Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Impact of titanium dioxide (TiO2) nanoparticle and liquid leachate of mushroom compost on agronomic and biochemical response of marigold (Tagetes erecta L.) under saline stress.

The cultivation of ornamental horticultural crops under salinity stress has been a challenge for growers all over the world. In this study, an attempt was made for pot cultivation of Marigold (Tagetes erecta L. var. Pusa Basanti Gainda) in salt-stressed (SS) soil (150 mM) with the combined use of mushroom compost leachate (CL) and foliar application of titanium dioxide nanoparticles (TiO2-NPs). For this purpose, a total of six pot treatments, i.e., borewell water (BW; control), T1 (BW with SS), T2 (BW with SS and TiO2-NPs), T3 (CL supplemented), T4 (CL with SS), and T5 (CL with SS and TiO2-NPs) were conducted in triplicate. The results of this study showed that CL supplementation significantly (p < 0.05) improved the physicochemical i.e., pH (14.5%), electrical conductivity (32.9%), total nitrogen (27.4%), total phosphorus (247.6%)), and nutrient (organic matter: 119.6%) profiles of soil which later helped in higher growth (30-35%) and yield (5.4-40.7%) of T. erecta. In CL-based treatments, the biochemical constituents were significantly (p < 0.05) higher than those in BW-irrigated ones. Also, the levels of selected stress defense enzymes were significantly increased under SS treatment but reduced under TiO2-NP application. Overall, it was observed that the combined application of CL and TiO2-NPs (T5 treatment) was the most helpful treatment for enhanced germination, growth, yield, biochemical parameters, and better plant enzymatic activities to cope with saline stress. This study provides a mechanistic understanding of T. erecta plants under saline stress which is crucial for the development of targeted interventions aimed at improving plant tolerance to saline conditions.

An experimental and prediction modeling study on water lettuce (Pistia stratiotes L.) assisted heavy metals removal from glass industry effluent.

The glass manufacturing industry produces hazardous effluent that is difficult to manage and causes numerous environmental problems when disposed of in the open. In this study, an attempt was made to study the phytoremediation feasibility of water lettuce (Pistia stratiotes L.), a free-floating aquatic macrophyte, for the removal of six heavy metals from glass industry effluent (GIE) at varying concentrations (0, 25, 50, 75, and 100%). After a 40-day experiment, the results showed that 25% GIE dilution showed maximum removal of heavy metals i.e., Cu (91.74%), Cr (95.29%), Fe (86.47%), Mn (92.95%), Pb (87.10%), and Zn (91.34%), respectively. The bioaccumulation, translocation, and Pearson correlation studies showed that the amount of heavy metals absorbed by vegetative parts of P. stratiotes was significantly correlated with concentrations. The highest biomass production, chlorophyll content, relative growth rate, and biomass productivity were also noted in the 25% GIE treatment. Moreover, the multiple linear regression models developed for the prediction of heavy metal uptake by P. stratiotes also showed good performance in determining the impact of GIE properties. The models showed a high coefficient of determination (R2 > 0.99), low mean average normalizing error (MANE = 0.01), and high model efficiency (ME > 0.99) supporting the robustness of the developed equations. This study outlined an efficient method for the biological treatment of GIE using P. stratiotes to reduce risks associated with its unsafe disposal.

Development and characterization of a novel flavored functional fermented whey-based sports beverage fortified with Spirulina platensis.

Spirulina platensis, a microalga known for its exceptional nutritional value, especially its bioactive compounds and protein content, holds promise for incorporation into functional food products. Ricotta cheese whey is a byproduct of the production of ricotta cheese that is difficult to use in industries due to its low pH and less favorable processing qualities. This research aimed to create a unique fermented ricotta cheese whey-based beverage supplemented with various Spirulina powder concentrations (0.25 %, 0.5 %, and 0.75 % w/w) cooperated with a mixture of lemon and peppermint juice 10 % and fermented by probiotic (ABT) culture. The physicochemical, rheological, bioactive compounds, microbiological, and sensory properties were evaluated over a storage period of 21 days at cold storage. Spirulina-fermented whey-based beverages with a mixture of lemon and peppermint juice increased the concentration of vitamins, minerals, antioxidants, and total phenolic compounds in the final product. The count of probiotic bacteria in all fermented beverage samples exceeded 7 log CFU/mL throughout storage, indicating that the fermented beverage kept its probiotic properties. The addition of 0.5 % Spirulina significantly improved the final product's structural qualities and sensory acceptance.

Doxorubicin, L-arginine, or their combination as a prophylactic agent against hepatic carcinoma in mice.

Hepatocellular carcinoma (HCC) is one of the ten most commonly diagnosed cancers. Doxorubicin is an antibiotic used in cancer treatment protocols that has several side effects. L-Arginine is a non-essential amino acid that is used as immune system activation and antitumor drugs. Therefore, the current study was designed to compare using doxorubicin, L-arginine, or their combination as a prophylactic agent against hepatic carcinoma induced by hepatocellular carcinoma cells (HepG2) injection in mice. The mice were divided into five groups: normal mice and mice that received HepG2, doxorubicin and HepG2, L-arginine and HepG2, and doxorubicin, L-Arginine, and HepG2, respectively. Liver function test as, aspartate transaminase (AST) and alanine transaminase (ALT), and alpha-fetoprotein (AFP), caspase 3, interleukin 6 (IL-6), tumor necrotic factor (TNF), lipid peroxidation (NDA), and some antioxidant parameters were determined. A significant increase in AST and ALT, α-fetoprotein, TNF-α, and cytokines IL6 and MDA and a significant decrease in the serum caspase and liver catalase were determined in HepG2-injected mice. Moreover, some large hyperchromatic heptocytes were observed and the percentage of the positive area/field of HepPar-1, the most specific HCC marker, was 9.56%. Interestingly, mice that received doxorubicin, L-arginine, or their combination showed an improvement in some of the previous parameters. The improvement was more prominent with L-arginine administration.