R. Nisha’s research while affiliated with Coimbatore Institute of Technology and other places

In the contemporary global food industry, prioritizing the safety and quality of food products stands as an imperative. Food and dairy process engineering encompasses a broad spectrum of procedures, including those for food preservation, heat treatment, fermentation, separation, and packaging, all meticulously aimed at augmenting both the longevity and sensory attributes of food and dairy items. Moreover, it assumes a pivotal role in tackling pertinent issues such as the reduction of food waste, the promotion of sustainability, and the optimization of resource utilization. This abstract delves into the fundamental tenets of food and dairy process engineering, underscoring the application of principles drawn from various engineering disciplines, including chemical, mechanical, and thermal engineering. These fundamental principles find application in the designing, refining, and controlling of processes to meet precise product requirements while remaining firmly in compliance with safety and regulatory standards. Furthermore, the seamless integration of emerging technologies, such as automation, data analytics, and biotechnology, is revolutionizing the landscape of food and dairy process engineering. These innovations are actively fostering the creation of innovative food products, enhancing process efficiency, and elevating the overall sustainability quotient of food production.

This study endeavors to employ non-destructive, machine learning-based techniques to predict and estimate the key quality parameters such as moisture content, total soluble solids, sugar content and pH throughout the ripening stage (unripe, ripen and over-ripen) of Mandarin orange fruit. Regression models compatible with Tiny-machine learning (TinyML) were used to track fruit development stages, crucially identifying the onset of spoilage from the unripe to ripe stages until the fruit becomes overripe. Additionally, Visible–Near–Infrared (VisNIR) spectral sensors were used to capture internal physicochemical attributes, facilitating precise predictions. These models, were trained on the Edge Impulse Platform and implemented on ESP8266 NodeMCU CP2102 Board microcontroller units. The optimal neural network architecture, comprising 18 input nodes representing spectral sensor data, two hidden layers with 20 and 10 nodes, and an output layer predicting ripening stage, achieves accuracy with R² values of 0.9912 for ripening stage, 0.8164 for pH, 0.9657 for total soluble solids (TSS), 0.9956 for sugar content (SC), and 0.9882 for moisture content (MC). Furthermore, by utilizing models for accurately predicting fruit quality parameters and estimating ripening stages, this approach not only aids in optimizing supply chain management by scheduling fruit consumption at the optimal time but also ensures consumers benefit from the nutritional advantages of mandarin oranges while minimizing economic losses due to spoilage.

This comprehensive review highlights the significant strides made in the field of food freshness detection through the integration of deep learning and imaging techniques. By leveraging advanced neural networks, researchers have developed innovative methodologies that enhance the accuracy and efficiency of freshness monitoring. The fusion of various imaging modalities, with sophisticated deep learning algorithms has enabled more precise detection of quality attributes and spoilage indicators. This multidimensional approach not only improves the reliability of freshness assessments but also provides a more holistic view of condition of the food. Additionally, the review underscores the growing potential for these technologies to be applied in real-time monitoring systems, offering valuable insights for both producers and consumers. The advancements discussed pave the way for future research and development, emphasizing the need for continued innovation in integrating these technologies to address the challenges of food safety and quality assurance in an increasingly complex and dynamic market. Graphical Abstract

Fruit and vegetable quality assessment is a critical task in agricultural and food industries, impacting various stages from production to consumption. Leveraging deep learning methods, particularly through sensor fusion, offers promising avenues to enhance the accuracy and robustness of quality assessment systems by amalgamating information from diverse sensor modalities such as visual, spectral, and tactile. The review scrutinizes a plethora of sensor fusion strategies, encompassing early fusion, late fusion, and hybrid fusion approaches, each with its distinct advantages and limitations. Furthermore, it explores the utilization of various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their combinations, tailored specifically for multimodal data fusion. Additionally, attention is paid to the challenges and considerations associated with sensor fusion in this domain, including data heterogeneity, synchronization, and feature alignment. Moreover, the review discusses the implications of dataset size, diversity, and annotation quality on the effectiveness of deep learning-based fusion models. Furthermore, it sheds light on the transferability of fusion models across different fruit and vegetable types and environmental conditions, highlighting the need for domain adaptation techniques. Moreover, the review delves into the real-world applications and commercial implementations of sensor fusion-based quality assessment systems, providing insights into their scalability, efficiency, and economic viability.

The storage of finger millet, an essential and resilient staple crop in many regions, poses a multifaceted challenge due to various factors such as susceptibility to pests, molds, and nutritional degradation, as well as the lack of proper infrastructure and knowledge. This comprehensive review seeks to shed light on the intricate web of challenges that afflict finger millet storage systems while also exploring a spectrum of innovative solutions and sustainable practices that have been developed to address these pressing issues. These solutions encompass a wide array of approaches, from the utilization of natural and synthetic storage protectants, improved storage structures, and enhanced post-harvest technologies, to the utilization of environmentally friendly and biodegradable materials. Additionally, the review underscores the importance of community-based knowledge sharing and capacity building initiatives, emphasizing the need for local communities to actively participate in and benefit from these innovations. By focusing on eco-friendly and culturally sensitive storage practices, this review aims to enhance food security, reduce post-harvest losses, and promote sustainability in finger millet cultivation, thereby contributing to the overall well-being of communities reliant on this vital crop.

Broken rice is an underutilized by‐product of the rice milling industry. It was added with green gram and barnyard millet to investigate as a source of nutrition‐based value‐added food product. The study was carried out to examine the influence of formulated composite flour obtained from broken rice (Oryza sativa) (50%–80%), barnyard millet (Echinochloa esculenta) (10%–30%), and green gram (Vigna radiata) (10%–30%) on the chemical properties, functional properties, pasting properties, oscillatory test, flour behavior, and microstructure of extrudate. The ash, crude fiber, crude protein, fat, and carbohydrate of the extrudate ranged from 1.62%–1.78%, 1.88%–2.43%, 13.47%–14.57%, 4.9%–7.48%, and 66.86%–70.8%, respectively. The water absorption index (WAI) and water solubility index (WSI) for the flour ranged from 5.06%–6.18 g/g and 2.34%–3.99%. The formulated composite flours having a higher percentage of broken rice showed significant differences (p < 0.05) in their pasting properties, pasting temperature, peak viscosity, breakdown viscosity, and setback and final viscosity. The pasting properties gave better results using a twin‐screw extruder with operating conditions of barrel temperature of 110°C, screw speed of 290 rpm, and 14% moisture content (w.b.). The microstructure of the extrudate obtained from 80% broken rice, 10% barnyard millet, and 10% green gram grits flour possessed a more integrated, well‐defined porosity, indicating a completely gelatinized structure. Practical application Determination of biochemical, structural, and rheological properties of ingredients are essential for the production desirable ready‐to‐eat snacks. In this context, the present study evaluated the functional and rheological properties of broken rice–barnyard millet–green gram blend for the production of nutritious snacks with desirable physical and textural properties. The findings of the study would be useful for food products industries using millets and broken rice as a major raw material.

Due to the more and more attention to environmental issues in recent years, the employment of eco-friendly corrosion inhibitors is the main topic of most studies. In this matter, the quince seed extract, which has been used for hundred years as traditional medicine was used as a new environmentally-friendly corrosion inhibitor of MS exposed to 1M HCl solution. Characterization techniques (i.e., FT-IR and UV-Vis spectroscopies) demonstrated that there are several physical/chemical adsorption active sites in the extracted compounds. EIS results showed that after 24h immersion of the electrode in the acid electrolyte containing 800 ppm extract, the Rp, and the Cdl increased about 95% and decreased about 87%, respectively. In addition, the PDP test results confirmed that the efficiency of the iron dissolution mitigation increased with the quince seed extract content increment and reached about 95% at 800 ppm. Furthermore, the Rp in the inhibitor-containing electrolyte (800 ppm) increased about 96% compared to the solution without the inhibitor. The electrochemical results proved the inhibitor action on both corrosion reactions (anodic (A)-cathodic (C)) control for MS in 1M HCl and obeyed a Langmuir adsorption isotherm. In addition, the obtained adsorption free energy (ΔG˚) of -32.48 kJ/mol indicates that the GOI interaction with the MS surface was a mix of physical and chemical adsorptions. Also, the theoretical results obtained from molecular simulations (Monte Carlo, MC and Molecular Dynamics, MD) approved the adsorption of the green ingredients on the steel. The DFT calculations supported the interfacial interactions of the inhibitors through their reactive sites.