Expiry Prediction and Reducing Food Wastage using IoT and ML

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage

Biosurfactants’ multifarious functional potential for sustainable agricultural practices

Increasing food demand by the ever-growing population imposes an extra burden on the agricultural and food industries. Chemical-based pesticides, fungicides, fertilizers, and high-breeding crop varieties are typically employed to enhance crop productivity. Overexploitation of chemicals and their persistence in the environment, however, has detrimental effects on soil, water, and air which consequently disturb the food chain and the ecosystem. The lower aqueous solubility and higher hydrophobicity of agrochemicals, pesticides, metals, and hydrocarbons allow them to adhere to soil particles and, therefore, continue in the environment. Chemical pesticides, viz., organophosphate, organochlorine, and carbamate, are used regularly to protect agriculture produce. Hydrophobic pollutants strongly adhered to soil particles can be solubilized or desorbed through the usage of biosurfactant/s (BSs) or BS-producing and pesticide-degrading microorganisms. Among different types of BSs, rhamnolipids (RL), surfactin, mannosylerythritol lipids (MELs), and sophorolipids (SL) have been explored extensively due to their broad-spectrum antimicrobial activities against several phytopathogens. Different isoforms of lipopeptide, viz., iturin, fengycin, and surfactin, have also been reported against phytopathogens. The key role of BSs in designing and developing biopesticide formulations is to protect crops and our environment. Various functional properties such as wetting, spreading, penetration ability, and retention period are improved in surfactant-based formulations. This review emphasizes the use of diverse types of BSs and their source microorganisms to challenge phytopathogens. Extensive efforts seem to be focused on discovering the innovative antimicrobial potential of BSs to combat phytopathogens. We discussed the effectiveness of BSs in solubilizing pesticides to reduce their toxicity and contamination effects in the soil environment. Thus, we have shed some light on the use of BSs as an alternative to chemical pesticides and other agrochemicals as sparse literature discusses their interactions with pesticides. Life cycle assessment (LCA) and life cycle sustainability analysis (LCSA) quantifying their impact on human activities/interventions are also included. Nanoencapsulation of pesticide formulations is an innovative approach in minimizing pesticide doses and ultimately reducing their direct exposures to humans and animals. Some of the established big players and new entrants in the global BS market are providing promising solutions for agricultural practices. In conclusion, a better understanding of the role of BSs in pesticide solubilization and/or degradation by microorganisms represents a valuable approach to reducing their negative impact and maintaining sustainable agricultural practices

Synergistic Activity of Rhamnolipid Biosurfactant and Nanoparticles Synthesized Using Fungal Origin Chitosan Against Phytopathogens

Phytopathogens pose severe implications in the quantity and quality of food production by instigating several diseases. Biocontrol strategies comprising the application of biomaterials have offered endless opportunities for sustainable agriculture. We explored multifarious potentials of rhamnolipid-BS (RH-BS: commercial), fungal chitosan (FCH), and FCH-derived nanoparticles (FCHNPs). The high-quality FCH was extracted from Cunninghamella echinulata NCIM 691 followed by the synthesis of FCHNPs. Both, FCH and FCHNPs were characterized by UV-visible spectroscopy, DLS, zeta potential, FTIR, SEM, and Nanoparticle Tracking Analysis (NTA). The commercial chitosan (CH) and synthesized chitosan nanoparticles (CHNPs) were used along with test compounds (FCH and FCHNPs). SEM analysis revealed the spherical shape of the nanomaterials (CHNPs and FCHNPs). NTA provided high-resolution visual validation of particle size distribution for CHNPs (256.33 ± 18.80 nm) and FCHNPs (144.33 ± 10.20 nm). The antibacterial and antifungal assays conducted for RH-BS, FCH, and FCHNPs were supportive to propose their efficacies against phytopathogens. The lower MIC of RH-BS (256 μg/ml) was observed than that of FCH and FCHNPs (>1,024 μg/ml) against Xanthomonas campestris NCIM 5028, whereas a combination study of RH-BS with FCHNPs showed a reduction in MIC up to 128 and 4 μg/ml, respectively, indicating their synergistic activity. The other combination of RH-BS with FCH resulted in an additive effect reducing MIC up to 128 and 256 μg/ml, respectively. Microdilution plate assay conducted for three test compounds demonstrated inhibition of fungi, FI: Fusarium moniliforme ITCC 191, FII: Fusarium moniliforme ITCC 4432, and FIII: Fusarium graminearum ITCC 5334 (at 0.015% and 0.020% concentration). Furthermore, potency of test compounds performed through the in vitro model (poisoned food technique) displayed dose-dependent (0.005%, 0.010%, 0.015%, and 0.020% w/v) antifungal activity. Moreover, RH-BS and FCHNPs inhibited spore germination (61–90%) of the same fungi. Our efforts toward utilizing the combination of RH-BS with FCHNPs are significant to develop eco-friendly, low cytotoxic formulations in future

Audio Source Separation as Applied to Vocals-Accompaniment Extraction

This paper presents two systems for extracting the vocals from a musical piece. Vocals extraction finds extensive application in automated lyrics transcription, music information retrieval systems and professional music remixing. However, the current research is dominated by training-intensive models and has not exploited several promising architectures for appreciably accurate yet computationally inexpensive inference. Neural networks can be leveraged to understand the underlying mathematical patterns of human speech and its harmonic overtones as distinguished from those of instruments. A first principles approach behind two distinct model architectures as well as the data processing steps has been described. Semantic segmentation techniques are used to discriminate between the magnitude spectrogram of the vocals and the mixture which is then applied to the first CNN-based model. The second architecture uses gated recurrent units to leverage the unique temporal dependencies of human speech. This model directly performs inference on the entire spectrogram to concurrently learn the frequency distribution of both the vocals as well as the accompaniment. The second architecture is shown to be an improvement upon the first model and intermittently approaches state-of-the-art predictions on the MIR-1K dataset. The novel GRU-based system in particular highlights the feasibility of rapid inference with smaller datasets. These features play a significant role towards source separation research oriented towards the deployment of real-time inference systems