Freshness Of Fruits Research Articles

Janus biopolymer nanocomposite coating with excellent antibacterial and water/oxygen barrier performance for fruit preservation

Fruit freshness is strongly influenced by the storage environment (water, oxygen), cellular respiration and microorganisms. Applying a biomass-based coating onto fruit surface is a facile and sustainable method to effectively slow down the decay rate and extend the freshness period of fruit. Inspired by the asymmetric characteristics and functionalizations of Janus structure, we developed a facile strategy to fabricate Janus biopolymer composite coating by combining modified quaternized chitosan (QCS) with aldehyde carboxycellulose nanofibres (ACCNF). Through a simple layer-by-layer self-assembly process, a Janus QCS/ACCNF composite coating can be easily and conformally generated on different fruit, where the outer QCS layer with positively-charged quaternary ammonium groups acts as an antibacterial barrier to effectively inhibit the growth of various microorganisms while the inner ACCNF layer strongly adheres to fruit surfaces and provides buffering effect to reduce the damage to fruit during vibration and collision. Moreover, a dense QCS/ACCNF interfacial layer is formed between QCS and ACCNF layers through strong electrostatic interaction and Schiff base reaction. This interfacial layer not only further enhances the mechanical properties of the coating, but also reduces its water and oxygen permeation, thus effectively reducing the dehydration shrinkage and oxidative deterioration of the coated fruits. The fruit preservation experiments, using mango, banana and loquat as model fruits, demonstrated this Janus QCS/ACCNF coating could confer the coated fruit with excellent antibacterial and oxygen/water permeation properties and significantly extend the freshness period of these fruits by 5–10 days, exhibiting great potential for its practical applications in fruit preservation.

Strategy for the implementation of sustainable green fuels in Indonesia

Global climate change has triggered the implementation of energy transition programs in many countries where renewable fuels have received great attention. While Indonesia successfully im- plements biofuels-based crude palm oil (CPO) such as biodiesel (up to B30). The program proceeds towards implementing other green fuels (such as green diesel, gasoline, and bio jet fuel). However, the current price of CPO (as a raw material) is highly fluctuated depending on the global market mechanism and mostly more expensive than the price of fossil fuels. This situation leads to an unattractive business of green fuels. So, the government's policies are strongly required to make more attractive businesses with enhanced competitiveness of green fuels. A qualitative approach was conducted by using forum group discussion to gain information about the current status of biofuel policy in Indonesia and its challenges. The quantitative method was conducted by calculat- ing the economic analysis of palm oil-based energy crops and processing plants. The concept of integrated palm energy plantation was proposed based on the result. It may be a kind of integrated business entity owned by the government which aims at producing CPO and the following green fuels. Economic analysis shows that from the plantation side, the selling price of Fruit Fresh Bunch (FFB) is USD 0.068/kg with an IRR of 12.6%. While in the CPO processing industry, using the FFB price of USD 0.082/kg obtained an IRR of 14.4%. This result shows promising results from the calculation, keeps the sustainability of raw material supply and enhances the competitiveness of green fuels.

Intelligent logistics system based on visual detection of fruit freshness

Because of its tremendous potential for financial advantage, the academic and business communities have begun to focus a significant amount of attention on the intelligent logistics system. For the purpose of addressing the issue of quality supervision of fruit while it is being transported logistically, this study presents a proposal for an intelligent logistics system that is based on the detection of fruit freshness. The physical, chemical, and image processing techniques that are utilized at various points during the logistics process make up the core detection methods utilized by this system. The ability of the system to perform monitoring of the freshness of the fruit in real time guarantees that the fruit will be transported in a manner that is both secure and effective. The results of the analysis show that this system plays an essential part in a number of steps, including fruit harvesting, fruit sorting, transportation planning, and fruit delivery right to consumers front doors.

The influence of multisensory indoor environment on the perception of orange juice

Drinking (as well as eating) represents a fundamental multisensory experience of our lives. Recent studies have proposed the implementation of multisensory contexts to improve the taste perception of food and beverages. This topic becomes relevant when considering the possibility of promoting the consumption of healthy beverages (e.g., orange juice). Only a few studies have concentrated on the multisensory features of the indoor environment (e.g., acoustic and visual context) reporting effects on the perception of healthy beverages. This study investigates how multisensory indoor environments modify the taste perception while drinking orange juice. By controlling the room microclimate and combining the correlated colour temperature of lighting (i.e. warm and cool) and the background wall colour (i.e., red, green) in the Sens i-Lab, with different audio stimuli (i.e. low and high-frequency pure tone), participants were invited to taste the low-sugar orange juice and to rate the fruit juice aroma, sweetness, sourness, bitterness, freshness, and thickness. The results showed that listening to a high-frequency tone in an environment with warm lighting and a red background increases the sweetness perception of orange juice compared to the control environment. In addition, compared to the control condition, the juice aroma was rated more intense in almost all multisensory conditions and thicker with cool lighting and a green background while listening to a low-frequency tone. Our findings suggested that the development of multisensory design can contribute to interior design strategies and design innovation in food architecture, where people can improve their taste in healthy foods and beverages.

CLASSIFICATION OF FRESH AND ROTTEN FRUITS USING DIFFERENT CNN MODELS

Fruit freshness automated classification is crucial to the agricultural sector. In the traditional procedure, a human being grades the fruit. Additionally, this process is labor-intensive, time-consuming, and ineffective. Additionally, it raises production costs. Therefore, a quick, precise, and automated system that may lessen human effort, enhance production, and decrease manufacturing time and cost is needed for industrial applications. The deep learning- based model for classifying fruit freshness is used in the current work. Various Convolution Neural Network (CNN) models are proposed, and they are implemented using the publicly available "fruit fresh and rotten for classification" kaggle dataset. Three fresh fruit varieties (Apple, Banana, and Oranges) and their rotting category are employed in an experiment using the dataset. From the given fruit photos, traits or attributes are extracted using a CNN model based on deep learning. The input photos are then divided into fresh and rotting categories by a softmax method. The classification of fresh and rotten fruits uses a variety of CNN models, including Resnet50 (50 Layers), InceptionV3 (48 Layers), and VGG16 (16 Layers). The proposed various CNN models accurately and efficiently evaluate the dataset. Later, the accuracy of the proposed CNN models is compared and the highest accuracy among the three CNN models is identified. In this way, the best accuracy CNN models will be identified for classifying the fresh and rotten fruits. KEYWORDS: Deep learning, CNN model, Inception V3, Resnet50, VGG16.

Perubahan Mutu Buah Pepaya Varietas IPB 9 (Calina) Selama Penyimpanan Pasca Simulasi Transportasi

<p class="AbstakIndo"><em>Papaya (Carica Papaya L.) IPB 9 (calina) variety is a papaya fruit that has thick, sweet flesh and very high fruit productivity. During the transportation process of papaya, mechanical damage (bruises, abrasions, weight loss) and physiological damage often occur, which can cause papaya decrease in quality during storage before consumption. The purpose of this study was to determine changes in the quality of papaya after 2-hour transportation simulation. The research design used a randomized block design (RAK) with the treatment of the position of the fruit in the packaging, which was stored horizontally (KS1) and vertically (KS2) during the transportation simulation process. Papaya hat has been simulated for transportation is stored at 18–2</em><em>0</em><em><sup>o</sup></em><em>C for 12 days. </em><em>Q</em><em>uality of papaya weight loss stored for 12 days for KS1 reached 7.34% and KS2 reached 7.43%. </em><em>H</em><em>ardness quality of papaya stored for 12 days for KS1 is 0.84 kgf, and for KS2, it is 0.76 kgf. </em><em>Q</em><em>uality of papaya skin color after 12 days of storage decreased the freshness of the papaya fruit, but it was still fit for consumption. </em><em>T</em><em>otal dissolved solids content after being stored for 12 days for KS1 reached 11.45% and KS2 reached 11.32%.</em><em></em></p>

Improving the post-harvest quality of fruits during storage through edible packaging based on guar gum and hydroxypropyl methylcellulose

Fruits are particularly susceptible to post-harvest decay. In this context, bio-based coatings could be a useful and sustainable approach to overcome this problem. The EU Commission is very restrictive about food additives and materials in contact with food, regulated respectively by Reg. UE (CE) 1333/2008 and Reg. UE (CE) 10/2011. In this research, different percentages of hydroxypropyl methylcellulose (H)-guar gum (G) (1–2% w/v) and potassium sorbate (PS) (0–2% w/v) were used to produce coatings to be applied on tomatoes (Solanum lycopersicum) and oranges (Citrus sinensis). The efficacy of the coatings was firstly evaluated through an accelerated test (28 °C 90% RH, 20 days) on artificially wounded fruits, and then under storage conditions (8 °C 95% RH, 6 weeks). The physical and chemical parameters of the fruits were periodically analyzed and used to evaluate the effect of the coatings on the fruit quality through a multivariate approach (PCA). All coatings were effective in preserving fruits under refrigeration against Penicillium spp. and Alternaria spp., but PS led to significant fruit weight loss despite its strong fungistatic activity. Biopolymers showed potential for preserving fruit freshness, but alternative antifungal agents without adverse weight effects should be explored using a similar statistical approach.

Development of pH-responsive intelligent and active films based on pectin incorporating Schiff base (Phenylalanine/syringaldehyde) for monitoring and preservation of fruits

This study aimed to develop pectin-based films by incorporating Schiff base compounds (SPS) synthesized by phenylalanine and syringaldehyde. The SEM images showed good compatibility between SPS and pectin matrix. The interaction of SPS and pectin matrix was analyzed by FTIR and XRD. Results indicated that the cross-linking effects between SPS and pectin matrix improved the thermal stability, water resistance and light shielding ability of the films. The incorporation of SPS in the films scavenged more than 80% of DPPH and ABTS free radicals, exhibited sustained antimicrobial ability against S. aureus, E. coli and B. cinerea, and showed significant color changes as pH-responsive films. Especially, the intelligent active coating/films inhibited the quality deterioration of cherry tomatoes and fresh-cut mangoes, and monitored the freshness of fresh-cut mangoes during storage. Therefore, the SPS/PE films have a potential application in maintaining fruit quality and monitoring the freshness of fresh-cut fruit.

A carrageenan-based edible coating incorporating with peppermint essential oils to increase shelf life of bananas (Musa acuminata cavendish)

Ambon bananas (Musa acuminata cavendish) are nutritious and economically valuable fruits. Bananas, being a climacteric fruit, have the potential to undergo deterioration subsequent to the harvesting process. The utilization of an edible coating on bananas has demonstrated potential in delaying spoilage, whereas the substantial use of peppermint oil has been observed in the preservation of fruit freshness. This research investigated how a carrageenan-based coating and peppermint essential oil enhance banana shelf life. This research compares uncoated bananas, carrageenan-coated bananas, and carrageenan-peppermint-coated bananas. Weight loss, total soluble solids (TSS), pH, colour, and water activity were tested. In this study, it was observed that the application of a carrageenan edible coating resulted in a reduction in weight loss and TSS as compared to bananas that were not coated. Duncan's test shows statistically significant discrepancy in weight loss among bananas that have undergone different treatments, thereby influencing the overall weight loss outcome. There are variations in pH levels and aw values. Meanwhile, pH and water activity of coated and uncoated bananas were relatively the same and stable during storage, so the carrageenan edible coating treatment and the addition of peppermint essential oil had no effect. The findings indicate that edible coatings made from carrageenan have the ability to inhibit the ripening process of bananas. Nevertheless, the efficacy of this inhibition decreases when peppermint essential oil is given as a supplementary constituent.

Dual-functional smart indicator for direct monitoring fruit freshness

Innovative smart packaging systems that inhibit bacterial growth and monitor food quality change are urgently needed in food industry. A dual-functional polyvinyl alcohol/polyethyleneimine/methylene blue (PVA/PEI/MB) nanofiber film was prepared for inhibiting bacterial growth and monitoring freshness of fruit. The nanofibers exhibited excellent mechanical property, breathability, water resistance and biocompatibility as characterized by the tensile test, air permeability test, water contact angle and cytotoxicity experiment. In vitro antibacterial experiment showed that the nanofibers had strong antibacterial activities against Escherichia coli and Staphylococcus aureus, and the antibacterial activities were improved under light irradiation. Nanofibers showed color variation from blue to colorless under bacterial infection in a range of 101–1010 CFU/mL, which can be used to detect the freshness of fresh-cut fruit. Effectiveness of nanofibers for freshness keeping and monitoring was confirmed via fruit tests, which suggests that the dual-functional nanofibers could potentially be used in food packaging.

Energy-efficient deep learning model for fruit freshness detection

Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from 55 to 73 Watts during the training process and 20 to 27 Watts during the testing process. For accuracy, the result is 98.64%. However, compared to previous studies with the MobileNetV2 model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F1-score in Rotten Banana.

Optimization Strategy on Deep Learning Model to Improve Fruit Freshness Recognition

The high fruit production during the harvest season is a challenge in the process of sorting fresh fruit and rotten fruit in plantations. Automatic fruit freshness classification based on deep learning can speed up the sorting process. However, building a model with high accuracy requires the right strategy based on the dataset's characteristics. This research aims to apply optimization strategies to deep learning models to improve model performance. The optimization strategy is implemented by optimizing the model using fine-tuning strategy by selecting the best parameters based on learning rate, optimizers, transfer learning, and data augmentation. The transfer learning process is applied based on the dataset's characteristics by training some parameters with a size of 30% and 60%, which were tested in four scenarios. The fine-tuning strategy is applied to three Deep Learning models, i.e., MobileNetv2, ResNet50, and InceptionResNetV2, which have various parameter sizes. Based on test results, fine-tuning strategy produces the best performance up to 100% with a learning rate of 0.01, the SGD optimizers on the InceptionResNetV2 model are trained on 60% of the parameters.

Eco-friendly and humidity-sensitive cellulosic triboelectric materials tailored by xylanase for monitoring the freshness of fruits

Detecting the freshness of fruits during the storage period plays crucial role in reducing food waste and food-borne contamination. In this study, for the first time, a novel humidity-sensitive tribopositive cellulosic material was successfully tailored by biological xylanase under mild conditions for monitoring the freshness of fruits within packing microenvironments. Benefiting from the more exposed -OH groups within created microstructures and the residual hydrophilic enzyme protein on the surface of fibers, the output voltage signal of the TENG decreased with gradual increasing the relative humidity (RH) from 25%RH to 95%RH. Moreover, the optimized TENG-72 h presents a higher humidity-sensitivity of V25% RH/V95% RH = 11.3 toward wide range humidity, which was about 3.65 times higher than pristine cellulose-based TENG. The constructed enzymatic cellulose TENG displayed discernible output signal response to the humidity within packaging microenvironments, which was highly correlated with the physiological parameters of fruits, further reveling the great potential application in monitoring the freshness of fruits. Overall, this research not only proposed a green and mild strategy for tailoring the humidity-sensitivity of cellulosic fibers, but also promotes the development of biodegradable triboelectric materials in the field of food safety.

Bio-Coatings for Preservation of Fresh Fruits and Vegetables

In response to increasing concerns over food waste and safety, and the environmental impacts of traditional conservation methods, this review aims to explore the potential of bio-coatings in preserving the freshness of fruits and vegetables. Our primary objective is to provide a comprehensive analysis of recent advancements in bio-coating technologies, detailing their benefits in terms of enhancing food safety, prolonging shelf life, and reducing waste. This paper delves into various forms of bio-coatings, their applications, and their effectiveness in maintaining post-harvest quality. We further elucidate the underlying mechanisms that govern their preservation efficacy. This review is intended for researchers, industry professionals, and policy makers who are interested in sustainable preservation alternatives and their implications for food security and environmental sustainability. By the end of this review, the audience will gain a thorough understanding of the current state of bio-coating technology and its prospects in the food preservation industry.

Active and smart antimicrobial food packaging film composed of date palm kernels extract loaded carboxymethyl chitosan and carboxymethyl starch composite for prohibiting foodborne pathogens during fruits preservation

The presented work aims to maximize the benefits of plant waste that does not degrade or decompose quickly and consumes an enormous financial burden for disposal or causes environmental pollution. Therefore, polymeric films of carboxymethyl chitosan (CMCs) and carboxymethyl starch (CMS) containing date kernel (DK) extract were prepared, intending to apply it as a material that can kill microbes that lead to spoilage of preserved fruits. It was noted from the results that DK extract did not affect the morphological structure of the formed films, and there is good compatibility with film polymers. The findings also illustrated that, DK extract aimed to form bonds with both polymers in the presence of the crosslinking agent, which eventually works to create insoluble films in water to a large extent. Results revealed that the CMCs/CMS/DK-3 had superior antimicrobial possibilities against all selected microbes without any hazardous effect while applied to the microbially contaminated solution. The current study recommended such promising edible film as a potential prototype for developing antimicrobial food packaging materials that promote the safety and freshness of fruits and vegetables.

Evaluation, Monitoring, and Improving the Airflow and Heat Transfer in a Cold Storage of Foodstuffs, CFD Simulation and Experimental Investigation

Freshness and quality of fruits and vegetables as well as product saleable weight depend on heat and moisture transfer rate during air-cooling in the cool storages. The main objective of this study was to evaluate and develop a validated CFD model for predicting the temperature and velocity fields in a cold storage. In this study the air temperature, velocity, and non-uniform airflow in cold storage have been studied experimentally and numerically. Experimental and numerical models were employed to investigate cold storage's airflow field, flow pattern, and temperature field. The temperature and the velocity fields were measured in the cold storage filled with two potato pallets by using a hot wire anemometer and thermocouple with a programmable data logger computer. The experimental data was recorded at 60 points of temperature, and 27 points of velocity from three levels; high, middle, and bottom. Numerically the SB (Solid Block) technique was used to assess four turbulence models (Standard k-ε, Realizable k-ε, Standard k-ω, and SST k-ω) to compare with the experimental results for validation. The results of the validation were agreed with the experimental. The comparisons between the numerical results and experimental results revealed that the Shear Stress Transport (SST k-ω) model shows the best performance with the smallest absolute errors for temperature and velocity respectively. As well as the results revealed that the bottom and the top of the room was subjected to fresher and cooler air with relatively higher velocity. According to the study validation and finding, using the SST k-ω model is the best model for investigation in a cold storage, and for the improvement and evaluation of more complex cold storage, to achieve the balance distributions of air velocity, air temperature, and other heat transfer phenomena.

Fruit Freshness Evaluation using a Real-Time Industrial Framework for Deep Learning Ensemble Approaches

Abstract: Consumers give a high value on fruits' freshness, and manual visual grading presents challenges due to labor effort and inconsistent results. This research suggests an effective machine vision system for automating a visual assessment of fruit freshness and attractiveness based on cutting-edge deep learning algorithms and ensemble methodologies. The suggested architecture enables the non-destructive and economical detection of fruit defects by utilizing convolutional neural networks (CNNs). To attain high classification accuracy, which acts as the performance metric, the system utilizes ensemble deep learning models. Fruit photographs are used to train the algorithm, enabling precise fruit quality assessment. This framework revolutionizes the inspection process by using computer vision in real-time for industrial applications in fruit freshness detection

Carvacrol/β-cyclodextrin inclusion complex as a fumigant to control decay caused by Penicillium digitatum on Shatangju mandarin slices

Preservation and microorganism control of fresh-cut fruit pose a persistent challenge in the food industry. To address this issue, we prepared a β-cyclodextrin (β-CD) inclusion complex containing carvacrol using a coprecipitation method and employed it for the non-contact fumigation of fresh-cut Shatangju mandarin slices. This biodegradable and safe preservative offers an effective means to combat spoilage and ensure product quality. We confirmed the formation of the encapsulated structure of the inclusion complex through various characterization methods, including scanning electron microscopy (SEM), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential thermal analysis (DTA). We also demonstrated the inhibitory effect of this preservative on Penicillium digitatum and its associated spoilage both in vitro and in vivo. The incidence and severity were significant lower in the inclusion complex-treated group (75.0% and 46.7%, respectively) compared to the group treated with pure carvacrol (100% and 69.2%, respectively). In addition, fruit freshness parameters and sensory evaluation showed that the inclusion complex treatment effectively maintained the overall quality of the fruit and achieved the highest consumer acceptance.

Advancements and Applications of Raman Spectroscopy in Rapid Quality and Safety Detection of Fruits and Vegetables

With the improvement in living standards, consumers have become more aware of healthy diets and pay more attention to the quality and safety of fruits and vegetables. Therefore, it is essential to strengthen the research on rapid detection of the quality and safety of fruits and vegetables. This study mainly outlines five Raman spectroscopy techniques. It introduces their principles and advantages and the current research progress of their application in fruit and vegetable quality and safety detection. Based on the characteristic Raman spectroscopy analysis of different fruits and vegetables, researchers found that Raman spectroscopy techniques can quickly and accurately detect classification identification, ripeness, freshness, disease infestation, and surface pesticide residues of fruits and vegetables. In addition, Raman spectroscopy techniques can also detect the content and distribution of material components of fruits and vegetables. This paper also discusses Raman spectroscopy’s current technology and application difficulties in fruit and vegetable quality and safety testing. It looks forward to its future development trend, expecting to promote the broad application of Raman spectroscopy in fruit and vegetable quality and safety testing.

Cu-MOF derived CuO@g-C3N4 nanozyme for cascade catalytic colorimetric sensing.

The use of peroxidase mimics has great potential for various real applications due to their strong catalytic activity. Herein, a facile strategy was proposed to directly prepare CuO@g-C3N4 by Cu-MOF derivatization and demonstrated its efficacy in constructing a multiple enzymatic cascade system by loading protein enzymes onto it. The resulting CuO@g-C3N4 possessed high peroxidase-like activity, with a Michaelis constant (Km) of 0.25 and 0.16mM for H2O2 and 3,3',5,5'-tetramethylbenzidine (TMB), respectively. Additionally, the high surface area of CuO@g-C3N4 facilitated the loading of protein enzymes and maintained their activity over an extended period, expanding the potential applications of CuO@g-C3N4. To test its feasibility, CuO@g-C3N4/protein oxidase complex was prepared and used to sense the ripeness and freshness of fruits and meat, respectively. The mechanism relied on the fact that the ripeness of fruits increasedand freshness of food decreased with the release of marked targets, such as glucose and xanthine, which could produce H2O2 when digested by the corresponding oxidase. The peroxidase mimics of CuO@g-C3N4 could then sensitively colorimetric detect H2O2 in present of TMB. The obtained CuO@g-C3N4/oxidase complex exhibited an excellent linear response to glucose or xanthine in the range of 1.0-120μmol/L or 8.0-350μmol/L, respectively. Furthermore, accurate quantification of glucose and xanthine in real samples is achieved with spiked recoveries ranging from 80.2% to 120.0% and from 94.2% to 112.0%, respectively. Overall, this work demonstrates the potential of CuO@g-C3N4 in various practical applications, such as food freshness detection.