A kiwifruit ripeness indicator was developed using potassium permanganate (KMnO4), an ethylene gas scavenger. A total of 500 g of kiwifruit was packaged in polypropylene (PP) containers (20 × 16 × ...11.5 cm) with a heat-sealed PP, polyethylene (PE), and ethylene-vinyl alcohol copolymer (EVOH) cover film (50 µm thick) with the indicator inside, and stored at 15, 20, and 25 °C. The ethylene gas permeability of the cover films, kiwifruit firmness (ripeness parameter, ≤ 8 N), ethylene gas concentration, and indicator color were measured. The indicator changed from purple to brown. When the firmness reached 7.9 N, the ethylene gas concentrations for each cover film were different due to film permeability. The indicator color index (ΔE) for each cover film was also different and ranged from 15.2 to 23.9, falling within the visually detectable change.
•KMnO4 was used as a sensor of ethylene gas colorimetric indicator.•KMnO4 oxidized ethylene leading to color change from purple to brown.•KMnO4-based indicator successfully determined kiwifruit ripening period.•Color endpoints depended on gas permeability of packaging films.
•Maturity classification algorithms that are developed and applied to sweet pepper.•Two methods to deal with adapting to different datasets are developed.•Mature-immature classification yielded 98.2% ...(red) and 97.3% (yellow) accuracy.•Four classes classification yielded 89.5% (red) and 97.3% (yellow) accuracy.•The random forest algorithm is recommended for the variable agricultural domain.
This paper presents maturity classification algorithms developed for small datasets and methods to deal with the highly variable and continuously changing agricultural environment. The algorithms were applied to the maturity classification of red and yellow sweet peppers, with data acquired from two different datasets, including 296 images. The maturity classification achieved 98.2 % and 97.3 % accuracy for classifying into two classes, between mature and immature classes of red and yellow peppers, respectively, and 89.5 % and 97.3 % accuracy for classifying into four maturity classes. The random forest algorithm is very robust and incurs a low computational cost, and therefore is recommended for the highly variable agricultural domain.
An improvement of 28.65 % in classification accuracy was achieved by applying the methods developed for adapting to new datasets.
The effect of hawthorn berries ripeness on the physicochemical, structural and functional properties of hawthorn pectin (HP) and its potential in sweet cherry preservation were investigated. With the ...advanced ripeness of hawthorn berries, the galacturonic acid (GalA) content decreased from 59.70 mol% to 52.16 mol%, the molecular weight (Mw) reduced from 368.6 kDa to 284.3 kDa, the microstructure exhibited variable appearance from thick lamella towards porous cross-linked fragment, emulsifying activity and emulsions stability, antioxidant activities, α-amylase and pancreatic lipid inhibitory capacities significantly increased. The heated emulsion stored for 30 d presented higher creaming index and more ordered oil droplets compared to the unheated emulsion. With the extended berries ripeness, the firmness of HP gels remarkably decreased from 225.69 g to 73.39 g, while the springiness increased from 0.78 to 1.16, HP exhibited a superior inhibitory effect in water loss, browning, softening, and bacterial infection in sweet cherries preservation.
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•GalA, Mw and particle size of HPs decreased with the advanced berries ripeness.•Adsorption and emulsification of HPs enhanced with the increasing berries ripeness.•Berry ripeness extension improved the antioxidation and lipase inhibition of HPs.•Increased ripeness promoted springiness while reduced firmness of HPs gels.•Higher ripeness brought superior effect of HPs coating on cherry preservation.
The robustness of visible/near-infrared spectral models in fruit quality assessment is challenged by differences in measurement conditions from instruments, environment, and season. Model transfer is ...considered to be an important method to solve such problems, however, model transfer of apple ripeness classification models under seasonal variation is difficult to achieve by measuring standard samples. Two model transfer methods without standard samples were implemented for this purpose: dynamic orthogonal projection (DOP) and transfer component analysis (TCA). Model transfer was accomplished on one source domain dataset and two target domain datasets under different seasons. The results show that both DOP and TCA can effectively improve the classification performance of the model, with DOP improving the precision and recall by up to 19.7 % and 40 %, and TCA improving the precision and recall by up to 25 % and 60 %. t-distributed Stochastic Neighbor Embedding (t-SNE) results after model transfer proved effective in reducing differences between datasets by DOP and TCA, and external validation ensures the robustness and generality of both methods. In conclusion, the apple ripeness classification model under seasonal variation can be accomplished by model transfer without relying on standard samples, in which TCA is expected to be a general tool for eliminating seasonal differences in apple NIR spectra.
•Model transfer effectively improved classification performance of ripe class samples.•Only a small number of target domain samples are required to complete model transfer.•·DOP and TCA have reduced the data differences between the source and target domains.•·SMOTE effectively solves sample imbalance in model transfer.
•Aldehyde emission as a marker for the determination of apple ripeness.•Introducing the preparation methods of commercially applicable printing inks for a sensor fabrication.•Simple, low-cost, and ...attachable sensor label.•Color change mechanism contributed by Cannizzaro reaction.
We developed an on-packaging colorimetric sensor label that can detect the aldehyde emission of apples based on Methyl Red. The sensor label was constructed using printable inks on paper medium and relied on the change in basicity caused by the nucleophilic addition reaction between aldehyde and hydroxide via the Cannizzaro reaction. The sensor can be used to detect aldehyde in solution and vapor. Sensitivity and stability toward changes in humidity were achieved by altering the concentration of OH−. Under exposure to ripening apples, the label changed color from yellow to orange, and then to red. The degree of ripeness was estimated by a sensory test and texture analysis. The color change of sensor label had showed a similar tendency to the changes in the parameters of the sensory test, soluble solid content, and hardness. Therefore, the sensor label can be used for real time on-package ripeness monitoring of apples during their shelf life.
Determining the right time to harvest is very important to do for agricultural commodities. This is related to post-harvest handling and extending the shelf life of fruits. Determining the level of ...melon maturity is still done manually by tapping the surface of the fruit by hand, but this method is still subjective. This research aims to study the maturity level of the 'Premier' melon to determine the appropriate harvest time based on acoustic properties and its relationship to the physic-chemical properties of the melon using a self-made tapping device. Based on the results obtained, the acoustic properties parameters showed a strong enough relationship to the physico-chemical parameters of melons. Based on linear regression analysis, it can be seen that the best acoustic parameters in estimating and determining the right harvest time for melons are the dominant frequency (f), magnitude (M), and zero moment power (Mo). 'Premier' melons can be harvested when the dominant frequency (f) is ≤ 219.92 Hz with a magnitude (M) of ≤ 39.72 dB, and the zero moment power (Mo) value is ≤ 68.99 according to the actual harvest age in the field conducted by farmers at the harvest age of 64 DAP.
•Mulberry maturity can be well distinguished based on fruit density.•Maturity affects physicochemical properties and drying of mulberries.•Ripening induced mulberry softening and pectin and ...hemicellulose depolymerization.•Ripening induced the main and side chains breakage of pectin.•Drying behavior were related to changes in tissue structure and pectin properties.
Mulberries were categorized into five stages of ripening (D1–D5, 0.905–1.055 g/cm3) based on their density, and their physicochemical properties, tissue structure, cell wall polysaccharide properties, and drying characteristics were investigated. As mulberry ripening progressed, the TSS and water-soluble pectin content rapidly increased, while the contents of TA, hardness, chelate-, sodium carbonate-soluble pectin, hemicellulose, and cellulose decreased gradually. Pectin nanostructure and monosaccharide composition indicated that both the main and side chains of pectin undergo depolymerization. Medium- and short-wave infrared drying time initially decreased and then increased during mulberry ripening, with D4 fruits exhibiting the shortest drying time. Compared with D4 fruits, the collapse of cell structure in D5 fruits prolongs their drying time. The results demonstrate that ripeness significantly affects mulberry drying characteristics, which is related to changes in cell structure and pectin properties. Utilizing mulberry density to determine ripeness and grading is an effective approach to achieving optimal drying.
CeOx-SnO2 nanocomposites (NCs) with different Ce:Sn compositions of 0:100, 20:80, 25:75, 33:67, 50:50 and 100:0 were synthesized via a two-reactant co-precipitation method. The phase, morphology, ...particles size, elemental composition and chemical state of as-prepared CeOx-SnO2 nanoparticles (NPs) were characterized by X-ray diffraction, nitrogen adsorption, electron microscopy and X-ray spectroscopy. The results revealed that the highly crystalline solid solution phases structure of CeOx-SnO2 were formed exhibiting approximately round morphologies with average particles sizes of ~5–20 nm. The sensor properties towards ethylene gas were characterized in terms of response, response times, stability and selectivity. The gas-sensing data showed that the addition of CeOx to SnO2 provided significant enhancement of ethylene response and the CeOx-SnO2 NPs (Ce:Sn = 33:67) offered the highest response of 5.18 with a short response time of 12 s to 10 ppm ethylene at 350 °C. Additionally, the sensor exhibited a low minimum detectable ethylene concentration of 0.3 ppm, high ethylene selectivity and good stability. Therefore, the sensor based on coprecipitated CeOx-SnO2 NPs could ce a promising candidate for detection of ethylene in fruit-ripeness monitoring applications.
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•CeOx-SnO2 nanocomposites with 0–50 wt% Ce were synthesized by coprecipitation.•Results suggested CeOx secondary crystallite were finely dispersed in SnO2 matrix.•Response to 30 ppm C2H4 at 350 °C was enhanced from 2.2 to 5.2 with 33 wt% Ce.•High C2H4 selectivity was attained against H2, C2H2, CH4, H2S, NO2, C2H5OH and C3H6O.
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