The accurate classification of banana ripeness is essential for optimising agricultural practices and enhancing food industry processes. This study investigates the classification of banana ripeness using Machine Learning (ML) and Deep Learning (DL) techniques. The dataset consisted of 1565 high-resolution images of bananas captured over a 20-day ripening period using a Canon EOS 90D camera under controlled lighting and background conditions. High-resolution images of bananas at different ripeness stages were classified into ‘unripe’, ‘ripe’, and ‘overripe’ categories. The training set consisted of 1398 images (89.33%), and the validation set consisted of 167 images (10.67%), allowing for robust model evaluation. Various ML models, including Decision Tree, Random Forest, KNN, SVM, CNN, and VGG models, were trained and evaluated for ripeness classification. Among these, DL models, particularly CNN and VGG, outperformed traditional ML algorithms, with the CNN and VGG achieving accuracy rates of 90.42% and 89.22%, respectively. These rates surpassed those of Decision Trees (71.86%), Random Forests (85.63%), KNNs (86.83%), and SVMs (89.22%). The study points out the importance of dataset quality, model selection, and preprocessing techniques in achieving accurate ripeness classification. Practical applications of these results include optimised harvesting practices, enhanced post-harvest handling, improved consumer experience, streamlined supply chain logistics, and automation in sorting systems. These results confirm the feasibility of using deep learning for the automated classification of ripening stages, with implications for reducing postharvest losses and improving supply chain logistics. These findings have significant implications for stakeholders in the banana industry, from farmers to consumers, and pave the way for the development of innovative solutions for banana ripeness classification.

Thermal and functional properties of starch extracted from American taro and Indian shot were determined to assess their use in food products. Starch was extracted by the wet-milling method. Physicochemical composition was determined following the Association of Official Agricultural Chemists (AOAC) protocols. Total fibre was measured by the Total Dietary Fiber Assay Kit. The morphology of starch granules was observed by scanning electronic microscopy (SEM). Gelatinization temperature and viscosity were measured by Differential Scanning Calorimetry (DSC) and with a rapid viscosity analyser (RVA), respectively. Swelling capacity, solubility index, and absorption index were measured at 15, 60, 70, 80, and 90 °C. The yield for Indian shot (72.5 %) was higher of that for taro (60.2 %). No significant differences (p > 0.05) were found for moisture, ashes, total fibres, and protein; significant differences were found for fat content, total carbohydrates, amylose, and amylopectin. Granules of Indian shot starch featured ovoid shapes (diameter, 30 µm), while granules of American taro starch presented round shapes (diameter, 15 µm. Gelatinization temperature for American taro (78.33 °C) was higher of that for Indian shot (65.28 °C). Maximum viscosity in Indian shot (3,535.5 cP) was higher of that in American taro (2,446.5 cP). Concerning functional properties, Indian shot starch yielded higher values. Moreover, at high temperature values, American taro starch presented better gelling results than those in Indian shot.

In this study, adsorbent materials prepared from common wastes generated in agricultural industries were evaluated, particularly the adsorption capacity for hydrocarbon compounds. Wastes from banana, cocoa, and rice were collected, and subsequently dried, milled, and chemically treated with NaOH. The physical properties of the treated wastes were assessed, including buoyancy and apparent density, and their adsorption capacity was tested under dynamic and static conditions. The results were subjected to an analysis of variance (ANOVA). The dynamic adsorption tests carried out indicated that the treated waste from banana leaves with a particle size of 1400 µm exhibited a buoyancy percentage above 90 % in the hydrophilic phase, while hydrocarbon compounds were adsorbed in the oleophilic phase. However, traces of hydrocarbons were still visible in the aqueous medium. In the short-term static adsorption tests, the waste materials showed adsorption capacity values ranging from 2.17 to 8.22 g oil/g adsorbent for motor oil; the highest adsorption index was determined in wastes from banana rachis with an 850 µm particle size. Moreover, in the adsorption of diesel and gasoline, the wastes from banana rachis with an 850 µm particle size showed the highest adsorption capacity for diesel (6.61 g diesel/g adsorbent) and gasoline (6.26 g gasoline/g adsorbent). This behavior was also observed in the long-term tests for all wastes tested, except for banana rachis, as some of the adsorbent material subsided, leading to the adsorption of both water and hydrocarbon compounds.

This study set out to determine the physical and mechanical properties of edible films made from native starch extracted from non-exportable Ecuadorian bananas (Musa sapientum L.). The following treatments were applied: 0.5% starch - 0.25% glucose (T1), 0.5% starch - 0.5% glucose (T2), 1% starch - 0.25% glucose (T3), and 1% starch - 0.5% glucose (T4). Variables such as physicochemical properties, amylose content, maximum gelation temperature, and the morphology of native starch granules were assessed. Variables in the film, such as thickness, opacity, film solubility, and tensile strength, were also measured. The fruit used in this study was composed of 88.55% starch (dry basis, 15.35% amylose). The starch granules had a dominant ovoid morphology with an average diameter of 25 - 30 μm. The peak gelatinization temperature was 73.80 °C. The maximum value for viscosity was 3,836 cP at 86.8 °C. Films that presented good malleability, thickness, transparency, tensile strength, and low solubility were obtained from T4, which turned out to be the best treatment.

Agave cocui vinasse was physicochemically characterized with reference to the relevant environmental regulations. The following results were obtained: COD: 71,000 mg.L-1, total solids: 21,000 mg.L-1, dissolved solids: 17,000 mg.L-1; pH: 4.06, conductivity: 9.45 mhoscm(-1), total Fe: 48.83 mg.L-1, total phenols: 8.66 mg.L-1; BOD: 30,000 mg.L-1. Fenton and photo-Fenton reactions were applied to treat the wastewater produced. For the Fenton process, the optimal oxidation conditions found were pH = 3.48, [COD]:[H2O2] mass ratio = 1:5, and [Fe+2]: [H2O2] mass ratio = 1:6. For the photo-Fenton process, the optimal parameters found were: pH = 3.98, [COD]:[H2O2] = 1:7.86, and [Fe+2]: [H2O2] = 1:5. The experimental data were adjusted to fit second order polynomial models with R-2 = 0.88 for the Fenton process and R-2 = 0.91 for the photo-Fenton process, respectively. The sludge produced featured the following characteristics: average COD: 41,000 mg.L-1, total Fe: 296,000 mg.L-1, pH: 7.7. The variables with the greatest influence in both processes were [Fe+2]:[H2O2] and [COD]:[H2O2].

The base for this research work was set in the study of the interaction between legume flours and tuber starch blends to be used for sweet biscuits formulations and the successive evaluation of quality parameters. As main inputs the following were used: legume flours, manioc starch, margarine, sugar, eggs, and baking soda. Basic utensils: plastic and aluminum trays, biscuits shapers, cutters, other biscuit items. Analytical balance, mixer, and oven. Methods: Establishment of experimental units, analysis of manioc starch and legume flours, moisture analysis, ash content determination, rheological analysis of blends, gluten analysis in the blend of starch and legumes flours, dough formulation and biscuit making, textural a color analysis, sensory analysis of biscuits, statistical analysis. The results of moisture and ash analyzes in the inputs for blends were within the limits established of 14,5%. Ash content found was 2.3% in starch, 6.3% in soybean meal, and 2.9% in chickpea flour. Gluten content was around 10 mg/g in average in the 3 samples. Concerning rheological characterization, water absorption for the 3 blends was relatively low. Values resulted for the torque were within 1.1 Nm; time required to reach this value did not exceed two minutes of kneading stability. The 3 blends denoted low amylasic activity, as well as low starch damage. Gel stability results were due to the high amylasic activity occurred in manioc starch. The retrogradation intensity predicted that the 3 blends would have a long shelf life since retrogradation processes were low for the 3 cases. Hardness presented in samples was due to protein supply provided by soybean meal. According to the sensory analysis average results for texture, flavor, and color attribute, differences were found. The ANOVA for sensory evaluation in biscuits showed differences regarding texture, flavor, and color attributes in biscuits. The moisture values obtained for each biscuit sample were similar to each other and did not exceed 3%. Tuber starch and legume flour incorporation in formulation to produce gluten-free biscuits contributes to the development of a flavorsome product and provides an alternative for people with gluten intolerance. The results obtained may be used as the basis for the production of gluten-free biscuits.