采用新的人工智能方法进行运营管理,并为植物生长床提供创新解决方案,该生长床由贝壳(R)组成,贝壳在食品加工业中被视为废弃物。为此,使用自回归积分滑动平均(ARIMA)监督学习方法来开发预测鱼类和植物生物量增长的解决方案,同时使用多元线性回归(MLR)、广义相加模型(GAM)和极端梯度提升(XGBoost)来开发水质黑箱虚拟传感器。对新型R基质的效率进行了评估,并与传统的轻质陶粒(LECA)水产养殖基质(H)进行了比较。考虑两种不同的技术方案(分别为A-高饲料投入、B-低饲料投入),分析了营养物质减少率、植物生物量生长性能以及植物质量。所得预测模型显示出良好的准确性,在预测工艺用水中N-NO浓度方面具有最佳指标。此外,主成分分析(PCA)显示溶解氧与pH值之间存在高度相关性。使用创新的R生长基质可确保罗勒具有更好的生长性能。实际上,这体现在每株罗勒植物的平均鲜重上,与AH相比,AR增加了22.59%,与BH相比,BR增加了16.45%;以及平均叶面积(LA)上,与AH相比,AR增加了8.36%,与BH相比,BR增加了9.49%。然而,与基于H的变体相比,使用R基质显示工艺用水中N-NH和N-NO的减少率较低(AR为19.58%,BR为18.95%,而AH的N-NH为20.75%,BH为26.53%;AR的N-NO为2.02%,BR为4.1%,而AH的N-NO为3.16%,BH为5.24%)。罗勒叶面积中Ca、K、Mg和NO的浓度在各实验变体之间呈现以下关系:AR > AH > BR > BH。然而,在低饲料投入的H变体中,根区的NO含量较高。实验变体罗勒根和地上部分的总酚和黄酮含量以及甲醇提取物的抗氧化活性表明,BH变体中的总酚和黄酮含量最高(根部分别为0.348%和0.169%,地上部分分别为0.512%和0.019%),而从同一变体的根中获得的甲醇提取物显示出最强的抗氧化活性(89.15%)。结果表明,基于监督学习的分析框架可成功应用于各种技术方案,以优化罗勒(L.)-鲟鱼()综合循环水养殖系统的运营管理。此外,R基质是替代传统水产养殖生长床的合适选择。这是因为它提供了更好的植物生长性能和植物质量,以及相当的氮化合物减少率。未来的研究应调查创新的R水产养殖生长床的长期效率。因此,应专注于在此开发的预测和预报模型在更广泛的技术方案中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ca3/9920146/1cef43cb1f0b/plants-12-00540-g0A1.jpg

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